Design and Evaluation of a Hierarchical Characterization and Adaptive Prediction Model for Cloud Workloads-Reference-Cited by-同舟云学术

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Design and Evaluation of a Hierarchical Characterization and Adaptive Prediction Model for Cloud Workloads

Published:2024-04 Issue:2 Volume:12 Page:712-724
ISSN:2168-7161
Container-title:IEEE Transactions on Cloud Computing
language:
Short-container-title:IEEE Trans. Cloud Comput.

Author:

Seshadri Karthick¹^ORCID,Sindhu Korrapati¹^ORCID,Bhattu S. Nagesh¹^ORCID,Kollengode Chidambaran²^ORCID

Affiliation:

1. Department of Computer Science and Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem, Andhra Pradesh, India

2. Data and AI Platforms, LinkedIn, Bangalore, Karnataka, India

Funder

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6245519/10551658/10508064.pdf?arnumber=10508064

Reference47 articles.

1. CPU Load Prediction Using Support Vector Regression and Kalman Smoother for Cloud

2. Data centers in the wild: A large performance study;Birke,2012

3. Empirical prediction models for adaptive resource provisioning in the cloud

4. Homeostatic and tendency-based CPU load predictions

5. Design, implementation, and performance of an extensible toolkit for resource prediction in distributed systems

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