Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing

TL;DR

This paper proposes energy-efficient, real-time job offloading and resource management strategies in mobile-edge computing, considering server resources, IoT device mobility, and communication constraints to optimize energy savings.

Contribution

It introduces novel offline and online algorithms for joint job scheduling, resource allocation, and mobility management in MEC, with proven performance guarantees and real-world validation.

Findings

The $ exttt{LHJS}$ algorithm achieves near-optimal energy savings.

The $ exttt{LBS}$ heuristic effectively manages online job arrivals.

Experimental results demonstrate significant energy efficiency improvements.

Abstract

Mobile-edge computing (MEC) has emerged as a promising paradigm for enabling Internet of Things (IoT) devices to handle computation-intensive jobs. Due to the imperfect parallelization of algorithms for job processing on servers and the impact of IoT device mobility on data communication quality in wireless networks, it is crucial to jointly consider server resource allocation and IoT device mobility during job scheduling to fully benefit from MEC, which is often overlooked in existing studies. By jointly considering job scheduling, server resource allocation, and IoT device mobility, we investigate the deadline-constrained job offloading and resource management problem in MEC with both communication and computation contentions, aiming to maximize the total energy saved for IoT devices. For the offline version of the problem, where job information is known in advance, we formulate it as an Integer Linear Programming problem and propose an approximation algorithm, $\mathtt{LHJS}$, with a constant performance guarantee. For the online version, where job information is only known upon release, we propose a heuristic algorithm, $\mathtt{LBS}$, that is invoked whenever a job is released. Finally, we conduct experiments with parameters from real-world applications to evaluate their performance.