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A quantum-inspired hybrid scheduling technique is proposed for multi-processor computing systems. The proposed algorithm is a hybridization of principles of quantum mechanics (QM) and a nature-inspired intelligence, gravitational search algorithm (GSA). The principles of QM such as quantum bit, superposition and rotation gate help to design an efficient agent representation as well as intense exploration capability of GSA enhances toward better converging rate. The fitness function is designed with the aim to minimize makespan, adequate balancing of loads and proper utilization of the deployed resources during the evaluation of agents. Several standard benchmarks as well as synthetic data sets are used to analyze and validate the work. The performance improvement of the proposed algorithm is compared with recently designed algorithms like quantum genetic algorithm, particle swarm optimization-based multi-criteria scheduling, Improved-GA, GSA and Cloudy-GSA. The significance of the algorithm is tested using a hypothesis analysis of variance. [ABSTRACT FROM AUTHOR]

Importance of workflow applications (WAs) is expediting in various fields of science and engineering. Scheduling of WAs is a non-deterministic polynomial-complete problem. One of the key challenges of scheduling the WAs is to create valid execution sequence. The validity of the execution sequence is ensured by preserving dependency constraints. Therefore, workflow scheduling algorithms (WSAs) are burning insight to researchers. In this paper, we have proposed a particle swarm optimization based workflow scheduling algorithm to address the problem. Our derived fitness function simultaneously considers several conflicting parameters, makespan, load-balancing, resource-utilization, and speed up ratio. The particle is represented in such a way that it produces a complete solution by preserving the dependency constraints. Moreover, the updated positions of the particles are also ensured to be valid in each iteration. The performance of the proposed work is extensively tested using several scientific WAs. Our simulation results show significant improvements in terms of the considered objectives. The effectiveness of the results is also validated using a statistical hypothesis, Analysis of Variance. [ABSTRACT FROM AUTHOR]

Scheduling in high-performance computing systems is experiencing potential challenges in modern computing applications due to different application sizes, computational requirements, resource utilization, rational completion time, etc. The scheduling problem is known to be an NP-complete problem. These challenges are moderated by the logical assignment of tasks to processors in a way to produce minimum schedule length and lesser load balance by utilizing system resources. In this paper, we proposed a novel genetic algorithm (GA)-based scheduling technique by considering four conflicting objectives, minimization of makespan, load balancing, and maximization of resource utilization, and speed up ratio. A novel mutation technique is proposed which helps to improve the considered multiple objectives. The performance of the proposed work is analyzed and validated through extensive simulation results using synthetic as well as benchmark data sets. It has been observed that the proposed work performs better than the existing algorithms, GA-based scheduling, priority-based performance-improved algorithm, and particle swarm optimization. A statistical hypothesis test ANOVA followed by post hoc analysis is conducted to demonstrate the significance of the work. [ABSTRACT FROM AUTHOR]