A multi-objective quantum-inspired genetic algorithm for workflow healthcare application scheduling with hard and soft deadline constraints in hybrid clouds

Recently, the use of quantum cloud computing for different applications has been increasing. For instance, weather forecasting, financial modeling, healthcare, and automation are geographically distributed in practice. These applications are workflows and consist of compute-intensive depen-dent tasks with precedence constraints. However, workflow processing on quantum-based cloud services still faces issues in the literature regarding makespan and energy consumption. This study presents the Multi-objective Quantum-inspired Genetic Algorithm (MQGA) to address the problems of workflow scheduling in the hybrid cloud, attempting to reduce makespan and energy consumption simultaneously. The proposed algorithm relies on the concept and principle of quantum mechanics, which explores the computational power of quantum computing. It adopted a qubit to represent the individual chromosome for better population diversity. It also uses a quantum rotation gate to lead the schedule to better convergence and avoids classical genetic operators. The simulation results show that the proposed algorithm can effectively reduce energy consumption by 23.36% and makespan 20% on average. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study proposes a Multi-objective Quantum-inspired Genetic Algorithm (MQGA) to address workflow scheduling problems in hybrid cloud environments. By reducing makespan and energy consumption simultaneously, the algorithm utilizes quantum principles, such as qubits and quantum rotation gates, to improve population diversity and convergence.