An Improved LSTM-Based Prediction Approach for Resources and Workload in Large-Scale Data Centers-Reference-Cited by-同舟云学术

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An Improved LSTM-Based Prediction Approach for Resources and Workload in Large-Scale Data Centers

Published:2024-06-15 Issue:12 Volume:11 Page:22816-22829
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Yuan Haitao¹^ORCID,Bi Jing²^ORCID,Li Shuang²,Zhang Jia³^ORCID,Zhou MengChu⁴^ORCID

Affiliation:

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

2. School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing, China

3. Department of Computer Science, Southern Methodist University, Dallas, TX, USA

4. Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10551641/10486896.pdf?arnumber=10486896

Reference50 articles.

1. TTSA: An Effective Scheduling Approach for Delay Bounded Tasks in Hybrid Clouds

2. Multiqueue Scheduling of Heterogeneous Tasks With Bounded Response Time in Hybrid Green IaaS Clouds

3. Application-Aware Dynamic Fine-Grained Resource Provisioning in a Virtualized Cloud Data Center

4. An Effective Transformation-Encoding-Attention Framework for Multivariate Time Series Anomaly Detection in IoT Environment

5. An efficient GAN-based predictive framework for multivariate time series anomaly prediction in cloud data centers

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