1. Adaptive Control of Virtualized Resources in Utility Computing Environments HP Labs: Xiaoyun Zhu, Mustafa Uysal, Zhikui Wang, Sharad Singhal University of Waterloo: Kenneth Salem Pradeep Padala, Kang G. Shin University of Michigan

2. 2 A typical scenario in data centers Customer A Shared Data Center Run auction siteRun news site Customer B Requirements Response time < 2s Throughput > 100 rq/sec Pay 100$ Requirements Response time < 5s Throughput > 50 rq/sec Pay 50$

3. 3 Hosting applications Data Center E-mail server Linux Web server Linux Database server Linux Common idiom: One-to-one mapping of applications to nodes

4. 4 Problem: Poor utilization Wasted Resources Ad-hoc resource allocation schemes waste resources

5. 5 Solution: Virtual data center Consolidate E-mail server Linux Web server Linux Database server Linux Virtualization ( Xen, OpenVZ, VMware ) E-mail server Linux Web server Linux Database server Linux Improved utilization using consolidation

6. 6 Problem: Provisioning Average Peak Wasted ResourcesBursty LoadBad response time Provisioning for dynamic workloads is hard! Solution: Adaptive controller

7. 7 Goals Good utilization Good performance QoS differentiation Average CPU utilization = 80% Average response time = 100ms Gold vs. Silver customers 2:1 resources

8. 8 Outline Motivation Background Modeling Design Evaluation Conclusion

9. 9 How do we provision the customers ? Virtualized Server IVirtualized Server II VM IVM IIVM IIIVM IV Web Server I DB Server I Web Server II DB Server II Auction Client News Client Customer A Customer B

10. 10 What are we controlling ? Xen scheduler VM I VM II Controller CPU Usage ? Goals Good performance Good utilization QoS differentiation Goals met ? NO Virtualized Server Mechanism Policy 50% 80% 20% Set CPU shares

11. 11 Related work Existing research –Cluster management –Load balancing –Resource allocation & scheduling –QoS differentiation Our contribution: Adaptive resource control –Quantitative model of system behavior –Fine-grained, adaptive control No wastage of resources High throughput, low response time QoS differentiation

12. 12 How do we design an adaptive resource controller? Model Design Experiment Evaluate Understand system variables Input Output Design controller PI, PID, I controller … Stress the controller Goals met ? A control theoretic approach to systems

13. 13 Outline Motivation Background Modeling Design Evaluation Conclusion

14. 14 QoS differentiation Modeling a virtual data center VM Shares Workload Virtualized Server IVirtualized Server II Web server I DB server I Web server II DB server II How to differentiate between two multi-tiered systems ? VM utilization Response time Throughput

15. 15 Modeling two multi-tiered systems QoS metric Linear Response time ratio is more controllable than loss ratio Non-Linear

16. 16 Outline Motivation Background Modeling Design Evaluation Conclusion

17. 17 Utilization controller: an example Solution: Self-tuning integral controller Set to 40% Using 20% Controller Utilization 20/40*100 = 50% Utilization goal = 80% Set to 25% New Utilization 20/25*100 = 80% Problems –Utilization is variable –Delays and errors in sensing & setting –Stability concerns VM

18. 18 Adjusts to varying demand Maintains goal utilization Knobs to control aggression (Kp) Proven stable [Wang DSOM’05] Utilization controller System Utilization goal Self-tuning controller - Workload Error in utilization e(k-1) Measured utilization u(k-1) CPU allocation u(k)

19. 19 Let there be controllers Container consumptions Problem: All controllers independent Want 40% Want 70% 110% Can’t fit (Saturation) Solution: Arbiter controller enforcing QoS differentiation UtilControl for WS I Virtualized Server I UtilControl for WS II UtilControl for DB I Virtualized Server II UtilControl for DB II

20. 20 Final controller Arbiter Controller Requested CPU shares Desired response time ratio Final CPU shares UtilControl for WS I Virtualized Server I UtilControl for WS II UtilControl for DB I Virtualized Server II UtilControl for DB II Container consumptions

21. 21 Outline Motivation Background Modeling Design Evaluation Conclusion

22. 22 Evaluation Multi-tiered systems –2 HP Proliant servers –Apache + MySQL –Xen 3.0 with SEDF scheduler Clients –RUBiS: auction client –2 RUBiS clients: 500 … 1000 threads Can we maintain 70% QoS ratio ?

23. 23 Varying load - throughput 500 threads 1000 threads

24. 24 Saturation Web I share Web II share Saturation Varying load - control Buffer to maintain good performance Penalized to maintain QoS ratio Saturation

25. 25 Varying load – QoS ratio Goal Goal ratio of 70% maintained!

26. 26 Conclusion Adaptive control of virtual data center –Good application performance High throughput Low response time –Good utilization Maintain goal CPU utilization –QoS differentiation Maintain goal QoS ratio Project page : http://kabru.eecs.umich.edu/twiki/bin/view/Main/ DynamicControl http://kabru.eecs.umich.edu/twiki/bin/view/Main/ DynamicControl E-mail: ppadala@eecs.umich.eduppadala@eecs.umich.edu Questions ?

27. 27 Backup and old slides

28. 28 Enterprise data centers Large data centers –100s/1000s of nodes –Shared infrastructure –Run critical applications –Should meet service levels Problems –Power costs –Management costs –Poor utilization –Unmet service levels

29. 29 Solution: Consolidate !

30. 30 Virtualized Server IIVirtualized Server I Customer B Hosting two multi-tiered systems Web Server I Web Server II DB Server I DB Server II Customer A Auction Client News Client Web Server I Web Server II DB Server I DB Server II

31. 31 Varying load Time Workload II Workload I 500 clients 1000 clients

32. 32 Saturation Web I share

33. 33 Web II share Saturation

34. 34 Modeling results - throughput Dom0 effect Saturation causes Real throughput < Offered throughput Web shareThroughput

35. 35 Arbiter controller features Is an integral controller Decides final shares based on QoS differentiation goals Integral gain: knobs for aggression Stable – gain value based on model

36. 36 Modeling a multi-tiered system Workload Web share DB share Web usage DB usage QoS metrics Stress the system in various scenarios Observe all variables Web server DB server Virtual Server

37. 37 Modeling results – response time Dom0 effect Web shareResponse time

38. 38 Questions ?