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
Department of Automation, Tsinghua University
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
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1.1 Introduction (Motivation)
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1.1 Introduction (Importance)
Department of Automation, Tsinghua University
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.
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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.
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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
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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
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3. Virtualized Resource Scheduling
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
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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
. . .
. . .
. . .
. . .
3. Virtualized Resource Scheduling
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)
Department of Automation, Tsinghua University
3. Virtualized Resource Scheduling
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
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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
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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.
Department of Automation, Tsinghua University
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.
Department of Automation, Tsinghua University
Thanks
Q&A