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

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 deadlineconstrained 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, LHJS, with a constant performance guarantee. For the online version, where job information is only known upon release, we propose a heuristic algorithm, LBS, that is invoked whenever a job is released. Finally, we conduct experiments with parameters from real-world applications to evaluate their performance.