Paper Info
Title
Accelerating the Energy Efficient Design of Traditional Data Centers Through Modeling<sup>*</sup>
Abstract
Power Management Strategies and the impact of carbon dioxide emission from the data centers across the globe have drawn significant attention worldwide. The rapid growth of energy consumed in data centers has lead to 1) huge costs 2) depletion of non-renewable resources such as coal and petroleum, and 3) emission of greenhouse gases like CO2 in the atmosphere. These greenhouse gas significantly contributes to the climate change of the earth. To tackle this challenge, our research deals with modeling the energy resources of data centers, thereby offering insights to reduce global carbon footprint and energy cost. In our model, we prioritize green energy consumption by eliminating the brown energy resources. In this process, we devise an algorithm that can determine the amount of CO2 emission in the atmosphere per hour by different energy resources. We create an energy model for data centers by incorporating the support vector regression algorithm. Our model is adroit at projecting energy consumed in data centers powered by green energy. Our experimental results confirm that our model consistently delivers high prediction accuracy in terms of energy usage in data centers. The model is expected to facilitate data analytic venues to optimize energy efficiency and sustainability for the development of future data centers.
Year
DOI
Venue
2022
10.1109/NAS55553.2022.9925343
2022 IEEE International Conference on Networking, Architecture and Storage (NAS)
Keywords
DocType
ISBN
energy modeling,power management,green energy,brown energy,carbon footprint
Conference
978-1-6654-5409-4
Citations 
PageRank 
References 
0
0.34
12
Authors
7
Name
Order
Citations
PageRank
Tathagata Bhattacharya100.34
Xiaopu Peng200.34
Taha Takreeti300.34
Jianzhou Mao400.34
Ting Cao500.34
Xiao Qin61836125.69
Mostafa Rahgouy700.34