Optimized scheduling techniques focused on powerful heuristics leveraging cloud services soft computing. Issue 1 (5th August 2021)

• Record Type: Journal Article
• Authors: P., Veerendra; Rao, Thirupathi
• Is Part Of: International journal of pervasive computing and communications. Volume 18:Issue 1(2022)
• Page Start: 5
• Page End: 17
• Publication Date: 2021-08-05
• Subjects: Genetic algorithm -- Particle swarm optimization -- Resource response time -- Ant colony optimization -- Hybridization of existing algorithms; Ubiquitous computing -- Periodicals; Mobile computing -- Periodicals; Computer network protocols -- Periodicals; Computer network architectures -- Periodicals; Application software -- Development -- Periodicals; 004.6
• DOI: 10.1108/IJPCC-04-2021-0087
• Languages: English
• ISSNs: 1742-7371

Abstract

Abstract : Purpose: Determining the roles of multiple CSPs is important because it affects job costs and time off. The primary objective of this work is to ensure an efficient and complex distribution of resources in cloud-based computing. Workflow study of various algorithms such as ant colony optimization (ACO), differential evolution algorithm, genetic algorithm, particle swarm optimization (PSO), hybridization of the above algorithms (ADGP). For research, CSP's tools are put all over the world. Design/methodology/approach: The main objective of this study is to effectively introduce cloud-based computing in CSPs. The algorithm minimizes resource response time and overall workflow tasks. It seeks to improve load balancing by modifying the algorithm to support load balancing. In the proposed multipurpose scheduling methods, the ADGP algorithm performs better than any other proposed algorithm during the resource response. This algorithm was found to be superior to the selected 200 sources and thousands of tasks. It reduces resource response time by copying service nodes through several sites. As this algorithm moves faster to the best solution, the response time of the resource is reduced compared to other algorithms. Findings: Hybrid ACOs perform best when it comes to resource management when workloads are uniformly spread across multiple virtual machines. However, hybrids PSOs are better suited to choosing the best options to minimize costs. Overall, an optimal cloud-based scheduling solution can be successfully simulated using CloudSim in CSP to share resources between end-users to support consumers and users effectively. Originality/value: Hybrid ACOs perform best when it comes to resource management when workloads are uniformly spread across multiple virtual machines. However, hybrids PSOs are better suited to choosing the best options to minimize costs. Overall, an optimal cloud-based scheduling solution can be successfully simulated using CloudSim in CSP to share resources between end-users to support consumers and users effectively.