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CPU Usage Prediction Model: A Simplified VM Clustering Approach

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Complex, Intelligent and Software Intensive Systems (CISIS 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 176))

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Abstract

Machine learning algorithms play an important role in resource management, allowing the improvement of the efficiency of resource usage in data centers (DCs) by predicting workload trends. In this paper, we propose a simplified system to predict the CPU usage of virtual machines (VMs) in a DC using Linear Regression Models while performing VM clustering based on common statistical characteristics of VM time series, which facilitates grouping VMs with similar behaviors and establishing clusters based on these characteristics. For each cluster, three representative VMs are established based on the time series of the closest VM to the cluster centroid, averaged time series for the cluster, and concatenated time series. Then, training of representative VMs is performed to finally choose the one with the lowest mean error per cluster. Simulation results show that, by performing clustering and training the model with representative time series, it is indeed possible to obtain a low mean error while reducing the local training time per VM.

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References

  1. Aceto, G., Botta, A., de Donato, W., Pescapè, A.: Cloud monitoring: a survey. Comput. Netw. 57(9), 2093–2115 (2013). https://doi.org/10.1016/j.comnet.2013.04.001

    Article  Google Scholar 

  2. Amazon: Time series forecasting principles with amazon forecast (2021)

    Google Scholar 

  3. Borkowski, M., Schulte, S., Hochreiner, C.: Predicting cloud resource utilization. In: IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC), pp. 37–42 (2016)

    Google Scholar 

  4. Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. John Wiley & Sons (2015)

    Google Scholar 

  5. Dai, L., Li, J.H.: An optimal resource allocation algorithm in cloud computing environment. Appl. Mech. Mater. 733, 779–783 (2015)

    Article  Google Scholar 

  6. Daraghmeh, M., Agarwal, A., Manzano, R., Zaman, M.: Time series forecasting using facebook prophet for cloud resource management. In: IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1–6 (2021)

    Google Scholar 

  7. Estrada, R., Asanza, V., Torres, D., Bazurto, A., Valeriano, I.: Learning-based energy consumption prediction. Procedia Comput. Sci. 203, 272–279 (2022)

    Article  Google Scholar 

  8. Farahnakian, F., Liljeberg, P., Plosila, J.: LiRCUP: Linear regression based CPU usage prediction algorithm for live migration of virtual machines in data centers. In: Proceedings - 39th Euromicro Conference Series on Software Engineering and Advanced Applications, SEAA 2013, pp. 357–364 (2013)

    Google Scholar 

  9. Gill, P., Jain, N., Nagappan, N.: Understanding network failures in data centers: measurement, analysis, and implications. SIGCOMM Comput. Commun. Rev. 41(4), 350–361 (2011)

    Article  Google Scholar 

  10. Gupta, S., Dinesh, D.A.: Resource usage prediction of cloud workloads using deep bidirectional long short term memory networks. In: IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), pp. 1–6 (2017)

    Google Scholar 

  11. Hindman, B., et al.: Mesos: a platform for fine-grained resource sharing in the data center. In: NSDI’11, pp. 295–308. USENIX Association, USA (2011)

    Google Scholar 

  12. Iqbal, W., Erradi, A., Mahmood, A.: Dynamic workload patterns prediction for proactive autoscaling of web applications. J. Netw. Comput. Appl. 124, 94–107 (2018)

    Article  Google Scholar 

  13. Janardhanan, D., Barrett, E.: Cpu workload forecasting of machines in data centers using lstm recurrent neural networks and arima models. In: 12th International Conference for Internet Technology and Secured Transactions (ICITST), pp. 55–60 (2017)

    Google Scholar 

  14. Kumar, J., Goomer, R., Singh, A.K.: Long short term memory recurrent neural network (LSTM-RNN) based workload forecasting model for cloud datacenters. Procedia Comput. Sci. 125, 676–682 (2018)

    Article  Google Scholar 

  15. Kumar, J., Singh, A.K.: Cloud datacenter workload estimation using error preventive time series forecasting models. Clust. Comput. 23(2), 1363–1379 (2019). https://doi.org/10.1007/s10586-019-03003-2

    Article  Google Scholar 

  16. Lindemann, B., Muller, T., Vietz, H., Jazdi, N., Weyrich, M.: A survey on long short-term memory networks for time series prediction. Procedia CIRP 99, 650–655 (2021)

    Article  Google Scholar 

  17. Mormul, M., Hirmer, P., Stach, C., Mitschang, B.: Dear: distributed evaluation of alerting rules. In: IEEE 13th International Conference on Cloud Computing (CLOUD), pp. 158–165 (2020)

    Google Scholar 

  18. Nashold, L., Krishnan, R.: Using lstm and sarima models to forecast cluster cpu usage. ArXiv abs/2007.08092 (2020)

    Google Scholar 

  19. Qiu, F., Zhang, B., Guo, J.: A deep learning approach for vm workload prediction in the cloud. In: 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 319–324 (2016)

    Google Scholar 

  20. Rao, S.N., Shobha, G., Prabhu, S., Deepamala, N.: Time Series Forecasting methods suitable for prediction of CPU usage. In: 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), vol. 4, pp. 1–5 (2019)

    Google Scholar 

  21. Sarikaa, S., Niranjana, S., Sri, K.V.D.: Time series forecasting of cloud resource usage. In: 6th International Conference on Computing, Communication and Automation (ICCCA), pp. 372–382 (2021)

    Google Scholar 

  22. TUDelft, D.U.o.T.: Dataset gwa-t-12-bitbrains. http://gwa.ewi.tudelft.nl/datasets/gwa-t-12-bitbrainss (2023)

  23. U-chupala, P., Watashiba, Y., Ichikawa, K., Date, S., Iida, H.: Container rebalancing: Towards proactive linux containers placement optimization in a data center. In: IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) 01, pp. 788–795 (2017)

    Google Scholar 

  24. Wang, J., Yan, Y., Guo, J.: Research on the prediction model of cpu utilization based on arimabp neural network (2016)

    Google Scholar 

  25. Xue, J., Yan, F., Birke, R., Chen, L.Y., Scherer, T., Smirni, E.: PRACTISE: robust prediction of data center time series. In: 11th International Conference on Network and Service Management (CNSM), pp. 126–134. IEEE (2015)

    Google Scholar 

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Authors and Affiliations

  1. Information Technology Center and Electrical Engineering and Computer Science Department, Escuela Superior Politecnica del Litoral, ESPOL´, Campus Gustavo Galindo Km 30.5 V´ıa Perimetral, Guayaquil, Ecuador

    Rebeca Estrada, Irving Valeriano & Xavier Aizaga

Authors
  1. Rebeca Estrada
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  2. Irving Valeriano
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  3. Xavier Aizaga
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Corresponding author

Correspondence to Rebeca Estrada .

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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Estrada, R., Valeriano, I., Aizaga, X. (2023). CPU Usage Prediction Model: A Simplified VM Clustering Approach. In: Barolli, L. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 176. Springer, Cham. https://doi.org/10.1007/978-3-031-35734-3_21

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