Computation Offloading for Partitionable Applications in Dense Networks: An Evolutionary Game Approach

Mobile-edge computing (MEC) is a burgeoning paradigm that performs computation close to the network edge. In MEC, computation offloading is an essential mechanism to alleviate the limitations of user equipment (UEs) by offloading computation-intensive and latency-sensitive tasks to edge servers deployed in base stations (BSs). For the partitionable task model, multi-BS offloading makes the parallel execution of tasks possible, which can further reduce the execution delay. In this article, we consider a MEC architecture in a dense network with multiple BSs and multiple UEs and formulate a computation offloading problem for partitionable tasks with the goal of minimizing the task completion time. We jointly optimize the computation and radio resources considering the heterogeneity of UEs’ geographic locations and task types. Specifically, we construct a task offloading game using the evolutionary game theory (EGT). We apply replicator dynamics to model the offloading decision process and obtain the evolutionary equilibrium, which is proved to be an evolutionary stable strategy. Based on the theoretical analysis, we propose an EGT-based offloading mechanism to achieve a suboptimal solution in the case of limited numbers of subtasks. The extensive evaluation results demonstrate the effectiveness of our proposed mechanism compared with the centralized optimal solution.