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		题名	作者	年代	出处	被引量
	1	Improving Scalability of Cloud Monitoring Through PCA-Based Clustering of Virtual Machines显示文摘Cloud computing has recently emerged as a leading paradigm to allow customers to run their applications in virtualized large-scale data centers. Existing solutions for monitoring and management of these infrastructures consider virtual machines(VMs) as independent entities with their own characteristics. However, these approaches sufer from scalability issues due to the increasing number of VMs in modern cloud data centers. We claim that scalability issues can be addressed by leveraging the similarity among VMs behavior in terms of resource usage patterns. In this paper we propose an automated methodology to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. The innovative contribution of the proposed methodology is the use of the statistical technique known as principal component analysis(PCA) to automatically select the most relevant information to cluster similar VMs. We apply the methodology to two case studies, a virtualized testbed and a real enterprise data center. In both case studies, the automatic data selection based on PCA allows us to achieve high performance, with a percentage of correctly clustered VMs between 80% and 100% even for short time series(1 day) of monitored data. Furthermore, we estimate the potential reduction in the amount of collected data to demonstrate how our proposal may address the scalability issues related to monitoring and management in cloud computing data centers.	Claudia Canali Riccardo Lancellotti	2014	Journal of Computer Science & Technology2014，29，1：	3
	2	Integration of Sensor Networks Monitoring in the Ganglia Scalable Grid Monitoring Tool显示文摘	Adel Nadhem Naeem Ali Abdulqader Bin Salem Sureswaran Ramadass	2011	通讯和计算机(中英文版)2011，8，8：	0

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