Energy Efficient Offloading in Mobile Edge Computing under Limited Resources

Energy consumption is one of the most important issues which wireless mobile devices are facing. This paper studies the minimum energy consumption problem under limited computing and channel resources by using dynamic voltage and frequency regulation (DVFS) techniques for mobile edge computing systems under time division multiple access(TDMA), and proposes a minimum energy consumption offloading strategy(MECOS) based on the internal penalty function method. The simulation results show that, depending on the number of users, the algorithm proposed in this paper can reduce energy consumption by 9.83% to 24.04% compared with the benchmark offloading strategy, and has good convergence, scalability and robustness.

I. Introduction

With the rapid development of Internet of Things (IoT) technology, an increasing number of mobile devices running computationally intensive tasks are connecting to the internet. However, these devices typically have limited battery life and computational resources, making them incapable of efficiently handling computationally intensive tasks. Addressing the contradiction between limited device resources and increasingly complex application services has become a significant challenge in the communications and internet fields. Inspired by service computing frameworks like cloud computing and fog computing, a new computing paradigm called Mobile Edge Computing (MEC) has emerged with the advent of 5G technology [1].