Performance Analysis of Machine Learning Regression Techniques to Predict Data Center Power Usage Efficiency

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : Rajendra Kumar , Sunil Kumar Khatri , Mario José Diván
DOI :  10.14445/22315381/IJETT-V70I5P236

Citation 

MLA Style: Rajendra Kumar, et al. "Performance Analysis of Machine Learning Regression Techniques to Predict Data Center Power Usage Efficiency." International Journal of Engineering Trends and Technology, vol. 70, no. 5, May. 2022, pp. 328-338. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P236

APA Style:Rajendra Kumar , Sunil Kumar Khatri & Mario José Diván.(2022). Performance Analysis of Machine Learning Regression Techniques to Predict Data Center Power Usage Efficiency. International Journal of Engineering Trends and Technology, 70(5), 328-338. https://doi.org/10.14445/22315381/IJETT-V70I5P236

Abstract
Data Centers & cloud hosting services are critical for IT workload. Datacenter organizations need to equip them with the latest technologies to estimate the power usage efficiency (PUE) to cater to their hosting customers` requirements. Power usage efficiency is one of the major metrics to check how efficiently Data Center consumes their power. To better understand whether machine learning technology can forecast PUE with more accuracy, we have used multiple machine learning regression methods to predict the PUE in a data center and compared their accuracy. The research`s originality resides in the fact that no previous research has examined the regression methods for PUE prediction in data centers. Once the accuracies are identified, future researchers can use the algorithm for effective PUE prediction. The experimental result shows that DT and KNN work effectively with the data center`s PUE data in the research scope. Further, the analysis clearly shows that the Decision tree and KNN predict the PUE with 97% & 98% accuracy, respectively, compared with other regression techniques.

Keywords
Cooling, data center, Machine learning, Optimisation, Power usage efficiency.

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