Energy Efficient Mobile Edge Computing in Dense Cellular Networks

TL;DR

This paper introduces ENGINE, an online algorithm that optimizes computation offloading and base station sleeping in dense cellular networks to reduce energy consumption while maintaining quality of service.

Contribution

It presents a novel energy-aware joint management algorithm for MEC-enabled dense networks using Lyapunov optimization, with proven near-optimal performance.

Findings

Significant energy savings achieved in simulations.

Maintains high quality of service despite energy optimization.

Operates effectively without future traffic information.

Abstract

Merging Mobile Edge Computing (MEC), which is an emerging paradigm to meet the increasing computation demands from mobile devices, with the dense deployment of Base Stations (BSs), is foreseen as a key step towards the next generation mobile networks. However, new challenges arise for designing energy efficient networks since radio access resources and computing resources of BSs have to be jointly managed, and yet they are complexly coupled with traffic in both spatial and temporal domains. In this paper, we address the challenge of incorporating MEC into dense cellular networks, and propose an efficient online algorithm, called ENGINE (ENErgy constrained offloadINg and slEeping) which makes joint computation offloading and BS sleeping decisions in order to maximize the quality of service while keeping the energy consumption low. Our algorithm leverages Lyapunov optimization technique, works online and achieves a close-to-optimal performance without using future information. Our simulation results show that our algorithm can effectively reduce energy consumption without sacrificing the user quality of service.