mceve a model for configuring efficient virtualized environment based on multiple weighted...

MCEVEA 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 YueDr. Albert Glock

Fall 2011

Outline

Introduction to Virtualization Technology Virtual Machine/Virtual Private Server The Problem The Proposed Model Evaluation Lessons Learned Future Work Conclusion Demo & Questions

2 MCEVE / Abdullah Almurayh 8/26/2011

Introduction to Virtualization


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

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


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.


VM vs. VPS

VM vs. VPS:Xen [ Para-Virtualization Virtual Machine]VMware [ Full Virtualization Virtual Machine]OpenVZ [ OS-level Virtualization Virtual Private Server]


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=4482669

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4482796&isnumber=4482669

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4482796&isnumber=4482669

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.


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.


Related Work

Quality in use: Meeting user needs for qualityBy: 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.


Related Work

A Quality of Service Management Framework Based on User ExpectationsVikas 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, theminimum and maximum expectations.

MCEVE is also based on user expectations; however, it allows the user to specify weights of these expectations .


Virtualization Solutions not Equal


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

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.

NativeXenVMwareUser-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..

Rel

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to n

ativ

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ighe

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bet

ter)

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.


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.


The Proposed Model


Different opinions lead to different decisions

combined views

The Algorithm of the Model


Conf: user configurationWeight: user configuration weightMeasure: benchmark measurementW: Weight valueM: measurement valueMix: overall calculationn: number of resulted solutionbest: best selection of the overall results.

The Flow Diagram of the Model


Benchmark Data Collection

SPCE virtualization measurements (SPECvirt)


Example

http://www.spec.org/virt_sc2010/results/res2011q2/virt_sc2010-20110419-00027-perf.html

MCEVE Implementation


Web Application Based Implementation

Input of MCEVE


Inputs:

1) Considerations2) Constraints3) Weighted priorities

Output of MCEVE Results


Results:

1) Considerations2) Weighted priorities3) List of solutions

Details of Virtualization Solution


Suggestions:

1) Hardware2) Platform3) Measurements

Details of MCEVE Configurations


Detailed configurations

MCEVE Software Efficiency


Execution time:

Test bed SpecificationsMachine Type Virtual MachineOS Name Windows XPWeb Server Apache 2.5.10System Manufacturer VMware Virtual PlatformTotal Physical Memory 128.00 MBTotal Virtual Memory 2.00 GB

ALL ALL-2011 Virtualiza-tion Plat-

form: ESX

Avg Resp. Time App

Server

SSD HP-RHEV/2011

Time 0.321323 0.191892 0.272906 0.241496 0.087024 0.0915742

0.025

0.075

0.125

0.175

0.225

0.275

0.325

User expectations

Exe

cuti

on

ti

me:

(S

ec)

• Execution time is the time between the submission and result delivering.

• Execution time is impacted by the quantity of the considered parameters.

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.


Referenced Configuration in MCEVE


Benchmarks Measurements Description

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.


http://localhost/mceve/pf_details.php?ID=5128174&PF=2&order=&by=hw


Impact of Weighted Priorities on Selection


Weighted prioritiesConf#14 score

Application Performance

10 1

Scenario (0)

Configurations Application Performance

conf#14 33.4983conf#13 33.4945conf#7 33.4929

conf#15 33.4751conf#8 33.4636

conf#12 33.4629conf#5 33.4407conf#4 33.4272conf#11 33.4156conf#1 33.4008conf#2 33.4007conf#6 33.3361conf#9 33.0474

conf#10 33.0474conf#3 32.1869

conf#14

conf#13

conf#7

conf#15

conf#8

conf#12

conf#5

conf#4

conf#11

conf#1

conf#2

conf#6

conf#9

conf#10

conf#3

31.5

32

32.5

33

33.5

34

Ap

p S

erve

r P

erfo

rman

ce (

Req

/Sec

)


Weighted prioritiesConf#14 score

Application Performance Avg Resp. Time App Server10 0 110 1 110 2 110 3 210 4 210 5 210 6 210 7 210 8 310 9 310 10 3

Scenario (1)

Configurations Latencyconf#15 1.17154conf#7 1.17929conf#5 1.18875

conf#14 1.20181conf#2 1.21857

conf#13 1.23182conf#4 1.29222conf#11 1.34103conf#12 1.34316conf#8 1.34714conf#3 1.37154conf#9 1.37684conf#1 1.38583

conf#10 1.50053conf#6 1.53393 conf#

15conf#

7conf#

5conf#

14conf#

2conf#

13conf#

4conf#

11conf#

12conf#

8conf#

3conf#

9conf#

1conf#

10conf#

6

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Lat

ency

(m

s)



Weighted prioritiesConf#14

scoreApplication

PerformanceAvg Resp. Time App

ServerOverall

Performance Score

10 10 0 310 10 1 110 10 2 110 10 3 110 10 4 110 10 5 110 10 6 110 10 7 110 10 8 110 10 9 110 10 10 1

Scenario (2)

Configurations

Overall Performance

Scoreconf#14 7067conf#15 3824conf#11 3802conf#12 3723conf#7 2742conf#6 2721

conf#13 2144conf#9 1820

conf#10 1811conf#4 1763conf#5 1763conf#2 1369conf#8 1367conf#3 1221conf#1 1169 conf#

14conf#

15conf#

11conf#

12conf#

7conf#

6conf#

13conf#

9conf#

10conf#

4conf#

5conf#

2conf#

8conf#

3conf#

1

0

1000

2000

3000

4000

5000

6000

7000

8000

Ov

era

ll P

erf

orm

an

ce

Sc

ore




ScoreApplication

PerformanceAvg Resp. Time

App ServerOverall Performance

Cost“price”

10 10 10 0 110 10 10 1 110 10 10 2 210 10 10 3 210 10 10 4 1110 10 10 5 1410 10 10 6 1410 10 10 7 1410 10 10 8 1410 10 10 9 1410 10 10 10 14

Scenario (3)

Configurations Costconf#3 2500

conf#15 4166.67conf#10 4167conf#1 4500conf#2 4500conf#4 4500conf#8 4500conf#9 4500

conf#13 4500conf#6 6667conf#7 6667

conf#12 8333conf#11 9000conf#5 18000

conf#14 18000 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

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

Co

st




scoreApplication Performance

Avg Resp. Time App Server

Overall Performance

Price Capacity

10 10 10 10 0 1410 10 10 10 1 1410 10 10 10 2 1410 10 10 10 3 1410 10 10 10 4 1410 10 10 10 5 1410 10 10 10 6 1410 10 10 10 7 1410 10 10 10 8 1410 10 10 10 9 1410 10 10 10 10 14

Scenario (4)

Configurations Number of VMconf#14 432conf#11 234conf#15 234conf#12 228conf#6 168conf#7 168

conf#13 132conf#9 114

conf#10 114conf#4 108conf#5 108conf#2 84conf#8 84conf#3 78conf#1 72 conf#

14conf#

11conf#

15conf#

12conf#

6conf#

7conf#

13conf#

9conf#

10conf#

4conf#

5conf#

2conf#

8conf#

3conf#

1

0

50

100

150

200

250

300

350

400

450

500

Nu

mb

er

of

VM



Configuration # 14 Scores based on the users weighted prioritiesLower is closer to the user’s expectation


0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Scenario 1 Scenario 2 Scenario 3 Scenario 4

Weights changes

Sco

res


benchmarks comparisons between Configurations #14 and its competitive Configurations #15

Benchmarks Conf#14 Measurements Conf#15 Measurements

Application Performance 33.4983 33.4751

Web Server Performance 54.0049 53.9654

Mail Server Performance 88.7385 88.6013

Overall Performance 7067 3824

Capacity 432 234

Avg Resp. Time App Server 1.20181 1.17154

Avg Resp. Time Idel 9.03028 5.44872

Price $18,000.00 $4,166.67

Conf#14 Conf#15

SPECvirt_sc2010 Result

Benchmarks Comparisons of Competitive Configurations



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.


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.


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.



Demo and

Questions

http://128.198.161.132/

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