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Volume 41 Issue 3
Mar.  2019
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Jiugen SHI, Hao XU, Jing ZHANG, Ji WANG. An Efficient Online Algorithm for Load Balancing in Software Defined Networks Based on Efficiency Range[J]. Journal of Electronics & Information Technology, 2019, 41(3): 694-701. doi: 10.11999/JEIT180464
Citation: Jiugen SHI, Hao XU, Jing ZHANG, Ji WANG. An Efficient Online Algorithm for Load Balancing in Software Defined Networks Based on Efficiency Range[J]. Journal of Electronics & Information Technology, 2019, 41(3): 694-701. doi: 10.11999/JEIT180464

An Efficient Online Algorithm for Load Balancing in Software Defined Networks Based on Efficiency Range

doi: 10.11999/JEIT180464
Funds:  The National Major Scientific Instruments Development Project (2013YQ030595)
  • Received Date: 2018-05-15
  • Rev Recd Date: 2018-09-20
  • Available Online: 2018-10-22
  • Publish Date: 2019-03-01
  • Due to the limitation of individual controller’s processing capacity in large-scale complex Software Defined Networks (SDN), an efficient online algorithm for load balancing among controllers based on efficiency range is proposed to improve load balancing among controllers and reduce the propagation delay between a controller and the switch. In the initial static network, the initial set of controllers is selected by a greedy algorithm, then M improved Minimum Spanning Trees (MST) rooted at the initial set of controllers are constructed, so initial M subnets with load balancing are determined. With the dynamic changes of load, for the purpose of making the controller work within efficiency range at any time, several switches in different subnets are reassigned by Breadth First Search (BFS). The initial set of controllers is updated for minimizing propagation delay in the algorithms’ last step. The algorithm is based on the connectivity of intra-domain and inter-domain. Simulation results show that the proposed algorithms not only guarantee the load balancing among controllers, but also guarantee the lower propagation delay. As to compare to PSA algorithm, optimized K-Means algorithm, etc., it can make Network Load Balancing Index (NLBI) averagely increase by 40.65%.

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