A coronavirus optimization-based algorithm for task offloading in mobile edge computing

Mobile Edge Computing (MEC) enables the offloading of tasks from vehicles to edge servers deployed at Roadside Units (RSUs), thereby improving vehicular task processing performance. However, traditional RSU-based offloading approaches relying on regional partitioning face two major challenges: first, task queue congestion leads to delays in resource allocation; second, due to uneven vehicle distribution, some RSUs become overloaded while others remain underutilized, resulting in an overall imbalance in system task load. To address these two issues, this paper proposes a collaborative offloading model integrating edge server, task offloading vehicles, and cloud servers, and designs a system utility function constrained by transmission delay and energy consumption, transforming the joint offloading problem into a constrained optimization problem. Therefore, we propose a Coronavirus optimization-based algorithm for Task Offloading in Mobile edge computing (CTOM) to solve this problem. Extensive simulation results demonstrate that the proposed algorithm effectively reduces system energy consumption and overall task latency, while maintaining high stability and robustness under various network conditions.