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IEEE Transactions on Software Engineering
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SIGMETRICS '91 Proceedings of the 1991 ACM SIGMETRICS conference on Measurement and modeling of computer systems
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IEEE/ACM Transactions on Networking (TON)
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INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 2)-Volume - Volume 2
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ACM Transactions on Modeling and Computer Simulation (TOMACS)
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Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Dependence modeling for stochastic simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
Autonomic storage system based on automatic learning
HiPC'04 Proceedings of the 11th international conference on High Performance Computing
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Proceedings of the 5th Annual International Systems and Storage Conference
Generating request streams on Big Data using clustered renewal processes
Performance Evaluation
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This article describes a method for analyzing, modeling, and simulating a two-level arrival-counting process. This method is particlarly appropriate when the number of independent processes is large, as is the case in our motivating application which requires analyzing and representing computer file system trace data for activity on nearly 8,000 files. The method is also applicable to network trace data characterizing communication patterns between pairs of computers. We apply cluster analysis to separate the arrival process into groups or bursts of activity on a file. We then characterize the arrival procss in terms of the time between bursts of activity on file, the time between file events within bursts, and the number of events in a burst. Finally, we model these three components individually, then reassemble the results to produce a synthetic trace generator. In order to gauge the effectiveness of this method, we use synthetically generated (simulated) trace data produced in this way to drive a discrete-event simulation of a distributed replicated file system. We compare the results of the simulation driven by the synthetic trace with the same simulation driven by the original trace data, and conclude that the synthetic data capture the essential characteristics of the empirical trace.