Optimizing Task Scheduling: Exploring Advanced Machine Learning in Dew-Powered Cloud Environments

Abstract

Research into Dew computing environments has recently emerged as a result of the increasing prevalence and processing power of mobile and IoT devices. In these settings, even low-powered devices can share some of their computational resources with their neighbors. This paper proposes a novel approach to workflow scheduling in dew enabled cloud computing environment, called Deep Q-learning (DQN) + Chronological Geese Migration Optimization (CGMO). DQN is a deep learningbased method for scheduling workflows, while CGMO is a hybrid optimization algorithm that combines the chronological idea and the Wild Geese Migration Optimization (GMO) algorithm. The proposed approach aims to optimize multiple objectives, including predicted energy, Quality of Service (QoS), and resource usage, by scheduling workflows in the cloud. The approach also takes into account the current state of the Virtual Machine (VM) and the job. The assessment measures employed for DCGM include maximum QoS, minimum energy usage, and maximum resource utilization. The results show that DCGM achieved the highest QoS (0.865), lowest energy usage (0.0322), and highest resource utilization (1.000) compared to other approaches.