NDLI: Self-Tuning Batching with DVFS for Performance Improvement and Energy Efficiency in Internet Servers

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Self-Tuning Batching with DVFS for Performance Improvement and Energy Efficiency in Internet Servers

Content Provider	ACM Digital Library
Author	Zhou, Xiaobo Guo, Yanfei Jiang, Changjun Cheng, Dazhao
Copyright Year	2015
Abstract	Performance improvement and energy efficiency are two important goals in provisioning Internet services in datacenter servers. In this article, we propose and develop a self-tuning request batching mechanism to simultaneously achieve the two correlated goals. The batching mechanism increases the cache hit rate at the front-tier Web server, which provides the opportunity to improve an application’s performance and the energy efficiency of the server system. The core of the batching mechanism is a novel and practical two-layer control system that adaptively adjusts the batching interval and frequency states of CPUs according to the service level agreement and the workload characteristics. The batching control adopts a self-tuning fuzzy model predictive control approach for application performance improvement. The power control dynamically adjusts the frequency of Central Processing Units (CPUs) with Dynamic Voltage and Frequency Scaling (DVFS) in response to workload fluctuations for energy efficiency. A coordinator between the two control loops achieves the desired performance and energy efficiency. We further extend the self-tuning batching with DVFS approach from a single-server system to a multiserver system. It relies on a MIMO expert fuzzy control to adjust the CPU frequencies of multiple servers and coordinate the frequency states of CPUs at different tiers. We implement the mechanism in a test bed. Experimental results demonstrate that the new approach significantly improves the application performance in terms of the system throughput and average response time. At the same time, the results also illustrate the mechanism can reduce the energy consumption of a single-server system by 13% and a multiserver system by 11%, respectively.
Starting Page	1
Ending Page	32
Page Count	32
File Format	PDF
ISSN	15564665
e-ISSN	15564703
DOI	10.1145/2720023
Volume Number	10
Issue Number	1
Journal	ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-25
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	DVFS Internet applications Self-tuning batching Energy efficiency Fuzzy model predictive control Performance improvement
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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