Optimized scheduling algorithm for soft Real-Time System using particle swarm optimization technique

Abstract

Scheduling of tasks in Real-Time Systems is based on static or dynamic priority like earliest deadline first (EDF) and rate monotonic, respectively. The static scheduler does not give assurance of scheduling all tasks during the underload scenario, whereas dynamic scheduler performs poorly during an overload scenario. This paper has proposed a swarm intelligence-based scheduling algorithm that can overcome both the situations. This paper has used particle swarm optimization (PSO) based swarm technique to design the new scheduling approach. It considers each task as a particle and applied modified PSO technique to identify the most critical task to execute. The efficiency of the newly proposed method has been compared with existing EDF and ACO based scheduling algorithms considering two significant parameters, the success ratio and the effective CPU utilization. All three algorithms have been tested on the simulator with a Soft Real-time periodic task set on 500 timelines. It has been observed that during the underload scenario, the proposed algorithm performs equally to EDF and ACO based algorithms. During overload and highly overload situations, the proposed algorithm performs batter compared to EDF and ACO based algorithms.