In a growing number of information processing applications,data takes the form of continuous data streams rather than traditional stored databases.Monitoring systems that seek to provide monitoring services in cloud e...In a growing number of information processing applications,data takes the form of continuous data streams rather than traditional stored databases.Monitoring systems that seek to provide monitoring services in cloud environment must be prepared to deal gracefully with huge data collections without compromising system performance.In this paper,we show that by using a concept of urgent data,our system can shorten the response time for most 'urgent' queries while guarantee lower bandwidth consumption.We argue that monitoring data can be treated differently.Some data capture critical system events;the arrival of these data will significantly influence the monitoring reaction speed which is called urgent data.High speed urgent data collections can help system to react in real time when facing fatal errors.A cloud environment in production,MagicCube,is used as a test bed.Extensive experiments over both real world and synthetic traces show that when using urgent data,monitoring system can lower the response latency compared with existing monitoring approaches.展开更多
Cloud computing system consists of private clouds and public clouds. It merges its resources on each layer(e.g. Iaa S, Paa S and Saa S), which poses a challenge for resource management. The cloud monitoring system is ...Cloud computing system consists of private clouds and public clouds. It merges its resources on each layer(e.g. Iaa S, Paa S and Saa S), which poses a challenge for resource management. The cloud monitoring system is a solution to managing cloud system data from the heterogeneous resources. This paper discusses the monitoring and collection of the heterogeneous resources, studies the adaptive system, and proposes a real-time extensible distributed framework of monitoring data processing. Based on this framework, a system of monitoring data distribution, publication and subscription is proposed. The simulation results show that the proposed mechanism can adaptively determine the distribution action of monitoring data flow, and effectively reduce the costs for data monitoring and distribution.展开更多
基金supported by the National Key Technology R&D Program(Grant NO. 2012BAH17F01)NSFC-NSF International Cooperation Project(Grant NO. 61361126011)
文摘In a growing number of information processing applications,data takes the form of continuous data streams rather than traditional stored databases.Monitoring systems that seek to provide monitoring services in cloud environment must be prepared to deal gracefully with huge data collections without compromising system performance.In this paper,we show that by using a concept of urgent data,our system can shorten the response time for most 'urgent' queries while guarantee lower bandwidth consumption.We argue that monitoring data can be treated differently.Some data capture critical system events;the arrival of these data will significantly influence the monitoring reaction speed which is called urgent data.High speed urgent data collections can help system to react in real time when facing fatal errors.A cloud environment in production,MagicCube,is used as a test bed.Extensive experiments over both real world and synthetic traces show that when using urgent data,monitoring system can lower the response latency compared with existing monitoring approaches.
基金the Scientific Research Foundation of Zhejiang Provincial Education Department of China(No.Y201431192)
文摘Cloud computing system consists of private clouds and public clouds. It merges its resources on each layer(e.g. Iaa S, Paa S and Saa S), which poses a challenge for resource management. The cloud monitoring system is a solution to managing cloud system data from the heterogeneous resources. This paper discusses the monitoring and collection of the heterogeneous resources, studies the adaptive system, and proposes a real-time extensible distributed framework of monitoring data processing. Based on this framework, a system of monitoring data distribution, publication and subscription is proposed. The simulation results show that the proposed mechanism can adaptively determine the distribution action of monitoring data flow, and effectively reduce the costs for data monitoring and distribution.
