Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.neucom.2011.01.009 | Neurocomputing |
Keywords | Field | DocType |
neuro-fuzzy system,cpu load prediction,resources monitoring,bayesian modeling,cpu load time point,bayesian inference,multi-user computational environment,single-step-ahead cpu load prediction,new process,future cpu load,metacomputing environment,c-means clustering process,dynamic environment,prediction model,fluctuating availability,effective prediction model,bayesian network,neuro fuzzy,bayesian model,distributed environment,distributed application | Data mining,Neuro-fuzzy,Bayesian inference,Naive Bayes classifier,Distributed Computing Environment,Computer science,Inference,Scheduling (computing),Artificial intelligence,Cluster analysis,Machine learning,Bayesian probability | Journal |
Volume | Issue | ISSN |
74 | 10 | Neurocomputing |
Citations | PageRank | References |
11 | 0.62 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kadda Beghdad Bey | 1 | 53 | 3.37 |
| Farid Benhammadi | 2 | 101 | 10.36 |
| Zahia Gessoum | 3 | 14 | 1.01 |
| Aicha Mokhtari | 4 | 56 | 5.02 |