Joint Subcarrier and CPU Time Allocation for Mobile Edge Computing

TL;DR

This paper proposes a joint resource allocation algorithm for mobile edge computing that optimally allocates radio subcarriers and CPU time, significantly improving offloading efficiency and energy savings.

Contribution

It introduces a novel joint scheduling algorithm for radio and compute resources in cloudlets, outperforming separate optimization methods.

Findings

The joint algorithm accommodates more offloading requests.

It achieves significant energy savings.

It outperforms separate resource optimization methods.

Abstract

In mobile edge computing systems, mobile devices can offload compute-intensive tasks to a nearby cloudlet,so as to save energy and extend battery life. Unlike a fully-fledged cloud, a cloudlet is a small-scale datacenter deployed at a wireless access point, and thus is highly constrained by both radio and compute resources. We show in this paper that separately optimizing the allocation of either compute or radio resource, as most existing works did, is highly suboptimal: the congestion of compute resource leads to the waste of radio resource, and vice versa. To address this problem, we propose a joint scheduling algorithm that allocates both radio and compute resources coordinately. Specifically, we consider a cloudlet in an Orthogonal Frequency-Division Multiplexing Access (OFDMA) system with multiple mobile devices, where we study subcarrier allocation for task offloading and CPU time allocation for task execution in the cloudlet. Simulation results show that the proposed algorithm significantly outperforms per-resource optimization, accommodating more offloading requests while achieving salient energy saving.