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
A parallel hybrid linear solver based on the Schur complement method has the potential to balance the robustness of direct solvers with the efficiency of preconditioned iterative solvers.However,when solving large-sca...A parallel hybrid linear solver based on the Schur complement method has the potential to balance the robustness of direct solvers with the efficiency of preconditioned iterative solvers.However,when solving large-scale highly-indefinite linear systems,this hybrid solver often suffers from either slow convergence or large memory requirements to solve the Schur complement systems.To overcome this challenge,we in this paper discuss techniques to preprocess the Schur complement systems in parallel. Numerical results of solving large-scale highly-indefinite linear systems from various applications demonstrate that these techniques improve the reliability and performance of the hybrid solver and enable efficient solutions of these linear systems on hundreds of processors,which was previously infeasible using existing state-of-the-art solvers.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supported in part by the Director,Office of Science,Office of Advanced Scientific Computing Research,of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231.
文摘A parallel hybrid linear solver based on the Schur complement method has the potential to balance the robustness of direct solvers with the efficiency of preconditioned iterative solvers.However,when solving large-scale highly-indefinite linear systems,this hybrid solver often suffers from either slow convergence or large memory requirements to solve the Schur complement systems.To overcome this challenge,we in this paper discuss techniques to preprocess the Schur complement systems in parallel. Numerical results of solving large-scale highly-indefinite linear systems from various applications demonstrate that these techniques improve the reliability and performance of the hybrid solver and enable efficient solutions of these linear systems on hundreds of processors,which was previously infeasible using existing state-of-the-art solvers.
