Embed Size (px)
Citation preview
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 [email protected]
The 44th International Conference on Parallel Processing (ICPP-15) Beijing, China, September 1-4, 2015
Outline
Background
Motivation
Approach
Evaluation
Conclusion
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
Background
ISPs (Internet Service Providers)
Background
The solution▫Restricting power budget
The problem▫May incur power budget violation
We need to evaluate the performance degradation with a evaluation method
Outline
Background
Motivation
Approach
Evaluation
Conclusion
MotivationState-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
Motivation
State-of-art▫PPL(percentage of performance loss)
Cannot reflect the delay of some parts of latency-sensitive applications
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
Outline
Background
Motivation
Approach
Evaluation
Conclusion
Approach
CPU Workload (Workload for short)
The actual CPU utilization will be capped under thrld.
Approach
Workload
▫Workload reflects the part of application affected
Approach
Workload
Approach
Differential Workload▫Workload in a very narrow time span
ApproachFunctions▫Delay(t). It is used to express the delay of differential
Workload at time t.
ApproachFunctions▫WA(t). It is used to express the accumulated Workload
at time t.
ApproachFunctions▫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.
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)
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
Approach
The algorithm▫Design an algorithm based on CPU utilization trace▫Obtain the result in O(n) time
Approach
Use Case in Datacenter
Transformation Map + CPU trace PDW & AD under different budget
The decision of power budget for all servers
Outline
Background
Motivation
Approach
Evaluation
Conclusion
Evaluation
The accuracy of methods
A synthetic CPU trace covering the range from 0% to 100%
Evaluation
The accuracy of methods
The average difference of PDW and AD is 2.8% and 3.4%, respectively
Evaluation
The accuracy of methods
The average difference of PBV and PPL is 34.9% and 86.3%, respectively
Evaluation
The accuracy of methodsA real trace from WorldCup98
Evaluation
The accuracy of methods
The average difference of PDW and AD is 3.3% and 7.5%, respectively
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.
Evaluation
Typical servers
We choose 9 servers in Tencent’s datacenter according to their application types and load
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.
Evaluation
Evaluating in datacenterEvaluate the space in saving power budget of about 25000 servers
Save about 1/3 power budget with almost no performance degradation
Outline
Background
Motivation
Approach
Evaluation
Conclusion
Conclusion
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
Huazhong University of Science and Technology
Thank you!
Any questions, pls. contact [email protected]
Approach
The derivation process
We can obtain the result of PDW & AD by simultaneous equations
