Quantum genetic algorithm with rotation angle refinement for dependent task scheduling on distributed systems

Distributed systems are efficient means of realizing High-Performance Computing (HPC). They are used in meeting the demand of executing large-scale high-performance computational jobs. Scheduling the tasks on such computational resources is one of the prime concerns in the heterogeneous distributed systems. Scheduling jobs on such systems are NP-complete in nature. Scheduling requires either heuristic or metaheuristic approach for sub-optimal but acceptable solutions. An application can be divided into a number of tasks which can be represented as Direct Acyclic Graph (DAG). To accomplish high performance, it is important to efficiently schedule these dependent tasks on resources with the satisfaction of the constraints defined for schedule generation. Inspired by Quantum computing, this work proposes a Quantum Genetic Algorithm with Rotation Angle Refinement (QGARAR) for optimum schedule generation. In this paper, the proposed QGARAR is compared with its peers under various test conditions to account for minimization of the makespan value of dependent jobs submitted for execution on heterogeneous distributed systems.