NDLI: Thermal-Aware Task Scheduling to Minimize Energy Usage of Blade Server Based Datacenters

Content Provider	IEEE Xplore Digital Library
Author	Qinghui Tang Gupta, S.K.S. Stanzione, D. Cayton, P.
Copyright Year	2006
Description	Author affiliation: Dept. Electr. Eng., Arizona State Univ. (Qinghui Tang)
Abstract	Blade severs are being increasingly deployed in modern datacenters due to their high performance/cost ratio and compact size. In this study, we document our work on blade server based datacenter thermal management. Our goal is to minimize the total energy costs (usage) of datacenter operation while providing a reasonable thermal environment for their reliable operation. Due to special characteristics of blade servers, we argue that previously proposed power-oriented schemes are ineffective for blade server-based datacenters and that task-oriented scheduling is a more practicable approach since the contribution to the total energy cost from cooling and computing systems varies according to the utilization rates. CFD simulations are used to evaluate scheduling results of three different task scheduling algorithms: uniform outlet profile (UOP), minimal computing energy (MCE), and uniform task (UT), under four different blade-server energy consumption models: discretenonoptimal (DNO), discreteoptimal (DO), analognonoptimal (ANO), and analogoptimal (AO). Simulation results show that the MCE algorithm, in most cases, results in a minimal total energy cost - a conclusion that differs from the findings of previous research. UOP performs better than UT at low datacenter utilization rates, whereas UT outperforms UOP at high utilization rates
Starting Page	195
Ending Page	202
File Size	317160
Page Count	8
File Format	PDF
ISBN	0769525393
DOI	10.1109/DASC.2006.47
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Energy consumption Costs Processor scheduling Cooling Blades Computational modeling Computational fluid dynamics Thermal management Power system reliability Scheduling algorithm
Content Type	Text
Resource Type	Article

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