BiPhase adaptive learning-based neural network model for cloud datacenter workload forecasting-Reference-Cited by-同舟云学术

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BiPhase adaptive learning-based neural network model for cloud datacenter workload forecasting

Published:2020-03-14 Issue:19 Volume:24 Page:14593-14610
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Kumar Jitendra,Saxena Deepika,Singh Ashutosh Kumar,Mohan Anand

Funder

Ministry of Electronics and Information Technology

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s00500-020-04808-9.pdf

Reference65 articles.

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2. Alam M, Shakil KA, Sethi S (2016) Analysis and clustering of workload in Google Cluster Trace based on resource usage. In: 2016 IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC) and 15th international symposium on distributed computing and applications for business engineering (DCABES). IEEE, pp 740–747

3. Amekraz Z, Hadi MY (2018) Higher order statistics based method for workload prediction in the cloud using ARMA model. In: 2018 international conference on intelligent systems and computer vision (ISCV). IEEE, pp 1–5

4. Amiri M, Mohammad-Khanli L (2017) Survey on prediction models of applications for resources provisioning in cloud. J Netw Comput Appl 82:93–113

5. Amiri M, Feizi-Derakhshi MR, Mohammad-Khanli L (2017) IDS fitted Q improvement using fuzzy approach for resource provisioning in cloud. J Intell Fuzzy Syst 32(1):229–240

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