Using Machine Learning Algorithms for Cloud Client Prediction Models in a Web VM Resource Provisioning Environment
AbstractIn order to meet Service Level Agreement (SLA) requirements, efficient scaling of Virtual Machine (VM) resources in cloud computing needs to be provisioned ahead due to the instantiation time required by the VM. One way to do this is by predicting future resource demands. The existing research on VM resource provisioning are either reactive in their approach or use only non-business level metrics. In this research, a Cloud client prediction model for TPC-W benchmark web application is developed and evaluated using three machine learning techniques: Support Vector Regression (SVR), Neural Networks (NN) and Linear Regression (LR). Business level metrics for Response Time and Throughput are included in the prediction model with the aim of providing cloud clients with a more robust scaling decision choice. Results and analysis from the experiments carried out on Amazon Elastic Compute Cloud (EC2) show that Support Vector Regression provides the best prediction model for random-like workload traffic pattern.
(1) Ajila A. S. and Bankole A. A., Cloud Client Prediction Models Using Machine Learning Techniques, COMPSAC 2013 - The 37th Annual International Computer Software & Applications Conference, Kyoto, Japan July 22-26, 2013
(2) “Amazon CloudWatch Developer Guide API Version 2010-08-01”, 2013. [Online]. Available: http://awsdocs.s3.amazonaws.com/AmazonCloudWatch/latest/acw-dg.pdf.
(3) “Amazon elastic compute cloud (amazon ec2)”, 2013. [Online]. Available: http://aws.amazon.com/ec2.
(4) “Amazon Web services Discussion Forums”, 2013. [Online]. Available: https://forums.aws.amazon.com/thread.jspa?threadID=67697
(5) Ali-Eldin, A., Tordsson, J and Elmroth E., “An adaptive hybrid elasticity controller for cloud infrastructures”. IEEE Network Operations and Management Symposium (NOMS) pp 204-212. Hawaii, USA. April,
(6) Armbrust, M. et al., “A view of cloud computing”. Commun. ACM. 53, 4 pp. 50–58, April, 2010.
(7) Armbrust, M. et al., Above the Clouds: A Berkeley View of Cloud Computing. 2009
(8) Bankole A., and Ajila S.A., “Cloud Client Prediction Models for Cloud Resource Provisioning in a Multitier Web Application Environment”, in 7th IEEE International Symposium on Service-Oriented System Engineering (IEEESOSE 2013), San Francisco Bay, USA March 25 – 28, 2013.
(9) Bertholon, B., Varrette, S., Bouvry, P., “Certicloud: A Novel TPM-based Approach to Ensure Cloud IaaS Security”. IEEE International Conference on Cloud Computing (CLOUD). pp. 121–130. 2011.
(10) Boniface, M. et al., “Platform-as-a-Service Architecture for Real-Time Quality of Service Management in Clouds”. 5th International Conference on Internet and Web Applications and Services (ICIW). pp. 155–160, Barcelona, Spain. May, 2010.
(11) Borgetto, D., Maurer, M., Da-Costa, G., Pierson, J., and Brandic, I., “Energy-Efficient and SLA-Aware Management of IaaS clouds”. 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet (e-Energy). pp. 1–10. Madrid, Spain. May, 2012.
(12) Cain , H. W., Rajwar, R., Marden, M., and Lipasti, M., “An Architectural Evaluation of Java TPC-W” in Proceedings of the Seventh International Symposium on High- Performance Computer Architecture, Nuevo Leone, Mexico. January, 2001.
(13) Caron, E., Desprez, F., and Muresan, A., "Forecasting for Grid and Cloud Computing On-Demand Resources Based on Pattern Matching" 2nd International Conference on Cloud Computing Technology and Science (CloudCom). pp.456-463, Indianapolis, USA. November, 2010.
(14) Chieu, T.C., Mohindra, A., Karve, A.A., and Segal A., “Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment”. IEEE International Conference on e-Business Engineering ,ICEBE ’09. pp 281-286. Macau, China. October, 2009.
(15) Chih-Chung, C. and Chih-Jen , L., “LIBSVM : a library for support vector machines”. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
(16) Chih-Wei, H., Chang, C.C, and Lin C., “A practical guide to support vector classification”. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 2003. [Online]. Available: http://www.csie.ntu.edu.tw/∼cjlin/libsvm/
(17) Dashevskiy, M. and Luo, Z., "Time series prediction with performance guarantee". IET Communications. Vol. 5, Issue 8, pp. 1044–1051. 2010.
(18) Dutreilh, X., Kirgizov, S., Melekhova, O., Malenfant, J., Rivierre, N., and Truck I., “Using Reinforcement Learning for Autonomic Resource Allocation in Clouds: Towards a Fully Automated Workflow” Proceedings of the 7th International Conference on Autonomic and Autonomous Systems. pp 67-74. Mestre, Italy. May, 2011.
(19) Fang, W., Lu, Z., Wu, J and Cao Z., “RPPS: A Novel Resource Prediction and Provisioning Scheme in Cloud Data Center”. IEEE Ninth International Conference on Services Computing (SCC). pp.609 –616, Washington DC, USA. June, 2012.
(20) Gandhi, A., Chen, Y, Gmach, D., Arlitt, M., and Marwah, M., “Hybrid resource provisioning for minimizing data center SLA violations and power consumption”. Sustainable Computing: Informatics and Systems. pp. 91–104. Orlando, Florida, USA. June, 2012.
(21) Ghanbari, H., Simmons, B., Litoiu, M., Barna, C., and Iszlai, G., “Optimal autoscaling in a IaaS cloud”. Proceedings of the 9th international conference on Autonomic computing. pp 173-178. San Jose, California, USA. September, 2012.
(22) Guosheng, H., Hu, L., Li, H., Li, K., and Liu, W., "Grid Resources Prediction with Support Vector Regression and Particle Swarm Optimization," 3rd International Joint Conference on Computational Science and Optimization (CSO), vol.1, pp.417-422, China. May, 2010.
(23) Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I.H., “The WEKA Data Mining Software: An Update”, SIGKDD Explorations, Volume 11, Issue 1. 2009.
(24) Han, R., Guo L., Ghanem, M.M., and Guo, Y., “Lightweight Resource Scaling for Cloud Applications”. 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp 644 –651, Ottawa Canada, May. 2012.
(25) Hasan, M.Z., Magana, E., Clemm, A., Tucker, L., and Gudreddi, S.L.D., “Integrated and autonomic cloud resource scaling”. IEEE Network Operations and Management Symposium (NOMS). pp 1327-1334. Hawaii, USA. April, 2012.
(26) Hilley, D., Cloud Computing: A Taxonomy of Platform and Infrastructure-level Offerings: 2009. https://smartech.gatech.edu/handle/1853/34402. Accessed: 2013-01-24.
(27) Holehouse A., “Stanford Machine Learning”. [Online]. Available: http://www.holehouse.org/mlclass/index.html
(28) Imam, M.T., Miskhat, S.F., Rahman, R.M., and Amin, M.A., “Neural network and regression based processor load prediction for efficient scaling of Grid and Cloud resources”. 14th International Conference on Computer and Information Technology (ICCIT). pp 333-338, Bangladesh, India. December, 2011.
(29) Keogh, E., Chu, S., Hart, D., and Pazzani, M., “An online algorithm for segmenting time series”. Proceedings of IEEE International Conference on Data Mining. pp 289-296. San Jose, California, USA. November, 2001.
(30) Khashman, A. and Nwulu, N.I., "Intelligent prediction of crude oil price using Support Vector Machines", in IEEE 9th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pp.165-169, Smolenice, Slovakia. January, 2011.
(31) Kulkarni, P., “Reinforcement and Systematic Machine Learning For Decision Making”, Wiley-IEEE Press, 2012.
(32) Kupferman, J., Silverman, J., Jara, P., and Browne, J., “Scaling Into
the Cloud”. University of California, Santa Barbara, Tech. Rep. http://cs.ucsb.edu/~jkupferman/docs/ScalingIntoTheClouds.pdf. 2009.
(33) Lim, H.C., Babu, S., and Chase, J.S., “Automated control for elastic storage”. Proceedings of the 7th international conference on Autonomic computing. pp 1-10. Washington DC, USA. June, 2010.
(34) Lim, H.C., Babu, S., and Chase, J.S., “Automated control in cloud computing: challenges and opportunities”. Proceedings of the 1st workshop on Automated control for datacenters and clouds. pp 13-18.
Barcelona, Spain. June, 2009.
(35) Lorido-Botran, T., Alonso-Miguel, J., and Lozano, J.A., “Auto-scaling Techniques for Elastic Applications in Cloud Environments” Department of Computer Architecture and Technology, University of Basque Country, Tech. Rep. EHU-KAT-IK-09-12. September, 2012.
(36) Muppala, S., Zhou X., and Zhang, L., “Regression-based resource provisioning for session slowdown guarantee in multi-tier Internet servers”. Journal of Parallel and Distributed Computing. pp. 362-375 March, 2012.
(37) Quiroz, A., Kim, H., Parashar, M., Gnanasambandam, N., and Sharma N., “Towards autonomic workload provisioning for enterprise Grids and clouds” in Grid Computing, 2009 10th IEEE/ACM International Conference. pp 50-57, Banff, Alberta, Canada. October, 2009.
(38) Richard S. S and Andrew B.B., “Reinforcement Learning an Introduction” http://www.scribd.com/doc/92878651/Reinforcement-Learning-an-Introduction-Richard-S-Sutton-Andrew-G-Barto. Accessed: 2013-01-02.
(39) Sadeka, I., Keung, J., Lee, K., and Liu, A., “Empirical prediction models for adaptive resource provisioning in the cloud”, Future Generation Computer Systems, vol. 28, no. 1, pp 155 – 165, January, 2012.
(40) Sakr, G.E., Elhajj, I.H., Mitri, G., Wejinya, U.C., "Artificial intelligence for forest fire prediction" IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp.1311-1316, Montreal, Canada. July, 2010.
(41) Sapankevych, N and Sankar, R., "Time Series Prediction Using Support Vector Machines: A Survey," Computational Intelligence Magazine, IEEE, vol.4, no.2, pp.24-38, May 2009.
(42) Smola, A.J and Scholkopf, B., “A Tutorial on Support Vector Regression” in Statistics and Computing vol 14, pp. 199 – 222, August, 2004.
(43) Tian, C., Wamg, Y., Qi, F., and Yin, B., "Decision model for provisioning virtual resources in Amazon EC2". 8th International Conference on Network and Service Management (CNSM), pp. 159–163, Las Vegas, USA. October, 2012.
(44) TPC, TPC-W Benchmark, Transaction Processing Performance Council (TPC), San Francisco, CA, USA, 2003.
(45) Trevor, H., Tibshirani, R., and Friedman, J., “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, New York: Springer, February, 2009.
(46) Wang, S. and Summers, R.M. "Machine learning and radiology". Medical Image Analysis. Vol 16, Issue 5. pp. 933-951. 2012.
(47) Witten, I. H and Frank, E., “Data Mining Practical Machine Learning Tools and Techniques with Java Implementations”, San Diego: Academic Press, 2000.
(48) Wood, T., Cherkasova, L., Ozonat, K., and Shenoy, P., “Profiling and Modeling Resource Usage of Virtualized Applications” Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, pp. 366-387, New York, USA. December, 2008.