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Adaptive algorithms for managing a distributed data processing workload
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Data mining: practical machine learning tools and techniques with Java implementations
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Host load prediction using linear models
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Experiences with predicting resource performance on-line in computational grid settings
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Predictive algorithms in the management of computer systems
IBM Systems Journal
Insights into providing dynamic adaptation of operating system policies
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Automatic Adaptation and Analysis of SIP Headers Using Decision Trees
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Data mining and model trees study on GDP and its influence factors
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Continuous numeric prediction techniques known as model trees which build decision trees and then use linear regression at the terminal nodes are used to characterize resource consumption in a computer system. An advantage of model trees over time series and other traditional statistical models is the ability to add background knowledge to the model. Models are built using production data from several banks in collaboration with domain experts at those institutions. A demonstration of improving the models by adding background expert knowledge is given. An example of using model predictions to allow adaptive elements of an operating system to become more self-managing with respect to memory usage is also presented. Comparisons with other predictive techniques are made and advantages and disadvantages of using this technique in the operating system are discussed.