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

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.

I. Introduction

Pervasive mobile devices and the Internet of Things are driving the development of many new applications, turning data and information into actions that create new capabilities, richer experiences and unprecedented economic opportunities. Although cloud computing enables convenient access to a centralized pool of configurable and powerful computing resources, it often cannot meet the stringent requirements of latency-sensitive applications due to the often unpredictable network latency and expensive bandwidth [1]–[3]. The growing amount of distributed data further makes it impractical or resource-prohibitive to transport all the data over today’s already-congested backbone networks to the remote cloud [4]. As a remedy to these limitations, mobile edge computing (MEC) [1]–[3] has recently emerged as a new computing paradigm to enable in-situ data processing at the network edge, in close proximity to mobile devices and connected things. Located often just one wireless hop away from the data source, edge computing provides a low-latency offloading infrastructure, and an optimal site for aggregating and analyzing bandwidth-hungry data from end devices.