Research on Fine-Grained Task Offloading in Mobile Edge Computing

Abstract

Edge computing is considered a promising architecture for handling latency-sensitive and computationally intensive tasks. The lack of consideration for the timing of jobs and their unique topology in the existing research on task scheduling in mobile edge computing settings results in performance deterioration and underutilization of edge servers. In this paper, we provide a two-tier, lightweight offloading mechanism. by carefully taking into account: 1) the job topology, 2) the task urgency, and 3) the rivalry for server resources. Simulation findings show that, as compared to baseline techniques, our proposed approach considerably increases application completion rates while reducing average system completion delay by 66.7% and 37.6% under various user and edge server counts.