Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks

TLDR

Mobile edge computing (MEC) provides cloud‑computing capabilities close to mobile devices in 5G networks. The paper investigates energy‑efficient computation offloading mechanisms for MEC in 5G heterogeneous networks. The authors formulate an optimization problem that minimizes energy consumption from task execution and file transmission, and design an EECO scheme that jointly optimizes offloading decisions and radio resource allocation to achieve minimal energy use under latency constraints. Numerical results show that the proposed EECO scheme improves energy efficiency.

Abstract

Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.