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Desktop to Cloud Transformation Planning

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Traditional desktop delivery model is based on …

Traditional desktop delivery model is based on
a large number of distributed PCs executing operating system
and desktop applications. Managing traditional desktop
environments is incredibly challenging and costly. Tasks like
installations, conguration changes, security measures require
time-consuming procedures and dedicated deskside support. Also
these distributed desktops are typically underutilized, resulting
in low ROI for these assets. Further, this distributed computing
model for desktops also creates a security concern as sensitive
information could be compromised with stolen laptops or PCs.
Desktop virtualization, which moves computation to the data
center, allows users to access their applications and data using
stateless “thin-client”devices and therefore alleviates some of
the problems of traditional desktop computing. Enterprises can
now leverage the exibility and cost-benets of running users'
desktops on virtual machines hosted at the data center to enhance
business agility and reduce business risks, while lowering TCO.
Recent research and development of cloud computing paradigm
opens new possibilities of mass hosting of desktops and providing
them as a service.
However, transformation of legacy systems to desktop clouds
as well as proper capacity provisioning is a challenging problem.
Desktop cloud needs to be appropriately designed and provisioned
to offer low response time and good working experience
to desktop users while optimizing back-end resource usage and
therefore minimizing provider's costs. This paper presents tools
and approaches we have developed to facilitate fast and accurate
planning for desktop clouds. We present desktop workload
proling and benchmarking tools as well as desktop to cloud
transformation process enabling fast and accurate transition of
legacy systems to new cloud-based model.

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  • An example of such a synchronization point is following a “double-click” event to open a window/application, the next action has to be delayed until the screen fully refreshes. In this case the tool user signals the synchronization point after the screen is fully refreshed.
  • The mechanism used at the synchronization point to determine event completion is to first compare the hash code of the observed screen with those (hash code) defined (or recorded) in the artifact for the current synchronization point.
  • Histograms also show mean resource usage as well as 99th percentile. Those values are used as estimates of aggregate resource consumption to beexpected on a virtualized environments. They may need to be scaled using benchmarking scaling factors and illustrated in next subsection
  • Transcript

    • 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  User’s 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