NDLI: A Multi-objective GRASP Algorithm for Joint Optimization of Energy Consumption and Schedule Length of Precedence-Constrained Applications

Content Provider	IEEE Xplore Digital Library
Author	Pecero, J.E. Bouvry, P. Huacuja, H.J.F. Khan, S.U.
Copyright Year	2011
Abstract	We address the problem of scheduling precedence-constrained scientific applications on a heterogeneous distributed processor system with the twin objectives of minimizing simultaneously energy consumption and schedule length. Previous research efforts on scheduling have focused on the minimization of a quality of service metric based on the completion time of applications (e.g., the schedule length). Recently, many researchers are working on the design of new scheduling algorithms that consider the minimization of energy consumption. We report a new scheduling algorithm accounting for both objectives. The new scheduling algorithm is based on a multi-start randomized adaptive search technique (GRASP framework) that adopts Dynamic Voltage Scaling technique to minimize energy consumption. This technique enables processors to operate in different voltage supply levels at the cost of sacrificing clock frequencies. This multiple voltage implies a trade-off between the quality of the schedules and energy consumption. Therefore, the new proposed approach is designed as a multi-objective algorithm that simultaneously optimize both objectives. Simulation results on a set of real-world applications emphasize the robust performance of the proposed approach.
Starting Page	510
Ending Page	517
File Size	2046462
Page Count	8
File Format	PDF
ISBN	9781467300063
DOI	10.1109/DASC.2011.97
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Schedules Energy consumption energy optimization Computational modeling Green computing scheduling Scheduling Scheduling algorithm Optimization distributed computing
Content Type	Text
Resource Type	Article

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