Efficient Resource Allocation Algorithm for Maximizing Operator Profit in 5G Edge Computing Network

Jing Liu, Yuting Huang, Chunhua Deng, Longxin Zhang, Cen Chen, Keqin Li

Abstract: Resource allocation in edge computing is a research hotspot and difficulty in academia and Industry. The nature like urgency and the priority of tasks are not taken into account, which is adverse to obtain a good solution. Meanwhile, 5G has the characteristics of higher network speed, high reliability, low latency, and low-power massive connections. In this article, we present a novel algorithm to solve the multi-objective resource allocation problem in 5G edge computing (EC) network, the objective is to maximize the operator profit and minimize the total completion time of tasks with priorities from the perspective of service operators under time and workload constraints. The algorithm is based on the beluga whale optimization algorithm, and it utilizes three methods to update the positions of beluga whales by swimming, predating, and migrating. In addition, to enhance the ability to escape from local optima during searching the best beluga whale position, it uses centroid information to improve the process of searching the optimal position, and adds the mutation operation in the process of position updating. Simulated results show that the proposed algorithm is high efficient in terms of reducing total task completion time and improving the revenue for operators, compared with existing strategies. For example, our algorithm reduces the time by 1.87% and improves the profit by 10.47% for 80 tasks in comparison with MOPSO.