Optimizing CPU Resources: A Deep Learning Approach for Usage Forecasting in Cloud Environments

Author:

Jha Sriti1ORCID,Sangwan Vaibhav1ORCID,Balani Sanket1ORCID,Nagrath Preeti1ORCID,Saini Dharmender1ORCID

Affiliation:

1. Department of Computer Science and Engineering, Bharati Vidyapeeth's College of Engineering, India

Publisher

ACM

Reference16 articles.

1. J. L. Berral C. Wang and A. Youssef. 2020. "AI4DL: Mining Behaviors of deep learning workloads for resource management." In Proceedings of the 12th USENIX Workshop HotCloud 2020.

2. J. Shetty and G. Shobha. 2016. "An ensemble of automatic algorithms for forecasting resource utilization in cloud." In Proceedings of the 2016 Future Technologies Conference (FTC) 2016 pp. 301–306.

3. A nonlinear autoregressive neural network for interference prediction and resource allocation in URLLC scenarios;Padilla R.;Proceedings of the IEEE International Conference on Information and Communication Technology Convergence,2021

4. CPU workload forecasting of machines in data centers using LSTM recurrent neural networks and ARIMA models

5. Xinyi Zhang, Hong Wu, Zhuo Chang, Shuowei Jin, Jian Tan, Feifei Li, Tieying Zhang, and Bin Cui. 2021. "Restune: Resource oriented tuning boosted by meta-learning for cloud databases." In Proceedings of the 2021 International Conference on Management of Data, pages 2102–2114, 2021.

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