An Efficient Dynamic Scheduling Algorithm for Soft Real-Time Tasks in Multiprocessor System Using Hybrid Quantum-Inspired Genetic Algorithm

摘要

This paper proposes a hybrid approach for dynamic scheduling of soft real-time tasks in multiprocessor environment using hybrid quantum-inspired genetic algorithm (HQIGA) combined with well-known heuristic earlier-deadline-first (EDF) algorithm. HQIGA exploits the power of quantum computation which relies on the concepts and principles of quantum mechanics. The HQIGA comprises variable size chromosomes represented as qubits for exploration in the Hilbert space 0–1 using the updating operator rotation gate. Earlier-deadline-first algorithm is employed in the proposed work for finding fitness values. In order to establish the comparison with the classical genetic algorithm-based approach, this paper demonstrates the usage of various numbers of processors and tasks along with arbitrary processing time. Simulation results show that quantum-inspired genetic algorithmbased approach outperforms the classical counterpart in finding better fitness values using same number of generations.