<scp>COSCO2</scp> : <scp>AI</scp> ‐augmented evolutionary algorithm based workload prediction framework for sustainable cloud data centers-Reference-Cited by-同舟云学术

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COSCO2 : AI ‐augmented evolutionary algorithm based workload prediction framework for sustainable cloud data centers

Published:2022-10-03 Issue:1 Volume:34 Page:
ISSN:2161-3915
Container-title:Transactions on Emerging Telecommunications Technologies
language:en
Short-container-title:Trans Emerging Tel Tech

Author:

Karthikeyan R.¹^ORCID,Balamurugan V.²,Cyriac Robin³,Sundaravadivazhagan B.³

Affiliation:

1. Department of Computer Science and Engineering (AI&ML) Vardhaman College of Engineering Hyderabad Telangana India

2. Department of Computer Science and Engineering Mohamed Sathak Engineering College Kilakarai Tamil Nadu India

3. Department of Information Technology University of Technology and Applied Sciences‐Al Mussanah Mussanah Oman

Publisher

Wiley

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.4652

Reference35 articles.

1. E2LG: a multiscale ensemble of LSTM/GAN deep learning architecture for multistep-ahead cloud workload prediction

2. A hybrid wavelet decomposer and GMDH-ELM ensemble model for Network function virtualization workload forecasting in cloud computing

3. Integrated deep learning method for workload and resource prediction in cloud systems

4. SaxenaD SinghAK BuyyaR.OP‐MLB: an online VM prediction based multi‐objective load balancing framework for resource management at cloud datacenter. Paper presented at IEEE Transactions on Cloud Computing;2021; IEEE.

5. An efficient forecasting approach for resource utilization in cloud data center using CNN-LSTM model

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3. CSO-based Efficient Resource Management for Sustainable Cloud Computing;Contemporary Mathematics;2024-03-29

4. How to use BAPV to alleviate the urban heat island effect: An evolutionary game perspective;PLOS ONE;2024-01-29

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