Please use this identifier to cite or link to this item: https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.40
Title: Estimating the energy consumption of executing software processes
Authors: Singh, V.K.
Dutta, K. 
VanderMeer, D.
Keywords: Energy
Green IT
Measurement
Modeling
Power meter
Issue Date: 2013
Source: Singh, V.K.,Dutta, K.,VanderMeer, D. (2013). Estimating the energy consumption of executing software processes. Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 : 94-101. ScholarBank@NUS Repository. https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.40
Abstract: Power consumption in data centers is significant across the globe. The use of cloud-based services, e.g., infrastructure as a service and software as a service (such as Google Docs, Microsoft Office 365, Salesforce.com), is becoming a standard practice in modern IT frameworks. This paradigm shift in the IT industry indicates that the demand for data-center-based services will continue to increase in the future, with concomitant increases in power consumption. In such a scenario, optimizing the IT resources to improve energy efficiency is a necessity. The first step of such an optimization at the application level is knowing how much energy an application is consuming. One of the main challenges in this domain is developing a software-based energy metering tool that can measure an OS processes' energy consumption. Many existing solutions depend on an external watt-meter or other hardware-based enhancements, these are not practical for real-world use in data centers. To overcome the limitations of existing solutions, we have developed an OS process-level power metering tool that can accurately estimate the energy usage of each OS process running on a Linux server without an online watt-meter. Based on a set of experiments, we demonstrated that our method and implementation provides energy consumption estimation for complex e-business applications with above 95% accuracy. © 2013 IEEE.
Source Title: Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
URI: http://scholarbank.nus.edu.sg/handle/10635/128457
ISBN: 9780769550466
DOI: 10.1109/GreenCom-iThings-CPSCom.2013.40
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

2
checked on Feb 21, 2018

Page view(s)

8
checked on Feb 17, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.