Abstract

Cloud computing is a new era in computing paradigm. It helps Information Technology (IT) companies to cut the cost by outsourcing data and computation on-demand. Cloud computing provides different kind of services which includes Hardware as a Service, Software as a Service (SaaS), Infrastructure as a Service (IaaS) etc. Despite these potential benefits, many IT companies are reluctant to do cloud business due to outstanding trust issues. Cloud consumer and provider are the most interested parties to maximize their benefits. In IaaS, the cloud provider operates the whole computing platform as a resource for the customer, which is accessed by customer as a Virtual Machine (VM) via the internet. The cloud provider must predict the best machine among the available machines to launch VM. This strategic prediction would avoid exodus of computation in middle due to machine heavy load or any failure which severely affect the benefits of both consumer and provider. Since VM allocation for IaaS request is a challenging task, in this study novel architecture is proposed for IaaS cloud computing environment in which VM allocation is done through genetically weight optimized neural network. In this scenario the host load of each machine is taken as its resource information. The neural network predicts the host load of each machine in near future based on the recent past host load. It would help the VM allocator to choose the right machine. Analysis is done on the performance of genetically weight optimized Back Propagation Neural Network (BPNN), Elman Neural Network (ELNN) and Jordan Neural Network (JNN) for prediction accuracy.

Keywords:

Back propagation neural network, Elman neural network, genetic algorithm, infrastructure as a service, Jordan neural network, virtual machine,

References

1. Alaknantha, E., 2005. A neural network based predictive mechanism for available bandwidth. Proceeding of the 19th IEEE International Parallel and Distributed Processing Symposium, pp: 33a.
   
2. Bohlouli, M., 2008. Grid-HPA: Predicting resource requirements of a job in the grid computing environment. J. World Acad. Sci. Eng. Technol., 42: 2008.
   
3. Che, X.L., 2010. A nu-support vector regression based system for grid resource monitoring and prediction. Acta Automat. Sinica, 36(1).
   
4. Dinda, P.A., 2001. Host load prediction using linear models. J. Clust. Comput., 3(4): 265-280.
   CrossRef    
5. Doulamis, N.D., A.D. Doulamis, A. Panagakis, K. Dolkas, T.A. Varvarigou and E.A. Varvarigos, 2004. A combined Fuzzy Neural model for nonlinear prediction of 3-D rendering workload in grid computing. IEEE T. Syst. Man Cy. B, 34(2): 1235-1247.
   CrossRef    
6. Duy, T.V.T., Y. Sato and Y. Inoguchi, 2009. Improving accuracy of host load predictions on computing grids by artificial neural network. Proceeding of IEEE International Symposium on Parallel and Distributed Processing, pp: 23-29.
   
7. Ioan, I., 2004. The optimization of feed forward neural structure using genetic algorithms. Proceeding of the International Conference on Theory and Applications of Mathematics and Informatics, pp: 223-234.
   
8. Jeffrey, E., 1990. Finding structure in time. Cognitive Sci., 14: 179-211.
   CrossRef    
9. Jin, H., 2007. Using NARX neural network based load prediction to improve scheduling decision in grid environments. Proceeding of the IEEE International Conference Natural Computation (NC, 2007).
   
10. Liang, H., C. Xi-Long and Z. Si-Qing, 2012. Online system for grid resource monitoring and machine learning-based prediction. IEEE T. Parall. Distr., 23(1).
    
11. Ming, W., 2006. Grid harvest service: A performance system of grid computing. J. Parallel Distr. Com., 66(10).
    
12. Neto, L.B., P.H.G. Coelho, J.C.C.B. Soares de Mello, L.A. Meza and M.L. Fernandes Velloso, 2004. Flow estimation using an Elman networks. Proceeding of the IEEE International Joint Conference on Neural Networks.
    
13. Zhen-Guo, C., 2011. Feed-forward neural networks back-propagation learning algorithm. Int. J. Innov. Comput. I., 7(10): 5839-5850.
    

Competing interests

The authors have no competing interests.

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The authors have no competing interests.