Adaptive Binary Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Abstract

Mobile edge computing (MEC) is an emerging technology that uses wireless networks to provide resource services for resource-constrained mobile devices. To address the problems of mobile device the percentage of offloading and system utility, the maximum the percentage of offloading and a system utility model are constructed in this paper. This model redefines the portion of task offloading combined with the task offloading scenario, making the offloading task more inclined to important user tasks and improving the quality of the task offloading. At the same time, an adaptive binary whale resource allocation (ABWRA) scheme is proposed to optimize the task offloading strategy and channel allocation strategy. In the evaluation, this paper simulates the computation offloading of ABWRA and existing works in the same scenario. Simulation results show that the proposed ABWRA scheme improves the system utility by 5.4% and the unloading rate by 9.8% compared with the BWRA algorithm.