Fairness-Aware Task Offloading and Resource Allocation in Cooperative Mobile-Edge Computing
Jiayun Zhou, Xinglin Zhang
Abstract
Currently, mobile-edge computing (MEC) becomes a burgeoning paradigm to tackle the contradiction between delay-sensitive tasks and resource-limited mobile/IoT devices. However, a single MEC server is usually not able to satisfy the heavy computation tasks considering its limited storage and computation capability. Thus, the cooperation of MEC servers provides an effective way to accommodate this issue. In this article, we study the joint task offloading and resource allocation problem in the scenario with cooperative MEC servers. We first define resource fairness among IoT devices from the user experience perspective. Then, we formulate a joint optimization problem by taking into account the system efficiency and fairness, which is shown to be NP-hard and thus, intractable. To solve this problem, we propose a two-level algorithm: the upper level algorithm, inspired by evolutionary strategies, is able to search superior offloading schemes globally; while the lower level algorithm, taking into account fairness among all tasks, is able to generate resource allocation schemes that make full use of server resources. Comprehensive evaluation results demonstrate the efficiency and fairness of the proposed algorithm compared to baselines.