Research on Resource Scheduling based on Improved Mutation Operator in Cloud Computing

DOI

10.2991/icmemtc-16.2016.91How to use a DOI?

Keywords

Cloud computing; task scheduling; mutation operator ;Genetic algorithm.

Abstract

With the increasing development of cloud computing,task scheduling problem become a crucial aspect.There are many scholars research on resource scheduling problems,but load balancing and scheduling in how to balance time and cost is still not achieved satisfactory results.Genetic algorithm(GA) has strong robustness and inherent parallel computer system,particularly suitable for the combinatorial optimization problems, but the basic genetic algorithm easy to fall into local optimal solution. So ,we propose a crossover-mutation operator in mutation to solve this problem. The performance is analyzed using Cloudsim simulator and compared with existing GA ,Min-min algorithm. Simulation results demonstrate that the proposed algorithm has better performance in load balancing ,finish time and costs.

Copyright

© 2016, 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  - Junwei Ge
AU  - Fangfang Sun
AU  - Yiqiu Fang
PY  - 2016/04
DA  - 2016/04
TI  - Research on Resource Scheduling based on Improved Mutation Operator in Cloud Computing
BT  - Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control
PB  - Atlantis Press
SP  - 469
EP  - 472
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmemtc-16.2016.91
DO  - 10.2991/icmemtc-16.2016.91
ID  - Ge2016/04
ER  -