Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks

TL;DR

This paper introduces OPEN, an online framework for energy-aware peer offloading among small-cell base stations in mobile edge computing, improving performance without future system knowledge.

Contribution

The paper proposes a novel online Lyapunov-based peer offloading framework for SBSs that maximizes long-term performance under energy constraints, with game-theoretic analysis of SBS strategic behavior.

Findings

Peer offloading significantly enhances MEC performance.

OPEN achieves near-optimal long-term system utility.

Decentralized decision-making is effective in SBS networks.

Abstract

The (ultra-)dense deployment of small-cell base stations (SBSs) endowed with cloud-like computing functionalities paves the way for pervasive mobile edge computing (MEC), enabling ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. To handle spatially uneven computation workloads in the network, cooperation among SBSs via workload peer offloading is essential to avoid large computation latency at overloaded SBSs and provide high quality of service to end users. However, performing effective peer offloading faces many unique challenges in small cell networks due to limited energy resources committed by self-interested SBS owners, uncertainties in the system dynamics and co-provisioning of radio access and computing services. This paper develops a novel online SBS peer offloading framework, called OPEN, by leveraging the Lyapunov technique, in order to maximize the long-term system performance while keeping the energy consumption of SBSs below individual long-term constraints. OPEN works online without requiring information about future system dynamics, yet provides provably near-optimal performance compared to the oracle solution that has the complete future information. In addition, this paper formulates a novel peer offloading game among SBSs, analyzes its equilibrium and efficiency loss in terms of the price of anarchy in order to thoroughly understand SBSs' strategic behaviors, thereby enabling decentralized and autonomous peer offloading decision making. Extensive simulations are carried out and show that peer offloading among SBSs dramatically improves the edge computing performance.