CC*DNI Integration: Enabling Big-Data Science at UO withSoftware-Defined Network Services

Reza RejaieDepartment of Computer and Information Science

University of Oregone–mail: reza@cs.uoregon.edu

Jose DominguezNetwork and Telecommunications Services Department

University of Oregone–mail: jad@network-services.uoregon.edu

Gregory BothunDepartment of PhysicsUniversity of Oregon

e–mail: dkmatter@uoregon.edu

Allen Malony, Jun Li, Hank ChildsDepartment of Computer and Information Science

University of Oregone–mail: {malony,lijun,hank}@cs.uoregon.edu

William CreskoDepartment of BiologyUniversity of Oregon

e–mail: wcresko@uoregon.edu

Walter WillingerNiksun Inc.

e–mail: wwillinger@niksun.com

March 23, 2015

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Project SummaryRecognizing the growing importance of data-intensive computational science, the University of Oregon (UO)has made major investment in computing resources to support the cutting-edge research across all scientificdisciplines during the past decade. The availability and growing use of these computing resources has quicklyled to realization that the general-purpose campus network (UOnet) does not offer the required capacity orthe capabilities for research needs of the UO community. Toward this end, the team acquired an NSF NIEfunding to design and build a new dedicated network for scientific computing at UO campus called BridgingOpen Networks for Scientific Applications and Innovation (BONSAI) network, or BONSAInet . The BONSAInetenables researchers across the campus (or even at other research institutions) to effectively access computationalresources, storage space and visualization capabilities at UO. The BONSAInet has high-bandwidth connectivityto Internet2 which enables UO researchers to effectively leverage the services offered by the DYNES project.However, further investment is required to develop or integrate an array of services on top of the BONSAInet inorder to turn this into a powerful operational network that can effectively serve domain scientists at UO.

We propose to develop, integrate and evaluate several core new techniques over the BONSAInet . Thesetechniques leverages the software-defined networking (SDN) capabilities of the switches in the BONSAInet inorder to offer the following essential services and support processes: (i) Supporting high throughout, low latencyend-to-end connections across the BONSAInet by deploying dynamic flow scheduling techniques (e.g., Hedera)and Data Center TCP (DSTCP) over the BONSAInet . (ii) Incorporating an array of network managementcapabilities to effectively deal with security issues, flexibly manage dynamics of users and resources, reliablyperform monitoring and logging, and widely support debugging & diagnosis tools. (iii) Enabling experimentalSDN-related research by Computer Scientists through flexible network partitioning and traffic re-routing. Thefirst set of services directly address the immediate computing needs of domain scientists at UO. The second setof services accommodate sustainability of the BONSAInet by ensuring that it can be effectively and efficientlymanaged beyond the duration of this project. The third set of services not only leads to contributing to researchon SDN but also provides the local expertise to cope with an array of systems issues that arise when integratingevolving technologies such as SDN in to an operational network. The proposed activities are conducted over atwo year period by two full-time programmers and a group of CIS graduate students.Intellectual Merit: The project participants have significant background on the related scientific and engi-neering issues that are essential for the success of this project. Computer scientists in our group have extensiveresearch knowledge in networking, security, computational science and scientific visualization. Members of theInformation Services network engineering group at UO have many years of experience in maintaining UOnetand continuously improving its capabilities. These members actively contribute to IETF, NANOG, SCinet,Internet2, and closely collaborate with other Regional Optical Networks (RON) in the country and major net-work infrastructure vendors. The participating domain scientists are involved in a wide range of computationaland data-enabled science applications including experimental astrophysics, high energy physics, climate sci-ence, the Cascadia initiative, scientific visualization and cancer biology. Therefore, these scientists are closelyinvolved in this project, and eager to leverage the proposed the proposed services for the BONSAInet.Broader Impact: Providing these services on top of the BONSAInet magnifies the impact of the significantrecent and planned investments by the UO (that are funded by NSF and other agencies) in networking, comput-ing, storage and visualization resources as well as several big-data NSF research awards by domain scientistsand CIS faculty at UO. More specifically, these services offer unique opportunities for distinct groups of stakeholders at UO as follows: (i) Domain scientists take advantage of high bandwidth connectivity between majorcomputing facilities across UO campus and to other organization through Internet2 to conduct data intensiveresearch in a more interactive and effective manner, (ii) Computer Scientists leverage SDN capabilities of theBONSAInet by using it as a powerful experimental testbed to develop and deploy a wide range of innovativenetworking and security protocols as well as cloud computing solutions. (iii) Network engineers gain a wealthof experience with SDN features to devise and easily deploy new network management and security policies inorder to cope with the growing size and complexity of campus network. The BONSAInet will directly impact

teaching and training opportunities for UO students by providing access to advanced computing infrastructureand networking capabilities. This project enables us to gradually expand the coverage and diversity of theseservices until they eventually serve the entire campus population for all of their networking needs.

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Contents

A Introduction 1

B University of Oregon Facilities 2B.1 Campus Network (UOnet) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2B.2 Computing Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

C The BONSAI Project 4

D Scientific Application Drivers 6D.1 High Energy Physics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6D.2 Climate Science via Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7D.3 The Cascadia Initiative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7D.4 Cancer Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8D.5 Scientific Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

E Design Objectives 9

F Core New Techniques 10F.1 Incorporating Dynamic Flow Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11F.2 Deploying Data Center TCP (DCTCP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11F.3 SDN Controller& Programming Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . 11F.4 SDN-Based Network Management Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . 12

G Plan of Work 12G.1 Planning & Preparation (September-November’14) . . . . . . . . . . . . . . . . . . . . . . . 12G.2 Examining Individual Components in the Testbed (December’14-February’15) . . . . . . . . 13G.3 Developing & Testing the Services on the Testbed(March’15-October’15) . . . . . . . . . . . 13G.4 Importing Services to BONSAInet (November’15-March’16) . . . . . . . . . . . . . . . . . . 13G.5 Developing Processes for Support from Network Engineers (January’16-May’16) . . . . . . . 13G.6 Integrating Developed Services into Applications (March’16-August’16) . . . . . . . . . . . . 13

H Management Plan 14

I Impact on Research and Training 15

J Prior NSF Support 16

K Bibliography 1

L Data Management Plan 1

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A IntroductionThe significant advances in computing power, networking bandwidth, storage density, and software technol-ogy have empowered today’s scientists and engineers to generate, share, analyze and visualize large and dis-tributed data sets at unprecedented scale and complexity. In the future, new opportunities for computation- andinformation-based discovery will have profound effects on research and development productivity across manydata-intensive scientific disciplines. However, in addition to providing next-generation computing resourcesfor data-intensive applications, it is essential to link these resources effectively and robustly to bridge sciencegroups within a university, across regional campuses, and between institutions over a wide area network.

Recognizing the growing importance of data-intensive computational science, the University of Oregon(UO) has made major investments in computing resources to support the cutting-edge research across all in-stitutional scientific disciplines during the past decade. For example, The Applied Computational Instrumentfor Scientific Synthesis (ACISS) is a science cloud based on high-performance heterogeneous computing andstorage components, acquired with $2M NSF MRI funding. The Lewis Integrative Science Building (LISB) isanother example that serves as the heart of inter-disciplinary science at UO, with leading-edge scientific in-struments, including a new MRI machine. UO also plans to develop a large data center at Allen Hall and astate-of-the-art visualization theater in the Knight Library.

The availability and growing use of these computing resources by scientists across UO campus has quicklyled to realization that the general-purpose campus network (UOnet) offers neither the capacity nor the capabil-ities necessary for the needs for data intensive research. For example, UO scientists have repeatedly reportedthe very long delay for transferring a large volume of data between their departments and ACISS machines thatadversely affected their rate of productivity. Furthermore, collaborations beyond the UO campus, regionally toother Oregon institutions, nationally, and internationally, are also beginning to suffer from network limitations.

In 2012, UO received a $500K NSF award (NIE-1246136) to design and build a new network at UO campuscalled Bridging Open Networks for Scientific Applications and Innovation (BONSAInet) that is targeted to therequirements of our scientific applications. This award from the “Data Driven Networking Infrastructure forthe Campus and Researcher” program provided support for the required equipments to achieve five goals: (i)Creating a Science DMZ platform among major computing facilities across UO, (ii) Adding a new 10Gbpsnetwork circuit between the UO and Internet2, (iii) Using Software-Defined Networking (SDN) technologiesthroughout the network, (iv) Promoting the development of IPv6- and service-aware scientific applications, and(v) Socializing the use of the UO’s membership to the InCommon federation. However, further investment isrequired to develop an array of services on top of the BONSAInet to turn it into a powerful operational network.

An interdisciplinary group of scientists along with the Information Services Division at the UO propose acollection of development and integration activities on top of the BONSAInet in order to provide the essentialservices and support processes to make this valuable resource fully functional and operational. The BONSAInetprovides a unique opportunity for innovation to three groups across the UO campus as follows:• Domain scientists take advantage of high bandwidth connectivity between major computing facilities across

UO campus and to other organization through Internet2 to conduct data intensive research in a more inter-active and effective manner,

• Computer Scientists leverage SDN capabilities of the BONSAInet by using it as a powerful experimentaltestbed to develop and deploy a wide range of innovative networking and security protocols as well assolutions related to cloud computing, and

• Network engineers can also use SDN features to devise and easily deploy new network management andsecurity policies in order to cope with the growing size and complexity of campus network (e.g., the requiredsupport by the users of BONSAInet to implement a programmable policy to route traffic from UOnet to theBONSAInet).

The proposed activities are conducted over a two-year period by two full-time programmers and a group ofCIS graduate students who broadly undertake the following tasks: (i) Identifying, developing and evaluating aset of basic services (e.g., route, connection, traffic, security management on the BONSAInet), (ii) Developing

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the required processes to receive support from network engineers for services that are offered by UO, (iii)Integration of the created services and processes into scientific applications and research projects across theUO campus, and (iv) Demonstrating and evaluating the operation of the BONSAInet by completing a few pilotprojects.

Our team members bring extensive background and experience that are required for the success of thisproject. Computer scientists in our group have extensive research experience in networking, security and com-putational science. The participating engineers from the Information Services (IS) are actively involved inIETF, NANOG, SCinet, Internet2, and closely collaborate with other Regional Optical Networks (RON) in thecountry and major network infrastructure vendors. The close collaborations between the CIS faculty and en-gineers at IS has created a strong synergy between them. The participating domain scientists are conductingresearch projects related to computational and data-enabled sciences. They are already heavy users of ACISSand are very interested and well-positioned to leverage the new networking capabilities offered by BONSAInet.The programmable connectivity of BONSAInet with UOnet turn this into a unique experimental testbed for CISresearchers to design, develop and deploy the next generation of networking and security protocols as well ascloud computing solutions on top of the services offered by the BONSAInet. The improved accessibility to com-puting resources coupled with adoption of good practices and advanced networking service will significantlyimprove the rate of innovation among UO scientists. Furthermore, BONSAInet is configured such that someof its features would be available to other UO users whose facilities are not directly connected to BONSAI atthis point. Finally, because research and education are coupled intimately at the UO, the success of this projectwill have significant impact on the educational experience on campus, especially in the use of high-bandwidth,data-intensive applications, such as the visualization of scientific simulations.

The rest of this proposal is organized as follows: In Section B, we present a short overview of currentand planned computing facilities across the UO campus to illustrate the growing needs to a high-performancescientific network that effectively bridges these facilities. Section C presents an overview of the BONSAIproject and its key elements along with their status. We present several scientific applications and demonstratetheir demand to frequently transfer large amount of data between different facilities across the network inSection D. Our proposed development and integration activities are outlined in Section ??. Section G describesour plan of work in several steps including the associated timeline and goals for each step. Our managementplan is presented in Section H. We describe the impact of the proposed effort on research and education at UOin Section I. Finally, some of our prior NSF supports are summarized in Section J.

B University of Oregon FacilitiesThis section presents a summary of the general-purpose campus network at UO (UOnet), and the main com-puting facilities across the campus as they relate to the proposed activities outline in this proposal.

B.1 Campus Network (UOnet)

At the heart of the UOnet, there are two points of presence (PoP) that house the head-end for our on-campusredundant multi-mode and single-mode fiber plants and external demarcation points for external fiber plantsoffered by other telecommunication service providers. These two facilities are built to comply with the TIA-942 specification for a Tier-2 data-center. Besides the fiber plant and core network and telecommunicationinfrastructure, these facilities also accommodate the bulk of the administrative and academic services providedby the central Information Services division. The architecture of the network follows a basic hierarchical modelwith a functional separation of the network across different layer as shown in Figure 1. The campus border isthe first zone that an incoming flow from outside enters. The campus border functions are satisfied by highperformance and high availability devices from two different vendors. The devices are configured in a highlyredundant architecture with dual routing engines, multiple switching engines and multiple power supplies toaccommodate the failure of any individual component with no impact to the operation of the network. Thenext layer is the campus layer-2 core. This is basically a redundant switching architecture. All devices at thecampus core will be dual connected to each of these switches. The last layer is the campus layer-3 core. This

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UOnet Functional Design

Campus Layer-3 Core

CampusLayer-2 Core

Open Acad/Admin Secure Acad/Admin Residence Halls/Wireless

NOC Satellite Sites

CampusBorder

SiSi SiSi

Off-campus

Network for Education & Research in Oregon

(NERO)

Oregon Gigapop(Internet2 Connector)

Oregon Internet Exchange Point

(OIX)

Figure 1: Functional view of the general-purpose campus network at UO (UOnet) with its external connectivity.

layer houses the bulk of the routing infrastructure. We have functionally grouped the infrastructure based on thecommunity that is being served. Buildings on the campus are dually connected to routes on the administrativeand academic groups. Devices at this layer do the bulk of routing edge functions, including access control,marking, policing, and routing aggregation.

The described architecture enabled us to introduce new technologies without incurring a forklift upgrade.The campus routing core and border utilize the Border Gateway Protocol (eBGP) to propagate routing infor-mation and make routing decisions based on well-defined traffic policies. Internally, iBGP, Open Shortest PathFirst (OSPF) and Intermediate System – Intermediate System (IS-IS) are utilized to carry internal routing in-formation. We have always been at the forefront of the integration of new technologies such as IP Multicast,IPv6 and wireless into the day-to-day operation of the campus network.

Today, UOnet supports 10Gbps connections in its core and distribution layers. We also support 10Gbpsconnection to buildings that require high speed connectivity. Buildings at the University of Oregon are provi-sioned with redundant fiber optic service to ensure their survivability in the event of a fiber cut or the failureon one of the core nodes that provide their connectivity. UOnet also offers a rich outbound connectivity byleveraging its partnership to operate three Internet transport activities as illustrated in Figure 1. The Networkfor Education and Research in Oregon (NERO) provides for 10Gbps connectivity for Internet commodity traf-fic and to R&E institutions in the state of Oregon. The Oregon Internet Exchange is a small Internet trafficexchange point that allows for the flow of traffic between its members for local ISPs and organizations. TheOregon Gigapop (OGIG) is an Internet2 connector serving the needs of the I2 members in the state of Oregon.The OGIG leverages the optical transport infrastructure that NERO operates.

B.2 Computing Facilities

We present a brief description for four computing facilities at the UO campus to illustrate their capabilities andcommunication needs.Applied Computational Instrument for Scientific Synthesis (ACISS): The University of Oregon (UO) re-ceived a Major Research Instrumentation (MRI) grant from the NSF on May of 2010. The three-year MRIgrant funded the development and deployment of an Applied Computational Instrument for Scientific Synthe-sis (ACISS) to enable an interdisciplinary group of UO scientists to advance their next-generation research

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objectives with a state-of-the-art, large-scale computational and storage resource. ACISS consists of a hetero-geneous computing environment with over 2000 compute cores, 156 GPUs, and a mix of regular and largememory systems. Additionally, ACISS has a 400 TB parallel shared filesystem. These resources are backed bya private 10 GigE network, allowing for fast compute interconnections and access to high-performance storage.

ACISS currently offers two primary research services, a traditional high-performance computing cluster,and a scientific cloud. To fully realize the potential of both systems, large amounts of data must be transferred toand from the high-performance storage system. Additionally, the cloud computing system allows for scientiststo allocate on-demand systems on the public facing network. As more scientists take advantage of the highlyconfigurable resources that ACISS provides, the demand for faster connections to ACISS through the UOnethas rapidly increased.Lewis Integrative Science Building (LISB): The LISB is home to strategic research clusters related to thehuman brain, molecular biology, nanotechnology, and solar energy. It will bring together researchers fromacross the spectrum of brain research from sensory and cognitive to molecular biologists and materials scientistsworking in green nanotechnology and solar energy. The LISB will also be the new home of the Lewis Centerfor Neuroimaging. The LCNI supports interdisciplinary, multifaceted research in cognitive neuroscience andbiological imaging. The Center will have a whole body 3T MRI unit, a variety of stimulus presentation anddata collection modalities, and full capabilities for the design and fabrication of MR coils to support a broadrange of research needs and applications.Visualization Lab & Machine Room at CIS: The visualization lab (Viz Lab) in the CIS Department is adedicated space for visualization research and presentation. It has three main components. For viewing ofhigh-resolution data, there is a 4x3 tiled LCD display wall, providing approximately 48M pixels of informa-tion. For large format visualization, a 6’x9’ rear-projection screen was installed. A CyViz 3D stereo projectorprovides a passive 3D graphics viewing experience. However, the highlight of the Viz Lab is a high-definitionreal-projection system based on 4 Sony VPL-VW100 1080p projectors organized in a 2x2 tiled fashion. Theseprojectors were recently upgraded with the edge-blending system to provide a seamless solution of approxi-mately 7.5M pixels that has significantly improved the visualization quality. The Viz Lab has been used fordiverse applications in neuroinformatics, geological sciences, and biology.

The CIS department also recently completed a significant upgrade on its machine room. The remodel roomhouses 15 racks that support about 10kVA per rack for power and cooling along with a battery-backed UPSand redundant air handlers. The planned phase two will add a second UPS and a connection to the University’sbackup power grid and a local chiller. These will provide redundancy for all the critical infrastructure. Highbandwidth connectivity to the CIS department is essential in order to effectively utilize the capabilities of thesefacilities.A new Data Center at Allen Hall: The University is also planning to construct a significant addition to AllenHall that is specifically designated as a facility to house key servers and co-located equipments, vastly im-proving the University’s capacity for data storage, security and access. The new facility will provide academicdepartments and research centers and institutes with server class hardware in a tier 3 redundant data center withan expected 99.982% uptime for power, cooling, and networking. The facility will include 10G connection.

C The BONSAI ProjectThe University of Oregon received $500K infrastructure award from NSF (NIE-1246136) in support of theBONSAI project. The main goal of this project is to build a “Bridging Open Network for Scientific Applicationsand Innovation (BONSAI)”. The BONSAI project consists of five components that are either in progress or havebeen completed as follows:Building a SDN-capable Network: The main component of the project is a dedicated, high bandwidth, SDNcapable network, called BONSAInet, that is independent from the UO campus network(UOnet). BONSAInetinitially interconnects several major facilities across the UO campus and connects these facilities to Internet2.These facilities are involved in data-intensive computing and include ACISS and the following departments:Physics, Biology, Chemistry, Neuroscience and Computer and Information Science. BONSAInet offers high

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Figure 2: The topology of BONSAInet and its connectivity to UOnet and Internet2

end-to-end throughput and a unique set of capabilities not only between the connected facilities at UO butalso between each facility and computing resources at other institutions throughout Internet2. Figure 2 depictsthe overall topology of BONSAInet along with its connectivity to UOnet and Internet2. The connections forUOnet and BONSAInet are shown with the dotted and solid lines, respectively. While these two networks arecompletely separated, users on UOnet can take advantage of services offered by the BONSAInet. Furthermore,a fraction of UOnet traffic can be routed through the BONSAInet in order to allow controlled experiments.Finally, the dual connectivity of individual facilities provide a great deal of flexibility to route traffic or partitionthe BONSAInet.Adding a new 10Gbps Circuit between UO Campus and Internet2: We will augment the UO’s connectivityto the Oregon Gigapop (OGIG) by a new 10-Gigabit circuit that exclusively connects the BONSAInet to In-ternet2. This dedicated connection facilitates the provisioning of dynamic network circuits across the nationalR&E network fabric by leveraging the Dynamic Network Systems offered by the NSF-funded Internet2 DYNESproject. This in turn allows UO researchers to effectively take advantage of computing resources and datasetsat many universities across the U.S. This new 10 Gpbs circuit has been added and would become online in thesummer of 2014.Creating a Science DMZ platform at UO: The BONSAInet operates as a Science DMZ using the dedicated10Gbps circuit to Internet2. The Science DMZ service model (i) provides an open, high bandwidth throughoutbetween the BONSAInet core and regional or national networks, and (ii) offers a programmable environmentwhere computing, storage, visualization and transport capabilities can be easily managed and application-driven, (iii) allows network engineers to customize the University’s security policies and their enforcementmechanisms without compromising the high performance of the BONSAInet, We leverage other initiatives,such as perfSONAR, for monitoring and diagnosis of failures as well as assessing the characterization of end-to-end network performance. All elements of the Science DMZ will be optimized for the common needs ofscience applications such as transferring a large volume of data, controlling an experiment at a remote site, anddata visualization. These capabilities making access to remote computing resources and datasets at 60+ univer-sity campuses across the U.S. easily accessible to scientists at our campus while, at the same time, making ourfacilities more reachable to their constituents.Promoting IPv6-aware Applications & Services: UOnet has widely supported IPv6 transport for a coupe ofyears. Project members from UO Information Services have made an orchestrated effort to raise awareness

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about the existing services and good practices for developing applications that leverages IPv6 capabilities. Theextensive expertise of these engineers in designing, deploying and operating IPv6 network has provide to be avaluable asset for promoting and facilitating these services among the UO community.Incorporating the InCommon Federation: The UO has been a member of the InCommon federation sinceFebruary 2010. As part of our participation, we can make use of the Shibboleth identity attribute sharingtechnologies to manage access to online resources that can be made available to the InCommon community.As we develop support services for the BONSAInet, we will use Shibboleth for data access management.The ability to utilize well-known and standardized access control techniques will facilitate access control forresearchers to BONSAInet ’s services.

D Scientific Application DriversIn this section, we present examples of collaborative projects by UO faculty members in the areas of high-energy physics, climate change, earthquake science, and cancer biology to demonstrate their immediate needsto effectively move large volumes of data which in turn motivate the proposed services for the BONSAInet.

D.1 High Energy Physics

UO faculty are involved in the ATLAS collaboration which is one of the two multi-purpose detectors at theLarge Hadron Collider (LHC) in Switzerland. The ATLAS detector typically records events at a 300 Hz rateover a nominal operational year of 107 seconds and a typical up-time of 25%. The raw data is reconstructed andstored at large Tier 1 (national) facilities, and with a typical event size of 1 MB/event, or approximately 1000TB per year. Clearly this is too much data for any typical user to analyze directly so the information stored perevent is reduced by filtering the raw data and eliminating detailed information which is not relevant for typicalusers. Currently, these filtered event samples are stored at Tier 1 and regional Tier 2 sites (the Oregon Tier 2site is the Stanford Linear Collider Center) with only a sub-sample of all defined samples available at each Tier2; see Figure 3. This is generally the starting point for a typical user analysis, where grid jobs are submittedto process data on the Tier 2 where a particular sample is stored, and more highly filtered data with reducedinformation is brought back to the user’s institution for further analysis. This model puts considerable demandson limited Tier 2 resources, however, and the filtering process can take up to one month of calendar time tocomplete. As local storage is limited, users may repeat this process several times per year in the course of atypical analysis. This leads to overall efficiency concerns which are best overcome by increasing local storageand compute space. Towards that end, the Oregon ATLAS group has purchased a so-called Tier 3 cluster whichincludes on order of 100 TB of usable disk and roughly 80 cores on 2.2 GHz Xeon processors. This allowseach individual analysis to customize the filtering of the total event sample tuned to their particular analysis,and samples of 10-20 TB.

Figure 3: Tier computing model for the LHC.

The 10-20 TB data sets forms the practical workingdata set around which real-time video-conferencing collab-oration can occur. Most of this real-time collaboration isbased on Monte Carlo simulations which are run againstthe 10-20 TB raw data set. Performing these runs on theACISS machine will improve turnaround time, especiallywhen the local Tier 3 cluster can use to pre-filter the data.By having the two machines to coordinate and share thedata will improve the efficiency of the analysis process andsignificantly reduce the turn-around time. However, this re-quires a high-bandwidth connection between the local ma-chine and the ACISS systems so that this 10-20 TB rawdata can continually be exchanged. The BONSAInet pro-vides the crucial capability to address this need.

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D.2 Climate Science via Simulation

co-PI Bothun is working on a large scale climate simulation that involves the potential increased efficacy of deeptropical convection in response to warming sea surface temperature. The problem of convection, since thereis no natural scale in the system, cannot be solved analytically and therefore extensive simulation is required.Simulations are generally limited by the amount of domain space that a single run can encompass as well aspoor treatment of boundary conditions. In particular, an important problem in tropical meteorology is betterunderstanding the tendency for convection to self-aggregate (i.e., evolve into highly organized clusters with noapparent external influence).

Direct simulations of self-aggregation require large domain sizes ([1, 2]), and the ability of a particularmodel to produce self-aggregated convection depends on many parameters ranging from dimensionality andgrid resolution to the treatment of radiation, wind shear, and even the terminal velocity of droplets. Conse-quently, identifying mechanisms responsible for self-aggregation via direct simulations is very computationallyexpensive. An alternate approach to direct simulations of self-aggregation exploits the analogy between mul-tiple equilibria in small domains which parameterize the large scale circulations via the weak temperaturegradient (WTG) approximation, and the dry and moist regions in a self-aggregated state. In the smaller WTGdomains, multiple equilibria refers either to a state which supports persistent precipitation or one that remainscompletely dry under identical forcing conditions [3].

In order to validate this approach, it is necessary to perform simulations on domains large enough to permitself-aggregation which will enable a direct comparison between the two approaches. In turn the analysis of thesesimulations then feedback to tweaking the different physics inputs into the simulation. Those tweaks come fromthe human network of real time collaboration and thinking. The faster that this human-interaction timescale canbe speed up by this improvement in hardware processing, the faster the rate at which the scientific process canhelp converge to the right set of initial physics conditions that might help explain the self-aggregation process.In particular, as far as human impacts on climate are concerned, the role of steadily increasing sea surfacetemperatures are particularly important to pin down, so that we may have some expectation of the amplitudeof the water vapor feedback loop on surface temperatures to better assess the overall predicted rate of surfacewarming. Using the ACISS machine as the agent to speed up real-time thinking about this critical process is animportant step towards better understanding climate change.

D.3 The Cascadia Initiative

Figure 4: Deployment of sensors in Cascadia initiative.

The NSF funded Cascadia Initiative is based on a largeamphibious array of detectors distributed over the Juan deFuca plate. The main site of this sensor network is hostedat the University of Washington, but analysis of the dataagain is occurring via a large scale distributed collaborativenetwork of seismologists and oceanographers in which UOfaculty member Doug Toomey is involved. Figure 4 showsthe deployment of sensors as of the end of 2011 (e.g., year1). Researchers run Monte Carlo and other types of sim-ulations to try and match the data set in real time. TheCascadia project provides a unique opportunity to study, inreal time, the dynamics of a subduction zone (the most ac-tive one in the world) in the context of understanding andmodeling the structure and evolution of the oceanic platesand the various forces acting upon them [4, 5, 6]. It is anunprecedented number of sensors which will provide anempirical database hundreds of times larger and with muchbetter sampling resolution than has been available before.As a result of number of new physical stresses will manifest themselves through the multiple detections of

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events throughout the sensor network. Real-time visualization of the response of the detector network to theseevents will be important in better understanding their overall 3D geometry. This in turn will inform the modelsand again, iteratively converge, through real-time data analysis and discussion tools to more optimal science.

The provided capabilities by the BONSAInet to transport, process, analyze and display large datasets in nearreal-time completely enable more productive collaboration among scientists.

D.4 Cancer Biology

wild type

pre-cancerous

pre-cancerous

tumor

tumor

p16 mutation

p53 mutation

gene?? mutation

1

2

4

5

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Figure 5: An oncogenetic tree of cancer somatic evolution.Somatic mutations can be related to one another in a nestedpattern called an oncogenetic tree that is similar to a phyloge-netic tree of organisms. Importantly, once the cancer states ofcell populations (right) are mapped onto the cell populationswith known mutations, an inference can be made regardingthe causative mutations. In this hypothetical example, muta-tions in p16 and p53 are necessary, but not sufficient, to causethe formation of a tumor. Cell populations 4 and 5, however,share the state of being a tumor, and therefore the mutation(s)that are shared by these cell types are likely to be causative.

Systems biology is a broad field of research that will sig-nificantly benefit from the proposed services on the BON-SAInet. We give one example from cancer biology todemonstrat these benefits. From a single mutant cell to acomplex tumor mass, cancer development is a complicatedprocess [7, 8]. Cancerous cells increase in fitness throughintrinsic genetic changes and co-adaptation with normalneighboring cells in a process very similar to adaptive evo-lution of organisms in populations in the wild. A partic-ularly vexing challenge has been identifying the networkof causal genes involved in this switch from pre-cancerousto tumor formation that increases the fitness of tumorgeniccells as compared to their neighbors; see Figure 5. Con-ventional endpoint studies with pathologically identifiabletumors have not been able to differentiate causal mutationsfrom by-standing “rider” mutations, and these approacheshave had little success in mapping out the order of muta-tions at each step of tumor progression.

The Cresko and Zong laboratories at UO have recentlyembarked on an exciting new project to apply tools fromevolutionary genetics to cancer biology and solve this problem. The Zong lab established a malignant gliomamodel and successfully identified a unique type of cells known as OPCs that are responsible for this deadlydisease. They are using this mouse model to generate sporadic tumorigenic cells and unequivocally label themwith GFP, thus revealing close interactions between tumor cells and normal neighboring neurons [9]. Thesecells are being precisely collected at distinct tumorigenic phases using a Laser Capture Microdissection system.The Cresko lab has developed novel next-generation sequencing approaches to scan for mutations in billions ofbase pairs of DNA in just a few days, including sequencing approaches, analytical theory and computer softwarepipelines for the multivariate analysis of massive amounts of sequence data. These approaches are being usedfor the complete genomic and transcriptomic analysis of these cells, to identify causal genetic changes acrossthe tumorigenic process and reveal critical supporting roles of normal neurons to glioma cells, opening up anew research paradigm in cancer evolution [10, 11, 12, 13].

A key bottleneck in this research is the transfer of the trillions of DNA sequences from each tumor sample[14, 15]. These labs are re-sequencing the entire genomes and transcriptomes of each of the hundreds ofsamples. Each sample comprises tens of billions of DNA sequences, and requires nearly 1.5 terabytes of storage.Terabytes of new data are generated every few days, and the analysis of these data is very time-consuming andspecific to certain cluster architectures. Because of this the data must be transferred numerous times acrosscampus between different computer systems, including the cluster connected directly to the sequencer, theCresko laboratory computer cluster, and the ACISS cluster. A significant bottleneck in this research is theinsufficient network bandwidth for transferring these data across the UO Oregon campus. Our project is onlyone of nearly two dozen utilizing large amounts of next generation sequencing data, and the proposed serviceson the BONSAInet would greatly enhance the productivity of all of these projects.

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D.5 Scientific Visualization

Another area that presents new requirements for the BONSAInet is scientific visualization. An emerging limitingfactor involving large data sets lies in the ability to display “all of the data” on a single display device (or anarray of devices on a video wall). Figure 7 is an illustrative example of this limitation (and comes from thekind of climate simulations discussed in this proposal. This is a convective mesh visualization of hurricaneformation and energy exchange. The entire simulation is an 8000x8000x8000 pixel cube. The figure is theresult of 10x smoothing and averaging of the data so that it fits on a standard 2 million pixel screen model eventhough there are actually 64 million pixels of information but, in general, the researcher can not view all of thatat the native resolution of the simulation. !

Figure 6: Pathway for visualization of simulations deliveredfrom ACISS and displayed on high-resolution domain por-tals.

Figure 7: Convective mesh visualization of hurricane forma-tion and energy exchange.

We anticipate the first generation of HXGA monitors at4096 x 3270 pixel native resolution to emerge on the mar-ket (those resolution projectors already exist). This givesone 12.5 million pixels per screen. As an example, a 72”HXGA monitor could be mounted in the Bothun lab pro-viding the visualization platform for one ACISS run andwould allow for a far faster processing and understandingof what can be done in the SDSS data set. Similarly, theabove simulation could be viewed at scale on that plat-form. An imagined 2x2 array of HXGA monitors wouldgive you 50 million sky pixels to display at one time. Thatdisplay capability would allow simulations like the one de-picted above to be seen nearly at scale and would allow fordisplay of a large number of sky pixels at once in eitherthe SDSS or coming LSST data. For real-time collabora-tion, the ability to display many pixels in some data cen-ter would be extremely valuable and we envision, throughthe BONSAInet, the ability to run high-resolution simula-tions on ACISS and the results are delivered back to thedata center in a timely manner (depending upon the codeand complexity of the simulation). The entire hypotheti-cal visualization pathway is shown in Figure 6. In practicewe can imagine constant iteration between the data centerand the ACISS machine as data and algorithms are passedto the ACISS machine in exchange for 560 million pixelsto be displayed using the visualization facility of the datacenter.

E Design ObjectivesOur design objectives are motivated by the described sci-entific application drivers with respect to data movement,high throughout, and predictable end-to-end performance. However, we also consider our long term goal ofextending BONSAI to the entire campus and therefore address scalability and extensibility of any proposedmechanism. Our proposed techniques should not make any explicit assumption about the composition of net-work traffic (e.g., temporal pattern, duration, size and bandwidth of flows across the Bnet). Toward this end, wepursue the following three related design objectives:1) Supporting High Throughout, Low Latency End-to-End Connections: Our examination of traffic asso-ciated with scientific applications at UO revealed that the traffic is mostly composed of TCP connections thatare query messages (2KB to 50KB in size), delay sensitive messages (100KB to 1MB), or throughput sensitiveflows (100MB to 2TB). These applications require low latency for short flows, high burst tolerance, and high

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throughput for long flows. The main limiting factor of the end-to-end throughout in a typical campus environ-ment is the routing and forwarding protocols that often consider a single static path for each source-destinationpair (in the absence of failures). In this approach, individual links can become bottleneck when a few large flowsare routed through the same link. While having a new network with higher link capacity, namely Bnet, is help-ful, we need to adopt new techniques to effectively leverage network resources and ensure that the bandwidthof individual flows is primarily limited by end hosts (i.e., disk bandwidth) rather than the network.2) Incorporating Network Management Capabilities: To ensure that the provided services over Bnet aresustainable by UO Information Services beyond the life of this project, we need to incorporate a minimumset of network management capabilities. Our discussion with the engineers at the UO Information Servicesrevealed that the following capabilities are essential for them to operate Bnet:• Security Techniques: Rather than dealing with the security as an afterthought, we identify and address any

security issues related to the Bnet as various services and capabilities are developed and deployed.• Flexiblbly Managing Users & Resources: In a large research university such as UO, the network and com-

puting infrastructure are constantly evolving. For example, a new building is getting connected to the Bnet;a computing, storage or visualization node is added or removed; and users may gain/lose their privilegesto access or use certain capabilities or resources of the Bnet. It is essential for network administrators toefficiently and effectively to deal with all these dynamics.

• Traffic Monitoring, Event Logging: Network administrators require to monitor various events across thenetwork in order to diagnose some problems, perform any forensic operation or conduct an audit. Therefore,the Bnet should offer such capabilities as its size and number of constituents grows over time.

• Debugging Capabilities: It is essential that we evaluate and deploy some basic debugging tools over Bnetto enable both network administrators and application developers detect any problems with their codes orconfiguration settings.

As we develop and deploy these network management capabilities over the Bnet using SDN features, the net-work engineers from the Information Services become gradually familiar with SDN features and these services,and take over the responsibility of operating the Bnet. This transition process develops local expertise on SDNtechnology among the operational staff which is an essential need to sustain and expand Bnet beyond the time-line of this project.3) Enabling SDN-related Research: We leverage SDN capabilities of the Bnet switches to develop the re-quired services by domain scientists. However, SDN is a relatively new and evolving technology. There arestill a range of technical and operational issues related to SDN deployment that are being explored by the net-working community. Having an SDN-capable network at UO provides a unique opportunity for the networkingresearchers at the Computer and Information Science department at UO to conduct experimental research in thisarea. To accommodate these researchers, it is essential to effectively partition a segment of the network for anexperiment and avoid any impact on its regular use by others. By conducting these experimental research overthe Bnet at UO, not only the CIS researchers contribute into the SDN-related investigations but the associatedoperational engineers also gain the required experience to deal with a range of inevitable technical issues thatarise as the size and complexity of Bnet grows over time. In essence, enabling the researchers accommodatesthe long term sustainability and scalability of the Bnet.

F Core New TechniquesThe design objectives that we sketched in the previous section, would be extremely challenging to achievewith the traditional networking technologies for campus networks. However, the recent emergence of newtechnologies, in particular Software-Defined Networking, has made it possible to achieve these objectives ina cost-effective and easy-to-deploy manner. Therefore, we identify and integrate a number of recent coretechnologies into the Bnet to meet our design objective. We note that, apart from its size, many features ofBnet are very similar to the Data Center environment [?]. These features include very short RTT between endnodes (in the absence of queuing), low statistical multiplexing (i.e., a single flow can dominate a particularpath), largely homogeneous network that is under a single administrative control. These similarities along with

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our long-term goal of expending Bnet to serve the entire campus suggest that recently proposed techniques foraddressing our design objectives in the data center environment could offer promising solutions. [XXX, addingsome background on SDN here, or provide a pointer to related resources/papers]

F.1 Incorporating Dynamic Flow Scheduling

A promising approach to support high bandwidth connectivity over the Bnet is to leverage multiple paths thatexist between each pair of end points across the network, are not used to reduce the over subscription ofindividual links. One such a technique is the Equal Cost Multipath (ECMP) forwarding [?] that spreads flowsamong the available paths using flow hashing. While this approach can utilize the availability of resourcesacross multiple paths, its static mapping of flows to paths does not consider the current utilization of individualinks or size of a flow. Given the variations of flow sizes coupled with the dynamic nature of flow arrivalpattern, we integrate a dynamic flow scheduling scheme such as Hedera []. This technique periodically detectslarge flows, estimates the bandwidth demand of large flows, runs flow placement algorithm to determine non-conflicting paths for major flows, and finally explicitly installs these paths on the SDN switches. In short,Hedera tries to maximize the aggregate network utilization and thus the bisection bandwidth without making.Hedera is an attractive approach as it does not make any assumption about the network topology or traffic, andit does not require any major change in existing software or protocols. Hedera has been presented as a researchproject. Therefore, we need to develop, deploy, and examine the impact of its key components (i.e., detectionof large flows, estimation of demand per link, choice of the scheduling algorithm and frequency of running thealgorithm) on its overall performance for the observed composition of flows over the Bnet.

F.2 Deploying Data Center TCP (DCTCP)

As we mentioned earlier, an absolute majority (>95%) of the connections over the Bnet are TCP. Recent studiesillustrated that accommodating high utilization for long flows, low latency for short flows and tolerating highburstiness is challenging for regular TCP in data center networks while DCTCP is designed to achieve thesegoals [?]. Given the similarity of the Bnet and the data center environment, we will adopt DCTCP as ourtransport protocol over the Bnet. DCTCP uses Explicit Congestion Notification (ECN) which is available onour switches. DCTCP outperforms TCP with respect to fairness, high throughput, high burst tolerance, and lowlatency when used over high bandwidth link (10Gbps) across shared-buffer switches [?]. Therefore, we believethat DCTCP offers a very promising transport protocol for all flows over the Bnet. However, care is requiredfor choosing assciated parameters to observe the desired behavior in real networks [?]. For example, setting theparameters to allow likely level of bustiness for the target setting. We will also examine recent extension of theDCTCP that accomodates incremental deployment when only one ends supports the protocol []

F.3 SDN Controller& Programming Framework

While there are many SDN-related projects in the community, only a few of them offer a stable and reliablesoftware that can be used as the starting point of (part of) a operational service. Furthermore, a completeSDN-based service often requires many components that need to be properly integrated. Therefore, our effortrequires a great deal of development and evaluation even for deploying a previously proposed and sound idea.

We use OpenDaylight1 as the basic platform for our controller since it is an open source and supported by anactive community of developers. To develop our desired services, it is desirable to use programming frameworkssuch as Ferentic [] or Maple [] that support high level programming and ensure consistency of the specified rules.Unfortunately, Ferentic is not compatible with OpenDaylight and is not actively supported/maintained whileno source code is currently available for Maple 2. Fortunately, Ferentic framework has a modular architecturewhich enables us to separate the compiler and run-time system portion of the framework and integrate themwith the OpenDaylight controller. We anticipate that most of our development to be performed either directlyover OpenDaylight or through the modified Ferentic framework.

1www.opendaylight.org2The developers of the Maple framework has promised to share their code with us by the end of the year.

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F.4 SDN-Based Network Management Capabilities

The central nature of control plane in SDN simplifies simplify certain network management tasks at the costof scalability and complexity of the controller. For example, the controller can directly manage the privilegeof individual users to request certain services through the Bnet using their UO credentials to authenticate them.Furthermore, it can also keep track of various resources that may become un/available at different times acrossthe campus. Regarding security mechanisms, we require that at least those security measures that are in placeover the campus network, can be replicated over the Bnet. Clearly, the security of the controller is of theoutmost importance. We adopt best known practices for security over SDN networks [?]. We are interestedin adopting modular composable security frameworks such as Fresco [?]. However, Fresco is integrated witha different controller (FortNOX). Therefore, we also need to explore whether its application-layer code forsecurity policy development can be separated and then integrated with the OpenDaylight controller. We willalso implement Resonance framework [18] that manages dynamic access control in Enterprise network. Inaddition its development from the presented high level ideas, we need to carefully examine a set of integrationissues including scaling, and responsiveness of this framework.

Measurement and monitoring of traffic is essential both for management and security purposes. How-ever, existing solutions either rely on customized hardware designed for a specific purpose, or result in a largeoverhead especially for high bandwidth link. We will implement the algorithmic frameworks for efficientlyidentifying heavy-hitters and estimating the aggregate traffic [16]. Given the inherent limitation of the existingmonitoring techniques, we will also deploy a commercial product that will be provided to us by Niksun Inc. 3

4 5, a worldwide leader in this area, through PIs long-term collaborations with Dr. Willinger who is a chief sci-entist at Niksun and Senior personnel on this proposal (see the provided support letter from Niksun Inc.). TheNiksun monitoring product enables us to get a handle of data collection, analysis, recording both for forensics-based cyber security and network performance assessment. Furthermore, our close collaborations with Dr.Willinger and Niksuns mutual interest to our deployment effort would be extremely valuable in addressing arange of challenges in network management that we need to confront. traffic monitoring [16]

Debugging SDN-based application could be particularly problematic due to subtle interplay of rule updatesand dynamics of packet arrival. To our knowledge, there is not stable, widely used debugger for SDN con-troller or SDN-based applications as this is considered as n active research area. Given the critical importanceof debugging tools, we will initiate our in-house development effort by leveraging the previously proposedapproaches [19, 20] as well as a recently-proposed technique for systematic debugging of the SDN controllersoftware [?] that is platform independent.

G Plan of WorkOnce the project commences on September 1, 2014, our plan of work and the associated timeline for theproposed activities over a two-year period are as follows.

G.1 Planning & Preparation (September-November’14)

The SDN ecosystem is rapidly evolving. Our first goal is to carefully re-examine SDN products, proposed ar-chitectures and controller software (and their features) and make any required change or extend our preliminaryplan (Section ??). This step involves a series of technical meetings with all project stake holders to present ourplan and collect and incorporate their feedback. Our plan will be vetted by network engineers with expertise inSDN (e.g., Dave Meyer). Other major goals in this step are (i) to recruit two capable programmers and a fewinterested and capable CIS graduate students (that we collectively refer to as “developers”) to execute the plan,and (ii) to form an advisory board for the project.

3https://niksun.com/solution.php?id=74https://niksun.com/Products_Cybersecurity.php5https://niksun.com/Products_Network_Performance.php

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G.2 Examining Individual Components in the Testbed (December’14-February’15)

The second goal is to conduct basic examination of the controller software (e.g., OpenDaylight) over our SDNtestbed. This step provides the basic experience with the existing switches and controllers to developers andalso exposes any obvious problems (e.g., incompatibility of controllers or its OpenFlow plugin with switchesfrom a particular vendor) that we should address. The major milestones in this step are: (i) Conducting theinitial examination and gaining the required confidence in the operation of various components (e.g., switchesand controller), (ii) Adopting or developing a test-suite for benchmarking a collection of basic features of aswitch or controller, (iii) Establishing the environment for the developers to effectively collaborate.

G.3 Developing & Testing the Services on the Testbed(March’15-October’15)

The primary goal of this step is to develop and extensively test those identified SDN services on our SDNtestbed. Using the testbed provides closer access to all elements and expedite testing and troubleshooting. Whilethe two programmers take the lead, selected graduate students will be actively involved (particularly full-timeover the summer) to ensure the completion of this labor-intensive step in a timely manner. Multiple servicesare developed and tested in parallel to speed up the process. The developers leverage existing prototypes ormature ideas related to the target services [16, 17, 18], as well as good practices for testing and debuggingthe services [19, 20]. The developers attend related venues (e.g., HotSDN, NSDI, OpenNetworkingSumit) toremain up-to-date with major developments in the SDN community. The major milestone at the end of thisstep is a collection of operating and fully tested services with a well-defined API. We schedule a coordinationmeeting with all project members half-way through this step to assess our progress and make any necessaryadjustments.

G.4 Importing Services to BONSAInet (November’15-March’16)

The next goal is to import and evaluate the developed services over the BONSAInet. We anticipate that moresubtle problems and bugs occur at this step despite extensive testing over the testbed. At this step, we alsoexplore how these services can be extended to inter-campus scenarios through the new connection from UOcampus to Internet2. The lessons learned in this step are used as input for defining and integrating the processesto obtain support from network engineers at UO. These services are demonstrated to the UO community in aday-long workshop to collect their feedback. The major milestone at the end of this step is the availability ofoperational services on top of the BONSAInet.

G.5 Developing Processes for Support from Network Engineers (January’16-May’16)

The goal at this stage is to develop a set of processes that users of the BONSAInet can request and receive supportfrom UO network engineers in an efficient and automated manner (e.g., through a web-based interface). Thisstep requires close collaboration and coordination between the developers and the related network engineers atthe UO Information Services to ensure proper integration of the BONSAInet services with the campus network.In particular, any security-related concerns for the BONSAInet should be discussed and addressed in this step.The major milestone at the end of this step is a working portal that allows UO users to request any of theoffered support (based on their privileges) from network engineers. At this point, the BONSAInet will be fullyoperational and can be used by users who have some experiences in using these services (e.g., CIS researchers,or domain scientists who were actively involved in the project).

G.6 Integrating Developed Services into Applications (March’16-August’16)

The goal of this last step is to inform the researchers and UO community at large about the provided servicesby the BONSAInet and educate them about the ways that they can take advantage of them. This goal is achievedthrough a series of presentations with hands-on demonstration to speed up the adoption and integration of theseservices by the UO community. We use this opportunity to introduce and train the campus about the newframework for the data-intensive computation that can be used over the BONSAInet. Finally, we will completea few pilot projects on top of the offered services and support processes to fully demonstrate that these servicesare indeed operational. We anticipate these pilot projects to be derived primarily by the CIS graduate students

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and other researchers who have been actively engaged with this projects and thus become “early-adopters” ofthe services. We also rely on these early adopters to inform and educate the next wave of researchers andstudents at UO in coming years.

The project members hold a coordination meeting at the end of each step in order to learn about the projectprogress, coordinate, and possibly revise the plan for the next step.

H Management PlanThe proposed activities are primarily managed by PIs Rejaie and Duminguez. Most of the activities are con-ducted by two full-time programmers as the core members of the project. The programmers form a new group,called BONSAI group, at the Information Services where their day-to-day activities are being supervised byco-PI Duminguez. This setting enables these programmers to closely interact with other network engineersto benefit from their extensive expertise and also coordinate for developing processes to obtain support fromnetwork engineers as we indicated in our plans.

We also recruit a few capable graduate students from the CIS (and possibly other sciences) department(s)who have some prior experience with SDN to closely work with the programmers and actively contribute to thelabor-intensive development and testing activities. In addition to their impact on productivity, the participationof these students offers a valuable training experience for them. These students can effectively act as “earlyadopters” of the services and contribute in propagating this “knowledge” by training the next wave of usersacross UO campus. This in turn offers a practical way to not only sustain but also expand the user populationfor the offered services which in turn further magnifies the impact of this project. We anticipate one or two ofthese graduate students act as the liaison between the BONSAI group and other departments in order to reducethe coordination overhead on the programmers.

Active project members hold monthly meetings to discuss the progress, devise solutions for existing prob-lems and plan accordingly. All coPIs and senior personnel from different science and CIS departments as wellas engineers with expertise in SDN (e.g., Dave Meyer) form an advisory board and attend the meeting once perquarter and at the end of each step to assess the progress and share their input. All information and updatesare shared through the project email list and will be posted on the project web site (bonsai.uoregon.edu)along with various deliverables and tutorial materials. All the developed software in this project will becomepublicly available under open source license through the project website.Anticipated Costs: The prior NSF award only provided funding for the SDN switches in the BONSAInet. TheUO covers all costs associated with building the BONSAInet before this project commences. The requestedfunding in the proposal is primarily used for funding the project personnel, namely the two programmers, onegraduate student as liaison, and one month of support for PI and coPI. We have also included a small amountof funding for the required servers, storage and desktops that are needed for the development and testing ofthe services. The final budget item is for traveling of programmer and graduate students to attend SDN-relatedvenues.

The budget does not include the contributions from network engineers in developing the required supportprocess, other participating graduate students, co-PIs, senior personnel who will be covered by other researchor infrastructure grants (possibly from NSF). The UO’s Information Services will assume all costs associatedwith maintaining the BONSAInet once all the proposed activities are completed and the network becomesoperational.BONSAInet Location & Operation: Our SDN testbed is located at the Information Services and will be eas-ily accessible by programmers and also remotely reachable by graduate students in early steps of the projects.BONSAInet infrastructure will be deployed within the University of Oregon campus. Core equipment for BON-SAInet will be installed at the Oregon Hall Datacenter Tier-2 facility, which houses the footprint for one of theUniversity’s dual fiber plant. This will ensure easy access to already existing fiber resources to reach each ofthe network nodes. BONSAInet will also leverage the rest of the University’s network services infrastructuresuch as InCommon, DNS, DHCP, NMS systems. Other BONSAInet infrastructure will be installed at two ofthe Network for Education and Research in Oregon (NERO) points of presence.

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Team of Network Engineers at UO’s Information Services: The team of participating network engineersfrom Information Services, who are led by co-PI Dominguez, is uniquely qualified to execute the proposedactivities. This team has over 100 years of combined experience and has been actively involved in a wide rangeof efforts related to campus and wide-area network design and operation including: maintaining and improvingthe capabilities of UOnet, planning and managing the connectivity of all UO new computing facilities and datacenters (ACISS, LISB), operating the Oregon Gigapop connector to Internet2 in the state of Oregon, monitoringnetwork usage and demand to detect security incidents, ensure availability of resources to all constituents ofthe campus network, and deploying and operating other advanced network technologies such as the MBONEand 6BONE. Our team members have provided leadership in a number of forums including participation inInternet2 technical groups, Quilt, NANOG, and supporting NERO. Participating engineers in the team pro-vided significant leadership in the deployment of dual stack IPv4 and IPv6 networking, operated a dual stacknetwork at UO for many years, and taught many workshops on IPv6 deployment. Finally, these network engi-neers have closely interacted with scientists from different disciplines to address their needs for connectivity toaccommodate their data-driven scientific applications.Synergy Between CIS and IS: There has been a strong synergy between the CIS department and InformationServices at UO that have been further strengthened by Rejaie and Duminguez in recent years through a seriesof collaborations, including the official agreement that IS shares anonymized Netflow data with CIS for thepurpose of networking research is in place. Furthermore, Rejaie is leveraging the experience of IS engineers inorder to create a couple of hands-on lab courses in network management for CIS students. Finally, IS regularlyhires CIS graduate students to help with their operations. We believe that the close relationship between CISand IS would be crucial for the success of this project.

I Impact on Research and TrainingThe capacity to conduct and share scientific research is increasingly dependent on seamless access to computa-tional resources, adequate data storage capacity, and visualization capabilities. The UO has a strong track recordof conducting innovative and integrative research with 25 interdisciplinary research centers and institutes thatengage UO researchers and collaborators at other institutions across the world. The BONSAI project advancesscientific discovery and understanding by enabling seamless access to University of Oregon resources thatsupport and promote integrative scientific research. The BONSAI project allows researchers across the Univer-sity of Oregon campus and collaborators and experts at other institutions to further progress on data-intensivescientific projects that will advance understanding and promote discovery. The researchers and principle inves-tigators participating in the BONSAI project will leverage new networking capabilities in a range of projects,from high energy physics to seismology and biology, making real-time data accessible to researchers aroundthe globe. Along with the Applied Computational Instrument for Scientific Synthesis (ACISS) and the ATLASdetector Tier-3 data analysis center, the BONSAI high speed network will connect the scientific facilities on thecampus with the scientist-users of these facilities and their remote collaborators, enhancing the infrastructurefor teaching and learning while simultaneously promoting collaborations between disciplines and institutionsand among the University of Oregon and international partners.

The BONSAI project will directly impact and promote teaching and training opportunities that will involveundergraduates, graduate students, and postdoctoral researchers in the use of advanced computing and networkinfrastructure along with the access to new kinds of information tools. Graduate students will benefit by theexpanded training and exchange programs that the BONSAI project will promote. The BONSAI project willenhance the infrastructure for research and education at UO, broadening the UO’s network of research partnersand lending our unique expertise and state of the science facilities to research projects that are taking place lo-cally, nationally, and globally. The BONSAI project will also allow for scientific results to be broadly accessedand disseminated enhancing scientific and technological understanding.

The offered services and capabilities by the BONSAInet are particularly valuable to three groups of stakeholders at UO. First, it enables students and researchers in science departments to effectively pursue data-intensive projects by providing high-bandwidth access to computing and visualization resources at UO and

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other universities. Second, it creates a unique environment for CIS faculty and students to conduct experimentalresearch with a wide range of next generation of network services on top of SDN. Third, network engineers atthe Information Services can examine and deploy emerging virtualization techniques that immensely simplifiesmanaging the campus network despite its growing size and complexity.

J Prior NSF SupportCC-NIE Network Infrastructure: Bridging Open Networks for Scientific Applications and Innovation(BONSAI) [NIE-1246136], R. Rejaie (PI), J. Duminguez (coPI); $507,000, 10/01/2012 – 9/30/2014. Thisaward funded all the equipment for the BONSAInet as well as upgarding the connectivity of UO campus net-work to Internet2.

NeTS: Small: Towards an Accurate, Geo-Aware, PoP-Level Perspective of the Internet’s Inter-AS Con-nectivity [NeTs-1320977], R. Rejaie (PI), $499,994, 10/01/2013 – 9/30/2016. This project focuses on accuratemapping of Internet AS-level topology that incorporates the geographic location of PoPs and physicaly inter-PoP connections.

NeTS: Small: Collaborative Research: Multi-Resolution Analysis & Measurement of Large-scale, Dy-namic Networked Systems with Applications to Online Social Networks [CNS-0917381] R. Rejaie (PI);$355,000, 09/15/09 – 08/31/12. This project is exploring the use of multi-resolution analysis as a powerfultechnique to characterize temporal evolution of large graphs.

NeTS-NBD: Characterizing Large-Scale, Dynamic Peer-to-Peer Networks: New Sampling and ModelingApproaches [CNS-0627202] R. Rejaie (PI, UO); $300K, 09/01/06 – 08/31/10. This project designed anddeveloped novel techniques for sampling and modeling large scale Peer-to-Peer networks.

CAREER: A Receiver-Driven Framework for Scalable and Adaptive Peer-to-Peer Streaming [CNS-0448639] R. Rejaie (PI, UO); $400K, 02/01/05 – 01/31/11. This project designed and developed scalable andadaptive approaches for live P2P video streaming over the Internet.

SI2-SSI: A Productive and Accessible Development Workbench for HPC Applications Using the EclipseParallel Tools Platform [OCI-1047956] J. Alameda (PI, UIUC), A. Malony (co-PI, UO), S. Shende (co-PI,UO); $2,999,297, 10/1/10 – 9/30/13. The project is conducting research and development of technologies toimprove Eclipse PTP as a viable to HPC applications. UO is integrating TAU tools and workflows in the EclipsePTP and workbench.

MRI-R2: Acquisition of an Applied Computational Instrument for Scientific Synthesis (ACISS) [OCI-0960354] A. Malony (PI); $1,998,560, 5/1/10 – 4/30/13. A large computational and data storage cloud systemis being created for interdisciplinary and integrative scientific research at the University of Oregon.

Evolution of Development of Facial Bone Morphology in Threespine Stickleback [IOS-0818738] Kimmel(PI), Cresko (co-PI, UO); $615,100, 09/01/08 – 8/31/12. The major goal of this project is to use cell-labelingand transgenic technologies to determine the molecular, cellular and developmental basis of variation in oper-cular bone morphology.

Collaborative Research: The Evolutionary Genomics of Rapid Adaptation in Threespine Stickleback[DEB-0919090] Cresko (PI, UO); $364,756, 07/01/09 – 6/30/12. The major goals of this project are to usenext generation Illumina sequencing to identify signatures of selection in very young, independently derivedpopulations of threespine stickleback.

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NeTS:Small:Buddyguard—A Buddy System for Reliable IP Prefix Monitoring [NSF-1118101] Jun Li(PI, UO); $300,000, September 1, 2011—August 31, 2014. This research addresses the critical issue of moni-toring the health of Internet routing at the granularity of every individual IP address block, i.e., every IP prefix.

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K Bibliography[1] C. Bretherton, P. Blossey, and M. Khairoutdinov, “An energy-balance analysis of deep convective self-

aggregation above uniform sst,” Journal of the atmospheric sciences, vol. 62, no. 12, pp. 4273–4292,2005.

[2] B. Mueller, S. Seneviratne, C. Jimenez, T. Corti, M. Hirschi, G. Balsamo, P. Ciais, P. Dirmeyer, J. Fisher,Z. Guo, et al., “Evaluation of global observations-based evapotranspiration datasets and ipcc ar4 simula-tions,” Geophysical Research Letters, vol. 38, no. 6, p. L06402, 2011.

[3] S. Sessions, S. Sugaya, D. Raymond, and A. Sobel, “Multiple equilibria in a cloud-resolving model usingthe weak temperature gradient approximation,” J. Geophys. Res, vol. 115, p. D12110, 2010.

[4] A. Trehu, G. Lin, E. Maxwell, and C. Goldfinger, “A seismic reflection profile across the cascadia sub-duction zone offshore central oregon: New constraints on methane distribution and crystal structure,”American Geophysical Union, 1995.

[5] D. Jousselin, R. Dunn, and D. Toomey, “Modeling the seismic signature of structural data from the omanophiolite: Can a mantle diapir be detected beneath the east pacific rise,” Geochem. Geophys. Geosyst,vol. 4, no. 7, p. 8610, 2003.

[6] R. Dziak, C. Fox, A. Bobbitt, and C. Goldfinger, “Bathymetric map of the gorda plate: Structural andgeomorphological processes inferred from multibeam surveys,” Marine Geophysical Research, vol. 22,no. 4, pp. 235–250, 2001.

[7] S. Frank et al., “Evolution in health and medicine sackler colloquium: Somatic evolutionary genomics:mutations during development cause highly variable genetic mosaicism with risk of cancer and neurode-generation.,” Proceedings of the National Academy of Sciences of the United States of America, vol. 107,p. 1725, 2010.

[8] M. Little, “Cancer models, genomic instability and somatic cellular darwinian evolution,” DNA, vol. 27,p. 28, 2010.

[9] H. Zong, J. Espinosa, H. Su, M. Muzumdar, and L. Luo, “Mosaic analysis with double markers in mice,”Cell, vol. 121, no. 3, pp. 479–492, 2005.

[10] W. Cresko, A. Amores, C. Wilson, J. Murphy, M. Currey, P. Phillips, M. Bell, C. Kimmel, and J. Postleth-wait, “Parallel genetic basis for repeated evolution of armor loss in alaskan threespine stickleback popula-tions,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 16,p. 6050, 2004.

[11] M. Miller, J. Dunham, A. Amores, W. Cresko, and E. Johnson, “Rapid and cost-effective polymorphismidentification and genotyping using restriction site associated dna (rad) markers,” Genome Research,vol. 17, no. 2, pp. 240–248, 2007.

[12] N. Baird, P. Etter, T. Atwood, M. Currey, A. Shiver, Z. Lewis, E. Selker, W. Cresko, and E. Johnson,“Rapid snp discovery and genetic mapping using sequenced rad markers,” PLoS One, vol. 3, no. 10,p. e3376, 2008.

[13] P. Hohenlohe, S. Bassham, P. Etter, N. Stiffler, E. Johnson, and W. Cresko, “Population genomics ofparallel adaptation in threespine stickleback using sequenced rad tags,” PLoS Genetics, vol. 6, no. 2,p. e1000862, 2010.

[14] E. Mardis, “The impact of next-generation sequencing technology on genetics,” Trends in genetics, vol. 24,no. 3, pp. 133–141, 2008.

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[15] J. Shendure and H. Ji, “Next-generation dna sequencing,” Nature biotechnology, vol. 26, no. 10, pp. 1135–1145, 2008.

[16] L. Jose, M. Yu, and J. Rexford, “Online measurement of large traffic aggregates on commodity switches,”in Proc. of the USENIX HotICE workshop, USENIX Association, 2011.

[17] R. Wang, D. Butnariu, J. Rexford, et al., “Openflow-based server load balancing gone wild,” in Pro-ceedings of the 11th USENIX conference on Hot topics in management of internet, cloud, and enterprisenetworks and services, pp. 12–12, USENIX Association, 2011.

[18] A. K. Nayak, A. Reimers, N. Feamster, and R. Clark, “Resonance: dynamic access control for enterprisenetworks,” in Proceedings of the 1st ACM workshop on Research on enterprise networking, pp. 11–18,ACM, 2009.

[19] N. Handigol, B. Heller, V. Jeyakumar, D. Mazieres, and N. McKeown, “Where is the debugger for mysoftware-defined network?,” in Proceedings of the first workshop on Hot topics in software defined net-works, pp. 55–60, ACM, 2012.

[20] B. Heller, C. Scott, N. McKeown, S. Shenker, A. Wundsam, H. Zeng, S. Whitlock, V. Jeyakumar, N. Hand-igol, J. McCauley, et al., “Leveraging sdn layering to systematically troubleshoot networks,” in Proceed-ings of the second ACM SIGCOMM workshop on Hot topics in software defined networking, pp. 37–42,ACM, 2013.

2

Reza Rejaie

Professional Preparation

University of Southern California Computer Science Ph.D. 1999University of Southern California Computer Science MS 1996Sharif University of Tech. (Tehran, Iran) Electrical Engineering B.S. 1991

Appointments

2008–present Associate Professor, CIS Department, University of Oregon, Eugene.2002–2008 Assistant Professor, CIS Department, University of Oregon, Eugene.1999–2002 Senior Technical Staff Member, AT&T Labs- Research, Menlo Park, CA.1996–1999 Research Assistant, USC Information Sciences Institute.1994–1996 Research Assistant, CS Department, Univ. of Southern California

Publications Related to Proposed Project• Nazanin Magharei, Reza Rejaie, Ivica Rimac, Volker Hilt, Markus Hofmann; ”ISP-friendly Live P2P

Streaming”; IEEE/ACM Transactions on Networking, Volume 22, Number 1, 2013• D. Stutzbach, R. Rejaie, N. Duffield, S. Sen, W. Willinger; “On Unbiased Sampling for Unstructured Peer-

to-Peer Networks”; IEEE/ACM Transactions on Networking, Volume 16, Number 6, 2008.• Nazanin Magharei, Reza Rejaie; ”Adaptive Receiver-Driven Streaming from Multiple Senders”, ACM/SPIE

Multimedia Systems Journal, Volume 11, Issue 6, Springer-Verlag, 2006• Reza Rejaie, Mark Handley, Deborah Estrin; ”Quality Adaptation for Congestion Controlled Video Play-

back over the Internet”, Proceedings of ACM SIGCOMM, Cambridge, 1999• Reza Rejaie, Mark Handely, Deborah Estrin ; ”RAP: An End-to-end Rate-based Congestion Control Mech-

anism for Realtime Streams in the Internet”; Proceedings of IEEE INFOCOM, New York, 1999

Other Publications• Nazanin Magharei, Reza Rejaie; ”PRIME: Peer-to-Peer Receiver-drIven MEsh-based Streaming”; IEEE/ACM

Transactions on Networking, Volume 17, Number 4, August 2009• Nazanin Magharei, Reza Rejaie, Yang Guo; ”Mesh or Multiple-Tree: A Comparative Study of Live P2P

Streaming Approaches”; Proceedings of IEEE INFOCOM, pp. 1424-1432, Anchorage, Alaska, May 2007• Daniel Stutzbach, Reza Rejaie; ”Understanding Churn in Peer-to-Peer Networks”; Proceedings of ACM

SIGCOMM/USENIX Internet Measurement Conference, pp. 189-202, Rio de Janeiro, Brazil, October 2006• Daniel Stutzbach, Reza Rejaie; ”Improving Lookup Performance over a Widely-Deployed DHT”; Proceed-

ings of IEEE INFOCOM, pp. 1-12, Barcelona, Spain, April 2006

Grants and Awards• European Union Marie Curie Fellowship (September 2009 - August 2010)• National Science Foundation CAREER Award, 2005

Synergistic Activities• Technical Program Committee: SIGCOMM Internet Measurement Conference (’07, ’11), IEEE Infocom

(’04 -’14), IEEE ICNP (’04), ICDCS (’04), IEEE GLobal Internet (’01, ’05, ’06, ’07 PC chair)• Associate Editor: Journal of Advances in Multimedia, Springer Journal in Peer-to-Peer Networking and

Applications, Springer/ACM Multimedia Systems Journal, ELSEVIER International Journal of Computerand Telecommunications Networking (COMNET)

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Collaborations and Other Affiliations

Collaborators: Paul Barford (Wisconsin U.), Lee Breslau (AT&T Labs - Research), Nick Duffield (AT&TLabs - Research), Brian Erikson (Boston U.), Deborah Estrin (UCLA), Yang Guo (Alcatel-Lucent Bell Labs),Mark Handley (UCL), Volker Hilt (Alcatel-Lucent Bell Labs), Markus Hofmann (Alcatel-Lucent Bell Labs),Jun Li (U. Oregon), Daniel Lowd (U. Oregon), Nazanin Magharei (Georgia Tech), Amy Reibman (AT&T Labs- Research), Ivica Rimac (Alcatel-Lucent Bell Labs), Shubho Sen (AT&T Labs - Research), Daniel Stutzbach(Google), Walter Willinger (AT&T Labs - Research), Haobo Yu (Packet Design),

Graduate Advisors: Deborah Estrin (UCLA), Mark Handley (UCL).

Current and Former Students: Reza Motamedi, Amir Rasti (PhD. ’11), Nazanin Magharei (PhD. ’10), M.Torkjazi (MS ’10), M. Valafar (MS ’10), Ghulam Memon (MS ’10), Aroon Nataraj (MS ’07),, Daniel Stutzbach(PhD. ’06), John Capehart (MS ’06), Ryan Kersh (MS ’06), Vikash Agarwal (MS ’05),

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Jose Dominguez

Network & Telecommunications Servicesjad@uoregon.edu 1212 University of Oregon(541) 346-1685 Eugene, OR 97403-1212

Professional Preparation

University of Oregon Computer Science MS 1994Santo Domingo Institute of Technology Systems Engineering B.S. 1991

Appointments

2009–present Network Architect & Asst. Director for Network Engineering, UO Network &Telecommunications Services, University of Oregon.

1998–2009 Senior Network Engineer, University of Oregon.1996–1998 Consultant for Several Projects in Dominican Republic, Organization of American States1996–1998 Assistant Professor, Santo Domingo Institute of Technology1996–1998 Director of Network Services, Santo Domingo Institute of Technology1994–1996 University of Oregon, Research Associate

Grants and Awards• Fulbright Scholar, 1992-1996.

Synergistic Activities• Oregon GigaPOP Connector Technical Representative to Internet2, 2000-present• Member, Internet2 Network Technical Advisory Committee, 2009-present• Advisory Council Member, IPv6 Council, Cisco Systems, Inc• Advisory Council Member, Wireless Networking Council, Cisco Systems, Inc.• Technical Adviser, Consorcio Ecuatoriano para el Desarrallo de Internet Avanzado (CEDIA), Ecuador.• Member, Program Committee, Internet Workshops for Latin America and the Caribbean, 2000-Present• Advisor and Instructor, a myriad of Data Networking workshops all around the world, Network Startup

Resource Center, 1998-Present• Instructor, Workshops on Network Design & Internet Architecture, LACNIC, 2002-2011• Presenter, Joint-Techs Meetings, Internet2/ESnet, Several years• Instructor, Multicast & IPv6 Workshops, Internet2, Several years

Collaborations and Other Affiliations

Ral Echeberra, Executive Director, Internet Address Registry for Latin America and the Caribbean (LACNIC),Montevideo, Uruguay; Steven G. Huter, Executive Director, Network Startup Resource Center, Eugene, Ore-gon, USA; Lucy Lynch, Director of Technical Projects, Internet Society (ISOC); Dr. Enrique Pelaez, ExecutiveDirector, Consorcio Ecuatoriano para el Desarrollo de Internet Avanzado (CEDIA), Ecuador

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Gregory Bothun

physics.uoregon.edu/faculty/bothun Department of Physicsdkmatter@uoregon.edu 1274 University of Oregon(541) 346-2569 Eugene, OR 97403-1274

Professional Preparation

University of Washington Astronomy Ph.D. 1981University of Washington Astronomy B.S. 1976

Postdoctoral Institution

Bantrell Fellow, California Institute of Technology 1983 – 1986Center Fellow, Harvard-Smithsonian Center for Astrophysics 1981 – 1983

Appointments

1995 – present Professor of Physics, University of Oregon1990 – 1995 Associate Professor of Physics, University of Oregon1986 – 1990 Assistant/Associate Professor of Astronomy, University of Michigan

Significant Publications• Obtaining a Fair Sample of SN Ia: A Robust Estimate of the Intrinsic SN Ia Rate (Elsa Johnson, Greg

Bothun, Department of Physics, University of Oregon, USA) Horizons in World Physics, Volume 278• Precision Cosmological Measurements: Independent Evidence for Dark Energy Physics Letters B (in press)

G. Bothun, S. Hsu and B. Murray) (2008)• Star Formation in Galaxies with Large Lower Surface Brightness Disks The Astronomical Journal 134, 547

(O’Neil, K. Oey, S. and G. Bothun) (2007)• The Luminosity of SN 1999by in NGC 2841 and the Nature of “Peculiar” Type 1a Supernovae The Astro-

physical Journal 613, 1120 (Garnavich et al). (2004)• Stellar Populations and the White Dwarf Mass Function: Connections To SNe Ia Luminosities, The Astro-

nomical Journal (T. von Hippel, G. Bothun and R. Schommer) 114, 1154-64 (1997).

Other Publications• The Great Observatories All-Sky LIRG Survey: Comparison of Ultraviolet and Far-Infrared Properties The

Astrophysical Journal 715, 572 (Howell etal) (2009)• The Spatial Extent of (U)LIRGs in the mid-Infrared I: The Continuum Emission in press (Diaz-Santos etal

2010)• The Buried Starburst in the Interacting Galaxy II ZW 096 as Revealed by the Spitzer Space Telescope The

Astronomical Joural 140, 63 (Inami etal) (2009)• An Electric Force Facilitator in Descending Vortex Tornadogenesis Journal of Geophysical Research-

Atmospheres 113, D07106 (F. Patton, G. Bothun and S. Sessions) (2007)• Star Formation in H I-Selected Galaxies. I. Sample Characteristics: The Astrophysical Journal, 613, 914

(Helmboldt, J. F.; Walterbos, R. A. M.; Bothun, G. D.; O’Neil, K.; de Blok, W. J. G.). (2004)

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Synergistic Activities• K12 Professional Development core content training for the last 5 years (with K. Carr)• Strong development in data driven curriculum (Astrophysics; Environmental Sciences)• Developed data visualization tools.• Strong record of professional outreach (in the guise of Dr. Dark Matter)• Director of the Pine Mountain Observatory and visitors program; undergraduate research camps and similar

activities.

Collaborations and Other Affiliations

Collaborators: Steve Hammond (NREL), Joe Mazzarella (IPAC), Rene Walterbos (NMSU), Kevin Carr (Pa-cific University), Steve Hsu (University of Oregon), Sharon Session (New Mexico Tech), Karen ONeill (NRAO),A. Evans (SUNY Stonybrook)Graduate Advisors: W.T. Sullivan (Univ. of Washington)

Most accomplished and recent graduate students sponsored (PH.D. Students sponsored = 21) McGaugh(Univ. of Maryland), C. Mihos (CWRU), G. Aldering (LLNL). E. Johnson (UO), Laura Rihiimaki (PNNL),Joe Helmboldt, (USNL), K. ONeill (NRAO)

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Allen D. Malony

cs.uoregon.edu/˜malony/ Department of Computer and Information Sciencemalony@cs.uoregon.edu 1202 University of Oregon(541) 346-4407 (voice), (541) 346-5373 (fax) Eugene, OR 97403-1202

Professional Preparation

University of California, Los Angeles Computer Science Ph.D. 1991University of California, Los Angeles Computer Science M.S. 1982University of Illinois, Urbana-Champaign Mathematics/CS B.S. 1980

Appointments

2003 present Professor, Computer and Information Science, University of Oregon2004 present CEO, ParaTools, Inc.1996 2003 Associate Professor, Computer and Information Science, University of Oregon1991 1996 Assistant Professor, Computer and Information Science, University of Oregon1991 1992 Visiting Assistant Professor, Computer Science, Utrech University, The Netherlands

Publications Related to Proposed Project• J. Hammond, S. Krishnamoorthy, S. Shende, N. Romero, and A. Malony, Performance Characterization

of Global Address Space Applications: A Case Study with NWChem, Concurrency and Computation:Practice and Experience, 24(2):135154, 2012.

• A. Malony, S. Biersdorff, S. Shende, H. Jagode, S. Tomov, G. Juckeland, R. Dietrich, D. Poole, and C.Lamb, Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs, InternationalConference on Parallel Processing (ICPP), September 2011.

• S. Shende, A. Malony, W. Spear, and K. Schuchardt, Characterizing I/O Performance using the TAU Per-formance System, International Conference on Parallel Computing (ParCo), September 2011.

• A. Malony and et al., Computational Modeling of Human Head Electromagnetics for Source Localizationof Milliscale Brain Dynamics, Multimedia Meets Virtual Reality / NextMed (MMVR 2011), pp. 329335,IOS Press, February 2011.

• A. Malony, J. Mellor-Crummey, and S. Shende, Methods and Strategies for Parallel Performance Measure-ment and Analysis: Experiences with TAU and HPCToolkit, D. Bailey, R. Lucas, and S. Williams (Eds.),Performance Tuning of Scientific Applications, CRC Press, New York, 2010.

Other Publications• D. Hammond, B. Scherrer, and A. Malony, Incorporating Anatomical Connectivity into EEG Source Esti-

mation via Sparse Approximation with Cortical Graph Wavelets, IEEE International Conference on Acous-tics, Speed, and Signal Processing (ICASSP 2012), pp. 573575, March 2012.

• A. Morris, A. Malony, S. Shende, and K. Huck, Design and Implementation of a Hybrid Parallel Per-formance Measurement System, International Conference on Parallel Processing (ICCP 2010), September2010.

• A. Morris, W. Spear, A. Malony, S. Shende, Observing Performance Dynamics using Parallel Profile Snap-shots, European Conference on Parallel Processing (EuroPar), August, 2008.

• A. Malony, S. Shende, A. Morris, S. Biersdorff, W. Spear, K. Huck, Aroon Nataraj, Evolution of a ParallelPerformance System, International Workshop on Tools for High Performance Computing, M. Resch et al.(Eds.), Springer-Verlag, pp. 169190, July, 2008.

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• K. Huck, A. Malony, S. Shende, and A. Morris, Knowledge Support and Automation for PerformanceAnalysis with PerfExplorer 2.0, The Journal of Scientific Programming, special issue on Large-Scale Pro-gramming Tools and Environments, 16(2-3): 123134, 2008.

Grants and Awards• NVIDIA Professor Partnership, 2009• Research Innovation Award, University of Oregon, 2006• Humboldt Research Award, Alexander von Humboldt Foundation, Germany, 2002• Fulbright Research Scholar, University of Vienna, Austria, 1999• National Young Investigator, National Science Foundation, 1994• Fulbright Research Scholar, Utrecht University, The Netherlands, 1991

Synergistic Activities• Director, NeuroInformatics Center (NIC), University of Oregon, 2002 present. The NIC is developing

advanced integrated neuroimaging tools that combine EEG and MRI methods for next-generation brainanalysis. Grid technologies and high-performance computing are being used by the NIC to prototypenetwork-based systems for medical service delivery.

• Director, TAU Performance System project, 1992 present. The University of Oregon is home to the TAUproject, a 18-year old research effort focusing on problems in performance analysis and optimization ofparallel applications on large-scale HPC systems. The project distributes the TAU Performance System, anopen source performance analysis toolsuite.

• Technical Program Committee (Performance area), SC12, Salt Lake City, UT, November 2012.• Global Chair, Performance Prediction and Evaluation, Euro-Par 2012, Rhodes, Greece, August 2012.• Tutorial, Scalable Heterogeneous Computing on GPU Clusters, with J. Vetter, P. Roth, and K. Spafford,

Supercomputing Conference (SC 2011), Seattle, WA, November 2011.

Collaborations and Other Affiliations

Collaborators: Jay Alameda (NCSA, Univ. of Illinois, Urbana-Champaign); Pete Beckman (Argonne NationalLab); John Cary (Tech-X Corp.); Bronis de Supinski (Lawrence Livermore National Lab); Gwen Frishkoff(Georgia State Univ.); Jeff Hollingsworth (Univ. of Maryland); Wen-Mei Hwu (Univ. of Illinois, Urbana-Champaign); Laxmikant Kale (Univ. of Illinois, Urbana-Champaign); Rick Kufrin (NCSA, Univ. of Illinois,Urbana- Champaign); Scott Makeig (Univ. of California, San Diego); Lois McInnes (Argonne National Lab);Bart Miller (Univ. of Wisconsin, Madison); Bernd Mohr (Research Centre Juelich); Shirley Moore (Univ.of Tennessee); Boyanna Norris (Argonne National Lab); Nick Nystrom (Pittsburgh Supercomputing Center);Craig Rasmussen (Los Alamos National Lab); Phil Roth (Oak Ridge National Lab); Jeff Vetter (Oak RidgeNational Lab); Rick Vuduc (Georgia Tech); Felix Wolf (Research Centre Juelich).Ph.D. Students and Postdocs: Ph.D. thesis advisor: Kai Li, Electrical Geodesics, Inc.; Kevin Huck, ParaTools,Inc.; Adnan Salman, University of Oregon Ph.D. students advised: Geoffrey Hulette, David Ozog, NicholasChaimov, Ahmadreza Khadem Total number of graduate students advised: 6 Postdoctoral scholars sponsored:David Hammond, Neuroinformatics Center, University of OregonGraduate Advisors: Daniel Reed, Microsoft Research

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Jun LiDept. of Computer and Information ScienceUniversity of OregonEugene, OR 97403-1202

http://www.cs.uoregon.edu/˜lijunlijun@cs.uoregon.eduPhone: 541-346-4424, Fax: 541-346-5373

Professional Preparation

Peking University, China Computer Science B.S. 1992Chinese Academy of Sciences Computer Engineering M.E. 1995UCLA Computer Science M.S. 1998UCLA Computer Science Ph.D. 2002 (with honors)

Appointments

Associate Professor 9/2008-present University of Oregon, Dept. of Computer ScienceChair of Excellence 2/2011-7/2011 Carlos III University of Madrid, SpainAssistant Professor 9/2002-9/2008 University of Oregon, Dept. of Computer ScienceResearch Assistant 7/1996-6/2002 UCLA Laboratory for Advanced Systems ResearchResearch Intern summer 1999 Network Associates Inc., Santa Clara, California

Five Most Relevant Products

1. David Koll, Jun Li, Joshua Stein, and Xiaoming Fu, “On the state of OSN-based Sybil defenses,” in IFIPNetworking, Trondheim, Norway, June 2014.

2. Jason Gustafson and Jun Li, “Leveraging the crowds to disrupt phishing,” in First IEEE Conference onCommunications and Network Security 2013 (CNS), Washington, DC, October 2013.

3. Jun Li, Toby Ehrenkranz, and Paul Elliott, “Buddyguard: A buddy system for fast and reliable detectionof IP prefix anomalies,” in 20th IEEE International Conference on Network Protocols (ICNP), Austin, TX,October 2012.

4. Jun Li and Scott Brooks, “I-seismograph: Observing and measuring Internet earthquakes,” in Proceedingsof IEEE INFOCOM, April 2011.

5. Shad Stafford and Jun Li, “Behavior-based worm detectors compared,” in 13th International Symposiumon Recent Advances in Intrusion Detection (RAID), September 2010.

Five Additional Significant Products

1. Lei Jiao, Jun Li, Wei Du, and Xiaoming Fu, “Multi-objective data placement for multi-cloud socially awareservices,” in Proceedings of IEEE INFOCOM, Toronto, Canada, April 2014.

2. Jun Li, “mSSL: A framework for trusted and incentivized peer-to-peer data sharing between distrusted andselfish clients,” Peer-to-Peer Networking and Applications, vol. 4, no. 4, pp. 325–345, December 2011.

3. Jun Li, Dejing Dou, Zhen Wu, Shiwoong Kim, and Vikash Agarwal, “An Internet routing forensics frame-work for discovering rules of abnormal BGP events,” ACM SIGCOMM Computer Communication Review,vol. 35, no. 5, pp. 55–66, October 2005.

4. Jun Li, Jelena Mirkovic, Mengqiu Wang, Peter L. Reiher, and Lixia Zhang, “SAVE: Source address validityenforcement protocol,” in Proceedings of IEEE INFOCOM, New York, June 2002, pp. 1557–66.

5. Jun Li, Peter L. Reiher, and Gerald J. Popek, Disseminating Security Updates at Internet Scale, KluwerAcademic Publishers, Boston, November 2002, ISBN 1-4020-7305-4. 157 pages.

Synergistic Activities• Established and directing the Network & Security Research Laboratory at the University of Oregon;• Established and taught multiple security and networking courses and seminars at the University of Oregon;

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• Head of the Graduate Education Committee (2011-2013) of the Computer Science Department at the Uni-versity of Oregon;

• Initiator and Faculty advisor of Upsilon Pi Epsilon (an honor society of CS) at the University of Oregon;• Currently an editor of Computer Networks; TPC co-chair of IEEE/ACM International Symposium on Qual-

ity of Service (IWQoS) 2015; TPC co-chair of 17th IEEE Global Internet Symposium (GI) 2014; TPCco-chair of 8th Workshop on Secure Network Protocols (NPSec) 2013; and lead TPC chair of the Commu-nication and Information System Security Symposium at IEEE Globecom 2014;

• Publicity chair of ACM Conference on Computer and Communications Security (CCS) 2013;• Travel grant chair of IEEE International Conference on Network Protocols (ICNP) 2013;• Served on 60+ technical program committees and multiple NSF proposal panels;• NSF CAREER award recipient; Senior member of ACM; Senior member of IEEE.

Collaborators

Kevin Butler, University of Oregon Yang Chen, Duke UniversityDejing Dou, University of Oregon Xiaoming Fu, University of GottingenTiffany Gallicano, University of Oregon Colin Koopman, University of GottingenPeter Laufer, University of Oregon Daniel Lowd, University of OregonAidong Lu, UNC Charlotte Olaf Maennel, Loughborough University (UK)Allen Malony, University of Oregon Andrzej Proskurowski, University of OregonPeter Reiher, UCLA Reza Rejaie, University of OregonDawn Song, UC Berkeley Matteo Varvello, Alcatel-LucentTao Wei, FireEye Inc. Chris Wilson, University of OregonXintao Wu, UNC Charlotte Yanwei Wu, Western Oregon UniversityChao Zhang, UC Berkeley Wei Zhao, UNC Charlotte

Graduate and Postdoctoral Advisor

Gerald Popek (deceased) and Peter Reiher, UCLA

Thesis Advisor and Postgraduate-Scholar Sponsor (total number = 17)

Ph.D. students (graduated): Ghulam Memon (Amazon), Toby Ehrenkranz (Amazon),Shad Stafford (Palo Alto Software)

Ph.D. students (current): Mingwei Zhang, David Koll (co-advisee), Lei Jiao (co-advisee)Master students (graduated): Paul Elliott (unknown)

Sruthi Kee Rangavajhula (Intel), Zhao Zhao (unknown)Jason Gustafson (Gaikai), Scott Brooks (Google)Dongting Yu (University of Cambridge), Yibo Wang (Intel)Paul Knickerbocker (Unknown), Zhen Wu (Yahoo!)Xun Kang (Microsoft)

Master students (current): Josh Stein

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Hank Childs

Professional Preparation

University of California at Davis Computer Science Ph.D. 2006University of California at Davis Computer Science/Mathematics B.S. 2006

Appointments

2013- Assistant Professor CIS Department, University of Oregon2009- Computer Systems Engineer Lawrence Berkeley National Laboratory2009-2013 Professional Researcher University of California at Davis1999-2009 Computer Scientist Lawrence Livermore National Laboratory

Main Publications Related to Proposed Project• H. Krishnan, J. Meyer, A. Romosan, H. Childs, and W. Bethel. “Enabling Advanced Environmental Man-

agement via Remote and Distributed Visual Data Exploration and Analysis.” In Journal of Computing andVisualization in Science (CAVS), Volume 15, Number 3, pp. 123-133, Spring 2014.

• L. Gosink, K. Bensema, T. Pulsipher, H. Obermaier, M. Henry, H. Childs, and K. I. Joy. “Characterizing andVisualizing Predictive Uncertainty in Numerical Ensembles Through Bayesian Model Averaging.” In IEEETransactions on Visualization and Computer Graphics (TVCG), Volume 19, Number 12, pp. 2703-2712,December 2013.

• H.Childs, B.Geveci, W.Schroeder, J.Meredith, K.Moreland, C.Sewell, T.Kuhlen, and E. W. Bethel. “Re-search Challenges for Visualization Software.” In IEEE Computer, Volume 45, Number 4, pp. 34-42, May2013.

• E. W. Bethel, H. Childs, and C. Hansen, editors. “High Performance Visualization Enabling Extreme-ScaleScientific Insight.” Textbook from Chapman & Hall, CRC Computational Science, October 2012.

• H. Childs, D. Pugmire, S. Ahern, B. Whitlock, M. Howison, Prabhat, G. Weber, and E. W. Bethel. “ExtremeScaling of Production Visualization Software on Diverse Architectures.” In IEEE Computer Graphics andApplications (CG&A), Volume 30, Number 3, pp. 22-31, May/June 2010.

Other Related Publications• M. Howison, E.W. Bethel, and H. Childs. “Hybrid Parallelism for Volume Rendering on Large-, Multi-,

and Many-Core Systems.” In IEEE Transactions on Visualization and Computer Graphics (TVCG), Volume18, Number 1, pp. 17-29, January 2012.

• H. Childs, E. Brugger, B. Whitlock, J. Meredith, S. Ahern, K. Bonnell, M. Miller, G. H. Weber, C. Harrison,D. Pugmire, T. Fogal, C. Garth, A. Sanderson, E. W. Bethel, M. Durant, D. Camp, J. M. Favre, O. Rubel, P.Navratil, M. Wheeler, P. Selby, and F. Vivodtzev. “VisIt: An End-User Tool For Visualizing and AnalyzingVery Large Data.” In Proceedings of SciDAC 2011, http://press.mcs.anl.gov/scidac2011.

• T. Fogal, H. Childs, S. Shankar, J. Kruger, R. D. Bergeron, and P. Hatcher. “Large Data Visualizationon Distributed Memory Multi-GPU Clusters.” In Proceedings of High Performance Graphics (HPG), pp.57-66, June 2010.

• H. Childs, E. S. Brugger, K. S. Bonnell, J. S. Meredith, M. Miller, B. J. Whitlock, and N. Max. “A Contract-Based System for Large Data Visualization.” In Proceedings of IEEE Visualization (Vis05), pp. 190-198,Oct. 2005.

• H. Childs and M. Miller. “Beyond Meat Grinders: An Analysis Framework Addressing the Scale andComplexity of Large Data Sets.” In SpringSim High Performance Computing Symposium (HPC 2006), pp.181-186, Apr. 2006.

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Synergistic Activities• Architect of VisIt, one of the most popular software programs for large data visualization.• Program Co-Chair for IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), Paris,

France, November 2014.• Co-Chair for Visualization Showcase at ACM/IEEE SuperComputing (SC), New Orleans, LA, November

2014.• Site Chair for Department of Energy Computer Graphics Forum (DOECGF), Portland, OR, April 2013.

Selected Panelists and Reviewers:• Panelist for NSF IIS, DOE SBIR• Reviewer for twenty-five different conferences and journals.

Collaborators

Sean Ahern (ORNL) Kathleen Bonnell (LLNL)Peer-Timo Bremer (LLNL) Eric Brugger (LLNL)John Clyne (NCAR) Christoph Garth (Kaiserslautern)Chuck Hansen (Utah) Cyrus Harrison (LLNL)Mark Howison (Brown) Kelly Gaither (UT-Austin)Chris Johnson (Utah) Peter Lindstrom (LLNL)Jeremy Meredith (ORNL) Mark Miller (LLNL)Paul Navratil (UT-Austin) George Ostrochov (ORNL)Valerio Pascucci (Utah) David Pugmire (ORNL)Allen Sanderson (Utah) Dean Williams (LLNL)Brad Whitlock (Intelligent Light Systems)

Advisor

Nelson Max, University of California at Davis

Advisees

David Camp, Ph.D. August 2012∗ Shaomeng Li, 1st year Ph.D. studentJames Kress, 1st year Ph.D. student Ryan Bleile, 1st year Ph.D. studentStephanie Labasan, 1st year Ph.D. student Matthew Larsen, 1st year Ph.D. student

∗ Ken Joy of UC Davis is the advisor of record.

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William A. Cresko

creskolab.uoregon.edu/ Institute of Ecology and Evolutionwcresko@uoregon.edu 5289 University of Oregon(541) 346-4779 (voice), (541) 346-2364 (fax) Eugene, OR 97403-5289

Professional Preparation

University of Oregon Developmental Genetics 2001-2005Clark University Evolutionary Genetics Ph.D. 2000University of Pennsylvania Biology B.A. 1992

Appointments

2011-present Associate Professor of Biology, University of Oregon2010-present Associate Director, Center for Ecology and Evolutionary Biology2009-present Director, University of Oregon High Throughput Sequencing Facility2005-present Assistant Professor of Biology, University of Oregon2001-2004 Postdoctoral Fellow (NRSA), National Institutes of Health2001-2004 Research Associate, IGERT Evolution, Development and Genomics1996-2000 STAR Graduate Fellow, U.S. Environmental Protection Agency1994-1995 NSF Graduate Fellowship, Honorable Mention1994-1995 University of Arkansas, Fulbright Graduate Scholarship

Publications Related to Proposed Project• Hohenlohe PA, Bassham S, Currey M, Cresko WA. 2012. Extensive linkage disequilibrium and parallel

adaptive divergence across threespine stickleback genomes. Phil Trans B 367: 395-408.• Catchen, J. M., Amores, A., Hohenlohe, P., Cresko, W. A. and Postlethwait, J. H. 2011. Stacks: building

and genotyping loci de novo from short read sequences. Genes, Genomes and Genetics. 1: 171-182.• Etter, P. D., Bassham, S., Hohenlohe, P., Johnson, E. and W. A. Cresko. 2011. SNP Discovery and Geno-

typing for Evolutionary Genetics using RAD sequencing. in Molecular Methods in Evolutionary Genetics,Rockman, M., and Orgonogozo, V., eds. (in press).

• Hohehlohe, P., S. Bassham, P. Etter, N. Stiffler, E. Johnson and W. A. Cresko. 2010. Population Genomicsof Parallel Adaptation in Threespine Stickleback using Sequenced RAD tags. PLoS Genetics 6(2). pp.e1000862

• Baird, N. A., P. D. Etter, T. S. Atwood, M. C. Currey, A. L. Shiver, Z. A. Lewis, E. U. Selker, W. A. Creskoand E. A. Johnson. 2008. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoSOne 3(10) e3376

Other Publications• Hohenlohe PA, Catchen JM, Cresko W. A. In press. Population genomic analysis using sequenced RAD

tags. In Bonin A, Pompanon F, eds. Data Production and Analysis in Population Genomics. Humana Press,New York.

• Miller, M., J. Dunham, A. Amores, W. A. Cresko and E. Johnson. 2007. Rapid and cost-effective poly-morphism identification and genotyping using Restriction site Associated DNA (RAD) markers. GenomeResearch 17(2): 240-248.

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• Emerson, K. J., C. R. Merz, J. M. Catchen, P. A. Hohenlohe, W. A. Cresko, W. E. Bradshaw, and C. M.Holzapfel. 2010. Resolving post-glacial phylogeography using high throughput sequencing. Proc. Natl.Acad. Sci. USA. 107(37): 16196-200.

• Kimmel, C., Ullmann, B., Walker, C., Wilson, C., Currey, M., Phillips, P., Bell, M. A., Postlethwait, J. H.,and W. A. Cresko. 2005 Evolution and Development of Facial Bone Morphology in Threespine Stickleback.Proc. Natl. Acad. Sci. USA. 102(16):5791-6.

• Cresko, W. A., Amores, A., Wilson , C., Murphy, J., Currey, M., Phillips, P., Bell , M. A., Kimmel, C.B., and Postlethwait, J. H. 2004 . Parallel genetic basis for repeated evolution of armor loss in Alaskanthreespine stickleback populations. Proc. Natl. Acad. Sci. USA 101:6050-5.

Synergistic Activities• CoPI for the NSF-sponsored IGERT graduate research training grant on Evolution of Development and

Genomics. This grant supports the training of graduate students, including including several from under-represented groups in the sciences.

• Meeting organizer and editor of the Proceedings: Fifth International Conference on Stickleback Behaviorand Evolution

• Training laboratory for University of Oregon SPUR (Science Participation for Undergraduate Researchers)program. This program allows undergraduate students from under-represented groups, and smaller collegesand universities, to take part in summer research at the U of O.

• Presented over 50 public seminars on evolutionary genetics since starting position in 2005. These includeeight plenary talks at meetings synthesizing evolution, development and genomics, as well as eight moregeneral seminars to large audiences with broader interests.

• Co-developed Restriction site Associated DNA (RAD) markers, which has become an important tool fornext generation sequencing in emerging model organisms. Developed and distributed open source softwarepipelines for the efficient analysis of these data.

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Walter Willinger

Professional Preparation

ETH Zurich Mathematics Dipl. Math., October 1980Cornell University School of ORIE M.S., August 1984Cornell University School of ORIE Ph.D., January 1987

Employment

1996–present Member of Technical Staff, AT&T Labs - Research, Florham Park, NJ.1986–1996 Member of Technical Staff, Bellcore Applied Research, Morristown, NJ.

Publications Related to Proposed Project• M. Sanchez, J. Otto, Z. Bischof, D. Choffnes, F. Bustamante, B. Krishanmurthy, and W. Willinger. “Dasu:

Pushing Experiments to the Internets Edge, Proc. USENIXENIX NSDI, 2013 (to appear).• B. Ager, N. Chatzis, A. Feldmann, N. Sarrar, S. Uhlig, and W. Willinger. “Anatomy of a large European

IXP, PROC. ACM SIGCOMM, 2012.• M. Roughan, Y. Zhang, W. Willinger, and L. Qiu. “Spatio-Temporal Compressive Sensing and Internet

Trafc Matric), IEEE/ACM TRANSACTIONS ON NETWORKING, 20(3), 2012.• M. Roughan, W. Willinger, O. Maennel, D. Perouli, and R. Bush. “10 Lessons from 10 Years of Measuring

and Modeling the Internets Autonomous Systems, IEEE JSAC, SPECIAL ISSUE ON “MEASUREMENTOF INTERNET TOPOLOGIES), 2011.

• R. Oliveira, D. Pei, W. Willinger, B. Zhang, and L. Zhang. “The (In)completeness of the Observed InternetAS-level Structure, IEEE/ACM TRANSACTIONS ON NETWORKING, 18(1), 2010.

• B. Augustin, B. Krishnamurthy, and W. Willinger. “IXPs: Mapped? PROC. ACM SIGCOMM INTERNETMEASUREMENT CONFERENCE (IMC09), 2009.

• W. Willinger, D. Alderson, and J.C. Doyle. “Mathematics and the Internet: A Source of Enormous Confu-sion and Great Potential, NOTICES OF THE AMS, 56(5), 2009. Reprinted in: THE BEST WRITING ONMATHEMATICS 2010, M. Pitici (Ed.), Princeton University Press.

• J.C. Doyle, D. Alderson, L. Li, S. Low, M. Roughan, S. Shalunov, R. Tanaka, and W. Willinger. “The robustyet fragile nature of the Internet, PROC. NAT. ACAD. SCI. USA, 102(41), 2005.

• D. Alderson, L. Li, W. Willinger, and J.C. Doyle. “Understanding Internet topology: Principles, models,and validation, IEEE/ACM Transaction on Nonworking, 13(6), 2005.

• H. Chang, S. Jamin, Z.M. Mao, and W. Willinger. “An empirical approach to modeling inter-AS trafcmatrices, PROC. 2005 ACM SIGCOMM Internet Measurement Conference (IMC05), 2005.

Grants and Awards• Co-recipient, the 1996 IEEE W.R.G. Baker Prize Award, the 1995 W.R. Bennett Prize Paper Award, and

the 2005 ACM/SIGCOMM Test of Time Paper Award for “On the Self-Similar Nature of Ethernet Traffic(Extended Version).”

• Co-author, ACM SIGCOMM 2004 Best Student Paper Award for “A first-principles approach to understand-ing the Internet’s router-level topology.”

• Fellow of ACM (2005), Fellow of IEEE (2005), AT&T Fellow (2007), Fellow of SIAM (2009).

Synergistic Activities• Co-chair, Tech. Program Committee, ACM SIGCOMM Internet Measurement Conf. (IMC) 2011.

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• Technical Program Committee: ACM SIGCOMM Conference 2006, 2007, 2010, 2011, 2013; ACM SIG-COMM/USENIX Internet Measurement Conference 2005, 2006, 2008, 2009, 2010; ACM CoNEXT 2012;ACM SIGCOMM Workshop on Online Social Network 2012; Passive and Active Measurement Confer-ence (PAM) 2011; INCP Workshop on Rigorous Protocol Engineering 2011; ACM HotMetrics Workshop2009, 2010; ACM Workshop on Experimental Computer Science 2007; IEEE Global Internet Symposium2007; European Conference on Complex Systems (ECCS) 2007; 5th Workshop on Algorithms and Modelsfor the Web-Graph (WAW) 2007; CAIDA/ISMA Workshop on Internet Topology 2006; ACM SIGCOMMWorkshop MineNet-05.

• Co-organizer, DAGSTUHL Seminar, Leibniz-Zentrum Fuer Informatik: Schloss Dagstuhl, on “the Criti-cal Internet Infrastructure,, August 2013

• Co-organizer, Center For Discrete Mathematics & Theoretical Computer Science (DIMACS), Rut-gers University, Piscataway, NJ: Workshop on “Internet Tomography, part of a 3-year Special Focus on“Algorithmic Foundations of the Internet, Spring 2008.

• Co-organizer, Institute for Pure and Applied Mathematics (IPAM), UCLA: Annual Program on InternetMulti-Resolution Analysis,” Fall of 2008; Workshop on “Random and Dynamic Graphs and Networks”,May 7–11, 2007; Annual Program on “Large-Scale Communication Networks,” March 11 – June 15, 2002.

• Co-organizer, National Academy of Sciences (NAS), Washington, D.C.: Workshop on “Statistics of Net-works,” September 26–27, 2005.

• Co-organizer, Statistical Research Center For Complex Systems (SRCCS), Seoul National University,Workshop on “Internet Measurement, Modeling, and Analysis, Jan. 9–12, 2005.

• Co-organizer, Institute for Mathematics and its Applications (IMA), University of Minnesota, MN:Annual Program on “Probability and Statistics in Complex Systems: Genomics, Networks, and FinancialEngineering,” September 2003 - June 2004 (www.ima.umn.edu/complex/).

• Co-organizer, Statistical and Applied Mathematical Sciences Institute (SAMSI), Research TrianglePark, NC: Annual Program on “Network Modeling for the Internet,” Fall 2003, (www.samsi.info/200304/int/int-home.html).

• Editorial Service, IEEE Journal On Selected Areas In Communications (JSAC), Guest Editor, SpecialIssue on “Measurement of Internet Topologies,”, Fall 2011.

• Editorial Service, Computing, Field Editor (2010 - present).• Editorial Service, IEEE Network Magazine, Guest Editor, Special Issue on “Internet Scalability: Proper-

ties and Evolution, Spring 2008.• Editorial Service, Computer Networks, Guest Editor, Special Issue on “Complex Computer and Commu-

nication Networks”, Fall 2008.• Editorial Service, Internet Mathematics, Associate Editor (2004 - present).• Editorial Service, IEEE Transactions On Information Theory, Guest Editor, Special Issue on “Multi-

scale Statistical Signal Analysis and its Applications, Vol. 45, No. 3, April 1999.• Editorial Service, The Annals Of Applied Probability, Associate Editor (1990 - 1995).

Collaborators: J. Doyle, S. Low (Caltech); D. Alderson (NPS); M. Roughan (Univ. of Adelaide, Australia);B. Krishnamurthy, S. Sen, N. Duffield, V. Vaishampayan (AT&T Labs-Research); P. Barford (Univ. of Wis-consin); R. Rejaie (Univ. of Oregon); L. Zhang (UCLA); F. Bustamante (Northwestern University); B. Maggs(Duke University) V. Sekar (CMU).

Graduate Advisors: Murad S. Taqqu (Boston University)

Postgraduate-Scholar Sponsor: A.C. Gilbert (Yale), D. Radulovic (Princeton), Abhishek Sharma (USC).

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L Data Management PlanOur proposed activities will result in developing open-source softwares and data sources that might be collectedover the BONSAInet as part of monitoring and measurement. All the developed softwares in this project arestored and backed up at secure servers in the Information Services at UO. We will make all the developedtools and software associated with the new services publicly available under an open source license through theproject web site.

Any collected data set is stored in a database at the CIS machine room. The machine room is equipped withlarge storage space that are organized into a RAID. Therefore, we are capable of full recovery of the data evenin the event of multiple disks failures. All these servers are located in the CIS department’s machine room (notaccessible from outside the CIS department) and are behind a firewall. The data on these servers are also beingbacked up regularly by CIS systems staff and can be recovered in case of an unplanned removal/loss.

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