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A feature extraction and time warping based neural expansion architecture for cloud resource usage forecasting

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Abstract

Accurate resource utilization estimation is crucial for efficient resource allocation, capacity planning, and cost optimization in cloud systems. In the past, several artificial intelligence, machine learning, and deep learning-based techniques have been evolved to forecast cloud cluster workload. Despite the abundance of available techniques, most existing techniques fail to achieve the desired prediction efficiency and generalization capability. They are computationally inefficient in accurately determining resource utilization at a machine-level granularity. The current research proposes a computationally less-expensive hybrid approach combining cluster analysis and deep neural learning with transfer learning to estimate the machine-level workload. The method implements clustering to identify the similarity patterns among the non-linear usage profiles of machines present in the input dataset. Subsequently, the generalized deep neural learning models are developed considering only a sample dataset belonging to each identified cluster. Lastly, the concept of transfer learning is deployed using pre-trained generalized models to estimate the workload for all remaining machines relating to the clusters. The performance validation of the proposed approach is carried out on the real-world traces dataset of the google cluster. The comparative evaluation of the proposed approach with benchmark approaches verifies the achieved performance benefits and accuracy.

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Data availability

The pre-processed data utilised in the current study will be made available on reasonable request from corresponding author.

References

  1. Jacks, T.: Research on remote work in the era of Covid-19 (2021). https://doi.org/10.1080/1097198X.2021.1914500

  2. Belgacem, A., Mahmoudi, S., Ferrag, M.A.: A machine learning model for improving virtual machine migration in cloud computing. J. Supercomput. (2023). https://doi.org/10.1007/s11227-022-05031-z

    Article  Google Scholar 

  3. Iftikhar, S., Ahmad, M.M.M., Tuli, S., Chowdhury, D., Xu, M., Gill, S.S., Uhlig, S.: Hunterplus: AI based energy-efficient task scheduling for cloud-fog computing environments. Internet of Things 21, 100667 (2023). https://doi.org/10.1016/j.iot.2022.100667

    Article  Google Scholar 

  4. Patel, Y.S., Jaiswal, R., Misra, R.: Deep learning-based multivariate resource utilization prediction for hotspots and coldspots mitigation in green cloud data centers. J. Supercomput. (2022). https://doi.org/10.1007/s11227-021-04107-6

    Article  Google Scholar 

  5. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur. Gener. Comput. Syst. 25(6), 599–616 (2009). https://doi.org/10.1016/j.future.2008.12.001

    Article  Google Scholar 

  6. Jeddi, S., Sharifian, S.: A hybrid wavelet decomposer and GMDH-ELM ensemble model for network function virtualization workload forecasting in cloud computing. Appl. Soft Comput. 88, 105940 (2020). https://doi.org/10.1016/j.asoc.2019.105940

    Article  Google Scholar 

  7. Zhang, Q., Yang, L.T., Yan, Z., Chen, Z., Li, P.: An efficient deep learning model to predict cloud workload for industry informatics. IEEE Trans. Ind. Inf. 14(7), 3170–3178 (2018). https://doi.org/10.1109/TII.2018.2808910

    Article  Google Scholar 

  8. Gill, S.S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., Golec, M., Stankovski, V., Wu, H., Abraham, A., et al.: Ai for next generation computing: emerging trends and future directions. Internet of Things 19, 100514 (2022). https://doi.org/10.1016/j.iot.2022.100514

    Article  Google Scholar 

  9. Bedi, J., Patel, Y.S.: Stowp: a light-weight deep residual network integrated windowing strategy for storage workload prediction in cloud systems. Eng. Appl. Artif. Intell. 115, 105303 (2022). https://doi.org/10.1016/j.engappai.2022.105303

    Article  Google Scholar 

  10. Bedi, J.: Attention based mechanism for load time series forecasting: an-LSTM. In: Artificial neural networks and machine learning—ICANN 2020: 29th international conference on artificial neural networks, Bratislava, Slovakia, 15–18 Sept 2020, Proceedings, Part I 29, Springer, pp. 838–849 (2020). https://doi.org/10.1007/978-3-030-61609-0_66

  11. Patriarca, R., Simone, F., Di Gravio, G.: Supporting weather forecasting performance management at aerodromes through anomaly detection and hierarchical clustering. Expert Syst. Appl. 213, 119210 (2023). https://doi.org/10.1016/j.eswa.2022.119210

    Article  Google Scholar 

  12. Chauhan, J., Bedi, J.: Effvit-Covid: a dual-path network for Covid-19 percentage estimation. Expert Syst. Appl. 213, 118939 (2023). https://doi.org/10.1016/j.eswa.2022.118939

    Article  Google Scholar 

  13. Singh, P., Gupta, P., Jyoti, K.: TASM: technocrat Arima and SVR model for workload prediction of web applications in cloud. Clust. Comput. 22(2), 619–633 (2019). https://doi.org/10.1007/s10586-018-2868-6

    Article  Google Scholar 

  14. Udayasankaran, M.P., Thangaraj, S.J.J.: Energy efficient resource utilization and load balancing in virtual machines using prediction algorithms. Int. J. Cogn. Comput. Eng. (2023). https://doi.org/10.1016/j.ijcce.2023.02.005

    Article  Google Scholar 

  15. Masdari, M., Khoshnevis, A.: A survey and classification of the workload forecasting methods in cloud computing. Clust. Comput. 23(4), 2399–2424 (2020). https://doi.org/10.1007/s10586-019-03010-3

    Article  Google Scholar 

  16. Gao, J., Wang, H., Shen, H.: Machine learning based workload prediction in cloud computing. In: 2020 29th international conference on computer communications and networks (ICCCN), IEEE, pp. 1–9 (2020). https://doi.org/10.1109/ICCCN49398.2020.9209730

  17. Ruta, D., Cen, L., Vu, Q.H.: Deep bi-directional LSTM networks for device workload forecasting. In: 2020 15th Conference on Computer Science and Information Systems (FedCSIS), IEEE, pp. 115–118 (2020). https://doi.org/10.15439/2020F213

  18. Bi, J., Li, S., Yuan, H., Zhou, M.: Integrated deep learning method for workload and resource prediction in cloud systems. Neurocomputing 424, 35–48 (2021). https://doi.org/10.1016/j.neucom.2020.11.011

    Article  Google Scholar 

  19. Xu, M., Song, C., Wu, H., Gill, S.S., Ye, K., Xu, C.: esdnn: deep neural network based multivariate workload prediction in cloud computing environments. ACM Trans. Internet Technol. (TOIT) 22(3), 1–24 (2022). https://doi.org/10.1145/3524114

    Article  Google Scholar 

  20. Devi, K.L., Valli, S.: Time series-based workload prediction using the statistical hybrid model for the cloud environment. Computing 105(2), 353–374 (2023). https://doi.org/10.1007/s00607-022-01129-7

    Article  Google Scholar 

  21. Dogani, J., Khunjush, F., Seydali, M.: Host load prediction in cloud computing with discrete wavelet transformation (DWT) and bidirectional gated recurrent unit (BIGRU) network. Comput. Commun. 198, 157–174 (2023). https://doi.org/10.1016/j.comcom.2022.11.018

    Article  Google Scholar 

  22. Patel, Y.S., Bedi, J.: Mag-d: a multivariate attention network based approach for cloud workload forecasting. Futur. Gener. Comput. Syst. (2023). https://doi.org/10.1016/j.future.2023.01.002

    Article  Google Scholar 

  23. Zhang, Q., Zhani, M.F., Zhang, S., Zhu, Q., Boutaba, R., Hellerstein, J.L.: Dynamic energy-aware capacity provisioning for cloud computing environments. In: Proceedings of the 9th international conference on Autonomic computing, pp. 145–154 (2012). https://doi.org/10.1145/2371536.2371562

  24. Khan, T., Tian, W., Ilager, S., Buyya, R.: Workload forecasting and energy state estimation in cloud data centres: Ml-centric approach. Futur. Gen. Comput. Syst. 128, 320–332 (2022). https://doi.org/10.1016/j.future.2021.10.019

    Article  Google Scholar 

  25. Wang, S., Yao, Y., Xiao, Y., Chen, H.: Dynamic resource prediction in cloud computing for complex system simulatiuon: a probabilistic approach using stacking ensemble learning. In: 2020 International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), IEEE, pp. 198–201 (2020). https://doi.org/10.1109/ICHCI51889.2020.00050

  26. Kumar, J., Singh, A.K., Buyya, R.: Self directed learning based workload forecasting model for cloud resource management. Inf. Sci. 543, 345–366 (2021). https://doi.org/10.1016/j.ins.2020.07.012

    Article  Google Scholar 

  27. Saxena, D., Kumar, J., Singh, A.K., Schmid, S.: Performance analysis of machine learning centered workload prediction models for cloud. IEEE Trans. Parallel Distrib. Syst. (2023). https://doi.org/10.1109/TPDS.2023.3240567

    Article  Google Scholar 

  28. Chen, Z., Hu, J., Min, G., Zomaya, A.Y., El-Ghazawi, T.: Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning. IEEE Trans. Parallel Distrib. Syst. 31(4), 923–934 (2019). https://doi.org/10.1109/TPDS.2019.2953745

    Article  Google Scholar 

  29. Ruan, L., Bai, Y., Li, S., He, S., Xiao, L.: Workload time series prediction in storage systems: a deep learning based approach. Cluster Comput. (2021). https://doi.org/10.1007/s10586-020-03214-y

    Article  Google Scholar 

  30. Patel, E., Kushwaha, D.S.: An integrated deep learning prediction approach for efficient modelling of host load patterns in cloud computing. J. Grid Comput. 21(1), 5 (2023). https://doi.org/10.1007/s10723-022-09639-6

    Article  Google Scholar 

  31. Zhao, Y., Ye, L., Li, Z., Song, X., Lang, Y., Su, J.: A novel bidirectional mechanism based on time series model for wind power forecasting. Appl. Energy 177, 793–803 (2016). https://doi.org/10.1016/j.apenergy.2016.03.096

    Article  Google Scholar 

  32. Jeong, B., Baek, S., Park, S., Jeon, J., Jeong, Y.-S.: Stable and efficient resource management using deep neural network on cloud computing. Neurocomputing 521, 99–112 (2023). https://doi.org/10.1016/j.neucom.2022.11.089

    Article  Google Scholar 

  33. Kaim, A., Singh, S., Patel, Y.S.: Ensemble CNN attention-based BILSTM deep learning architecture for multivariate cloud workload prediction. In: 24th international conference on distributed computing and networking, pp. 342–348 (2023). https://doi.org/10.1145/3571306.3571433

  34. Tabrizchi, H., Razmara, J., Mosavi, A.: Thermal prediction for energy management of clouds using a hybrid model based on CNN and stacking multi-layer bi-directional LSTM. Energy Rep. 9, 2253–2268 (2023). https://doi.org/10.1016/j.egyr.2023.01.032

    Article  Google Scholar 

  35. Reiss, C., Wilkes, J., Hellerstein, J.L.: Google cluster-usage traces: format + schema, Technical report, Google Inc., Mountain View (2011), revised 2014-11–17 for version 2.1. https://github.com/google/cluster-data

  36. Van Der Donckt, J., Van Der Donckt, J., Deprost, E., Van Hoecke, S.: tsflex: Flexible time series processing & feature extraction. SoftwareX 17, 100971 (2022). https://doi.org/10.1016/j.softx.2021.100971. https://www.sciencedirect.com/science/article/pii/S2352711021001904

  37. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to data mining. Pearson Education India (2016)

    Google Scholar 

  38. Senin, P.: Dynamic time warping algorithm review. Inf. Comput. Sci. 855(1–23), 40 (2008)

    Google Scholar 

  39. Bedi, J., Toshniwal, D.: Deep learning framework to forecast electricity demand. Appl. Energy 238, 1312–1326 (2019). https://doi.org/10.1016/j.apenergy.2019.01.113

    Article  Google Scholar 

  40. Kumar, K.D., Umamaheswari, E.: Ewptnn: an efficient workload prediction model in cloud computing using two-stage neural networks. Procedia Comput. Sci. 165, 151–157 (2019). https://doi.org/10.1016/j.procs.2020.01.097

    Article  Google Scholar 

  41. Leka, H.L., Fengli, Z., Kenea, A.T., Sharma, D.P., Tegene, A.T.: Workload prediction of virtual machines using integrated deep learning approaches over cloud data centers. In: Human-centric smart computing: proceedings of ICHCSC 2022, Springer, pp. 55–65 (2022). https://doi.org/10.1007/978-981-19-5403-0_5

  42. Bishop, C.M., Nasrabadi, N.M.: Pattern recognition and machine learning, vol. 4. Springer (2006)

    Google Scholar 

  43. Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT Press (2016)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Gurjot Singh, Prajit Sengupta, Anant Mehta & Jatin Bedi

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Contributions

Credit Author Statement Gurjot Singh conceived the study, performed the numerical experiments, analyzed the data, performed comparative evaluation and wrote the manuscript. Prajit Sengupta and Anant Mehta performed the literature survey, and wrote the manuscript. Jatin Bedi conceived the study, analyzed the data, performed comparative evaluation and wrote the manuscript. Furthermore, the article was proofread by two English experts and after incorporating the suggestions of the experts, the author has approved and submitted the final version of the manuscript. Thank You Dr. Jatin Bedi +91-9991015545

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Correspondence to Jatin Bedi.

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Singh, G., Sengupta, P., Mehta, A. et al. A feature extraction and time warping based neural expansion architecture for cloud resource usage forecasting. Cluster Comput 27, 4963–4982 (2024). https://doi.org/10.1007/s10586-023-04224-2

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