Particle Swarm Optimization-Based Task Migration in Mobile-Edge Cloud Computing

Qixin Peng, Xinde Chen, Yujing Huang, Songkang Ma, Zhenli He

Abstract: As users increasingly rely on edge servers for computationally intensive tasks, the limitations of edge nodes become evident. While edge nodes indeed provide superior computing resources compared to mobile devices, they are not immune to constraints. To address these constraints, task migration emerges as a key solution, allowing computationally intensive tasks to be offloaded to other nodes over wireless networks. However, prior research often overlooked a critical aspect: user mobility, a crucial consideration in Mobile-Edge Cloud Computing (MECC). In response to the challenge of users moving out of edge server coverage, we propose a Particle Swarm Optimization (PSO)-Based approach. This approach leverages task migration to ensure uninterrupted service continuity while taking into account user mobility, latency constraints, and cost efficiency. Through extensive simulations, we demonstrate the effectiveness of our approach in significantly reducing task latency and costs.