Optimizing cloud resource management with an IoT-enabled optimized virtual machine migration scheme for improved efficiency

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Chunjing Liu, Lixiang Ma, Minfeng Zhang, Haiyan Long
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引用次数: 0

Abstract

Cloud computing manages many resources and alterations to meet the demands made by consumers at multiple locations and in numerous applications. Cloud computing presents a significant obstacle to efficient resource usage and balance of loads due to the dynamic nature of consumer requirements and tasks. The inflexibility of conventional methods guarantees inadequate outcomes and waste of resources. Motivated by improved cloud infrastructure management, the present research introduces a novel approach to load optimization and migrating Virtual Machines (VMs) based on agents modelled and Internet of Things (IoT) devices. This research aims to boost cloud performance primarily by optimizing the utilization of resources and distribution of workloads. Hence, a novel approach, the Optimized Virtual Machine Migration Scheme (OVMMS), is introduced that uses the Squirrel Search Algorithm (SSA) for migrating VMs. By emulating squirrel behaviour during migration and search, these agents maximize load balance and the distribution of resources. During the analysis, IoT devices were enabled to monitor and control cloud resources to minimize wastage. Results from experimental analysis demonstrate that the proposed strategy outperforms the state-of-the-art in numerous key areas, including service dissemination, load mitigation, managing failures, mitigating time, and endurance of VM. The results show that the number of failures and the time it takes to mitigate them have dropped dramatically, while services' efficiency and distribution rates have improved substantially. The results illustrate that the squirrel-driven approach holds significant potential for addressing vital issues in cloud computing scenarios. This method asserts that optimizing the distribution of resources and the allocation of workloads may improve systems adaptability, service dependability, and cloud infrastructure operations. The proposed scheme maximizes load mitigation by 11.59%, service dissemination by 8.1%, and VM availability by 8.56%, reducing failures by 12.12% for the maximum service providers.
通过支持物联网的优化虚拟机迁移方案优化云资源管理,提高效率
云计算管理许多资源和变更,以满足消费者在多个位置和众多应用程序中的需求。由于消费者需求和任务的动态性,云计算对有效的资源使用和负载平衡构成了重大障碍。传统方法的不灵活性保证了不充分的结果和资源浪费。受改进的云基础设施管理的激励,本研究引入了一种基于代理建模和物联网(IoT)设备的负载优化和迁移虚拟机(vm)的新方法。这项研究旨在通过优化资源的利用和工作负载的分配来提高云性能。因此,本文提出了一种基于松鼠搜索算法(SSA)的虚拟机迁移优化方案(OVMMS)。通过模拟松鼠在迁移和搜索过程中的行为,这些代理可以最大限度地平衡负载和分配资源。在分析过程中,启用物联网设备来监控和控制云资源,以最大限度地减少浪费。实验分析结果表明,该策略在服务分发、负载缓解、故障管理、缩短时间和虚拟机耐久性等诸多关键领域都优于当前最先进的策略。结果表明,故障数量和缓解故障所需的时间大幅下降,而服务的效率和分配率大幅提高。结果表明,松鼠驱动的方法在解决云计算场景中的关键问题方面具有巨大的潜力。该方法认为,优化资源的分配和工作负载的分配,可以提高系统的适应性、服务的可靠性和云基础设施的运行。该方案最大限度地减少了11.59%的负载缓解、8.1%的服务分发和8.56%的VM可用性,最大限度地减少了12.12%的服务提供商的故障。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Network and Computer Applications
Journal of Network and Computer Applications 工程技术-计算机:跨学科应用
CiteScore
21.50
自引率
3.40%
发文量
142
审稿时长
37 days
期刊介绍: The Journal of Network and Computer Applications welcomes research contributions, surveys, and notes in all areas relating to computer networks and applications thereof. Sample topics include new design techniques, interesting or novel applications, components or standards; computer networks with tools such as WWW; emerging standards for internet protocols; Wireless networks; Mobile Computing; emerging computing models such as cloud computing, grid computing; applications of networked systems for remote collaboration and telemedicine, etc. The journal is abstracted and indexed in Scopus, Engineering Index, Web of Science, Science Citation Index Expanded and INSPEC.
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