Effective task scheduling based on interactive autodidactic school algorithm for cloud computing

Abstract

The topic of load balanced task scheduling has emerged as a prominent and intricate area of study within the realm of Cloud computing. Swarm intelligence-based meta-heuristic algorithms are commonly considered more suitable for the purposes of Cloud scheduling and load balancing. These algorithms employ a combination of local and global search strategies in order to ascertain the ideal location. To achieve an optimal mapping strategy for task allocation to resources, it is imperative to find a suitable equilibrium between local and global search techniques, since this approach has demonstrated significant efficacy. This research introduces a new approach to task scheduling using the Autodidactic Interactive School Optimization Algorithm (IASOA). The objective of this method is to decrease the time required for job execution while also enhancing throughput. The assessment of the suggested methodology has been executed, and a comparative analysis has been performed with five established algorithms in relation to makespan and throughput. The tests were subsequently extended to encompass a comparative analysis of the suggested methodology alongside four other established meta-heuristic scheduling methodologies. The study of the simulated experimentation reveals that the proposed approach yielded noteworthy advantages in makespan and throughput, with improvements of up to 10% and 60% respectively.