MAO: An Efficient Resource Utilization of Task Scheduling in Cloud Fog Environment

Abstract

In the context of Cloud-Fog Computing (CFC) efficient Task Scheduling (TS) plays a critical role in optimizing resource allocation and meeting the increasingly complex performance demands of modern computing infrastructures. This study introduces an advanced iteration of the Mexican Axolotl Optimization (MAO) algorithm, a bio-inspired metaheuristic designed to address the intricate challenges of task scheduling in dynamic and distributed computing environments. The MAO algorithm presented here incorporates advanced heuristics, self-optimization mechanisms, and innovative features to significantly improve task scheduling processes. By imitating the axolotl's capacity for self-healing, the algorithm offers a solution that is both robust and adaptable to dynamic computing conditions, making it well-suited to the challenges posed by CFC. The primary objectives of the MAO algorithm are to improve resource allocation, reduce latency, and enhance overall operational efficiency in cloud and fog computing ecosystems. It achieves this by optimizing the allocation of computing tasks to available resources, taking into account factors such as makespan time, success rate and response time, while dynamically adapting the fluctuations in workloads and resource availability. By enhancing resource utilization and reducing response time, this algorithm is poised to make significant contributions to the efficiency, reliability, and overall performance of CFC infrastructures. The experimental findings indicate that the proposed algorithm exhibits superior performance in terms of makespan and response time reduced by 28% and 36%, success rate increased by 31 % and compared to alternative approaches.