Computation Offloading and Resource Allocation for Mobile Edge Computing

Abstract

Mobile edge computing (MEC) is an emerging paradigm that integrates service environment and cloud computing service and technology at the edge of a network to reduce network traffic and enhance quality of service (QoS). It has thus attracted extensive attention as a solution to the low delay and massive computation demand in 5G. In this work, we consider a multi-user MEC scenario with an MEC server in which user equipments (UEs) can choose to offload their tasks via wireless access point to an MEC server. To ensure the best QoS and minimize system cost, we formulate the sum of the task delay and energy consumption of all UEs as the optimization target, and jointly optimize the UE's offload decision and the computing resource allocation of the MEC server. Then, we propose a dynamic optimization algorithm based on ACO to tackle the proposed optimization problem. Simulation results show that the proposed method can more effectively reduce energy consumption and achieve lower latency than other baselines.