1. MCEVE A Model for Configuring Efficient Virtualized Environment Based on Multiple Weighted Considerations Abdullah Almurayh MSCS Graduate Candidate Master Thesis Committee members: Dr. Edward Chow (Advisor) Dr. Chuan Yue Dr. Albert Glock Fall 2011

2. Outline  Introduction to Virtualization Technology  Virtual Machine/Virtual Private Server  The Problem  The Proposed Model  Evaluation  Lessons Learned  Future Work  Conclusion  Demo & Questions 1 MCEVE / Abdullah Almurayh 8/26/2011

3. Introduction to Virtualization 2 MCEVE / Abdullah Almurayh 8/26/2011 Widely used technology Benefits of Virtualization  Consolidation and isolation  Reduced power and cooling  Green computing  Ease of deployment and administration  High availability and disaster recovery Applications of virtualization  Education  Software Evaluation  Enabling the dynamic data center  Cloud computing  Load Balancing  Information Technology Departments  Disaster Recovery  Personal use

4. Introduction to Virtualization Virtualization types: Full-virtualization Para-virtualization Operating system-level virtualization Emulation Virtualization Projects: Xen VMware Windows Server 2008 R2 – Hyper-V OpenVZ Red Hat Virtualization RHEV Virtual box Many companies, datacenters, organizations, universities, and IT have virtualized their servers. Even Small business and individuals started using their virtualization solutions. Availability of Low-cost Public Clouds, e.g. Amazon AWS 3 MCEVE / Abdullah Almurayh 8/26/2011

5. Virtual Guest Virtual Machine (VM): Guest Operating System. More flexibility. Full Virtualization, Para- Virtualization. Xen, Vmware, Virtual box, RHEV. Virtual Private Server(VPS): Share host Operating System. Less flexibility when the host Kernel more efficient. OS-level Virtualization. Linux-Vserver, OpenVZ. 4 MCEVE / Abdullah Almurayh 8/26/2011

6. VM vs. VPS VM vs. VPS: Xen [ Para-Virtualization  Virtual Machine] VMware [ Full Virtualization  Virtual Machine] OpenVZ [ OS-level Virtualization  Virtual Private Server] 5 MCEVE / Abdullah Almurayh 8/26/2011 Figure: Comparison of read and write performance by different file sizes Figure: Comparison of latency performance by different message sizes Figures are Cited from: Chaudhary, V.; Minsuk Cha; Walters, J.P.; Guercio, S.; Gallo, S.;, "A Comparison of Virtualization Technologies for HPC," Advanced Information Networking and Applications, 2008. AINA 2008. 22nd International Conference on, vol., no., pp.861-868, 25-28 March 2008, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4482796&isnumber=4482669http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4482796&isnumber=4482669

7. The Problem  Diversity of virtualization solutions has opened the door to an endless array of choices.  Virtualization technologies operate in slightly different manners.  Virtualization technologies have different architectures and requirements.  By having vast choices, people sometimes become so confused and unable to choose the right virtualization solutions. 6 MCEVE / Abdullah Almurayh 8/26/2011

8. Related Work Optimizing utilization of resource pools in web application servers By: Alexander Totok, Vijay Karamcheti, Concurrency and Computation: Practice and Experience, vol. 22 (2010), pp. 2421-2444.  Research work in the area of modeling underlying server environments produces different results.  This research work can also include bottleneck identification and tuning to identify system metrics for performance enhancement.  Includes identification of different application configuration parameters to determine performance goals. The process of configuring virtualized environments is to achieve performance goals by producing better decisions of making virtualized environments. The proposed research is focused on the model of application configuration. 7 MCEVE / Abdullah Almurayh 8/26/2011

9. Related Work Quality in use: Meeting user needs for quality By: NigelBevan. Journal of Systems and Software. ACM. Dec,1999.Pages 89-96 http://dl.acm.org/citation.cfm?id=340343 This paper describes a framework for software product quality developed for: Internal quality: static properties of the code. External quality: behaviour of the software when it is executed. Quality in use: the extent to which the software meets the needs of the user. The paper defines the quality in use as a broader view of different concepts such as functionality, reliability, usability, efficiency, and the like. This framework is needed for evaluating the MCEVE software or application. 8 MCEVE / Abdullah Almurayh 8/26/2011

10. Related Work A Quality of Service Management Framework Based on User Expectations Vikas Deora, J. Shao, W. Alex Gray and Nick J. Fiddian, Service-Oriented Computing - ICSOC 2003, p.104-114 This paper presents a quality of service management framework based on user expectations by collecting expectations as well as ratings from the users of a service then calculating the quality of the service. This approach does not allow the user to specify, for example, the minimum and maximum expectations. MCEVE is also based on user expectations; however, it allows the user to specify weights of these expectations. 9 MCEVE / Abdullah Almurayh 8/26/2011

11. Virtualization Solutions not Equal 10 MCEVE / Abdullah Almurayh 8/26/2011 CPU scalability of different leading virtualization solutions in the UNIX and distributed server market Cited from: Not All Server Virtualization Solutions are Created Equal By: Andre Metelo IBM SWG Competitive Project Office. 08/13/2010

12. Trade-off Complexity of Platforms vs. Benchmarks 11 MCEVE / Abdullah Almurayh 8/26/2011 Native VMware Xen Figure: Passmark – CPU results compared to native (higher is better).. Cited From: VMware, "A Performance Comparison of Hypervisors" 2007. Native Xen VMware User-mode Figure : Relative performance of native Linux (L), XenoLinux (X), VMware workstation 3.2 (V) and User-Mode Linux (U) (higher is better). Cited From: P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, et a, "Xen and the art of virtualization," in In Proc. Of the 19th ACM Symposium on Operating System Principles, Bolton Landing, NY, Oct. 2003.. Relative score to native (higher is better)

13. The Proposed Model Develop a model for suggesting better solutions of virtualized environments based on the user weighted considerations. Use user weighted considerations as inputs for algorithmic outputs To have suggested solutions in easy way and low cost. A user can get a good overview of configurations that may meet his expectations. 12 MCEVE / Abdullah Almurayh 8/26/2011

14. Page 13 The Goals for the Proposed Model  Cost: Adopting the appropriate decisions resulting in hours configuring virtualized environments instead of spending days researching and comparing existing results. The cost can be reduced by the use of existing reliable results instead of performing tests and experiments that cost money and time.  Performance: Performance can be enhanced by using solutions that are based on the best performance comparisons. Trying different unbeknown solutions may have potential failures and lead to inefficient virtualized environments. 13 MCEVE / Abdullah Almurayh 8/26/2011

15. Page 14 The Proposed Model 14 MCEVE / Abdullah Almurayh 8/26/2011 Different opinions lead to different decisions combined views

16. Page 15 The Algorithm of the Model 15 MCEVE / Abdullah Almurayh 8/26/2011 Conf: user configuration Weight: user configuration weight Measure: benchmark measurement W: Weight value M: measurement value Mix: overall calculation n: number of resulted solution best: best selection of the overall results.

17. Page 16 The Flow Diagram of the Model 16 MCEVE / Abdullah Almurayh 8/26/2011

18. Page 17 Benchmark Data Collection SPCE virtualization measurements (SPECvirt) 17 MCEVE / Abdullah Almurayh 8/26/2011 Example

19. Page 18 MCEVE Implementation 18 MCEVE / Abdullah Almurayh 8/26/2011 Web Application Based Implementation

20. Page 19 Input of MCEVE 19 MCEVE / Abdullah Almurayh 8/26/2011 Inputs: 1)Considerations 2)Constraints 3)Weighted priorities

21. Page 20 Output of MCEVE Results 20 MCEVE / Abdullah Almurayh 8/26/2011 Results: 1)Considerations 2)Weighted priorities 3)List of solutions

22. Page 21 Details of Virtualization Solution 21 MCEVE / Abdullah Almurayh 8/26/2011 Suggestions: 1)Hardware 2)Platform 3)Measurements

23. Page 22 Details of MCEVE Configurations 22 MCEVE / Abdullah Almurayh 8/26/2011 Detailed configurations

24. Page 23 MCEVE Software Efficiency 23 MCEVE / Abdullah Almurayh 8/26/2011 Execution time: Execution time is the time between the submission and result delivering. Execution time is impacted by the quantity of the considered parameters.

25. Page 24 Evaluation  Effectiveness:  The functionality of the Model depends on the user inputs that any failure of a specific input can be effective.  The Model responds to user changes and functions relatively to these changes.  Accuracy:  This demonstrates how precisely and accurately the Model produces the results.  Compare a human perspective to the Model results  Data Transparency:  Data transparency in the Model indicates the data independency which exists when the code is not subjected to change when any change in the data occurs. 24 MCEVE / Abdullah Almurayh 8/26/2011

26. Page 25 Referenced Configuration in MCEVE 25 MCEVE / Abdullah Almurayh 8/26/2011 BenchmarksMeasurementsDescription Application Server Performance 33.4983 Request per second in application virtual server Web Server Performance 54.0049 Request per second in Web Server virtual server Mail Server Performance 88.7385 Request per second in Mail virtual server Overall Performance 7067 Overall Performance of the above performance benchmarks Capacity 432 Number of virtual servers per one physical server Avg Resp. Time App Server 1.20181 Average response time of application virtual server performance Avg Resp. Time Idel 9.03028 Average response time of Idle virtual server performance Price $18,000.00 The price of purchasing/licensing a virtualization platform Configuration # 14: Intel Xeon E7- 4870 2.4 GHz, 80 cores, 8 chips, 10 cores/chip, 2 threads/core, 2 TB RAM (128 x 16 GB, Quad Rank x4 PC3-8500 CL7 ECC DDR3 1066MHz LP RDIMM), 576 x 73 GB 15k RPM SAS storage, 2 x Broadcom NetXtreme II Gigabit Ethernet, 4 x Intel x520 10Gb, RedHat RHEV Virtualization Platform.RHEV http://www.spec.org/virt_sc2010/results/res2011q2/virt_sc2010-20110419-00027-perf.html

27. Page 26 Impact of Weighted Priorities on Selection 26 MCEVE / Abdullah Almurayh 8/26/2011 Weighted priorities Conf#14 score Application Performance 101 Scenario (0)

28. Page 27 27 MCEVE / Abdullah Almurayh 8/26/2011 Weighted priorities Conf#14 score Application PerformanceAvg Resp. Time App Server 1001 11 21 32 42 52 62 72 83 93 3 Scenario (1) Impact of Weighted Priorities on Selection

29. Page 28 28 MCEVE / Abdullah Almurayh 8/26/2011 Weighted priorities Conf#14 score Application Performance Avg Resp. Time App Server Overall Performance Score 10 03 11 21 31 41 51 61 71 81 91 1 Scenario (2) Impact of Weighted Priorities on Selection

30. Page 29 29 MCEVE / Abdullah Almurayh 8/26/2011 Weighted priorities Conf#14 Score Application Performance Avg Resp. Time App Server Overall Performance Cost “price” 10 01 11 22 32 411 10 514 10 614 10 714 10 814 10 914 10 14 Scenario (3) Impact of Weighted Priorities on Selection

31. Page 30 30 MCEVE / Abdullah Almurayh 8/26/2011 Weighted priorities Conf#14 score Application Performance Avg Resp. Time App Server Overall Performance PriceCapacity 10 014 10 114 10 214 10 314 10 414 10 514 10 614 10 714 10 814 10 914 10 14 Scenario (4) Impact of Weighted Priorities on Selection

32. Page 31 31 MCEVE / Abdullah Almurayh 8/26/2011 Configuration # 14 Scores based on the users weighted priorities Lower is closer to the user’s expectation Impact of Weighted Priorities on Selection

33. Page 32 32 MCEVE / Abdullah Almurayh 8/26/2011 benchmarks comparisons between Configurations #14 and its competitive Configurations #15 Benchmarks Conf#14 Measurements Conf#15 Measurements Application Performance33.4983 33.4751 Web Server Performance54.0049 53.9654 Mail Server Performance88.7385 88.6013 Overall Performance7067 3824 Capacity432 234 Avg Resp. Time App Server1.20181 1.17154 Avg Resp. Time Idel9.03028 5.44872 Price$18,000.00 $ 4, 166.67 Conf#14 Conf#15 SPECvirt_sc2010 Result Benchmarks Comparisons of Competitive Configurations

34. Page 33 Lessons Learned  Field Research  Needed a lot of time to read and research in many different topics related to Virtualization.  Needed to implement parameters prediction algorithms, but could not be validated.  Tested Virtualization solutions to understand the differences between them. Testing Xenserver on UCCS HP blade servers due to unsatisfied requirements in my desktop.  Needed a lot of effort for calculating converting SPEC data into data that MCEVE uses.  Proposed Solution  I developed a model (MCEVE ) for suggesting better solutions of virtualized environments based on the user weighted considerations.  MCEVE still needs a lot of data to ensure that MCEVE yields dependable results.  The execution time of MCEVE increases as the data grows  The accuracy of MCEVE needs to be normalized since it is impacted by big values such as the cost. 33 MCEVE / Abdullah Almurayh 8/26/2011

35. Page 34 Future Work  Working in future on a different evaluation method by testing the suggested configurations.  Identifying fundamentally different opportunities to provide vast data comes from trustworthy sources  The Model needs a Data Standard that includes naming agreements for data elements and other system components.  There is a need for an offset that can be associated to a large benchmark such as the “Price” to reduce its negative effectiveness.  The Model application can be published in the real world and surveyed. 34 MCEVE / Abdullah Almurayh 8/26/2011

36. Page 35 Conclusion  I proposed the MCEVE Model that could help users to efficiently configure virtualized environments in an easier and reduced cost manner.  The model uses user considerations and configurations as inputs for algorithmic outputs /suggestions.  The proposed Model helps to minimize unexpected events driven by inefficient configurations.  The model saves user’s time by adopting the right decisions in hours instead of spending days researching and comparing existing results and reduces the cost of performing tests and experiments.  Performance can be enhanced by using solutions accordingly to real solutions rather than trying different solutions that can lead to high cost. 35 MCEVE / Abdullah Almurayh 8/26/2011

37. Page 36 36 MCEVE / Abdullah Almurayh 8/26/2011 Demo and Questions

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