Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Pursuing failure: the distribution of program failures in a profile space
Proceedings of the 8th European software engineering conference held jointly with 9th ACM SIGSOFT international symposium on Foundations of software engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Gnort: High Performance Network Intrusion Detection Using Graphics Processors
RAID '08 Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection
IEEE Transactions on Parallel and Distributed Systems
Application classification through monitoring and learning of resource consumption patterns
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Preprocessing time series data for classification with application to CRM
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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In this paper two classifiers have been derived in order to determine if identical computer tasks have been executed at different processors. The classifiers have been developed analytically following a classical hypothesis testing approach. The main assumption of this work is that the probability distribution function (pdf) of the random times taken by the processors to serve tasks are known. This assumption has been fulfilled by empirically characterizing the pdf of such random times. The performance of the classifiers developed here has been assessed using traces from real processors. Further, the performance of the classifiers is compared to heuristic classifiers, linear discriminants, and non-linear discriminants among other classifiers.