A Multi-Objective Hybrid Parallel-Batch Job Scheduling in Edge Computing

Edge computing is an emerging technique for enhancing the computing experience on mobile devices by offloading computationally demanding jobs to the edge cloud. One of the critical scientific issues in edge computing is scheduling jobs on edge servers with restricted computational resources. In order to address this issue, the paper first suggests a parallel batch processing solution to multi-objective task scheduling using a hybrid P-BPM and PSO algorithm (P-MPSO) method that optimizes objectives while minimizing running costs and the amount of failed jobs running behind schedule in edge clouds. In order to maximize the effectiveness of our approach in light of the variable server performance and the various lags in the processing of jobs, this study then defines two parameters: the processing effectiveness of the server's performance by the P-BPM algorithm and the order of importance of the job by PSO algorithm. The simulation examined in this work were designed to compare the hybrid scheduling algorithms with several already-in-use scheduling techniques. Experiments demonstrate that the hybrid algorithms presented in this research are effective and dependable in lowering running costs and ensuring the scheduling jobs in edge clouds in a timely manner.