Bio-Inspired Crossover Cosine Swarm Optimization Algorithm Based Task Scheduling for Quality of Service Improvement Under Cloud Environment

• Published in: Journal of signal processing systems Vol. 96; no. 1; pp. 51 - 65
• Main Authors: Santhiya, C., Padmavathi, S.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2024; Springer Nature B.V
• Subjects: Algorithms; Batch processing; Circuits and Systems; Cloud computing; Computer Imaging; Crossovers; Data exchange; Efficiency; Electrical Engineering; Engineering; Genetic algorithms; Image Processing and Computer Vision; Linear programming; Optimization; Optimization algorithms; Optimization techniques; Pattern Recognition; Pattern Recognition and Graphics; Polynomials; Quality of service; Scheduling; Signal,Image and Speech Processing; Task scheduling; Virtual environments; Vision; Workloads; Cloud computing; Task scheduling, map-reduce; Bio-inspired crossover cosine swarm optimization; Polynomial lasso regression optimization; Quality of service
• ISSN: 1939-8018; 1939-8115
• DOI: 10.1007/s11265-023-01900-9

Cloud computing has developed into a critical information technology utility in the Internet age. It allows data exchange and on-demand service offering online. There is also a lot of research being done on task scheduling in the context of cloud computing. In the cloud, tasks, and resources are coordinated to produce the desired result. This is the process of allocating jobs to virtual machines (VMs) on a server depending on the server's capacity for the workload. To reduce traffic and delay, tasks are placed on the server as early as possible. This article presents a better method based on the mapping reduction concept and Bio-inspired crossover cosine swarm optimization algorithm (BCCSOA) for optimal task scheduling in a particular server. To begin, the enormous tasks are divided into smaller ones to use a map-reduce architecture. Then the tasks are effectively arranged with the help of the BCCSOA algorithm. For detecting VM polynomial lasso regression algorithm overload, finally. To run the experiment models, Cloudsim is utilized. Regarding the various "Quality of Service (QoS)" measures used in the assessment, the results demonstrate that the suggested technique, BCCSOA performs better than the others.