A Multi-Objective JAYA-Based Workflow Scheduling in Fog-Cloud Computing Environment

The rapid growth of cloud-fog computing has introduced significant challenges in improving system performance and resource efficiency for dependent tasks. The inherent complexity and variability of tasks, coupled with fluctuating resource availability, make it difficult to identify an optimal scheduling algorithm. This study addresses these challenges by proposing a novel multi-objective optimization workflow scheduling approach based on the JAYA algorithm. The proposed method effectively tackles the complexities of scheduling within a distributed computing framework by integrating both fog and cloud resources. The approach aims to simultaneously minimize key performance metrics such as makespan, energy consumption, and cost, thereby enhancing overall system performance through optimized task allocation across heterogeneous resources. The Simulations conducted using the FogWorkflowSim toolkit compare the proposed approach with established algorithms, including Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). The results demonstrate the superiority of the JAYA algorithm in achieving enhanced time efficiency, energy savings, and cost reduction. These findings highlight its significant contribution to advancing cloud-fog resource management by providing a robust framework for multi-objective optimization in workflow scheduling scenarios.