DocumentCode
3706538
Title
Evaluating Latency-Sensitive Applications: Performance Degradation in Datacenters with Restricted Power Budget
Author
Song Wu;Chuxiong Yan;Haibao Chen;Hai Jin;Wei Guo;Zhen Wang;Deqing Zou
Author_Institution
Services Comput. Technol. &
fYear
2015
Firstpage
639
Lastpage
648
Abstract
For data centers with limited power supply, restricting the servers´ power budget (i.e., The maximal power provided to servers) is an efficient approach to increase the server density (the server quantity per rack), which can effectively improve the cost-effectiveness of the data centers. However, this approach may also affect the performance of applications in servers. Hence, the prerequisite of adopting the approach in data centers is to precisely evaluate the application performance degradation caused by restricting the servers´ power budget. Unfortunately, existing evaluation methods are inaccurate because they are either improper or coarse-grained, especially for the latency-sensitive applications widely deployed in data centers. In this paper, we analyze the reasons why state-of-the-art methods are not appropriate for evaluating the performance degradation of latency-sensitive applications in case of power restriction, and we propose a new evaluation method which can provide a fine-grained way to precisely describe and evaluate such degradation. We verify our proposed method by a real-world application and the traces from Ten cent´s date enter with 25328 servers. The experimental results show that our method is much more accurate compared with the state of the art, and we can significantly increase datacenter efficiency by saving servers´ power budget while maintaining the applications´ performance degradation within controllable and acceptable range.
Keywords
"Degradation","Servers","Power demand","Delays","Google","Guidelines"
Publisher
ieee
Conference_Titel
Parallel Processing (ICPP), 2015 44th International Conference on
ISSN
0190-3918
Type
conf
DOI
10.1109/ICPP.2015.73
Filename
7349619
Link To Document