Optimal Fairness-Aware Resource Supply and Demand Management for Mobile Edge Computing

• Published in: IEEE wireless communications letters Vol. 10; no. 3; pp. 678 - 682
• Main Authors: Guo, Chongtao, He, Wei, Li, Geoffrey Ye
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bandwidth; convex optimization; Edge computing; Iterative methods; min-max fairness; Mobile computing; Mobile edge computing; Optimization; Resource allocation; resource demand; Resource management; resource supply; Servers; Supply & demand; Task analysis; Wireless communication; Wireless sensor networks
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2020.3046023

This letter focuses on fairness-aware resource management in a multi-user and multi-server mobile edge computing (MEC) network, where the resource supply and demand are jointly considered for resource allocation and task assignment, respectively. In particular, we aim to minimize the maximum task execution latency of all users subject to task and resource constraints. Although the optimization problem includes power, spectrum, hashrate, and task variables and is nonconvex in its primal form, it can be equivalently transformed to a more tractable programming. Then, a low-complexity iteration based algorithm is proposed to find the global optimum of the primal problem since only a convex feasibility problem is tackled in each iteration. Simulation results in typical scenarios show that the proposed resource management strategy can reduce the maximum task execution latency of users by more than 15% comparing with the available baseline approaches.