Research on Scheduling Optimization of Cloud Computing Resource Load Based on Culture Firefly Algorithm

DOI

10.2991/iceeecs-16.2016.48How to use a DOI?

Keywords

Firefly Algorithm; Cloud Computing; Resource Load Scheduling Optimization

Abstract

Cloud computing is a new computing technology with great potential value. It utilizes large-scale hardware and virtual resources to provide users with dynamic application services. In order to maximize the use of cloud resources, give full play to the maximum potential of cloud computing, mining efficient resource scheduling strategy is our top priority. By comparing with the classic function, the optimized algorithm has a great improvement on the search precision and convergence speed. Firefly algorithm can effectively improve the performance of resource scheduling in cloud computing, shorten the time to complete the task, improve the overall processing capacity of the system. On the basis of this improved idea, the problem of balanced network load and extended network can be solved better, the global convergence of the algorithm is improved, and the effect of network operation is enhanced to find the optimal scheduling scheme.

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/).

Download article (PDF)

TY  - CONF
AU  - Kexin Zhang
PY  - 2016/12
DA  - 2016/12
TI  - Research on Scheduling Optimization of Cloud Computing Resource Load Based on Culture Firefly Algorithm
BT  - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
PB  - Atlantis Press
SP  - 214
EP  - 217
SN  - 2352-538X
UR  - https://doi.org/10.2991/iceeecs-16.2016.48
DO  - 10.2991/iceeecs-16.2016.48
ID  - Zhang2016/12
ER  -