Abstract
Cloud services connect user with cloud computing platform where services range from Infrastructure as a Service, Software as a Service and Platform as a Service. It is important for Cloud Service Provider to provide reliable cloud services which are fast in performance and to predict possible service violation before any issue emerges so then remedial action can be taken. In this paper, we therefore experiment with five different machine learning algorithms namely Support Vector Machine, Random Forest, Naïve Bayes, Neural Network, and k-Nearest Neighbors for the detection and prediction of cloud quality of service violations in terms of response time and throughput. Experimental results show that the model created using SVM incorporated with 16 derived cloud quality of service violation rules has consistent accuracy of greater than 99%. With this machine learning model coupled with 16 decision rules, the Cloud Service Provider shall be able to know before hand, whether violation of services based on response time and throughput is occurring. When transactions tend to go beyond the threshold limits, system administrator shall be alerted to take necessary preventive measures to bring the system back to normal conditions. This shall reduce the chance for violation to occur, hence mitigating lose or costly penalty.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mell, P., Grance, T.: The NIST definition of cloud computing. national institute of standards and technology. U.S. Department of Commerce, Special Publication 800-145 (2011)
Mirobi, G.J., Arockiam, L.: Service level agreement in cloud computing: an overview. In: International Conference on Control, Instrumentation, Communication and Computational Technologies, ICCICCT, pp. 753–758. IEEE, Kumaracoil (2015). https://doi.org/10.1109/iccicct.2015.7475380
OSG Cloud Working Group: Report on Cloud Computing to the OSG Steering Committee. https://www.spec.org/osgcloud/docs/osgcloudwgreport20120410.pdf. Accessed 20 July 2017
WSDREAM Data Set. https://github.com/wsdream/wsdream-dataset/tree/master/dataset2. Accessed 20 July 2017
R version 3.4.3: A language and environment for statistical computing. https://wbc.upm.edu.my/cran/. Accessed 20 July 2017
Emeakaroha, V.C., Ferreto, T.C., Netto, M.A.S., Brandic, I., De Rose, C.A.F.: CASViD: application level monitoring for SLA violation detection in clouds. In: IEEE 36th Annual Computer Software and Applications Conference. IEEE, Izmir (2012). https://doi.org/10.1109/compsac.2012.68
Musa, S.M., Yousif, A., Bashi, M.B.: SLA violation detection mechanism for cloud computing. Int. J. Comput. Appl. 133, 8–11 (2016)
Leitner, P., Wetzstein, B., Rosenberg, F., Michlmayr, A., Dustdar, S., Leymann, F.: Runtime prediction of service level agreement violations for composite services. In: Dan, A., Gittler, F., Toumani, F. (eds.) ICSOC/ServiceWave -2009. LNCS, vol. 6275, pp. 176–186. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16132-2_17
Hani, A.F.M., Paputungan, I.V., Hassan, M.F.: Support vector regression for service level agreement violation prediction. In: International Conference on Computer, Control, Informatics and its Applications, IC3INA. IEEE, Jakarta (2013)
Tang, B., Tang, M.: Bayesian model-based prediction of service level agreement violations for cloud services. In: Theoretical Aspects of Software Engineering Conference, TASE. IEEE, Changsha (2014)
Sheng, D., Kondo, D., Cirne, W.: Host load prediction in a Google compute cloud with a Bayesian model. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC 2012. IEEE, Salt Lake City (2012)
Hemmat, R.A., Abdelhakim, H.: SLA violation prediction in cloud computing: a machine learning perspective. eprint arXiv:1611.10338 (2016)
Smola, A., Vishwanathan, S.V.N.: Introduction to Machine Learning, 1st edn. Cambridge University Press, Cambridge (2008)
Chang, C.C., Lin, C.J.: LIBSVM: a library of support vector machine. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011)
Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13, 415–425 (2002)
Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, pp. 41–46 (2011)
Popescu, M.C., Balas, V.E., Perescu-Popescu, L., Mastorakis, N.: Multilayer perceptron and neural networks. WSEAS Trans. Circ. Syst. 8, 579–588 (2009)
Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995)
Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: KNN model-based approach in classification. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) OTM 2003. LNCS, vol. 2888, pp. 986–996. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39964-3_62
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Zheng, Z., Zhang, Y., Lyu, M.R.: Investigating QoS of real-world web services. IEEE Trans. Serv. Comput. 7, 29–32 (2014)
IBM Informix Documentation Team: IBM Informix Performance Guide. Version 12.10. IBM, USA (2016)
Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process (IJDKP) 5(2), 1–11 (2015). https://doi.org/10.5121/ijdkp.2015.5201
Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861–874 (2006)
McHugh, M.L.: Interrater reliability: the kappa statistic. Biochem. Med. 22(3), 276–282 (2012)
Acknowledgement
This work is supported by the funding of Fundamental Research Grant Scheme (FRGS) from the Ministry of Higher Education of Malaysia with grant number FRGS/1/2016/ICT01/MMU/02/1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Wong, TS., Chan, GY., Chua, FF. (2018). A Machine Learning Model for Detection and Prediction of Cloud Quality of Service Violation. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10960. Springer, Cham. https://doi.org/10.1007/978-3-319-95162-1_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-95162-1_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95161-4
Online ISBN: 978-3-319-95162-1
eBook Packages: Computer ScienceComputer Science (R0)