CPU load prediction using neuro-fuzzy and Bayesian inferences

Authors:
Kadda Beghdad bey;Farid Benhammadi;Zahia Gessoum;Aicha Mokhtari
Affiliations:
Laboratory of Informatics Systems, Polytechnic Military School, 16111 Algiers, Algeria;Laboratory of Informatics Systems, Polytechnic Military School, 16111 Algiers, Algeria;TMAS Team, Laboratory of Informatics of Paris 6 (LIP6), 75016 Paris, France;Laboratory of Artificial Intelligence, USTHB, Bab-Ezzouar, Algiers, Algeria
Venue:
Neurocomputing
Year:
2011

Citing 23
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Abstract

Ensuring adequate use of the computing resources for highly fluctuating availability in multi-user computational environments requires effective prediction models, which play a key role in achieving application performance for large-scale distributed applications. Predicting the processor availability for scheduling a new process or task in a distributed environment is a basic problem that arises in many important contexts. The present paper aims at developing a model for single-step-ahead CPU load prediction that can be used to predict the future CPU load in a dynamic environment. Our prediction model is based on the control of multiple Local Adaptive Network-based Fuzzy Inference Systems Predictors (LAPs) via the Naive Bayesian Network inference between clusters states of CPU load time points obtained by the C-means clustering process. Experimental results show that our model performs better and has less overhead than other approaches reported in the literature.