MGOKA: A Multi-Objective Optimization Algorithm for Controller Placement Problem Combining Network Partition with Cluster Fusion in Software Defined Network
Jue CHEN, Changwei Xiao, Xihe Qiu, Wenjing LÜ · Wuhan University Journal of Natural Sciences · 2024
Software Defined Network (SDN) has been developed rapidly in technology and popularized in application due to its efficiency and flexibility in network management. In multi-controller SDN architecture, the Controller Placement Problem (CPP) must be solved carefully as it directly affects the whole network performance. This paper proposes a Multi-objective Greedy Optimized K-means Algorithm (MGOKA) to solve this problem to optimize worst-case and average delay between switches and controllers as well as synchronization delay and load balance among controllers for Wide Area Networks (WAN). MGOKA combines the process of network partition based on the K-means algorithm with cluster fusion based on the greedy algorithm and designs a normalization strategy to convert a multi-objective into a single-objective optimization problem. The simulation results depict that in different network scales with different numbers of controllers, the relative optimization rate of our proposed algorithm compared with K-means, K-means++, and GOKA can reach up to 101 . 5%, 109 . 9%, and 79 . 8%, respectively. Moreover, the error rate between MGOKA and the global optimal solution is always less than 4%.