NDLI: Energy-efficient scheduling algorithm of task dependent graph on DVS-Unable cluster system

Content Provider	IEEE Xplore Digital Library
Author	Yan Ma Bin Gong Lida Zou
Copyright Year	2009
Description	Author affiliation: Information Technology School, Women's Academy at Shandong Jinan Shandong, China (Lida Zou) \|\| School of Computer Science and Technology, Shandong University Jinan Shandong, China (Yan Ma; Bin Gong)
Abstract	At present, energy-efficient scheduling algorithm in High-Performance Computing (HPC) environment is becoming a research hotspot owning to its high operation cost and low reliability. In this paper, we investigate energy-efficient scheduling algorithm of data dependent tasks in DVS-Unable cluster system. The proposed ESTD algorithm efficiently integrates task clustering with task duplication technologies to reduce data transmission time and communication energy consumption. In order to decrease the static power of processing elements, it also uses one of the power-saving techniques in system level — Dynamic Power Management on the premise that application execution is non-preemptive and predictive. ESTD algorithm not only optimizes the makespan of task dependent graph, but also confines its energy consumption into a certain extent. Compared with EAD algorithm and PEBD algorithm, experimental results and case studies demonstrate that ESTD algorithm can reduce more energy consumption while not affecting scheduling performance of applications.
Starting Page	217
Ending Page	224
File Size	415523
Page Count	8
File Format	PDF
ISBN	9781424451487
DOI	10.1109/GRID.2009.5353056
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-13
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Energy efficiency Scheduling algorithm Energy consumption Clustering algorithms Costs Power system reliability Data communication Energy management Power system management Prediction algorithms
Content Type	Text
Resource Type	Article

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