PSO-Optimized Hybrid Scheduler Integrating RR, HRRN and SRTF for Cloud Task Scheduling

Nowadays, in cloud computing, task scheduling is a key factor to enhance the performance of the distributed computing environment. As workloads vary dynamically, traditional scheduling algorithms like RR and HRRN have limitations in achieving optimal performance. To overcome these constraints, this paper proposes a PSO-Optimized Hybrid Scheduler that integrates Particle Swarm Optimization (PSO) with RR, HRRN, and Shortest Remaining Time First (SRTF) scheduling approaches. In this integrated approach, the PSO role is to find the optimal task execution order, the RR role is to ensure fair time-sharing among tasks, and the HRRN role is to prioritize processes to dynamically reduce excessive waiting time and prevent starvation. The SRTF provides short-job bias through the inverse remaining time term. The proposed mechanism is evaluated by using AWT and ATAT as key metrics. A Python-based simulation and a data set of six processes are used for validation and implementation, and a mathematical analysis is also presented to ensure correctness. The proposed PSO-optimized Hybrid Scheduler shows lower waiting and turnaround times than classical RR and HRRN scheduling approaches. In comparison with HRRN, the improvements of the proposed mechanism in AWT, ATAT, ART, and AS are 3.6%, 2.3%, 3.6%, and 7.9%, respectively. The findings are that the proposed mechanism provides an effective, balanced solution for resource scheduling in cloud environments.