Optimized scheduling techniques focused on powerful heuristics leveraging cloud services soft computing

P. Veerendra, T. Rao
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引用次数: 2

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.
优化调度技术侧重于利用云服务软计算的强大启发式方法
目的确定多个csp的角色很重要,因为它会影响工作成本和休假时间。这项工作的主要目标是确保在基于云的计算中有效和复杂地分配资源。工作流的各种算法研究,如蚁群优化(ACO)、差分进化算法、遗传算法、粒子群优化(PSO)、上述算法的杂交(ADGP)。为了研究,CSP的工具遍布世界各地。设计/方法/途径本研究的主要目的是在csp中有效地引入基于云的计算。该算法最大限度地减少了资源响应时间和整体工作流任务。它试图通过修改算法以支持负载平衡来改进负载平衡。在提出的多目的调度方法中,ADGP算法在资源响应方面的性能优于其他提出的算法。结果表明,该算法优于所选的200个源和数千个任务。它通过在多个站点复制服务节点来减少资源响应时间。由于该算法更快地移动到最佳解决方案,因此与其他算法相比,减少了资源的响应时间。当工作负载均匀分布在多个虚拟机上时,混合型aco在资源管理方面表现最佳。然而,混合动力pso更适合选择最佳方案以最小化成本。总体而言,可以在CSP中使用CloudSim成功模拟基于云的最佳调度解决方案,从而在最终用户之间共享资源,从而有效地支持消费者和用户。当工作负载均匀分布在多个虚拟机上时,hybrid aco在资源管理方面表现最佳。然而,混合动力pso更适合选择最佳方案以最小化成本。总体而言,可以在CSP中使用CloudSim成功模拟基于云的最佳调度解决方案,从而在最终用户之间共享资源,从而有效地支持消费者和用户。
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