JointCloud计算的分布式监控架构

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Yadi Wu , Lina Wang , Rongwei Yu , Xiuwen Huang , Jiatong Liu
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引用次数: 0

摘要

JointCloud计算支持多个云服务提供商之间的大规模资源整合和协作,为用户提供强大的性能和充分的服务。面对资源的指数级增长,监控是有效的资源管理不可或缺的一部分。监控提供了审查和管理JointCloud资源和服务的性能状态的方法,以更好地表征JointCloud系统的整体运行状态。然而,JointCloud中云服务提供商之间的协作和资源规模是动态变化的,并且不容易以灵活和可扩展的方式执行监控。为了涵盖JointCloud环境中与资源监控相关的所有方面,我们提出了一种用于JointCloud计算的分布式监控架构。该体系结构侧重于获取信息的能力,以模块化的方式组织监视组件,并支持按需启动以提供动态监视功能。分布式监控方法提供了负载均衡和容错服务,保证了监控的可靠性和性能。该体系结构还考虑了JointCloud的服务质量(QoS),并设计了一种旨在提高资源利用效率的虚拟资源编排方法。我们开发了一个原型架构,并给出了实验结果来评估我们的设计。原型架构可以很容易地部署在公共或私有JointCloud基础设施中,以实现灵活和可扩展的监控。评估结果表明,该体系结构在性能和可扩展性方面是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A distributed monitoring architecture for JointCloud computing
JointCloud computing supports large-scale resource consolidation and collaboration among multiple cloud service providers to provide users with powerful performance and adequate services. In the face of exponential scaling of resources, monitoring is an indispensable part of effective resource management. Monitoring provides methods for reviewing and managing the performance status of JointCloud resources and services to better characterize the overall operating status of JointCloud system. However, the collaboration between cloud service providers and the scale of resources in JointCloud are dynamically changing, and it is not easy to perform monitoring in a flexible and scalable way. In order to cover all aspects related to resource monitoring in JointCloud environments, we propose a distributed monitoring architecture for JointCloud computing. The architecture focuses on the ability to obtain information, organizes monitoring components in a modular way, and supports on-demand startup to provide dynamic monitoring capabilities. The proposed distributed monitoring approach provides load balancing and fault tolerance services to ensure reliability and performance of monitoring. The architecture also considers the JointCloud quality of service (QoS) and designs a virtual resource orchestration approach aimed at improving the efficiency of resource utilization. We have developed a prototype architecture and presented experimental results to evaluate our design. The prototype architecture can be easily deployed in public or private JointCloud infrastructures for flexible and scalable monitoring. The evaluation results show that our architecture is feasible in terms of performance and scalability.
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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