Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An Empirical Study of the Multiscale Predictability of Network Traffic
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
Characterizing and Predicting TCP Throughput on the Wide Area Network
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Embedded predictive modeling in a parallel relational database
Proceedings of the 2006 ACM symposium on Applied computing
Fast pattern-based throughput prediction for TCP bulk transfers
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid - Volume 01
A machine learning approach to TCP throughput prediction
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
USING AUTONOMOUS SYSTEM TOPOLOGICAL INFORMATION IN A WEB SERVER PERFORMANCE PREDICTION
Cybernetics and Systems
Bandwidth estimation: metrics, measurement techniques, and tools
IEEE Network: The Magazine of Global Internetworking
Predicting network throughput for grid applications on network virtualization areas
Proceedings of the first international workshop on Network-aware data management
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This paper presents the application of data mining algorithms to the prediction of Web performance. Our domain-driven data mining uses historic HTTP transactions data reflecting Web performance as perceived by the end-users located in the Internet domain of Wroclaw University of Technology, Wroclaw, Poland. The predictive modeling features of two general data mining systems, Microsoft SQL Server and IBM Intelligent Miner, are compared. The neural networks, decision tree, time series, and transform regression models are evaluated. It is shown that the data mining algorithms return quite accurate prediction results. The best results are achieved using the IBM's transform regression algorithm.