Tailoring Genetic Algorithm for Resource Scheduling in Many-Core Processors

DOI

10.2991/icecee-15.2015.97How to use a DOI?

Keywords

Many-core processors; Resource scheduling; Genetic algorithm

Abstract

With the development of multi-core and many-core processors, how to leverage the hardware improvement to boost application performance remains a challenge for ensuring the continuously improvement of computing and data processing capabilities. Specifically, in this paper, we focus on how to efficiently assign many-core resources to computing tasks to improve the processing capability of systems. We propose to employ Genetic Algorithm (GA) for many-core resource scheduling. Considering the issues with GA, such as the low convergence speed, low efficiency of the search process, and limitations of the genetic operators, we propose two modifications. First, to increase the discrimination of fitness value and therefore accelerate the convergence speed, we introduce the idea from Simulated Annealing (SA) to design the fitness function. Second, to deal with the limitations of genetic operators, we introduce Cellular Automata (CA), and therefore to overcome the problem of premature convergence. With the extensive experiments, our modified algorithm exhibits fast convergence and efficient performance for many-core scheduling.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiande Hu
AU  - Jingming Li
AU  - Jiaxing Cheng
PY  - 2015/06
DA  - 2015/06
TI  - Tailoring Genetic Algorithm for Resource Scheduling in Many-Core Processors
BT  - Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics
PB  - Atlantis Press
SP  - 465
EP  - 471
SN  - 2352-538X
UR  - https://doi.org/10.2991/icecee-15.2015.97
DO  - 10.2991/icecee-15.2015.97
ID  - Hu2015/06
ER  -