A Self-decoupled Interpretable Prediction Framework for Highly-Variable Cloud Workloads
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-30637-2_39
Reference24 articles.
1. 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)
2. 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)
3. Lecture Notes in Computer Science;Z Zhao,2022
4. 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)
5. 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)
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