RPTCN: Resource Prediction for High-dynamic Workloads in Clouds based on Deep Learning-Reference-Cited by-同舟云学术

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RPTCN: Resource Prediction for High-dynamic Workloads in Clouds based on Deep Learning

Published:2021-09 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International Conference on Cluster Computing (CLUSTER)
language:
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Author:

Chen Wenyan,Lu Chengzhi,Ye Kejiang,Wang Yang,Xu Cheng-Zhong

Funder

Research and Development

National Natural Science Foundation of China

Technology Development

Youth Innovation Promotion Association

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9555841/9555930/09556034.pdf?arnumber=9556034

Cited by 17 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DuCFF: A Dual-Channel Feature-Fusion Network for Workload Prediction in a Cloud Infrastructure;Electronics;2024-09-10

2. Optimization of containerized application deployment in virtualized environments: a novel mathematical framework for resource-efficient and energy-aware server infrastructure;The Journal of Supercomputing;2024-06-25

3. Exploiting sequence characteristics for long-term workload prediction in cloud data centers;Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024);2024-06-08

4. Computing Power Scheduling Method Based on Long-Term Workload Prediction for Computing Power Platform;2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE);2024-05-10

5. Forecasting resource usage pattern changes in clouds via contrast graph-evolution learning;Future Generation Computer Systems;2024-05

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