Accurate Prediction of Workloads and Resources With Multi-Head Attention and Hybrid LSTM for Cloud Data Centers-Reference-Cited by-同舟云学术

Accurate Prediction of Workloads and Resources With Multi-Head Attention and Hybrid LSTM for Cloud Data Centers

Author:

Bi Jing¹^ORCID,Ma Haisen¹^ORCID,Yuan Haitao²^ORCID,Zhang Jia³^ORCID

Affiliation:

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

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

3. Department of Computer Science, Lyle School of Engineering, Southern Methodist University, Dallas, TX, 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)

Subject

Computational Theory and Mathematics,Control and Optimization,Hardware and Architecture,Renewable Energy, Sustainability and the Environment,Software

Link

Reference35 articles.

1. Semantic part segmentation using compositional model combining shape and appearance;yuille;Proc IEEE Conf Comput Vis Pattern Recognit,2015

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