Abstract
Cloud workloads prediction plays a crucial role in the various tasks of cloud computing, such as resource scheduling, performance optimization, cost management, etc. However, current time series prediction methods suffer instability and inefficiency issues when addressing cloud workloads, due to the high variability of workload patterns and the high fluctuation within a workload. To address these issues, we propose DeIP4CW, a Self-Decoupled Interpretable Prediction framework for highly-variable Cloud Workloads. It can accurately forecast future job arrival rates in a cloud environment. The core idea of DeIP4CW is to first introduce the periodic and residual states as hidden variables to decouple complicated dependencies in cloud workload signals. Then it adopts a deep expansion learning framework with the block structure to perform workload prediction layer by layer. Each block consists of some periodic modules and some compensation modules. The periodic module with a self-attention mechanism can effectively capture the global trend of cloud workload, while the compensation module is employed to compensate for the local volatility information. Moreover, our two customized modules also have interpretable abilities, such as attributing the predictions to either global trends or local compensation. We conduct extensive experiments on the real-world cloud workload traces to evaluate the effectiveness of the proposed DeIP4CW. The experimental results demonstrate the DeIP4CW achieves significant improvements over the best baseline in most cases, and the error reduction can even reach up to 20.66%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Luo, C., et al.: Correlation-aware heuristic search for intelligent virtual machine provisioning in cloud systems. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 12363–12372 (2021)
Ran, L., Shi, X., Shang, M.: SLAs-aware online task scheduling based on deep reinforcement learning method in cloud environment. In: 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 1518–1525. IEEE (2019)
Zhao, Z., Shi, X., Shang, M.: Performance and cost-aware task scheduling via deep reinforcement learning in cloud environment. In: Troya, J., Medjahed, B., Piattini, M., Yao, L., Fernández, P., Ruiz-Cortés, A. (eds.) ICSOC 2022. LNCS, vol. 13740, pp. 600–615. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20984-0_43
Arbat, S., Jayakumar, V.K., Lee, J., Wang, W., Kim, I.K.: Wasserstein adversarial transformer for cloud workload prediction. arXiv preprint arXiv:2203.06501 (2022)
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), pp. 1–9. IEEE (2020)
Nelson, B.K.: Time series analysis using autoregressive integrated moving average (ARIMA) models. Acad. Emerg. Med. 5(7), 739–744 (1998)
Jayakumar, V.K., Lee, J., Kim, I.K., Wang, W.: A self-optimized generic workload prediction framework for cloud computing. In: 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 779–788. IEEE (2020)
Graves, A.: Long short-term memory. In: Graves, A. (ed.) Supervised Sequence Labelling with Recurrent Neural Networks. SCI, vol. 385, pp. 37–45. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24797-2_4
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)
Dinda, P.A., O’Hallaron, D.R.: Host load prediction using linear models. Cluster Comput. 3(4), 265–280 (2000)
Vecchia, A.V.: Maximum likelihood estimation for periodic autoregressive moving average models. Technometrics 27(4), 375–384 (1985)
Winters, P.R.: Forecasting sales by exponentially weighted moving averages. Manag. Sci. 6(3), 324–342 (1960)
Roy, N., Dubey, A., Gokhale, A.: Efficient autoscaling in the cloud using predictive models for workload forecasting. In: 2011 IEEE 4th International Conference on Cloud Computing, pp. 500–507. IEEE (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 (2014)
Kumar, J., Singh, A.K.: Workload prediction in cloud using artificial neural network and adaptive differential evolution. Futur. Gener. Comput. Syst. 81, 41–52 (2018)
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)
Fan, W., et al.: DEPTS: deep expansion learning for periodic time series forecasting. In: International Conference on Learning Representations (2021)
Salinas, D., Flunkert, V., Gasthaus, J., Januschowski, T.: DeepAR: probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast. 36(3), 1181–1191 (2020)
Qiu, F., Zhang, B., Guo, J.: A deep learning approach for VM workload prediction in the cloud. In: 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 319–324. IEEE (2016)
Oreshkin, B.N., Carpov, D., Chapados, N., Bengio, Y.: N-beats: neural basis expansion analysis for interpretable time series forecasting. In: International Conference on Learning Representations (2019)
Wu, H., Xu, J., Wang, J., Long, M.: AutoFormer: decomposition transformers with auto-correlation for long-term series forecasting. In: Advances in Neural Information Processing Systems, vol. 34, pp. 22419–22430 (2021)
Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021)
Medsker, L.R., Jain, L.C.: Recurrent neural networks. Des. Appl. 5, 64–67 (2001)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP (2014)
Acknowledgments
This work is supported in part by the National Natural Science Foundation of China under Grants 62072429, in part by the Chinese Academy of Sciences “Light of West China” Program, and in part by the Key Cooperation Project of Chongqing Municipal Education Commission (HZ2021008, HZ2021017), and the “Fertilizer Robot” project of Chongqing Committee on Agriculture and Rural Affairs.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, B., Shi, X., Shang, M. (2023). A Self-decoupled Interpretable Prediction Framework for Highly-Variable Cloud Workloads. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13943. Springer, Cham. https://doi.org/10.1007/978-3-031-30637-2_39
Download citation
DOI: https://doi.org/10.1007/978-3-031-30637-2_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-30636-5
Online ISBN: 978-3-031-30637-2
eBook Packages: Computer ScienceComputer Science (R0)