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

Self-Tuning Batching with DVFS for Performance Improvement and Energy Efficiency in Internet Servers Self-Tuning Batching with DVFS for Performance Improvement and Energy Efficiency in Internet Servers DAZHAO CHENG and YANFEI GUO, University of Colorado, Colorado Springs CHANGJUN JIANG, Tongji University, China XIAOBO ZHOU, University of Colorado, Colorado Springs 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

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

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References (36)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2015 by ACM Inc.
ISSN
1556-4665
DOI
10.1145/2720023
Publisher site
See Article on Publisher Site

Abstract

Self-Tuning Batching with DVFS for Performance Improvement and Energy Efficiency in Internet Servers DAZHAO CHENG and YANFEI GUO, University of Colorado, Colorado Springs CHANGJUN JIANG, Tongji University, China XIAOBO ZHOU, University of Colorado, Colorado Springs 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

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS)Association for Computing Machinery

Published: Mar 25, 2015

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