Mixture of ANFIS systems for CPU load prediction in metacomputing environment

Authors:
Kadda Beghdad Bey;Farid Benhammadi;Aicha Mokhtari;Zahia Gessoum
Affiliations:
Laboratory of Informatics Systems, Polytechnic Military School, BP 17, Bordj-El-Bahri 16111 Algiers, Algeria;Laboratory of Informatics Systems, Polytechnic Military School, BP 17, Bordj-El-Bahri 16111 Algiers, Algeria;Laboratory of Artificial Intelligence, University of Sciences and Technology, Houari Boumediène, Bab-Ezzouar, Algiers, Algeria;TMAS Team, Laboratory of Informatics of Paris 6 (LIP6), 104 avenue Kennedy, 75016, Paris, France
Venue:
Future Generation Computer Systems
Year:
2010

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Abstract

The metacomputing environments are becoming real distributed running platforms for compute-intensive services. One of the most difficult problems to be solved by metacomputing systems is ensuring accurate and fast prediction of available performance on each resource. The main objective of the present study is to develop a new prediction model that can be used to predict the future CPU load in a distributed computing environment. This prediction model is based on a mixture of Adaptive Network based Fuzzy Inference Systems (ANFIS) via the naive Bayes assumption. Experimental results for different load time series confirm that the new prediction model performs better than other CPU load prediction methods. In addition, a comparison with previous prediction methods to evaluate their accuracy is presented.