Computer Science and Information Technology Vol. 2(2), pp. 72 - 78
DOI: 10.13189/csit.2014.020203
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Resource Demand Prediction and Carbon Emission Estimation for Data Centers

San Hlaing Myint ^*, Thandar Thein
University of Computer Studies, Yangon

ABSTRACT

The energy consumption of data centers has become a key issue in today’s ICT sector and a significant factor of green environment. A substantial reduction in energy consumption can be made by powering down servers when they are not in use. In cloud data center, it is very hard to manage and allocate their resource to incoming dynamic workload demands. Predicting the required resource demand, it can save the data center’s resource wasting and achieve max-profit and min-risk. Without proper prediction, data center can have overprovision and underprovision, which can cause resource waste and significant financial penalties. So an efficient resource management scheme is needed to reduce energy consumption and carbon dioxide (CO₂ ) emission. The aim of the present study is to develop model for predicting the future resource demand and estimation of CO₂ emission by comparatively assessing the suitability of several machine learning techniques. In order to reduce processing overheads, feature selection is conducted in prediction model. To estimate the CO₂ emission, Power model and Carbon model are also developed. Experiment is conducted on real world workload traces and results show that prediction model can predict future resource demand with acceptable accuracy.

KEYWORDS
CO₂ Emission, Data Centers, Energy Consumption, Machine Leaning, Resource Demand Prediction

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] San Hlaing Myint , Thandar Thein , "Resource Demand Prediction and Carbon Emission Estimation for Data Centers," Computer Science and Information Technology, Vol. 2, No. 2, pp. 72 - 78, 2014. DOI: 10.13189/csit.2014.020203.

(b). APA Format:
San Hlaing Myint , Thandar Thein (2014). Resource Demand Prediction and Carbon Emission Estimation for Data Centers. Computer Science and Information Technology, 2(2), 72 - 78. DOI: 10.13189/csit.2014.020203.