Virtual Edge-Based Smart Community Network...

32 Published by the IEEE Computer Society 1089-7801/16/$33.00 © 2016 IEEE IEEE INTERNET COMPUTING

Virtual Edge-Based Smart Community Network Management

Kim-Khoa Nguyen and

Mohamed Cheriet

University of Quebec, Canada

Providing multitenant, multiaccess, and multiservice solutions in communities is

key to sustaining innovations and economic development. This article investigates

a solution for rearchitecting a telecommunications company’s (telco’s) central

office to offer services in a smart community, enabled by virtual network

function elements running on a smart edge. These elements manage a multiaccess

underlying infrastructure and deploy telco services with minimal resource

consumption. The virtual smart edge #exibly routes traf%c, aggregates, and splits

#ows across a heterogeneous network to achieve network operational ef%ciency,

and optimizes computing resource allocation for last-mile Internet of Things

services hosted close to the end user.

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To accelerate the deployment of next generation broadband networks, as well as to provide next-genera-

tion network services and applications, national projects have recently been cre-ated, such as Gig.U in the US (www.gig-u.org/about). The aim of these projects is to create hubs of innovation and productiv-ity at the center of regional and urban development, based on universities and their knowledge networks of researchers, faculty, and students. It’s an open inte-grated environment with multilayer wire and wireless infrastructures connected to various institutions and other points of presence in communities (see Figure 1a). A key issue when deploying agile broadband telco service in such a campus network is the optimization of resources

and traf"c to avoid excessive overprovi-sioning, which results in high costs and damaging environmental impacts.

In terms of physical infrastructure, an integrated system of WiFi and optical access networks1 is a promising solution for campus network provisioning. Such hybrid architecture, called wireless-opti-cal broadband access network (WOBAN), consists of a wireless network in the front end, supported by a passive optical network (PON)2 in the backend. Figure 1. 1 over-views a WOBAN with a "ber backbone connecting university buildings, as well as community anchor institutions and public housing. This backbone serves as foundation for a wireless mesh network (WMN), which is a device-as-infrastruc-ture network architecture that operates


NOVEMBER/DECEMBER 2016 33

using common standards such as 802.11ac Wi-Fi and modi"ed, but easily accessible, hardware com-ponents. WOBAN has complementary features of optical and wireless networks: particularly, the high capacity of optical networks, and the unte-thered connectivity, mobility, and other facets of wireless networks. Thanks to the front-end WMN, traf"c that would be disrupted by a network fail-ure can be rerouted through other parts of the net-work, maintaining overall connectivity. Existing solutions often deal with multiple paths between a user and the telecommunications company’s (telco’s) central of"ce (CO) to establish alterna-tive routing protocols. While this low-cost solution is quite #exible, the traditional control and man-agement approach intertwining data and control planes imposes technical challenges regarding fast service deployment and the implementation of new services. In response to these challenges, net-work operators recently reinvented their networks — using software-de"ned networking (SDN) and network function virtualization (NFV) — to better leverage practices in cloud elasticity and agility.

In this article, we investigate the problem of virtualizing core telco service components, and then deploy them onto an edge datacenter, tradi-tionally employed as a CO. Unlike Internet-based management solutions, which centrally manage the community with services running on a pub-lic cloud, our solution is based on a smart edge running at the last-mile close to end users. This solution leverages software-de"ned mechanisms to allocate resources optimally according to user requirements, minimize backhaul bandwidth

and energy consumption, and guarantee quality of service (QoS). The relocation of virtual telco services in cases of link or node failures will be transparent to users, and hence an arbitrary routing algorithm is no longer required.

Prior research has presented the convergence of WiFi and optical access networks.1 How-ever, a cloud-based management model involv-ing both underlying infrastructure and service layers hasn’t been investigated yet. Based on a realistic model of a smart campus, this article formulates the problem of service provisioning in a smart community, and then discusses the sustainability aspects of a smart community and home access network models, taking into account requirements of new smart applica-tions. Our contribution is two-fold: we present a smart community management solution that enables direct control of telco-grade services, such as an Internet multimedia subsystem (IMS), in proximity to end users; and the integrated management of the telco IMS, Internet, and home automation at the edge that enables the global optimization of a smart community net-work, including compute, network, and storage resources, which isn’t available in public cloud-based solutions.

Smart Community Network: CO-WOBAN IntegrationUntil recently, the WOBAN model has usually been de"ned as “a combination of PON and wire-less networks in which Gigabit Ethernet PON (for example, EPON) serves as the backhaul for the

Figure 1. Gig.U and wireless-optical broadband access network (WOBAN) models. (a) Gig.U network model integrating "ber and wireless access. (b) Existing WOBAN model. (OLT stands for optical line terminal; ONU stands for optical network unit.)

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34 www.computer.org/internet/ IEEE INTERNET COMPUTING

wireless network, which builds a mesh topol-ogy.”1,3 The PON backhaul consists in an opti-cal line terminal (OLT) located in a CO connected via optical "ber to multiple optical network units (ONU). The front end of WOBAN is a WMN where locations of wireless nodes (access points, or APs) are generally "xed. End users, both mobile and stationary, connect to the network through these wireless nodes, whose locations are generally "xed. Users send data packets to a nearby wireless node. Then, packets travel over multiple wireless hops on the WMN, known as wireless backhaul, to reach the OLT via the gate-way nodes, which connect wireless and optical parts of WOBAN. Usually, gateways are attached to one of the ONUs, as Figure 1b shows. Wireless nodes can also be equipped with multiple radios to carry higher traf"c over wireless backhaul. Unlike residential WiFi APs, the community wireless network is carrier-grade, and often has a central control unit (a mesh network control-ler), which automatically manages and monitors all APs according to administrative tasks.

Although it’s a cost-effective model, the existing WOBAN model might mismatch some requirements of home and building networks such as the following:

Unlike the theoretically traditional WOBAN model where the optical part serves exclu-sively as backhaul for the WMN, the smart community’s optical network is also respon-sible for providing high-speed services to resi-dential houses, independently with the WMN.WOBAN isn’t designed to #exibly provide differentiated services with various require-ments, such as voice, video, and data.High-security network segmentation schemes (for example, enterprise virtual private net-works) often require in-campus "xed sockets.WOBAN isn’t designed to optimize bandwidth on backhaul links, which is often costly.Multitenancy support isn’t present in WOBAN. The isolation and differentiation of different #ows, such as video or data moni-toring, are required to provide QoS for dif-ferent houses in a community.

The aforementioned remarks eventually lead to the need for new dynamic resource sharing and tenant isolation mechanisms for optical and wireless parts, in particular for upstream links. The high mobility of WMN

users can also result in local bottlenecks in some of the gateways (for example, when there is a high concentration of mobile users) and in the entire network, which prevents achiev-ing QoS service level agreements for PON subscribers. QoS violations could become par-ticularly critical in cases of multistage PON because a violation in an ONU might affect other ONUs in a chain of faults. In addition, the PON might also be used to provide high-speed access to neighbor buildings, as shown in the Gig.U model (Figure 1). Some of these connections might require a constant bit rate for mission-critical applications (for example, e-Health) regardless of WMN use. Therefore, the WOBAN model should be designed and planned with service awareness to support multiple traf"c-privileged classes in addition to best-effort WiFi users. This can take place through the virtualization of the CO, and the integration of white box network gears and computing resources called smart edge in the central office rearchitected as a datacenter (CORD) approach, which provides last-mile ser-vices. Evolving from the CO, the smart edge is located in proximity to end users, for example, in a resident building or a small campus. This smart edge is orchestrated by a virtual man-agement and orchestration layer and a SDN controller, which computes optimal routing schemes and resource sharing plans, as well as controls WMN access, and optical elements. In addition to QoS provisioning, the collocation of network and compute resources in the smart edge helps improve energy ef"ciency of com-munications services through the consolida-tion of virtualized resources.

On the other hand, such integration will increase the complexity of the resource alloca-tion problem, which has been proven NP-hard.4 In a traditional WOBAN, there’s no computation resource; therefore, the key issue of WOBAN is the optimal de"nition of network topology. In the CO-WOBAN architecture, computation resources must be coordinated with network resources. As both types of resources have limi-tations and capabilities, a global optimization problem must be addressed.

Virtualized Wireline Access Network in a Smart CommunityThe Gigabit Passive Optical Network (GPON) is rapidly becoming the dominant technology in



the last-mile network due to its low-cost, simple architecture and especially passive components (splitters). In a practical scenario, a GPON com-munity might exist within the range of 20 kilo-meters of the OLTs. Each community will have a passive (no power required) "ber distribution cabinet (FDT) installed in the smart edge, which covers a radius of 500 meters (to all houses or apartments in a multidwelling building). Each home in the community will get a minimum one "ber connectivity without protection directly from the FDT or through a splitter. Figure 2a shows a network model, and in Figure 2b we present a virtualized model. The FDT and ONUs are hosted in the smart edge and are OpenFlow-enabled5 through a preinstalled virtual switch, which is controlled by a central SDN controller. The smart edge’s computation resources are vir-tualized and controlled by a virtual infrastruc-ture manager (such as OpenStack). Intra- and inter-smart-edge communications are achieved by the SDN controller with rate-limiting feature supports (for bandwidth allocation).

The advantage of such SDN/NFV architec-ture is as follows:

Both installation costs and hardware upgrade for new services can be reduced, because they can be done centrally at the smart edge level.Because software upgrades can be done at the operator’s premises transparently to end users, the hardware device won’t be shut down during operations. The smart community network’s

topology is completely visible to operators.The development and deployment of new services will become easier. Principally, new services will be implemented at the smart edge level, and then provided to all homes in the community. SDN #ows will link new components to create new services using a service function chaining model.Virtual resources can be aggregated to ful-"ll tasks that can’t be done by a single hard-ware resource. Thus, the overall capacity is optimized for processing distributed tasks in the community.

In contrast to the virtualized wireline access network, next we look at the virtualization wire-less access network in a smart community.

Virtualized Wireless Access Network in a Smart CommunityKey challenges when deploying software-de"ned wireless parts include virtualizing low-cost wireless APs — for example, a WiFi router — and implementing programmability features. Each AP will be provided with an OpenFlow agent, which exposes the necessary hooks for the SDN controller running on the smart edge to orchestrate the WiFi network and report mea-surements. Time-critical aspects of the WiFi MAC protocol (such as IEEE 802.11 acknowl-edgment packets) are processed by the WiFi net-work interface controller (NIC) hardware, and non-time-critical functionality (such as client

Figure 2. Optical access plans for a smart community: (a) traditional passive optical network (PON) plan versus (b) virtualized PON network with smart edge integration. (FDT stands for "re distribution terminals; ODB stands for optical distribution box; SDN stands for software-de"ned networking; and SDU stands for single dwelling unit.)

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association management) is implemented in software on the controller and agents. In addi-tion, the agents perform matching on incoming frames to support a publish subscribe system, wherein network applications can subscribe to per-frame events. Traditionally, the WiFi pro-tocol provides no mechanism for centralized control over the client’s association, because the client makes the association decision entirely on its own. Thus, dynamically building a soft-ware-de"ned mesh network is challenging. This challenge can be addressed by implementing a virtual AP inside a physical AP.6

Figure 3a depicts the SDN controller and the software-de"ned AP. To help the SDN con-troller con"gure the underlying hardware, additional functionalities are required. Open vSwitch includes an Open vSwitch Database management protocol, which is one alterna-tive for con"guring the switch instances as well as queue management. The physical layer (PHY) management is handled by a PHY agent that con"gures the wireless interface param-eters, such as frequency, transmission power, and operation mode. The agent is also used by a PHY manager running in the controller to con"gure hardware parameters and to monitor the status of interfaces. Both the manager and controller run on virtual instances hosted by the smart edge.

In this integrated environment, APs and ONUs are shared between different homes and users. Thus, it’s challenging to provide security and privacy at the physical home gateway level in each home because security rules might be frequently changed, which makes the con"gu-ration tasks costly. An automatic solution based

on centralizing virtualized network function components at the smart edge can be used to deal with the issue. Figure 3b depicts key com-ponents of a virtual access point running on the smart edge, which corresponds to a real hard-ware device installed in a home. Each compo-nent of the virtual access point is a virtualized network function hosted by a virtual machine or container, and is managed by the virtual infra-structure manager. The access point logic com-ponent implements basic functions of the AP, such as routing, traf"c characterization, #ow scheduling, and Session Initiation Protocol (SIP) functions. The home automation logic compo-nent processes communications and man-ages different smart objects in a home network. Finally, the "rewall component is responsible for security and privacy. Security rules are de"ned by this component, and then pushed to the data plane in the hardware device. This architecture allows dynamic customization of traf"c "lters and privacy methods according to application and user requirements.

Routing in a Smart CommunityThanks to built-in SDN capabilities of optical and wireless network elements, optimal routing algo-rithms can be implemented in the SDN control-ler to dynamically change the forwarding table of each network element. A variety of objectives can be considered when building an optimized routing scheme, such as costs, QoS, and energy saving. In this article, we investigate the prob-lem of optimizing energy consumption. Basically, each ONU can support several APs and optical subscribers at the same time. Mobile users con-nect to the network through the APs. The WMN

Figure 3. Virtualized wireless access point (AP) and the smart edge. (a) Virtualized wireless AP and the controller. (b) Virtual AP running on the smart edge. (DHCP stands for Dynamic Host Con"guration Protocol; PHY stands for physical layer.)

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enables traf"c to be rerouted through alternate paths in case of failures. Because there are user-load #uctuations, the network is often planned to support the peak traf"c load, which might result in overprovisioning during low-load peri-ods. Recent advances in optical networks enable setting network interfaces into low-power states during idle periods.7 This allows reducing energy consumption of an interface from more than 10 W during an active state down to less than 1 W during an idle state; similarly for the APs. The aforementioned ingredients inspired us to seek out an algorithm that affords bandwidth demand while activating the smallest num-ber possible of optical interfaces and APs. This problem can be formulated as a mixed-integer linear program; however, prior work4,8 considers only mobile users and ignores optical subscrib-ers. We can reformulate the problem as follows: The graph G(V, E) represents the topology of the network, in which V is the set of vertices and E

V V is the set of edges. Then V = H S, in which H is the set of APs and S is the set of optical switch ports. Each edge (i, j) E repre-sents a communication link between two nodes i and j, which are APs or optical ports. The band-width requirement of an edge is denoted by cij. Available bandwidth of an optical port i is Bi, and available bandwidth capacity of an AP i is Ci. If there’s no link between i and j, we assign cij = 0. We use two matrices X and Y to represent results:

=

=

xf u

yf v

1 if optical port isactive

0 otherwise,

1 if the AP isactive

0 otherwise.

u

v

The objective function is

∑ ∑+∈ ∈ax byMinimize ,uu S vv H (1)

subject to

∑ ≤ ∀ ∈=

c B u S, ,i

Niu u1 and (2)

∑ ∑ ∑+ ≤ ∀ ∈∈ ∈

∈

c c C i H, .j S ij j S

j Sji i (3)

The objective function (Equation 1) mini-mizes energy consumed by active optical ports and APs, in which each optical port consumes

a and each AP consumes b amount of energy, respectively. The constraint in Equation 2 states that total bandwidth requirements of links going to a switch port can’t be greater than the capacity of the port. The constraint in Equation 3 states that total bandwidth requirements of links going to an AP can’t be greater than the capacity of the AP. A solution to this integer linear program-ing optimization model can be obtained using a mathematical solver or a heuristic algorithm.4

It’s worth noting that an issue in a dense envi-ronment is assigning users to the APs. In this arti-cle, we deal principally with access from homes in a smart community. Each home’s AP is protected by a password-based mechanism. In an open sce-nario where users might enter the system from any AP, an advanced sharing mechanism in which heavy-loaded AP might direct users to another AP is required. Such a mechanism lets users select the appropriate AP based on a cost model (for example, distance and load). Game-theoretic algorithms are preferred to compute optimal solutions that bene"t both users and providers.9

Telco Cloud Service Provisioning in a Smart CommunityCross-domain and multilayer virtualization is the key challenge for creating virtual slices (VIs) of the network, including optical and wireless network resources for community ser-vices. The virtualization process involves the abstraction of physical resources into logical resources that can then be assigned as indepen-dent entities to different VIs, and shared by a variety of virtual operators and end users. The objective is to implement dynamically recon-"gurable uni"ed VIs over the underlying con-verged optical and wireless network segments satisfying the VI operators’ requirements and end users’ needs, while maintaining cost effec-tiveness and other speci"c requirements such as energy ef"ciency. This process requires the identi"cation of the optimal VI that can support the service demand in terms of both topology and available resources, and includes mapping of virtual resources to physical resources.

In addition, such an optimal solution must take into account smart edge’s computing capac-ity, and the nature of services. We model a typi-cal telco service, IMS, which provides text, voice, and data services to end users in a smart com-munity. The IMS system comprises a set of SIP servers, user databases (home subscriber servers),



application servers, media resource functions, public switched telephone network gateways, and so on. The core IMS system consists of SIP servers, called call/session control functions (CSCF), which include proxy-CSCF, interrogating-CSCF, and serving-CSCF. Traditionally, the entire IMS system made up of several components is cen-trally hosted by a telco operator’s datacenter (see Figure 4a). The centralization of all telco func-tions at a remote site imposes challenges in terms of load processing, QoS, availability, and band-width costs when providing service to a commu-nity with a high user concentration. Such issues

can be addressed through distributing telco service functions to the smart edge (see Figure 4b), and the connection to the remote datacenter can be controlled by SDN mechanisms to bal-ance load, optimize use, and afford additional demand. Further distributions might also be pos-sible — for example, the smart edge can directly host some application servers.

Basically, IMS provides integrated services-like capabilities for voice, video conferencing, and Internet Protocol television. IMS only man-ages a speci"c class of forwarding in the net-work — typically the real-time services. This

Figure 4. Example of a telecommunication company (telco) function distribution between the smart edge and core telco cloud. (a) Centralized telco service provisioning. (b) Distributed telco service provisioning with smart edge control. (GbE stands for Gigabit Ethernet; HSS stands for home subscriber server; I-CSCF stands for interrogating called call/session control functions; MGW stands for media gateway; MGCF stands for media gateway control function; and S-CSCF stands for serving called call/session control functions.)

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section formulates the problem of providing such services using uni-"ed #ow-and-computation manage-ment capabilities of an SDN/NFV smart edge. In addition, such inte-gration allows orchestrating both underlying (WOBAN and the smart edge) and service (virtual IMS com-ponents) layers at the same time.

The problem of joint optimization of routing and the telco service provi-sioning model in a smart community determines a routing mechanism over WOBAN network, and allocates com-puting resources in the smart edge to afford a given request (for example, the number of messages to be pro-cessed by the IMS per second) with minimal resource consumption or costs (in terms of energy) while meet-ing QoS requirements. Constraints of the problem are link capacity (wired, wireless, and backhaul links), user requirements, and the available capacity of the smart edge servers.

Consider a virtual IMS system run-ning on K identical virtual machines in the smart edge. Here,Rm

k and Rpk

are respectively memory capacity and processing resource requirements of a virtual machine k, and ∅m

t and ∅pt are

respectively available memory capac-ity and processing resource of the server t in the smart edge. We use a matrix W to represent results in which

=w 1tk,1 if the virtual machine k is

hosted by the server t for a demand from the optical port i. The objec-tive function is to minimize the total energy consumption of the network and smart edge:

∑ ∑ ∑+ +∈ ∈ ∈ ∈

ax by zwMin ,uu S vv H tk i

u U k K,

,

(4)

subject to

∑ ∑≤∅ ≤∅ ∀R w R w t* ; * ; , mk

k tk i

mt

pk

k tk i

pt, ,

in which z is a function to calculate energy consumption of the virtual machine k based on its memory and processing resource require-ments as de"ned in other work.10 In addition to

the constraints shown in Equations 2 and 3, the optimization problem (Equation 4) is formulated as an integer linear programing model. Its solu-tion can be obtained by a mathematical solver, or by a heuristic (such as greedy, genetic algo-rithm, or tabu search).

Example of Internet of Things Service ProvisioningThe Smart Residence is an ultra-broadband tes-tbed supporting smart city Internet of Things (IoT) applications for a sustainable smart uni-versity residential campus at the École de

Figure 5. The testbed and experimental results. (a) The Smart Residence testbed. (b) Comparison of network function virtualization (NFV) smart edge technology, traditional provisioning model, and some other NFV models. (MiFi stands for mobile WiFi.)

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Technologie Supérieure, University of Quebec, Canada, and is joining with Gig.U. Currently, the testbed provides ultra-broadband service to 150 residential units (student apartments and single family houses).

The testbed is composed of three compo-nents (see Figure 5a):

A white box core switching platform pro-vides optical multiservice to link the Smart Residence to Internet providers, as well as to international partners. This switching plat-form is integrated into the smart edge, and programmable to provide SDN functions, such as dynamic routing and traf"c "ltering.An optical access platform consists of vir-tual home gateways and optical aggregation switches linking smart home and WiFi APs to the core switch.

A set of telco-grade blade servers provides various telco services as well as monitoring, power management, and emergency alerting. Based on the monitoring services, a database containing user, power, and resource data is built and Big Data analytical services are developed to extract information from data, and then to optimize resource and service provisioning.

Each residential unit in Smart Residence has a residential gateway providing optical access2 and WiFi technology (IEEE 802.11ac). It also serves as a concentration point for monitoring services based on the ZigBee protocol.

Previous work has presented a similar SDN/NFV architecture for the deployment and con-trol of the WiFi access network in the univer-sity dorm.11 However, this research didn’t take into account any computation resources.

To jointly provide telco access and comput-ing services in the Smart Residence, the CO has been rearchitected. In this section, we compare the amount of energy required for network ele-ments to provide smart community control functions using our proposed architecture (see Figure 4b) and consider a traditional control method when all functions are centralized on the remote cloud (see Figure 4a).

In the traditional approach, each smart home has an active optical connection directly linked to the remote datacenter through which smart control service is provided. This connection is always active regardless of user traf"c condi-tions. All WiFi APs are also activated. Because power consumption of each element (for exam-ple, computation or network functions) is con-stant and their number increases linearly with user requests, the total system consumption increases linearly with the number of smart homes in the community.

In the proposed approach, routes are estab-lished dynamically to afford service require-ment, and unused ports and APs are set to idle state with minimal power consumption. In Fig-ure 5b, the proposed approach might give better results, depending on traf"c demand and avail-able resources of the smart edge. As shown, energy consumed by the traditional model (non-NFV) is far higher than the proposed model. When the number of users is 1,000, the difference between the two models is roughly six times.

We carried out the experiments using data collected from a virtual IMS system. We cal-culated system energy consumption based on bandwidth requirements for IMS control mes-sages. We derived home activity patterns from an open dataset.12 Power consumption of an AP is 12 W at full capacity, an ONU is 5 W, and an optical switch is 450 W. We calculated power consumption of each virtual machine based on their memory and CPU consumption, as explained in our prior work.10

Figure 5b also presents energy consumption of two other NFV solutions, namely NFV-server only and NFV-WiFi only, which are discussed in prior research. The "rst solution, NFV-server only, virtualizes the IMS system13 and deploys it onto the edge without considering the access network. The second solution, NFV-WiFi only, virtualizes wireless APs,11,14 and uses IMS ser-vice running on a remote cloud. As shown in

Our proposed solution (that is, NFV-

smart edge) virtualizes all the involved

elements ... and then consolidates them

into the smart edge, thus giving a

better result in terms of energy savings.



the "rst solution, the virtual IMS, which can be provisioned dynamically when the user base grows, and consumes power proportionally to user requests that #uctuate. However, network resources, which aren’t virtualized, consume power constantly, resulting in total consumption higher than our proposed solution. On the other hand, the second solution dynamically provi-sions virtual APs according to user requests, but computation resources running on bare metal servers consume a high amount of energy. Our proposed solution (that is, NFV-smart edge) vir-tualizes all the involved elements — namely the IMS system, APs, and optical access equipment — and then consolidates them into the smart edge, thus giving a better result in terms of energy savings.

I n this article, we reviewed a telco service pro-visioning model in a smart community, along

with its ability to provide sustainable service based on NFV and SDN capabilities. Integrating WOBAN and smart edge functionalities looks quite promising, because it allows the deploy-ment of service in small- and medium-scale proj-ects with low cost and #exible business models. This method addresses the green information and communications technology (ICT) realm.

Our future work will include an integrated framework for the environmental assessment of network and cloud computing.

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Infrastructure,” J. Lightwave Technology, vol. 24, no.

12, 2006, pp. 4568-4583.

3. A. Maha et al., “Service Class Resource Management

for Green Wireless-Optical Broadband Access Net-

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Kim-Khoa Nguyen is an assistant professor at the École

de Technologie Supérieure’s Department of Electrical

Engineering, University of Quebec. He is the key archi-

tect of the GreenStar Network project and responsible

for R&D of the Green Telco Cloud project. His research

interests include software-de"ned networking (SDN),

network function virtualization (NFV), green infor-

mation and communications technology, cloud com-

puting, smart city, router architectures, and wireless

networks. Nguyen has a PhD in electrical and com-

puter engineering from Concordia University. Contact

him at [email protected].

Mohamed Cheriet is a full professor at the École de Technol-

ogie Supérieure’s Department of System Engineering,

University of Quebec. His research interests include

document image analysis, optical character recogni-

tion, mathematical models for image processing, pat-

tern classi"cation models, learning algorithms, and

perception in computer vision. Cheriet has a PhD in

computer science from the University of Pierre et Marie

Curie. Contact him at [email protected].

Selected CS articles and columns are also available

for free at http://ComputingNow.computer.org.

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