A distributed monitoring architecture for JointCloud computing

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

Abstract

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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