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A moving average method for predicting process resource usage based on a state transition model
Description
We develop a prediction algorithm for process resource usage based on a state transition model. The state transition model is built by using a k-means clustering algorithm applied to a series of 2-dimensional observed parameters on process resource usage such as load average and free memory in a computer. Our prediction algorithm estimates the parameters from state transition probabilities of the model. To reduce prediction error, we introduce a moving average method in the prediction algorithm.
Journal
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- 1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.98TH8353)
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1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.98TH8353) 82-85, 2002-11-27
IEEE
