Abdullah Almurayh
MSCS Graduate Candidate
Committee members:
Dr. Edward Chow (Advisor)
Dr. Chuan Yue
Dr. Albert Glock
Fall 2011

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MCEVE / Abdullah Almurayh
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8/26/2011

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Widely used technology
Benefits of Virtualization

Consolidation and isolation
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Reduced power and cooling
 Green computing
 Ease of deployment and administration
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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
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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
MCEVE / Abdullah Almurayh 8/26/2011

Virtual Machine (VM):
• Guest Operating System.
• More flexibility.
• Full Virtualization, Para- Virtualization.
• Xen, Vmware, Virtual box, RHEV.
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Virtual Private Server(VPS):
• Share host Operating System.
• Less flexibility when the host Kernel
• more efficient.
• OS-level Virtualization.
• Linux-Vserver, OpenVZ.
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VM vs. VPS:
Xen [ Para-Virtualization  Virtual Machine]
VMware [ Full Virtualization  Virtual Machine]
OpenVZ [ OS-level Virtualization  Virtual Private Server]
Figure: Comparison of latency performance by different message sizes
Figure: Comparison of read and write performance by different file 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=4482669
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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.
MCEVE / Abdullah Almurayh 8/26/2011
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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.
MCEVE / Abdullah Almurayh 8/26/2011
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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 .
MCEVE / Abdullah Almurayh 8/26/2011
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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
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MCEVE / Abdullah Almurayh 8/26/2011

Trade-off Complexity of Platforms vs. Benchmarks
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..
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• 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.
MCEVE / Abdullah Almurayh 8/26/2011
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 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.
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Different opinions lead to different decisions
MCEVE / Abdullah Almurayh combined views

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.
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SPCE virtualization measurements (SPECvirt)
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MCEVE / Abdullah Almurayh
Example

MCEVE Implementation
Web Application Based
Implementation
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Inputs:
1) Considerations
2) Constraints
3) Weighted priorities
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Results:
1) Considerations
2) Weighted priorities
3) List of solutions
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Suggestions:
1) Hardware
2) Platform
3) Measurements
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Detailed configurations
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Execution time:
• Execution time is the time between the submission and result delivering.
• Execution time is impacted by the quantity of the considered parameters.
Test bed Specifications
Machine Type
OS Name
Virtual Machine
Windows XP
Web Server Apache 2.5.10
System Manufacturer VMware Virtual Platform
Total Physical Memory 128.00 MB
Total Virtual Memory 2.00 GB
0,35
0,3
0,25
0,2
0,15
0,1
0,05
0
ALL
Time 0,321323
ALL-2011
0,191892
Virtualizatio n Platform:
ESX
Avg Resp.
Time App
Server
0,272906 0,241496
User expectations
SSD
0,087024
HP-
RHEV/2011
0,0915742
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 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.
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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.
Benchmarks
Application Server Performance
Web Server Performance
Mail Server Performance
Overall Performance
Capacity
Avg Resp. Time App Server
Avg Resp. Time Idel
Price
Measurements
33.4983
54.0049
88.7385
7067
432
1.20181
9.03028
$18,000.00
Description
Request per second in application virtual server
Request per second in Web Server virtual server
Request per second in Mail virtual server
Overall Performance of the above performance benchmarks
Number of virtual servers per one physical server
Average response time of application virtual server performance
Average response time of Idle virtual server performance
The price of purchasing/licensing a virtualization platform
25 http://www.spec.org/virt_sc2010/results/res2011q2/virt_sc2010-20110419-00027-perf.html
MCEVE / Abdullah Almurayh

Impact of Weighted Priorities on Selection
Scenario (0)
Weighted priorities
Application Performance
10
Conf#14 score
1
Configurations Application
Performance conf#14 33.4983
conf#13 conf#7
33.4945
33.4929
conf#15 conf#8 conf#12 conf#5 conf#4
33.4751
33.4636
33.4629
33.4407
33.4272
conf#11 conf#1 conf#2 conf#6 conf#9 conf#10 conf#3
33.4156
33.4008
33.4007
33.3361
33.0474
33.0474
32.1869
26
32,5
32
31,5
34
33,5
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MCEVE / Abdullah Almurayh

Impact of Weighted Priorities on Selection
Scenario (1)
Weighted priorities
Application Performance Avg Resp. Time App Server
10
10
10
10
0
1
2
3
10
10
10
10
10
10
10
4
5
6
7
8
9
10
Conf#14 score
2
2
3
2
2
3
3
1
1
1
2
Configurations conf#15 conf#7 conf#5 conf#14 conf#2 conf#13 conf#4 conf#11 conf#12 conf#8 conf#3 conf#9 conf#1 conf#10 conf#6
Latency
1.17154
1.17929
1.18875
1.20181
1.21857
1.23182
1.29222
1.34103
1.34316
1.34714
1.37154
1.37684
1.38583
1.50053
1.53393
1,8
1,6
1,4
1,2
1
0,8
0,6
0,4
0,2
0
MCEVE / Abdullah Almurayh
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Impact of Weighted Priorities on Selection
Scenario (2)
Configurations conf#14 conf#15 conf#11 conf#12 conf#7 conf#6 conf#13 conf#9 conf#10 conf#4 conf#5 conf#2 conf#8 conf#3 conf#1
Overall
Performance
Score
7067
3824
3802
3723
2742
2721
2144
1820
1811
1763
1763
1369
1367
1221
1169
6000
5000
4000
3000
2000
1000
0
8000
Application
Performance
10
10
10
10
10
10
10
10
10
10
10
7000
Weighted priorities
Avg Resp. Time App
Server
10
10
10
10
10
10
10
10
10
10
10
Overall
Performance Score
0
1
2
3
4
5
6
7
8
9
10
Conf#14 score
1
1
1
3
1
1
1
1
1
1
1
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Impact of Weighted Priorities on Selection
Scenario (3)
Configurations conf#3 conf#15 conf#10 conf#1 conf#2 conf#4 conf#8 conf#9 conf#13 conf#6 conf#7 conf#12 conf#11 conf#5 conf#14
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Cost
2500
4166.67
4167
4500
4500
4500
4500
4500
4500
6667
6667
8333
9000
18000
18000
18000
16000
14000
12000
10000
8000
6000
Application
Performance
10
10
10
10
10
10
10
10
10
10
10
20000
4000
2000
0
Weighted priorities
Avg Resp. Time
App Server
Overall Performance
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
8
9
10
4
5
6
7
Cost
“price”
0
1
2
3
MCEVE / Abdullah Almurayh
Conf#14
Score
11
14
14
14
1
1
2
2
14
14
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Impact of Weighted Priorities on Selection
Scenario (4)
Configurations Number of VM conf#14 432 conf#11 conf#15
234
234 conf#12 conf#6 conf#7 conf#13
228
168
168
132 conf#9 conf#10 conf#4 conf#5 conf#2 conf#8 conf#3 conf#1
114
114
108
108
84
84
78
72
30
200
150
100
50
0
Application
Performance
10
10
10
10
10
10
10
10
10
10
10
500
450
400
350
300
250
Weighted priorities
Avg Resp. Time App
Server
10
10
10
10
10
10
10
10
10
10
10
Overall
Performance
10
10
10
10
10
10
10
10
10
10
10
MCEVE / Abdullah Almurayh
Price
10
10
10
10
10
10
10
10
10
10
10
Capacity
6
7
4
5
0
1
2
3
8
9
10
Conf#14 score
14
14
14
14
14
14
14
14
14
14
14

Impact of Weighted Priorities on Selection
Configuration # 14 Scores based on the users weighted priorities
Lower is closer to the user’s expectation
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11
10
9
8
7
6
5
15
14
13
4
3
2
1
0
Scenario 1
Weights changes
Scenario 2 Scenario 3
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Scenario 4

Benchmarks Comparisons of Competitive Configurations benchmarks comparisons between Configurations #14 and its competitive
Configurations #15
Benchmarks
Application Performance
Web Server Performance
Mail Server Performance
Overall Performance
Capacity
Avg Resp. Time App Server
Avg Resp. Time Idel
Price
SPECvirt_sc2010 Result
Conf#14 Measurements
33.4983
54.0049
88.7385
7067
432
1.20181
9.03028
$18,000.00
Conf#15 Measurements
33.4751
53.9654
88.6013
3824
234
1.17154
5.44872
$ 4 , 166.67
Conf#14 Conf#15
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
 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.
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
 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.
MCEVE / Abdullah Almurayh

 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.
MCEVE / Abdullah Almurayh
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 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.
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Demo and
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MCEVE / Abdullah Almurayh
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