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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2021

03.09.2020

Performance Assessment of Time Series Forecasting Models for Cloud Datacenter Networks’ Workload Prediction

verfasst von: Jitendra Kumar, Ashutosh Kumar Singh

Erschienen in: Wireless Personal Communications | Ausgabe 3/2021

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Abstract

The resource scaling has been influential in enabling the cloud service providers to provision the resources on-demand effectively. The prior estimation of workloads helps in addressing the scaling issues arises due to dynamic nature of the resource demands. In this paper, we evaluate six different forecasting approaches over real world workload data traces of web and cloud servers. The entire analysis is carried out three times as three different functions are used to measure the deviation in forecasts. The three forecast error measures are root mean squared error, mean absolute error, and mean absolute scaled error. We also carried out a statistical evaluation using Friedman test and Finner post-hoc analysis. The study concludes that the auto ARIMA process outperforms other models and achieves the best rank in the statistical analysis.
Vorheriger Artikel Analysis of Outage Probability in Wavelength Diversity Based FSO Link Under Gamma–Gamma Fading with Varying Atmospheric Attenuation
Nächster Artikel Improved Performance of Alamouti Scheme Using Cyclic Delay Diversity and Doppler Diversity for MIMO Systems-Based

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Literatur
1.
Sebrechts, M., Van Seghbroeck, G., Wauters, T., Volckaert, B., & De Turck, F. (2018). Orchestrator conversation: Distributed management of cloud applications. International Journal of Network Management, 28(6), e2036.CrossRef
2.
Singh, A. K., & Kumar, J. (2019). Secure and energy aware load balancing framework for cloud data centre networks. Electronics Letters, 55(9), 540–541.CrossRef
3.
Amiri, M., & Mohammad-Khanli, L. (2017). Survey on prediction models of applications for resources provisioning in cloud. Journal of Network and Computer Applications, 82(Supplement C), 93–113.CrossRef
4.
Li, T.-H. (2005). A hierarchical framework for modeling and forecasting web server workload. Journal of the American Statistical Association, 100(471), 748–63.MathSciNetCrossRef
5.
Roy, N., Dubey, A., & Gokhale, A. (2011). Efficient autoscaling in the cloud using predictive models for workload forecasting. In 2011 IEEE 4th International Conference on Cloud Computing, pp. 500–507.
6.
Tran, V. G., Debusschere, V., & Bacha, S. (2012). Hourly server workload forecasting up to 168 hours ahead using seasonal arima model. IEEE International Conference on Industrial Technology, 2012, 1127–1131.
7.
Calheiros, R. N., Masoumi, E., Ranjan, R., & Buyya, R. (2015). Workload prediction using arima model and its impact on cloud applications’ qos. IEEE Transactions on Cloud Computing, 3(4), 449–458.CrossRef
8.
Cao, J., Zhu, X., Dong, F., Liu, B., Ma, Z., & Min, H. (2016). Time series based bandwidth allocation strategy in cloud datacenter. International Conference on Advanced Cloud and Big Data (CBD), 2016, 228–233.CrossRef
9.
Khan, A., Yan, X., Tao, S., & Anerousis, N. (2012). Workload characterization and prediction in the cloud: A multiple time series approach. IEEE Network Operations and Management Symposium, 2012, 1287–1294.
10.
Ye, Z., Mistry, S., Bouguettaya, A., & Dong, H. (2016). Long-term qos-aware cloud service composition using multivariate time series analysis. IEEE Transactions on Services Computing, 9(3), 382–393.CrossRef
11.
Messias, V. R., Estrella, J. C., Ehlers, R., Santana, M. J., Santana, R. C., & Reiff-Marganiec, S. (2016). Combining time series prediction models using genetic algorithm to autoscaling web applications hosted in the cloud infrastructure. Neural Computing and Applications, 27(8), 2383–2406.CrossRef
12.
Liu, C., Liu, C., Shang, Y., Chen, S., Cheng, B., & Chen, J. (2017). An adaptive prediction approach based on workload pattern discrimination in the cloud. Journal of Network and Computer Applications, 80(C), 35–44.CrossRef
13.
Cetinski, K., & Juric, M. B. (2015). Ame-wpc: Advanced model for efficient workload prediction in the cloud. Journal of Network and Computer Applications, 55, 191–201.CrossRef
14.
Rahman, Z.U., Hussain, O.K., & Hussain, F.K. (2014). Time series qos forecasting for management of cloud services. In 2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications, pp. 183–190.
15.
Khan, M.N.A.H., Liu, Y., Alipour, H., & Singh, S. (2015). Modeling the autoscaling operations in cloud with time series data. In Proceedings of the 2015 IEEE 34th Symposium on Reliable Distributed Systems Workshop (SRDSW’15), IEEE Computer Society, Washington, DC, USA, pp. 7–12.
16.
Baldan, F. J., Ramirez-Gallego, S., Bergmeir, C., Herrera, F., & Benitez, J. M. (2016). A forecasting methodology for workload forecasting in cloud systems. IEEE Transactions on Cloud Computing, 6(4), 929–941.CrossRef
17.
Kim, I.K., Wang, W., Qi, Y., & Humphrey, M. (2016). Empirical evaluation of workload forecasting techniques for predictive cloud resource scaling. In 2016 IEEE 9th International Conference on Cloud Computing (CLOUD), (pp. 1–10).
18.
Kumar, A. S., & Mazumdar, S. (2016). Forecasting hpc workload using arma models and ssa. International Conference on Information Technology (ICIT), 2016, 294–297.
19.
Kumar, J., & Singh, A. K. (2018). Workload prediction in cloud using artificial neural network and adaptive differential evolution. Future Generation Computer Systems, 81, 41–52.CrossRef
20.
Bibal Benifa, J. V., & Dharma, D. (2018). Has: Hybrid auto-scaler for resource scaling in cloud environment. Journal of Parallel and Distributed Computing, 120, 1–15.CrossRef
21.
Kaur, G., Bala, A., & Chana, I. (2019). An intelligent regressive ensemble approach for predicting resource usage in cloud computing. Journal of Parallel and Distributed Computing, 123, 1–12.CrossRef
22.
Mohiuddin, I., & Almogren, A. (2019). Workload aware vm consolidation method in edge/cloud computing for IoT applications. Journal of Parallel and Distributed Computing, 123, 204–214.CrossRef
23.
Moghaddam, S. K., Buyya, R., & Ramamohanarao, K. (2019). Acas: An anomaly-based cause aware auto-scaling framework for clouds. Journal of Parallel and Distributed Computing, 126, 107–120.CrossRef
24.
Kumar, J., Goomer, R., & Singh, A. K. (2018). Long short term memory recurrent neural network (LSTM-RNN) based workload forecasting model for cloud datacenters. Procedia Computer Science, 125, 676–682.CrossRef
25.
Kumar, J., & Singh, A. K. (2016). Dynamic resource scaling in cloud using neural network and black hole algorithm. Fifth International Conference on Eco-friendly Computing and Communication Systems (ICECCS), 2016, 63–67.
26.
Pontius, R. G., Thontteh, O., & Chen, H. (2008). Components of information for multiple resolution comparison between maps that share a real variable. Environmental and Ecological Statistics, 15(2), 111–142.MathSciNetCrossRef
27.
Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate Research, 30, 79–82.CrossRef
28.
Willmott, C. J., & Matsuura, K. (2006). On the use of dimensioned measures of error to evaluate the performance of spatial interpolators. International Journal of Geographical Information Science, 20(1), 89–102.CrossRef
29.
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.CrossRef
30.
Tsay, R. S. (2000). Time series and forecasting: Brief history and future research. Journal of the American Statistical Association, 95(450), 638–643.CrossRef
31.
Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and applications (3rd ed.). New York: Wiley.
32.
Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2008). Time series analysis. New York: Wiley.CrossRef
33.
Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, 26(3), 1–22.
34.
35.
Reiss, C., Wilkes, J., & Hellerstein, J. L. (2011). Google cluster-usage traces: Format + schema. Google Inc., White Paper, 1–14.
36.
Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200), 675–701.CrossRef
37.
Conover, W. J., & Conover, W. J. (1980). Practical nonparametric statistics. New York: Wiley.MATH
38.
Rodríguez-Fdez, I., Canosa, A., Mucientes, M., & Bugarín, A. (2015). STAC: a web platform for the comparison of algorithms using statistical tests. In Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
39.
Finner, H. (1993). On a monotonicity problem in step-down multiple test procedures. Journal of the American Statistical Association, 88(423), 920–923.MathSciNetCrossRef
Metadaten
Titel
Performance Assessment of Time Series Forecasting Models for Cloud Datacenter Networks’ Workload Prediction
verfasst von
Jitendra Kumar
Ashutosh Kumar Singh
Publikationsdatum
03.09.2020
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2021
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-07773-6

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