GEWO: An Efficient Prioritised Task Scheduling in Cloud Fog Computing Environment

Abstract

The rapid expansion of Cloud-Fog Computing (CFC) underscores the need for effective task scheduling (TS) strategies to optimize resource utilization and bolster system performance. This paper introduces a novel optimization algorithm, Golden Eagle Whale Optimization (GEWO), which combines the unique behaviors of Golden Eagles and Whales by integrating Golden Eagle Optimization (GEO) and Whale Optimization Algorithm (WOA). GEWO strives to achieve a balance between global exploration, emulating the soaring capabilities of Golden Eagles, and local exploitation, inspired by the strategic diving behaviors of Whales. Specifically designed to tackle the challenges posed by the dynamic and heterogeneous nature of cloud and fog computing environments, GEWO takes into account factors such as task characteristics, resource availability, and network conditions. By incorporating these elements into the optimization process, GEWO enhances convergence speed and solution quality, presenting a promising solution for efficient TS in CFC. To evaluate GEWO's efficacy, comprehensive experiments were conducted using diverse benchmark tasks and real-world CFC scenarios. Comparative analyses against state-of-the-art optimization algorithms reveal the superior performance of GEWO, showcasing a 26% reduction in task completion time, a 32 % improvement in resource utilization, and a 29% increase in energy efficiency. GEWO contributes to ongoing efforts aimed at enhancing the efficiency and scalability of cloud-fog systems, marking advancements in resource management and overall system performance.