Task Scheduling Scheme for Mobile Edge Computing via Bayesian Classifier

Abstract The enormous computing tasks in the Mobile Edge Computing (MEC) beget challenges correlated with cost, energy consumption and quality of service. However, existing studies are not optimized enough for MEC terms selection task classification scheduling schemes. Inspired by spam idea, a novel scheme is proposed via Bayesian Classifier. first categorized into three priority categories using Naive Bayes. In addition, priority-based (PCTS) to schedule classified different priorities. Finally, experimental evaluation conducted metrics that include latency, processing time, consumption. Simulation results show outperforms baseline schemes accordance completion time throughout rate.