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

TLDR

The ultra‑dense deployment of small‑cell base stations with cloud‑like computing enables pervasive mobile edge computing, but uneven workloads, limited energy, and dynamic uncertainties make effective peer offloading essential. This work proposes an online SBS peer‑offloading framework, OPEN, to maximize long‑term system performance while keeping each SBS’s energy consumption within its long‑term budget. OPEN uses Lyapunov optimization and a peer‑offloading game to analyze equilibrium and price‑of‑anarchy, enabling decentralized, autonomous decision making. Simulations show that OPEN achieves near‑optimal performance without future knowledge and that peer offloading among SBSs markedly improves edge computing performance.

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, 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 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 online peer offloading (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 with the oracle solution that has the complete future information. In addition, this paper formulates a peer offloading game among SBSs and analyzes its equilibrium and efficiency loss in terms of the price of anarchy 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.