Improved Binary Whale Optimization-based Resource Allocation in Edge Computing Environment

In an edge computing environment, a large number of interconnected smart devices can provide effective resources to solve the contradiction between the supply and demand of computing. How to make full use of the diversified and flexible idle resource supply with scattered hardware locations to improve the task execution efficiency and overall support capability has become a great challenge. Thus, we investigated the optimal allocation scheme of edge computing resources with the objective of system delay optimization. First of all, we established a time-minimizing resource allocation model for the edge computing environment, with the decision method of selecting pairs among nodes and satisfying the device energy constraint. Secondly, we proposed a heuristic algorithm, namely Improved Binary Whale Optimization Algorithm (IBWOA), to solve the problem and demonstrated the effectiveness of the heuristic algorithm in a small-scale case. Finally, the performance evaluation and comparative analysis demonstrated that the proposed algorithm outperforms the Random Assignment (RAN) algorithm and Binary Particle Swarm Optimization (BPSO) algorithm in terms of final results, convergence speed, and stability.