Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks

TLDR

Mobile edge computing brings computation to the network edge, saving device energy but increasing network load and transmission latency. The study investigates the tradeoff between energy consumption and latency by proposing an energy‑aware offloading scheme that jointly optimizes communication and computation resources under limited energy and strict latency constraints. The authors model single‑ and multi‑cell MEC scenarios, incorporate residual device battery into a weighting factor, formulate a mixed‑integer nonlinear problem, and solve it with an iterative search that combines interior‑penalty functions and difference‑of‑convex programming. Numerical results demonstrate that the algorithm reduces the weighted sum of energy consumption and execution latency compared with baseline methods, and that the energy‑aware weighting factor is crucial for extending the lifetime of smart mobile devices.

Abstract

Mobile edge computing (MEC) brings computation capacity to the edge of mobile networks in close proximity to smart mobile devices (SMDs) and contributes to energy saving compared with local computing, but resulting in increased network load and transmission latency. To investigate the tradeoff between energy consumption and latency, we present an energy-aware offloading scheme, which jointly optimizes communication and computation resource allocation under the limited energy and sensitive latency. In this paper, single and multicell MEC network scenarios are considered at the same time. The residual energy of smart devices' battery is introduced into the definition of the weighting factor of energy consumption and latency. In terms of the mixed integer nonlinear problem for computation offloading and resource allocation, we propose an iterative search algorithm combining interior penalty function with D.C. (the difference of two convex functions/sets) programming to find the optimal solution. Numerical results show that the proposed algorithm can obtain lower total cost (i.e., the weighted sum of energy consumption and execution latency) comparing with the baseline algorithms, and the energy-aware weighting factor is of great significance to maintain the lifetime of SMDs.