用于云计算环境中多目标负载均衡任务调度的草原犬和白鲸混合优化算法

K. Ramya, Senthilselvi Ayothi
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引用次数: 0

摘要

云计算技术用于实现远程虚拟计算机的资源利用,为消费者提供快速、准确的海量数据服务。它采用按需配置资源的方式,但由于快速周转时间、最小执行成本、高资源利用率和有限时间跨度等必要条件的限制,基于负载均衡(LB)进程的任务调度(TS)问题变成了一个 NP-困难的优化问题。本文提出了 "草原犬和白鲸混合优化算法"(HPDBWOA),用于将任务精确映射到虚拟机,以解决云环境的动态特性。HPDBWOA 的这一功能有助于通过优化资源管理来减少违反服务水平协议(SLA)的情况和时间跨度(Makespan)。它被模拟为一种调度策略,利用 PDOA 和 BWOA 的优点,通过考虑任务的优先级,在将任务分配到虚拟资源的过程中实现被动决策。它通过均衡的探索和利用来解决预收敛问题,以达到必要的服务质量(QoS),从而最大限度地减少 TS 过程中产生的等待时间。它进一步平衡了探索和利用率,以便在完全了解虚拟机状态的情况下减少任务分配过程中的时间间隔。提议的 HPDBWOA 的结果证实,与调查中使用的方法相比,能源利用率降低了 32.18%,成本降低了 28.94%。利用方差分析对所提出的 HPDBWOA 进行的统计调查证实,它在吞吐量、系统和响应时间方面都优于基准系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Prairie Dog and Beluga Whale optimization algorithm for multi-objective load balanced-task scheduling in cloud computing environments
The cloud computing technology is utilized for achieving resource utilization of remote-based virtual computer to facilitate the consumers with rapid and accurate massive data services. It utilizes on-demand resource provisioning, but the necessitated constraints of rapid turnaround time, minimal execution cost, high rate of resource utilization and limited makespan transforms the Load Balancing (LB) process-based Task Scheduling (TS) problem into an NP-hard optimization issue. In this paper, Hybrid Prairie Dog and Beluga Whale Optimization Algorithm (HPDBWOA) is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment. This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management. It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account. It addresses the problem of pre-convergence with well-balanced exploration and exploitation to attain necessitated Quality of Service (QoS) for minimizing the waiting time incurred during TS process. It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state. The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation. The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput, system, and response time.
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