Pufferfish: Cost-aware based task scheduling algorithm using pufferfish optimization algorithm in cloud computing

Abstract

Due to the dynamic and diverse nature of workloads on cloud platforms, task scheduling in the cloud computing paradigm presents a substantial challenge to scholars. Effective scheduling of these varied tasks on appropriate virtual resources is a major challenge. Incorrect task assignments can degrade service quality and violate SLA metrics, ultimately increasing costs for cloud providers. To address these issues and improve scheduling efficiency, propose an efficient cost aware based task scheduling algorithm that considers the priorities of tasks and VMs, ensuring accurate assignment of tasks to appropriate VMs. Our scheduling algorithm is based on the Pufferfish Optimization (PFO) algorithm and is implemented using the CloudSim simulation environment. Compare our approach with reference methods, such as SMA, HHO, and HBO. Simulation results demonstrate that our proposed method significantly reduces turnaround time, response time, and processing time compared to baseline approaches.