1. Adaptive Virtual Machine Provisioning in Elastic Multi-tier Cloud Platforms Fan Zhang, Junwei Cao, Hong Cai James J. Mulcahy, Cheng Wu Tsinghua University, IBM, FAU 2011.07.28

2. Department of Automation, Tsinghua University Outline Introduction & Related Works 1 Virtualized Resource Scheduling 3 Experimental Studies 42 System Architecture Overview

3. 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

4. Department of Automation, Tsinghua University 1.1 Introduction (Motivation) Operational Cost Response Time Use more VMs Resp. Time estimation Workload estimation Use less VMs Proper VM usage Users How many VM to use? Vendors How many VM to provide?

5. Department of Automation, Tsinghua University 1.1 Introduction (Importance)

6. 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. None of the previous work considers the cost of using VMs. Cost considering None of the previous work considers providing large/small VMs. Various VM Most of the previous work calculate response time estimation based on simulation. We use mathematical prediction, which is easier. Mathematical prediction We differentiate our work from the following three aspects.

7. Department of Automation, Tsinghua University 2. System Architecture Overview Virtual CPU Virtual Memory Virtual Machines Virtual Clusters Virtual Everything Small VM 1 CPU, 1 GB M. Large VM 2 CPU, 2 GB M.

8. Department of Automation, Tsinghua University 2. System Architecture Overview j (i) (AAR j ) =AAR j-1,j + AAR j+1,j (j  [1, J-1]) J (i) (AAR J ) =ADR J-1,J

9. Department of Automation, Tsinghua University 3. Virtualized Resource Scheduling L. Kleinrock, Queueing Systems, Volume 2: Computer Applications. John Wiley and Sons, Inc., 1976. Average Service Time Tier j Small VM Average Service Time Tier j Large VM Average Departure Rate Tier j Average Departure Rate Tier j to Tier j+1 Average Departure Rate Tier j to Tier j - 1

10. Department of Automation, Tsinghua University 3. Virtualized Resource Scheduling Calculating Response Time AAR J-1,J A function of AAR J-1,J A function of AAR j+1,j AAR j-1, j A function of AAR j-1,j A function of AAR 1,2 AAR 1, 2 A function of AAR 2,3 A function of AAR 1,2 AAR 0, 1

11. Department of Automation, Tsinghua University 3. Virtualized Resource Scheduling R(i)(1–P 1 )*AST 1 R(i)P 1 (1–P 2 )*(AST 2 +2*AST 1 ) R(i)P 1 P 2 --- P j-1 (1–P j )*(AST j +2*AST j-1 +…+2*AST 1 )

12. Department of Automation, Tsinghua University 3. Virtualized Resource Scheduling Optimization problem Cost Min Res. Time Large VM Small VM

13. Department of Automation, Tsinghua University 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

14. Department of Automation, Tsinghua University 4. Experimental Studies Our model suits the workload very well. Our model predicts the response time very well.

15. Department of Automation, Tsinghua University 4. Experimental Studies 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.

16. Department of Automation, Tsinghua University Thanks Q&A