An Efficient Load Distribution Approach for Optimizing Resources in SDN-Based Edge Computing Environment

Abstract

In the rapidly evolving networking and communication technology era, the emergence of novel edge computing paradigms helps reduce latency and improve communication efficiency. The advancements of edge computing bring data processing closer to its source, reducing communication distance. Moreover, integrating Software-Defined Networking (SDN) in edge computing enhances network management by decoupling the control plane from the data plane, enabling more flexible and efficient resource allocation in distributed environments. However, scheduling, resource allocation, and load balancing are significant obstacles to enhancing the edge computing resources' performance. Besides, efficient resource allocation and load balancing help to use all resources and optimize the system's performance effectively. To address these issues, this paper proposed an Average-Based Resource Allocation and Load Balancing (ABRL) algorithm for task allocation and load balancing, which aims to minimize the task's completion time and enhance the system's resource utilization. A three-layer SDN-based edge architecture is designed to implement the algorithm that improves the system's performance. The simulation studies have been conducted using the OpenDaylight (ODL) controller and implemented in Python. Experimental results demonstrate that the proposed strategy optimizes makespan, average resource utilization, and level of load balancing under consideration and exhibits better performance than the existing state-of-the-art techniques.