Improved snake optimization-based task scheduling in cloud computing

Abstract

The recent focus on cloud computing is due to its evolving platform and features like multiplexing users on shared infrastructure and on-demand resource computation. Efficient use of computer resources is crucial in cloud computing. Effective task-scheduling methods are essential to optimize cloud system performance. Scheduling virtual machines in dynamic cloud environments, marked by uncertainty and constant change, is challenging. Despite many efforts to improve cloud task scheduling, it remains an unresolved issue. Various scheduling approaches have been proposed, but researchers continue to refine performance by incorporating diverse quality-of-service characteristics, enhancing overall cloud performance. This study introduces an innovative task-scheduling algorithm that improves upon existing methods, particularly in quality-of-service criteria like makespan and energy efficiency. The proposed technique enhances the Snake Optimization Algorithm (SO) by incorporating sine chaos mapping, a spiral search strategy, and dynamic adaptive weights. These enhancements increase the algorithm’s ability to escape local optima and improve global search. Compared to other models, the proposed method shows improvements in cloud scheduling performance by 6%, 4.6%, and 3.27%. Additionally, the approach quickly converges to the optimal scheduling solution.