Mixture of ANFIS systems for CPU load prediction in metacomputing environment

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 naïve 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.