Analysis of Meta Heuristic Algorithms in Task Scheduling for Cloud-Fog Computing: A future Perspective

This review explores the applications of Meta Heuristic (MH) algorithms in the dynamic and complex domains of cloud and fog computing (CFC). Recognizing the crucial role of effective Task Scheduling (TS) in optimizing resource utilization and meeting performance objectives in this dynamic environment, the demand for efficient resource allocation, TS, and optimization strategies becomes paramount as these paradigms continue to evolve. MH algorithms, renowned for their ability to navigate complex solution spaces, have gained increasing attention in addressing the intricacies of CFC environments. In this paper the literature review comprehensively synthesizes current research to evaluate the effectiveness of various MH algorithms, such as GA, PSO, GWO, ACO. The primary emphasis is on assessing their impact on optimizing resource utilization, minimizing energy consumption, and improving the holistic performance of systems, the review provides crucial insights into the strengths, limitations, and emerging trends linked to the utilization of MH algorithms in tackling the unique challenges of CFC. These discoveries provide valuable guidance for researchers, practitioners, and decision-makers as they navigate the intricate landscape of these computing paradigms.