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 infrastructure...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 suffer from scalability issues due to the increasing number of VMs in modern cloud data centers. We claim that scalability issues can bc 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.展开更多
Multimedia content,user mobility and heterogeneous client devices require novel systems that are able to support ubiquitous access to the Web resources.In this scenario,solutions that combine flexibility,efficiency an...Multimedia content,user mobility and heterogeneous client devices require novel systems that are able to support ubiquitous access to the Web resources.In this scenario,solutions that combine flexibility,efficiency and scalability in offering edge services for ubiquitous access are needed.We propose an original intermediary framework,namely Scalable Intermediary Software Infrastructure （SISI）,which is able to dynamically compose edge services on the basis of user preferences and device characteristics.The SISI framework exploits a per-user profiling mechanism,where each user can initially set his/her personal preferences through a simple Web interface,and the system is then able to compose at run-time the necessary components.The basic framework can be enriched through new edge services that can be easily implemented through a programming model based on APIs and internal functions.Our experiments demonstrate that flexibility and edge service composition do not affect the system performance.We show that this framework is able to chain multiple edge services and to guarantee stable performance.展开更多
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 suffer from scalability issues due to the increasing number of VMs in modern cloud data centers. We claim that scalability issues can bc 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.
文摘Multimedia content,user mobility and heterogeneous client devices require novel systems that are able to support ubiquitous access to the Web resources.In this scenario,solutions that combine flexibility,efficiency and scalability in offering edge services for ubiquitous access are needed.We propose an original intermediary framework,namely Scalable Intermediary Software Infrastructure （SISI）,which is able to dynamically compose edge services on the basis of user preferences and device characteristics.The SISI framework exploits a per-user profiling mechanism,where each user can initially set his/her personal preferences through a simple Web interface,and the system is then able to compose at run-time the necessary components.The basic framework can be enriched through new edge services that can be easily implemented through a programming model based on APIs and internal functions.Our experiments demonstrate that flexibility and edge service composition do not affect the system performance.We show that this framework is able to chain multiple edge services and to guarantee stable performance.
