1. Huazhong University of Science and Technology Evaluating Latency-Sensitive Applications’ Performance Degradation in Datacenters with Restricted Power Budget Song Wu, Chuxiong Yan, Haibao Chen, Hai Jin, Wei Guo, Zhen Wang, Deqing Zou chenhaibao@hust.edu.cn The 44th International Conference on Parallel Processing (ICPP-15) Beijing, China, September 1-4, 2015

2. Outline Background Motivation Approach Evaluation Conclusion

3. Background ISPs (Internet Service Providers) Power budget ▫ The reserved space of power for servers Power margin ▫ The part of the power budget that is not consumed by the servers

4. Background ISPs (Internet Service Providers)

5. Background The solution ▫ Restricting power budget The problem ▫ May incur power budget violation We need to evaluate the performance degradation with a evaluation method

6. Outline Background Motivation Approach Evaluation Conclusion

7. Motivation State-of-art ▫ PBV(percentage of budget violation) ▫ In these two cases, the performance degradation values given by PBV are both Cannot reflect the affected percentage of the application

8. Motivation State-of-art ▫ PPL(percentage of performance loss) Cannot reflect the delay of some parts of latency- sensitive applications

9. Motivation Latency-sensitive applications ▫ Sensitive to brief variation in response time ▫ Common application of Internet service The problem ▫ The state-of-art methods are too coarse-grained Our target ▫ Design a evaluation method for latency-sensitive applications

10. Outline Background Motivation Approach Evaluation Conclusion

11. Approach CPU Workload (Workload for short) The actual CPU utilization will be capped under thrld.

12. Approach Workload ▫ Workload reflects the part of application affected

13. Approach Workload

14. Approach Differential Workload ▫ Workload in a very narrow time span

15. Approach Functions ▫ Delay(t). It is used to express the delay of differential Workload at time t.

16. Approach Functions ▫ WA(t). It is used to express the accumulated Workload at time t.

17. Approach Functions ▫ TotalWorkload(t). The amount of total Workload submitted to the server between time 0 and t. ▫ DelayedWorkload(t). The summation of delayed differential Workload between time 0 and t.

18. Approach Metrics ▫ In what percentage the application is delayed? —— PDW (Percentage of Degraded Workload) ▫ What is the average delay of this part of application? —— AD (Average Delay)

19. Approach Metrics’ expression ▫ PDW is the percentage of workload whose delay is greater than 0 ▫ AD is the division between workload-delay product and delayed workload

20. Approach The algorithm ▫ Design an algorithm based on CPU utilization trace ▫ Obtain the result in O(n) time

21. Approach Use Case in Datacenter Transformation Map + CPU trace  PDW & AD under different budget The decision of power budget for all servers

22. Outline Background Motivation Approach Evaluation Conclusion

23. Evaluation The accuracy of methods A synthetic CPU trace covering the range from 0% to 100%

24. Evaluation The accuracy of methods The average difference of PDW and AD is 2.8% and 3.4%, respectively

25. Evaluation The accuracy of methods The average difference of PBV and PPL is 34.9% and 86.3%, respectively

26. Evaluation The accuracy of methods A real trace from WorldCup98

27. Evaluation The accuracy of methods The average difference of PDW and AD is 3.3% and 7.5%, respectively

28. Evaluation The accuracy of methods The average difference of PBV and PPL is 49.6% and 95.8%, respectively Summary: PDW and AD can accurately evaluate the performance degradation, but PBV and PPL cannot. Fluctuant CPU trace may bring about more difference.

29. Evaluation Typical servers We choose 9 servers in Tencent’s datacenter according to their application types and load

30. Evaluation Typical servers  PDW and AD increase with lower CPU utilization threshold;  More space in reducing power budget with light load servers;  There could be a maximum-benefit point.

31. Evaluation Evaluating in datacenter Evaluate the space in saving power budget of about 25000 servers Save about 1/3 power budget with almost no performance degradation

32. Outline Background Motivation Approach Evaluation Conclusion

33. The state-of-art ▫ Inaccurate for latency-sensitive applications Our evaluation method ▫ Two metrics (PDW and AD) ▫ A fine-grained method Experimental result ▫ Our evaluation method is more accurate ▫ Substantial space in power budget restriction

34. Huazhong University of Science and Technology Thank you! Any questions, pls. contact chenhaibao@hust.edu.cn

35. Approach The derivation process We can obtain the result of PDW & AD by simultaneous equations