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Cluster Computing

Erschienen in:

23.11.2018

TASM: technocrat ARIMA and SVR model for workload prediction of web applications in cloud

verfasst von: Parminder Singh, Pooja Gupta, Kiran Jyoti

Erschienen in: Cluster Computing | Ausgabe 2/2019

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Abstract

Workload patterns of cloud applications are changing regularly. The workload prediction model is key for auto-scaling of resources in a cloud environment. It is helping with cost reduction and efficient resource utilization. The workload for the web applications is usually mixed for different application at different time span. The single prediction model is not able to predict different kinds of workload pattern of cloud applications. In this paper, an adaptive prediction model has been proposed using linear regression, ARIMA, and support vector regression for web applications. Workload classifier has been proposed to select the model as per workload features. Further the model parameters are selected through a heuristic approach. We have used real trace files to evaluate the proposed model with existing state-of-the-art models. The experiment results describe the significant improvement in root-mean-squared error and mean absolute percentage error metrics, and improve the quality of service of web applications in a cloud environment.

Vorheriger Artikel A novel water pressure change optimization technique for solving scheduling problem in cloud computing

Nächster Artikel Network-aware energy saving multi-objective optimization in virtualized data centers

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Balbach, S.: ClarkNet web server logs. http://ita.ee.lbl.gov/html/contrib/ClarkNet-HTTP.html (2018). Accessed 25 Feb

Bankole, A.A., Ajila, S.A.: Cloud client prediction models for cloud resource provisioning in a multitier web application environment. In: 2013 IEEE 7th International Symposium on Service Oriented System Engineering (SOSE), pp. 156–161. IEEE (2013)

Bonvin, N., Papaioannou, T.G., Aberer, K.: Autonomic SLA-driven provisioning for cloud applications. In: Proceedings of the 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 434–443. IEEE Computer Society (2011)

Calheiros, R.N., Masoumi, E., Ranjan, R., Buyya, R.: Workload prediction using ARIMA model and its impact on cloud applications QoS. IEEE Trans. Cloud Comput. 3(4), 449–458 (2015)CrossRef

Calzarossa, M.C., Massari, L., Tessera, D.: Workload characterization: a survey revisited. ACM Comput. Surv. (CSUR) 48(3), 48 (2016)CrossRef

Doshi, P.S., Goel, M., Agarwal, A., Punjabi, K.: Performance provisioning using machine learning based automated workload classification (2018). US Patent App. 15/257,491

Dumoulin, J.: NASA web server logs. http://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html (2018). Accessed 25 Feb

Eldin, A.A., Rezaie, A., Mehta, A., Razroev, S., de Luna, S.S., Seleznjev, O., Tordsson, J., Elmroth, E.: How will your workload look like in 6 years? Analyzing Wikimedia’s workload. In: 2014 IEEE International Conference on Cloud Engineering (IC2E), pp. 349–354. IEEE (2014)

Fox, A., Griffith, R., Joseph, A., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I.: Above the Clouds: A Berkeley View of Cloud Computing. Report UCB/EECS 28(13), 2009. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (2009)

10.

Gunn, S.R., et al.: Support Vector Machines for Classification and Regression. ISIS technical report 14(1), pp. 5–16 (1998)

11.

Halavais, A.M.C.: The slashdot effect: analysis of a large-scale public conversation on the world wide web. PhD Thesis (2001)

12.

Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice. OTexts (2014)

13.

Islam, S., Keung, J., Lee, K., Liu, A.: Empirical prediction models for adaptive resource provisioning in the cloud. Future Gener. Comput. Syst. 28(1), 155–162 (2012)CrossRef

14.

Jiang, Y., Perng, C.S., Li, T., Chang, R.N.: Cloud analytics for capacity planning and instant VM provisioning. IEEE Trans. Netw. Serv. Manag. 10(3), 312–325 (2013)CrossRef

15.

Liu, C., Liu, C., Shang, Y., Chen, S., Cheng, B., Chen, J.: An adaptive prediction approach based on workload pattern discrimination in the cloud. J. Netw. Comput. Appl. 80, 35–44 (2017)CrossRef

16.

Ljung, G.M., Box, G.E.: On a measure of lack of fit in time series models. Biometrika 65(2), 297–303 (1978)CrossRefMATH

17.

Makridakis, S., Wheelwright, S.C., Hyndman, R.J.: Forecasting Methods and Applications. Wiley, New York (2008)

18.

Messias, V.R., Estrella, J.C., Ehlers, R., Santana, M.J., Santana, R.C., Reiff-Marganiec, S.: Combining time series prediction models using genetic algorithm to autoscaling web applications hosted in the cloud infrastructure. Neural Comput. Appl. 27(8), 2383–2406 (2016)CrossRef

19.

Panneerselvam, J., Liu, L., Antonopoulos, N., Bo, Y.: Workload analysis for the scope of user demand prediction model evaluations in cloud environments. In: Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp. 883–889. IEEE Computer Society (2014)

20.

Patel, J., Jindal, V., Yen, I.L., Bastani, F., Xu, J., Garraghan, P.: Workload estimation for improving resource management decisions in the cloud. In: 2015 IEEE Twelfth International Symposium on Autonomous Decentralized Systems (ISADS), pp. 25–32. IEEE (2015)

21.

Roy, N., Dubey, A., Gokhale, A.: Efficient autoscaling in the cloud using predictive models for workload forecasting. In: 2011 IEEE International Conference on Cloud Computing (CLOUD), pp. 500–507. IEEE (2011)

22.

Sapankevych, N.I., Sankar, R.: Time series prediction using support vector machines: a survey. IEEE Comput. Intell. Mag. 4(2), 24–38 (2009)CrossRef

23.

Seber, G.A., Lee, A.J.: Linear Regression Analysis, vol. 329. Wiley, New York (2012)MATH

24.

RC Team, et al.: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2013)

25.

Teräsvirta, T., Lin, C.F., Granger, C.W.: Power of the neural network linearity test. J. Time Ser. Anal. 14(2), 209–220 (1993)CrossRef

26.

Wang, K., Lin, M., Ciucu, F., Wierman, A., Lin, C.: Characterizing the impact of the workload on the value of dynamic resizing in data centers. Perform. Eval. 85, 1–18 (2015)CrossRef

27.

Wu, C.H., Wei, C.C., Su, D.C., Chang, M.H., Ho, J.M.: Travel time prediction with support vector regression. In: 2003 IEEE Intelligent Transportation Systems, 2003. Proceedings, vol. 2, pp. 1438–1442. IEEE (2003)

28.

Xiaofang, Y., Yaonan, W.: Parameter selection of support vector machine for function approximation based on chaos optimization. J. Syst. Eng. Electron. 19(1), 191–197 (2008)CrossRef

29.

Yan, X., Su, X.: Linear Regression Analysis: Theory and Computing. World Scientific, Singapore (2009)CrossRefMATH

30.

Yang, J., Yu, T., Jian, L., Qiu, J., Li, Y.: An extreme automation framework for scaling cloud applications. IBM J. Res. Dev. 55(6), 410–421 (2011)CrossRef

31.

Yin, J., Lu, X., Zhao, X., Chen, H., Liu, X.: BURSE: a bursty and self-similar workload generator for cloud computing. IEEE Trans. Parallel Distrib. Syst. 26(3), 668–680 (2015)CrossRef

32.

Zhang, H., Jiang, G., Yoshihira, K., Chen, H., Saxena, A.: Intelligent workload factoring for a hybrid cloud computing model. In: 2009 World Conference on Services-I, pp. 701–708. IEEE (2009)

33.

Zhang, H., Jiang, G., Yoshihira, K., Chen, H.: Proactive workload management in hybrid cloud computing. IEEE Trans. Netw. Serv. Manag. 11(1), 90–100 (2014)CrossRef

34.

Zhu, Q., Agrawal, G.: Resource provisioning with budget constraints for adaptive applications in cloud environments. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, pp. 304–307. ACM (2010)

Titel: TASM: technocrat ARIMA and SVR model for workload prediction of web applications in cloud
verfasst von: Parminder Singh
Pooja Gupta
Kiran Jyoti
Publikationsdatum: 23.11.2018
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 2/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-2868-6

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