Quantum-inspired binary chaotic salp swarm algorithm (QBCSSA)-based dynamic task scheduling for multiprocessor cloud computing systems

• Published in: The Journal of supercomputing Vol. 77; no. 9; pp. 10377 - 10423
• Main Authors: Mishra, Kaushik, Pradhan, Rosy, Majhi, Santosh Kumar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2021; Springer Nature B.V
• Subjects: Algorithms; Cloud computing; Compilers; Computer Science; Data centers; Energy consumption; Fitness; Interpreters; Load balancing; Multiprocessing; Particle swarm optimization; Performance measurement; Processor Architectures; Programming Languages; Provisioning; Quantum computing; Resource allocation; Resource utilization; Task scheduling; Task scheduling; Quantum-inspired computing; Salp Swarm Algorithm (SSA); Binary chaotic SSA; Load balancing; Multiprocessor computing
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-021-03695-7

Scheduling in multiprocessor computing systems is experiencing prolific challenges in datacenters due to the alarmingly growing need for dynamic on-demand resource provisioning. This problem has become a challenge for the cloud broker due to the involvement of the numerous conflicting performance metrics such as minimization of makespan, energy consumption and load balancing, and maximization of resource utilization. These challenges are to be alleviated by the practical assignments of tasks onto VMs in a way to disperse loads among VMs with high utilization of resources uniformly. In this research, authors propose a quantum-inspired binary chaotic salp swarm algorithm for scheduling the tasks in multiprocessor computing systems by considering the above conflicting objectives. The principles of quantum computing are amalgamated with the BCSSA with the aim to intensify the exploration capability. Besides, a load balancing approach is incorporated with the algorithm for uniformly dispersing the loads. This algorithm considers a multi-objective fitness function to evaluate the fitness of the particles in the problem space. The performance of the proposed algorithm is validated and analyzed through extensive experimental results using the synthetic as well as the benchmark datasets in both homogeneous and heterogeneous environments. It is evident that the proposed work shows considerable improvements over Bird Swarm Optimization, Modified Particle Swarm Optimization, JAYA, standard SSA, and GAYA (a hybrid approach) with the considered objectives.