Maximizing the number of completed tasks in MEC considering time and energy constraints.

Many tasks generated by mobile user devices are computation-intensive and latency-sensitive, such as autonomous driving and video analysis. However, due to limited energy and computing capacity, a user device may not be able to complete its task within a given time, leading to a poor user experience. Mobile edge computing (MEC) can address this challenge by offloading tasks to edge servers with stronger computing capacity and more resources for execution, which can save energy of user devices and reduce the task computation time. Different offloading strategies will impact the number of tasks completed, latency, energy overhead and so on. This paper investigates the problem of maximizing the number of completed tasks while minimizing the average completion time, energy overhead and cost in MEC under both time and energy constraints. To solve the problem, we develop the mayfly genetic algorithm (MGA), which jointly optimizes task offloading locations and ratios, central processor unit (CPU) frequencies of user devices and computing capacities allocated to user devices by edge servers. Simulation experiments indicate that MGA outperforms state-of-the-art algorithms in terms of the number of completed tasks. [ABSTRACT FROM AUTHOR]