Abstract Cloud computing is a most fascinated technology that is being utilized by IT companies to reduce their infrastructure setup cost by outsourcing data and computation on demand. Cloud computing offer services in three basic models such as SaaS, PaaS and Infrastructure as a Service (IaaS). Where IaaS is one of the fundamental cloud service model in which cloud provider offers Virtual Machines (VMs) as resources to cloud customers through virtualization. The VMs act as dedicated computer system to consumers which are created on physical hosts of cloud provider. Making decision of physical host selection for VMs creation is a challenging task for cloud provider. Any deficiency of this selection causes VMs migration in middle of computation or restart computation from the scratch; these would sternly affect profit and trust of cloud provider. In this paper, we proposed a novel methodology to handle VMs creation and allocation for IaaS service. The proposed methodology employs a genetically weight optimized neural network component in each host to predict their near future availability during VMs creation. We analyses the host load prediction performance of various neural networks through real time host load values. Also we proposed a proficient decision making algorithm named Future Load Based Virtual machine Creation (FLBVC) to choose appropriate launching hosts for VMs. The performance of our methodology is validated using CloudAnalyst tool. The results demonstrated that our proposed approach reduces response time of cloud customers and rental cost of VMs.

References

[1] Eswaradass A., Sun X., and Wu M., A Neural Network Based Predictive Mechanism for Available Bandwidth, in Proceeding of 19th International Parallel and Distributed Symposium, Colorado, 2005.

[2] Baptista D. and Dias F., A Survey of Artificial Neural Networks Training Tools, Neural 322 The International Arab Journal of Information Technology, Vol. 14, No. 3, May 2017 Computing and Applications, vol. 23, no. 3, pp. 609-615, 2013.

[3] Beloglazov A., Abawajy J., and Buyya R., Energy Aware Resource Allocation Heuristics for Efficient Management of Datacentres for Cloud Computing, Journal of Future Generation Computer System, vol. 28, no. 5, pp. 755-768, 2012.

[4] Bohlouli M. and Analoui M., Grid-HPA: Predicting Resource Requirements of a Job in the Grid Computing Environment, International Journal of Electrical and Computer Engineering, vol. 3, no. 3, pp. 137- 141, 2008.

[5] Buyya R., Pandey S., and Vecchiola C., Market Oriented Cloud Computing and the Cloudbus toolkit, in Proceeding of the 1st International Conference on Cloud Computing, Beijing, pp. 24-44, 2009.

[6] Buyya R., Yeo C., Venugopal S., Broberg J., and Brandic I., Cloud Computing and Emerging IT Platforms; Vision, Hype and Reality for Delivering Computing as 5th Utility, Journal of Future Generation Computer System, vol. 25, no. 6, pp. 599-616, 2009.

[7] Che Z., Chiang T., and Che Z., Feed-Forward Neural Network Training: A Comparison Between Genetic Algorithm and Back- Propagation Learning Algorithm, International Journal of Innovative Computing, Information and Control, vol. 7, no. 10, pp. 5839-5858, 2011.

[8] Das P. and Choudhury S., Prediction of Retail Sales of Footwear using Feed Forward and Recurrent Neural Networks, Journal of Neural Computing and Applications, vol. 16, no. 4, pp. 491-502, 2007.

[9] Dinda P. and O Hallaron D., Host Load Prediction Using Linear Models, Journal of Cluster Computing, vol. 3, no. 4, pp. 265-280, 2000.

[10] Doulamis N., Doulamis A., Panagakis A., Dolkas K., Varvarigou T., and Varvarigou E., A Combined Fuzzy-Neural Network Model for Nonlinear Prediction of 3-D Rendering in Grid Computing, IEEE Transaction on Systems Man and Cybernetics, vol. 34, no. 2, pp. 1235-1247, 2004.

[11] Duy T., Sato Y., and Inoguchi Y., Improving Accuracy of Host load Predictions on Computing Grids by Artificial Neural Network, International Journal of Parallel, Emergent, and Distributed System, vol. 26, no. 4, pp. 275-290, 2011.

[12] Ferrer A., Hernndez F., Tordsson J., Elmroth E., Ali-Eldin A., Zsigri C., Sirvent R., Guitart J., Badia R., Djemame K., Ziegler W., Dimitrakos T., Nair S., Kousiouris G., Konstanteli K.,Varvarigou T., Hudzia B., Kipp A.,Wesner S., Corrales M., Forg N., Sharif T., and Sheridan C., OPTIMIS: A Holistic approach to Cloud Service Provisioning, Journal of Future Generation Computer System, vol. 28, no. 1, pp. 66-77, 2012.

[13] Habib S., Hauke S., Ries S., and Muhlhauser M., Trust as a Facilitator: A Survey, Journal of Cloud Computing: Advances, Systems and Applications, vol. 1, no. 19, pp. 1-18, 2012.

[14] James J. and Verma B., Efficient VM load Balancing algorithm for a Cloud Computing Environment, International Journal of Computer Science and Engineering, vol. 4, no. 9, pp. 1658-1663, 2012.

[15] Gao K., Wang Q., and Xi L., Reduct Algoritham Based Execution Times Prediction in Knowledge Discovery Cloud Computing Environment, International Arab Journal of Information Technology, vol. 11, no. 3, pp. 268- 275, 2014.

[16] Hu L., Che X., and Zheng S., Online System for Grid Resource Monitoring and Machine Learning-Based Prediction, IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 1, pp. 134-145, 2012.

[17] Mahalee H., Kaveri P., and Chavan V., Load Balancing on Cloud Data Centers, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 1, pp.1-4, 2013.

[18] Subashini S. and Kavitha V., A Survey on Security Issues in Service Delivery Models of Cloud Computing, Journal of Network and Computer Applications, vol. 34, no. 1, pp. 1-11, 2011.

[19] TaheriMonfared A. and Jaatun M., Handling Compromised Components in an IaaS Cloud Installation, Journal of Cloud Computing, vol. 1, no. 16, 2012.

[20] Vouk M., Cloud Computing Issues, Research and Implementation, in Proceeding of 30th International Conference on Information Technology Interfaces, Croatia, pp. 31-40, 2008.

[21] Wickremasinghe B., Calirous R., and Buyya R., A Cloudsim-based Visual Modeler for Analyzing Cloud Computing Environment and Application, in Proceeding of 24th IEEE conference on Advanced Information Networking and Applications, Washington, pp. 446-452, 2010.

[22] Wu L., Garg S., and Buyya R., SLA-Based Resource Allocation for SaaS in Cloud Computing Environments, in Proceeding of 11th IEEE/ACM International Symposium on Cluster, Washington, pp. 195-204, 2011. Proficient Decision Making on Virtual Machine Creation in IaaS 323

[23] Wu M. and Sun X., Grid Harvest Service: A Performance of Grid Computing, Journal of Parallel and Distributed Computing, vol. 66, no. 10, pp. 1322-1337, 2006.

[24] Yeo C. and Buyya R., Service Level Agreement based Allocation of Cloud Resources: Handling Penalty to Enhance Utility, in Proceeding of International Conference on Cluster Computing, Bostan, 2005. Radhakrishnan Ayyapazham received his M.E. degree in Computer Science and Engineering from Anna University Chennai, India. He is currently persuing PhD degree and working as Assistant Professor, Department of Computer Science and Engineering at Anna University, Tirunelveli Region, India. He has 12 years of academic experience. He participated many national and international confernces. His research interest on cloud computing focusing on Infrastructure as a Service. Kavitha Velautham received her B.E degree in Computer Science and Engineering in 1996 from M.S. University and M.E degree in Computer Science and Engineering in 2000 from Madurai Kamaraj University. Presently she is working as Associate Professor in the Department of CSE at University College of Engineering Nagercoil, Anna University, India. Currently, under her guidance 11 Research Scholars are pursuing Ph.D as full time and part time. Her research interests are Wireless networks, Mobile Computing, Network Security, Wireless Sensor Networks, Image Processing, Cloud Computing. She has published many papers in Conference, National and International Journal in areas such as Network security, Mobile Computing, wireless network security, and Cloud Computing.