ARIMA time Series Model vs. K-Means Clustering for Cloud Workloads Performance-Reference-Cited by-同舟云学术

ARIMA time Series Model vs. K-Means Clustering for Cloud Workloads Performance

Published:2023-02-08 Issue: Volume: Page:
ISSN:
Container-title:2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON)
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Author:

Mishra Vishnu Kumar¹,Mishra Megha¹,Tekale Sunil²,Praveena T Naga²,Venkatesh Rachakonda²,Dewangan Bhupesh Kumar³

Affiliation:

1. CSVTU University,Shri Shankaracharya Technical campus,Department of Computer Science and Engineering,Bhilai,Chhattisgarh,India,491003

2. Malla Reddy College of Engineering,Department of Computer Science and Engineering,Secunderabad,Telangana,India

3. OP Jindal University,Department of Computer Science and Engineering,Raigarh,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10113898/10113899/10113979.pdf?arnumber=10113979

Reference23 articles.

1. Latency-based Analytic Approach to Forecast Cloud Work- load Trend for Sustainable Datacentres;panneerselvam;IEEE Transactions on Sustainable Computing,2019

2. Defense in Depth for Data Storage in Cloud Computing;baghel;International Journal of Technology,2012

3. Estimating Power Consumption of Servers Using Gaussian Mixture Model;dai;5th Int Symp on Computing and Networking,2017

4. A survey on the ietf protocol suite for the internet of things: standards, challenges, and opportunities

5. Cluster analysis: basic concepts and algorithms;kumar;Introduction to Data Mining,2006

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