The Load Balancing Research of SDN based on Ant Colony Algorithm with Job Classification

DOI

10.2991/wartia-16.2016.95How to use a DOI?

Keywords

SDN, Cloud computing, Load balance, ant colony algorithm, job classification.

Abstract

In this paper, we propose a dynamic load balancing algorithm conbined with job classification and ant colony algorithm in SDN network cloud computing environments. After scene analysis was done, we classify the server nodes that have the same processing capability of the CPU into a small cluster sub-network in SDN with the classify SDN network server nodes.With the help of SDN control plane and forwarding layer separation characteristics, we create a central controller whose work is to monitor the entire network the network, and there is a sub-controller int each sub-network. Ant colony algorithm runs in each controller.When a job comes, the central controller first finds the corresponding sub-network based on the job demand for CPU performance and send the job to the sub-network controller,then in the sub-network, the ant colony algorithm can calculate the minimum load Link Road with the real-time network load provide by the controller.In the end,the controller send forwarding policy to the switch. We build the SDN environment with the Mininet and OpenDayLight controllers, and through the algorithm experiment with other load-balancing algorithm ,the experimental results proves: the optimized ant colony algorithm can achieve better load balancing in each link which results in improving the utilization of resources and enhancing the usability of the system.

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  - WuCai Lin
AU  - LiChen Zhang
PY  - 2016/05
DA  - 2016/05
TI  - The Load Balancing Research of SDN based on Ant Colony Algorithm with Job Classification
BT  - Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications
PB  - Atlantis Press
SP  - 472
EP  - 476
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-16.2016.95
DO  - 10.2991/wartia-16.2016.95
ID  - Lin2016/05
ER  -