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.

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

2. Contents
I. Introduction
II. Transformation Planning for Desktop Clouds
A. User Profiling - UPROF
B. Desktop Benchmarking - DeskBench
C. Computing Resources Requirement for Virtualized System
and Desktop Placement
III. Examples and Experimental Studies
A. Desktop Workload Analysis
B. Benchmarking for Capacity Planning
IV. Related Work
V. 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 Cloud
Desktops 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