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Precise Power Capping for Latency-Sensitive Applications in Datacenter
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DOI：10.1109/TSUSC.2018.2881893.png)
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Power capping is widely used in cloud datacenters to mitigate power over-provisioning problem, thus improve datacenter capacity and cut off their operation cost. However, inappropriate or aggressive power capping may lead to performance degradation of applications (especially latency-sensitive ones), and there are few effective methods that can accurately evaluate and control such negative impact caused by aggressive power capping. In this paper, we propose Fine-Grained Differential Method (FGD) to quantitatively analyze how inappropriate power capping degrades the performance of latency-sensitive applications. By using FGD, we can minimize the provisioned power for each server by setting a precise power budget according to application's Service Level Agreement (SLA). And we further propose Precise Power Capping (PPCapping) which is designed to increase the datacenter capacity with a fixed power supply by means of FGD. Our research also provides an insight of precise tradeoff between applications' SLAs and datacenter capacity. We verify FGD and PPCapping by using real world traces from Tencent's datecenter with 25,328 servers. The experimental results show that FGD can accurately analyze the impact of power capping on the performance of latency-sensitive applications, and PPCapping can effectively increase datacenter capacity compared with the typical power provisioning strategy.
Keyword:
Servers
Degradation
Power demand
Cloud computing
Web and internet services
Power measurement
Datacenter capacity
power provision
latency-sensitive application
cloud computing
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