Title
An Evolutionary Technique for Performance-Energy-Temperature Optimized Scheduling of Parallel Tasks on Multi-Core Processors
Abstract
This paper proposes a static multi-objective evolutionary algorithm (MOEA)-based task scheduling approach for determining Pareto optimal solutions with simultaneous optimization of performance (P), energy (E), and temperature (T). Our algorithm includes problem-specific techniques for solution encoding, determining the initial population of the solution space, and the genetic operators that collectively work on generating efficient solutions in fast turnaround time. Multiple schedules offer a diverse range of values for makespan, total energy consumed, and peak temperature and thus present an efficient way of identifying trade-offs among the desired objectives, for a given application and architecture pair. We also propose a methodology to select one solution from the Pareto front given the user’s preference. The proposed algorithm for solving the task to core scheduling effectively achieves 3-way optimization and does so with fast turnaround time. We show that the proposed algorithm is advantageous because it reduces both energy and temperature together rather than in isolation. The proposed algorithm is evaluated using both implementation and simulation and is compared with integer linear programming solutions as well as with other scheduling algorithms that are energy- or thermal-aware. The time complexity of the proposed scheme is also considerably better than the compared algorithms.
Year
DOI
Venue
2016
10.1109/TPDS.2015.2421352
IEEE Transactions on Parallel and Distributed Systems
Keywords
Field
DocType
energy-efficient computing,evolutionary algorithms,static scheduling,task allocation,task graphs,thermal-efficient computing
Job shop scheduling,Evolutionary algorithm,Fair-share scheduling,Computer science,Parallel computing,Multi-objective optimization,Real-time computing,Schedule,Integer programming,Turnaround time,Time complexity,Distributed computing
Journal
Volume
Issue
ISSN
PP
99
1045-9219
Citations 
PageRank 
References 
12
0.59
26
Authors
3
Name
Order
Citations
PageRank
Hafiz Fahad Sheikh1796.41
Ishfaq Ahmad22884192.17
FAN Dong-Rui322238.18