Adaptive Virtual Machine Provisioning in Elastic Multi-tier Cloud Platforms Fan Zhang, Junwei Cao,...

Adaptive Virtual Machine Provisioning in Elastic

Multi-tier Cloud Platforms

Fan Zhang, Junwei Cao, Hong CaiJames J. Mulcahy, Cheng Wu

Tsinghua University, IBM, FAU2011.07.28

Department of Automation, Tsinghua University

Outline

Introduction & Related Works1

Virtualized Resource Scheduling3

Experimental Studies4

2 System Architecture Overview


1.1 Introduction (Background)

Virtualized Cloud Platform S(Software)aaS P(Platform)aaS I(Infrastructure)aaS

Virtual Machines Virtual Clusters

Advantages:(1)Creating/

Destroying VM(2)Data/Processing

locality(3)Service migration


1.1 Introduction (Motivation)


1.1 Introduction (Importance)


1.2 Related Works L. Slothouber. A model of web server performance. In Proceedings of the 5th International World

Wide Web Conference (WWW). Paris, France. 1996. J. Chase, and R. Doyle. Balance of power: Energy management for server clusters. In Proceedings

of the 8th Workshop on Hot Topics in Operating Systems (HotOS-VIII). Elmau, Germany. 2001. B. Urgaonkar, and P. Shenoy, Cataclysm: Handling extreme overloads in internet services. In

Proceedings of the 23rd Annual ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing (PODC’04). St. John’s, Newfoundland, Canada. 2004.

R. Levy, J. Nagarajarao, G. Pacifici, M. Spreitzer, A. Tantawi, and A. Youssef. Performance management for cluster based web services. In IFIP/IEEE 8th International Symposium on Integrated Network Management. Vol. 246, pp. 247–261. 2003.

D. Menasce, Web server software architectures. IEEE Internet Computing. Vol. 7 no. 6, 2003. D. Villela , P. Pradhan, and D. Rubenstein. Provisioning servers in the application tier for e-

commerce systems. ACM Transactions on Internet Technology (TOIT). Vol. 7, no. 1, 2007. S. Ranjan, J. Rolia, H. FU, and E. Knightly. QoS-driven servermigration for internet data centers.

In Proceedings of the 10th International Workshop on Quality of Service(IWQoS), Miami, FL. 2002.

A. Kamra, V. Misra, E.M. Nahum, Yaksha: a self-tuning controller for managing the performance of 3-tiered Web sites, In Proceedings of the 12th International Workshop on Quality of Service(IWQoS), Passau, Germany, 2004.

B. Urgaonkar, G. Pacifici, P. Shenoy, M.Spreitzer, and A. Tantawi. Analytic Modeling of Multi-tier Internet Services and its Applications. ACM Transactions on the Web (TWEB 2007), Vol. 1, No. 1, pp. 1-35, May 2007.

B. Urgaonkar, P. Shenoy, A. Chandra, P. Goyal, and T. Wood. Agile Dynamic Provisioning of Multi-tier Internet Applications. ACM Transactions on Adaptive and Autonomous Systems (TAAS), Vol. 3, No. 1, pp. 1-39, March 2008.

We differentiate our work from the following three aspects.


2. System Architecture OverviewPhysical Cluster 1 Physical Cluster 2 Physical Cluster 3

VC 1 VC 2 VC 3 VC 4 VM

Small VM Pool Large VM Pool

VM Resource Pool

•Virtual CPU•Virtual

Memory•Virtual

Machines•Virtual

Clusters

Virtual Everything

Virtual Everything

•Small VM•1 CPU, 1 GB

M.

•Large VM•2 CPU, 2 GB

M.


2. System Architecture Overview

1

1-Pj+1

1-P2

1-P1

P2P1

S1

Sn

...

Q0

Tier 2

Tier 1

1-Pj

Tier j

1-P3

Tier J

Pj-1Pj

. . .

. . .

. . .

. . .

1 1SjN i

...

1 1LjN i

...

1 1SjN i

...

1 1LjN i

...

Load Balancer

ADRj

AARj = AARj-1,j + AARj+1,j

j i

j i j i j i j i j i j i j i j i

ASTS ASTS ASTS ASTS ASTL ASTL ASTL ASTL

Pj1-Pj

AARj-1,j = Pj-1 * ADRj-1 AARj+1,j = (1-Pj+1 )* ADRj+1

j(i) (AARj) =AARj-1,j + AARj+1,j (j[1, J-1])

J(i) (AARJ) =ADRJ-1,J


3. Virtualized Resource Scheduling

11

,(1 )

j

j

C

j S jS Sj jS C S

j j jj S

C iAST i

i i ii

11

,(1 )

j

j

C

j L jL Lj jL C L

j j jj L

C iAST i

i i ii

L. Kleinrock, Queueing Systems, Volume 2: Computer Applications. John Wiley and Sons, Inc., 1976.

S S L Lj j j j jADR N i AST N i AST

, 1 *j j j jAAR ADR P

, 1 * 1j j j jAAR ADR P

Average Service TimeTier jSmall VMAverage Service TimeTier jLarge VM

Average Departure RateTier jAverage

Departure RateTier j to Tier j+1Average Departure RateTier j to Tier j - 1



1

1-Pj+1

1-P2

1-P1

P2P1

S1

Sn

...

Q0

Tier 2

Tier 1

1-Pj

Tier j

1-P3

Tier J

Pj-1Pj

. . .

. . .

. . .

. . .

Calculating Response Time

AARJ-1,J

A function of AARJ-

1,J

A function of AARj+1,j

AARj-1, j

A function of AARj-1,j

A function of AAR1,2

AAR1, 2

A function of AAR2,3A function of

AAR1,2

AAR0, 1


1

1-Pj+1

1-P2

1-P1

P2P1

S1

Sn

...

Q0

Tier 2

Tier 1

1-Pj

Tier j

1-P3

Tier J

Pj-1Pj

. . .

. . .

. . .

. . .


R(i)(1–P1)*AST1

R(i)P1(1–P2)*(AST2+2*AST1)

R(i)P1P2--- Pj-1(1–Pj)*(ASTj+2*ASTj-1+…+2*AST1)



Optimization problem

1

1 10

Re

* 1 * 2*jJ j

k j l jj lk

sponseTime

P P AST AST SLA

,

1 1,

* *

S Lj j

S Lj j

N i N i

J JS LS j L jj j

N i N i

Cost i

Cost N i Cost N i

Min

Min

1

J Sj Sj

N i

1

J Lj Lj

N i


4. Experimental Studies Simulation Toolkits:

Matlab SimEvents

Real Testbed IBM X3950, 16 CPUs 24 GB (Opensuse 11.1) Apache 2.0.55 (1 large VM, 1 small VM) tomcat 5.5 (2 large VMs, 4 small VMs) MySQL (1 large VM)

Transaction Data: Rubis (an auction site like ebay)

Workload Data: Web trace from the 1998 Soccer World Cup site

0 10 20 30 40 50 600.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

Time (Min)R

eque

sts

Arr

ival

Fre

quen

cy


4. Experimental Studies

0 10 20 30 40 50 600

1

2

3

4

5

6

7

Time(Min)

Num

ber

of V

Ms

# of Small Apache VM# of Large Apache VM# of Small Tomcat VM# of Large Tomcat VM

0 10 20 30 40 50 606

6.5

7

7.5

8

8.5

9

9.5

10

10.5

Time (Min)

Res

pons

e T

ime

(Sec

)

Measured Response TimePredicted Response Time

Our model suits the workload very well.

Our model predicts the response time

very well.


4. Experimental Studies

0 10 20 30 40 50 606

7

8

9

10

11

12

13

14

Time (Min)

Res

pons

e T

ime

(Sec

)

Our methodUtilization based method

0 10 20 30 40 50 600.02

0.04

0.06

0.08

0.1

0.12

0.14

Time (Min)

Cos

t ($)

Our methodUtilization based method

Utilization based method: Increase or

decrease VM based on the utilization of the

previous stage.

Our method is better than utilization based method. The SLA is satisfied bounded

below 10 Sec. The cost is generally less.


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Adaptive Virtual Machine Provisioning in Elastic Multi-tier Cloud Platforms Fan Zhang, Junwei Cao,...

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