PFS: A Productivity Forecasting System for Desktop Computers to Improve Grid Applications Performance in Enterprise Desktop Grid

An Enterprise Desktop Grid (EDG) is a low cost platform that gathers desktop computers spread over different institutions. This platform uses desktop computers idle time to run Grid applications. We argue that computers in these environments have a predictable productivity that affects a Grid application execution time. In this paper, we propose a system called PFS for computer productivity forecasting that improves Grid applications performance. We simulated 157.500 applications and compared the performance achieved by our proposal against two recent strategies. Our experiments show that a Grid scheduler based on PFS runs applications faster than schedulers based on other selection strategies

Mixture of ANFIS Systems for CPU Load Prediction in Metacomputing Environment

International audienceThe 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