Novel PSO-Based Algorithm for Workflow Time and Energy Optimization in a Heterogeneous Fog Computing Environment

Abstract

The dynamic and heterogeneous nature of fog computing environments presents significant challenges for efficient resource management, especially in the context of workflow optimization. To address this problem, we propose a novel particle swarm optimization (PSO)-based algorithm for workflow time and energy optimization in heterogeneous fog computing environments. Our algorithm leverages the collective intelligence of particles to efficiently explore the solution space and adapt to the dynamic and uncertain nature of fog computing resources. We evaluate the performance of our algorithm using simulations and experiments, demonstrating significant improvements in workflow completion time, energy consumption, and resource utilization compared to existing PSO-based algorithms and state-of-the-art methods. Our main contributions are twofold: a novel PSO-based algorithm that effectively addresses the challenges of workflow optimization in fog computing environments and empirical evidence of its efficacy and potential impact on real-world scenarios. Our results provide valuable insights for practitioners and researchers in the field of fog computing, demonstrating the feasibility of efficient resource management in dynamic and heterogeneous environments.