An Intelligent Regressive Ensemble Approach for Predicting Resource Usage in Cloud Computing
Abstract
Cloud Computing has become a prime infrastructure for scientists to deploy scientific applications as it provides a parallel and distributed environment for large-scale computations. Significant prediction of resource use is essential during deployment to achieve optimal scheduling for scientific applications. An Intelligent Regressive Ensemble Approach for Predicting Resource Usage in Cloud Computing Because of high variances in cloud metrics, existing resource prediction models are short in providing reasonable accuracy. Therefore, it is necessary to accurately predict the future resource requirements for the automatic provision of resources in order to handle the varying cloud resource requirements.
In this An Intelligent Regressive Ensemble Approach for Predicting Resource Usage in Cloud Computing paper, an Intelligent Regressive Ensemble Approach for Prediction (REAP) has been proposed that integrates feature selection and resource prediction techniques to achieve high performance. In a real cloud environment, the effectiveness of the proposed approach is evaluated by conducting a series of experiments.
System Configuration
H/W System Configuration
Speed : 1.1 GHz
RAM : 256 MB(min)
Hard Disk : 20 GB
Floppy Drive : 1.44 MB
Key Board : Standard Windows Keyboard
Mouse : Two or Three Button Mouse
Monitor : SVGA
S/W System Configuration
Platform : cloud computing
Operating system : Windows Xp,7,
Server : WAMP/Apache
Working on : Browser Like Firefox, IE
Conclusion
We presented an adaptive resource utilization prediction system for IaaS cloud in this paper. We used real trace, i.e. Bitbrains data center FastStorage, to evaluate our system. The system predicts four hundred future data samples, i.e. 400 minute workload demand.
