A Multi-Objective Machine Learning Approach for Controller Placement in Software-Defined Networks
Subject Areas : مهندسی برق و کامپیوترM. Gholizadeh 1 , N. Mozayeni 2 *
1 - Dept. of Comp. Eng., Iran University of Science and Technology, Tehran, Iran
2 - Dept. of Comp. Eng., Iran University of Science and Technology, Tehran, Iran
Keywords: Controller placement, software-defined networking (SDN), machine learning,
Abstract :
With the rapid growth of computer networks and the increasing complexity of communication infrastructures, the need for efficient resource management and reduced operational and maintenance costs has become a major challenge. Software-Defined Networking (SDN), by decoupling the control and data planes, provides flexibility, scalability, and centralized management. In the control plane, controllers act as the core components responsible for network management and decision-making, and their optimal placement remains a fundamental challenge. The distribution and deployment of controllers directly affect network performance parameters such as delay reduction, load balancing, and stability enhancement. In this paper, a multi-objective machine learning–based approach is proposed for controller placement in SDN. The problem is formulated with the objectives of minimizing control-plane delay, inter-domain delay, controller load, and the failure cost of network nodes and links, while maximizing network throughput. Considering the computational complexity of the problem, a deep reinforcement learning (DRL) algorithm is employed as the solution approach. Simulation results on multiple network topologies demonstrate that the proposed algorithm improves the control-plane delay, inter-domain delay, and network throughput by approximately 20%, 30%, and 15%, respectively, compared with previous works. Therefore, the proposed approach not only addresses common objectives such as delay minimization and load balancing but also incorporates crucial factors such as stability and fault tolerance into the controller placement decision-making process.
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