Fuzzy logic-driven scheduling for cloud computing operations: a dynamic and adaptive approach

Abstract

Cloud computing is a decentralized approach of providing and accessing computer services through the internet. The phrase «cloud computing» is commonly used to describe this setup. The term «cloud computing» refers to a method of running computer programs, data, and services over the internet from a central location rather than on individual users’ local machines. Cloud computing environments face the challenge of efficiently managing and scheduling diverse tasks to ensure optimal resource utilization and system performance. This paper introduces a fuzzy logic-based approach for scheduling cloud computing operations designed to handle the uncertainty and dynamic nature of task execution requirements. The proposed method incorporates fuzzy rules and membership functions to evaluate key parameters such as task priority, resource availability, and execution time. By modeling these uncertainties, the fuzzy logic system dynamically adjusts scheduling decisions to optimize load balancing and minimize delays. The approach offers flexibility in allocating resources and prioritizing tasks in real-time, adapting to fluctuating workloads and system conditions. Experimental simulations demonstrate the effectiveness of the fuzzy logic approach in enhancing system throughput and reducing task completion time, offering a robust solution for scheduling in heterogeneous and complex cloud environments. This method shows promise for improving the scalability and responsiveness of cloud-based operations. Comparisons with three separate scheduling algorithms the first come, first serve (FCFS) algorithm, the round robin (RR) strategy, and the Honeybee foraging (HF) algorithm, show that our method is quite effective. The experimental findings validate the efficacy of our algorithm.