1. Desktop to Cloud Transformation PlanningAuthor: Kirk Beaty, Andrzej Kochut, Hidayatullah Shaikh IBM T.J. Watson Research Center Presenter: SOK Phearin MBC Lab., Konkuk University

2. ContentsI. IntroductionII. Transformation Planning for Desktop Clouds A. User Profiling - UPROF B. Desktop Benchmarking - DeskBench C. Computing Resources Requirement for Virtualized System and Desktop PlacementIII. Examples and Experimental Studies A. Desktop Workload Analysis B. Benchmarking for Capacity PlanningIV. Related WorkV. Conclusion and Future Work

3. Introduction

4. Introduction Traditional desktop delivery model Costly Time-consuming procedures Security concerns Deskside supports Desktop Virtualization is an emerging alternative. OS and application reside at a remote data center Lightweight end-user computer/device Lower management cost Improved data and application security management

5. Introduction Cloud computing - an emerging paradigm whereby services and computing resources are delivered to customers over the Internet from a service provider who owns and operates the cloud. Service models: IaaS SaaS PaaS Desktop as a Service (DaaS) : a natural environment of virtual desktop paradigm whereby desktops would be delivered as a service from a Desktop Cloud.

6. Introduction

7. Introduction Major contribution of the paper provides method and transformation planning algorithm that: Accounts for realistic scaling factors between application execution on legacy system and execution on virtualized servers Provides validation mechanism using benchmarking driven by realistic action sequences based on workload analysis Allows for estimating networking needs and effects of remoting protocol and network conditions on user experience.

8. Transformation Planning for Desktop Clouds

9. Transformation Planning for Desktop Clouds Server to CloudDesktops to Cloud Transformation Transformation Interactive desktop User interactions with applications servers Desktop is generally Transaction based with single-user aggregate user load Unpredictable utilization Predictable resources requirement requirements Users bursts of interatction, computing and think time Similarity of the both transformation is Hardware Transparency

10. Transformation Planning for Desktop Clouds

11. Input: user profiling data and activities Process: analyze Output: profiling of both system and user applications to determine the key applications in terms of usage frequency and resource requirements.

12. Capture and replay the completion event in a precise timing Provides the necessary data to determine how resource utilization and execution times will scale from one hardware platform to another

13. Scaling data (3) and resource utilization data (2) are both used by the cloud administrator

14. Uses the Knowledge and Models based rules (6) and Cloud operator (4) to help guide allocation of user desktops to the cloud. Output: a transformation plan

15. Provision and place the legacy desktop images onto hardware within the desktop cloud (8)

16. Transformation Planning for Desktop Clouds Shared Server A single operating system Provides shared services to many users Virtualized Server Full administrative access Application libraries required by less than limited amount of users Dedicated Server Provide a dedicated 1-to-1 instantiation Additional resources required such as heavy graphics processors for 3D rendering.

17. User Profiling - UPROF

18. User Profiling - UPROF User Profiling tool or agent running on users desktops Gathers details for all processes in 10 second interval Prototype version - Microsoft Windows Management Interface (WMI) to obtain statistics of interests including: number, speed, type and utilization of processors size and utilization of memory size and utilization of local disks utilization of network interfaces names, user/owner, command line arguments, utilization of CPU, memory, network for all processes names, frequency, resource utilities of applications

19. User Profiling UPROF (cont. ) Uploader: CURL HTTP Collects data at all times the desktop is in operation, regardless of network connection Data is stored and uploaded on a subsequence attempt when connection is re-established Categorized data for desired details Ex. Administrator, developer, business manager

20. Desktop Benchmarking DeskBench

21. Desktop Benchmarking - DeskBench DeskBench: an implementation of the window manager software or an independent layer between the application and the window manager library (as a shim). The primitives that need to be intercepted and injected are common throughout all major window managers both the open source and proprietary.

22. Desktop Benchmarking DeskBench (Cont.) A tool capable of replaying and timing previously recorded user actions (keyboard and mouse events) Actions recorded are stored as Artifacts Artifact - combination of playlist, a set of actions, can be played back with directives included for controlling repetitions, random or fixed think times, and random or sequential order. Two phases: Recording Replaying

23. Desktop Benchmarking DeskBench (Cont.) Recording Phase All events (mouse and keyboard), generated by window manager and passed to application, arerecorded. A synchronization point - a screen state that logically is a necessary point to reach before proceeding with subsequent actions, or is a point that the tool user wants to mark for measured execution time. Hash codes (MD5) of the screen image buffer are recorded along with each synchronization point to expect the completion of the corresponding event element of the artifact being played.

24. Desktop Benchmarking DeskBench (Cont.) Replaying Phase Processes each ordered event found in the artifact file and injects into window manager.

25. Computing Resources Requirement for Virtualized System and Desktop Placement

26. Computing Resources Requirement for Virtualized System and Desktop Placement A method for calculating the proper capacity planning using scaling factor for resource usage. Produces a ratio of amount of the resource used by the same application executing in the cloud and legacy desktop. The next step involves placing of virtual desktops to servers in the cloud using standard techniques, such as binpacking algorithm where item sizes correspond to resource requirements of virtual desktops

27. Examples and Experimental Studies

28. Desktop Workload Analysis Desktop workload analysis gives detailed view of activity on legacy systems. Various outputs from UPROF tool. CPU utilization over the measurement period Large cumulative CPU usage important cloud capacity planning

29. Desktop Workload Analysis CPU consumption when Critical for finding peak application is loaded in usage application memory

30. Desktop Workload Analysis Read and write transfer rate of the top application

31. Desktop Workload Analysis Scaling factors for resource usage on legacy (IBM T42) and virtualized system (HS20 blade running ESX 3.0 hypervisor) Windows XP WMP requires almost 3 times more CPU resources on virtualized system

32. Desktop Workload Analysis Aggregate usage of resources for a given user group In this example, workload of 9 workstations were aggregated Time-series of aggregate CPU utilization for all desktops

33. Desktop Workload Analysis Histograms of aggregate CPU usage and memory usage accordingly Used as estimates of aggregate resource consumption to be expected on a virtualized environment

34. Desktop Workload Analysis aggregate disk write transfers

35. Desktop Workload Analysis Resource usage due to a single application across all of the users Provisioning shared services environment

36. Benchmarking for Capacity Planning results of benchmarking experiments for a set of typical applications Sensitive operations to the concurrence the acceptable density of operations per core to maintain a given responsiveness

37. Benchmarking for Capacity Planning Effects of latency on responsiveness of rendering a picture Picture rendering is significantly affected because it requires significant network transfers. DeskBench can be used to estimate how far (in terms of network latency and bandwidth) user terminals can be from the virtualized servers to maintain desirable level of response time

38. Related Work &Conclusion and Future Work

39. Related Work Cloud computing is a new architectural approach designed to conceal complexities of the large scale computer systems and provide users with easy to use, flexible, and massively scalable services. Desktop cloud: an example of services provided by cloud computing At the data center, techniques and approaches used are: Various virtualization technologies Several protocols to access remote desktops Shared same concept of relaying the keyboard and mouse events to the server

40. Related Work Many approaches applicable to server consolidation: Static heuristic-based vector bin-packing algorithm Optimization algorithm based on the expected financial gains Integrated resource management framework for QoS Allocation algorithm minimizing the number of migrations Algorithm minimizing the number of VM migrations Grid-based resource management algorithm Theoretical algorithm for scheduling of tasks Con: Not desktop consolidation Not consider application level statistics Purely analyze without the use of benchmark component

41. Conclusion and Future Work A set of tools and an approach in legacy desktops to desktop cloud transformation model: Assessment of workload on legacy machines Benchmarking of target virtualized environment Future Work: Research on automation of actual transformation execution

42. Key References [10] Desktone, .Desktop as a Service,. http://desktone.com/, 2008. [11] J. Rhee, A. Kochut, and K. Beaty, .DeskBench: Flexible Virtual Desktop Benchmarking Toolkit,. to appear in Integrated Management Symposium (IM), 2009.

43. Thank You