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Time Series Analysis: Forecasting and Control
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Host load prediction using linear models
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Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing
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HPDC '97 Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing
A Resource Query Interface for Network-Aware Applications
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Predicting the CPU Availability of Time-Shared Unix Systems on the Computational Grid
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Proceedings of the 2004 ACM/IEEE conference on Supercomputing
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Proceedings of the 2003 ACM/IEEE conference on Supercomputing
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IEEE Transactions on Parallel and Distributed Systems
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PDCN'06 Proceedings of the 24th IASTED international conference on Parallel and distributed computing and networks
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Computer Communications
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Proceedings of the twelfth international conference on 3D web technology
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Future Generation Computer Systems
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Future Generation Computer Systems
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GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
Multi-scale Real-Time Grid Monitoring with Job Stream Mining
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Platform-independent modeling and prediction of application resource usage characteristics
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Future Generation Computer Systems
Task profiling model for load profile prediction
Future Generation Computer Systems
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Journal of Parallel and Distributed Computing
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We describe and evaluate the Running Time Advisor (RTA), a system that can predict the running time of a compute-bound task on a typical shared, unreserved commodity host. The prediction is computed from linear time series predictions of host load and takes the form of a confidence interval that neatly expresses the error associated with the measurement and prediction processes – error that must be captured to make statistically valid decisions based on the predictions. Adaptive applications make such decisions in pursuit of consistent high performance, choosing, for example, the host where a task is most likely to meet its deadline. We begin by describing the system and summarizing the results of our previously published work on host load prediction. We then describe our algorithm for computing predictions of running time from host load predictions. We next evaluate the system using over 100,000 randomized testcases run on 39 different hosts, finding that is indeed capable of computing correct and useful confidence intervals. Finally, we report on our experience with using the RTA in application-oriented real-time scheduling in distributed systems.