Optimizing Task Scheduling in Multi-thread Real-Time Systems using Augmented Particle Swarm Optimization

This paper proposes an intelligent approach for task scheduling and power management in multi-core systems using augmented particle swarm optimization. The algorithm prioritizes tasks based on slack times to prevent deadline misses and enhance system performance. It maintains a balance between processing capacity and energy consumption by dynamically altering core voltage and frequency in response to activities. Enhancing decisions about tasks and power distribution, enhanced augmented PSO takes into account both individual and group best solutions. Experimental investigations show that multi-core computers greatly boost performance while using less energy. As a result of the method’s successful exploration of the solution space, real-time systems with multithreading and temporal predictability now have a solid and practical framework. This strategy has the ability to maximize the performance of multicore machines while fostering green computing environments.