NDLI: Power consumption model based on feature selection and deep learning in cloud computing scenarios

Content Provider	IET Digital Library
Author	Liang, Yang Hu, Zhigang Li, Keqin
Abstract	High power consumption of cloud data centres is a crucial challenge in modern cloud computing. To comply with the conceptions of green computing, power consumption prediction of the computing cluster has a major role to play in these energy conservation efforts. However, due to complexity and heterogeneity in cloud computing scenarios, it is difficult to accurately predict the power consumption using conventional approaches. To this end, this study presents a power consumption model based on feature selection and deep learning to powerfully cope with low energy efficiency. Different from other methods focusing on only a few performance attributes, the proposed method takes into account up to 12 energy-related features and introduces deep neural network architecture, aiming at making full use of massive data to train model completely. In particular, this approach is composed of three main phases including (i) performance monitoring and energy-related feature acquisition, (ii) essential feature selection, and (iii) model establishment and optimisation. Representative results of comprehensive experiments, in terms of the relative error, reveal that the proposed power consumption model can undoubtedly achieve state-of-the-art predictive capability when compared with other models in most cases.
Starting Page	1610
Ending Page	1618
Page Count	9
ISSN	17518628
Volume Number	14
e-ISSN	17518636
Issue Number	Issue 10, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/14/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2019.0717
Journal	IET Communications
Publisher Date	2020-03-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Cloud Computing Cloud Computing Scenarios Cloud Data Centres Computer Centres Computer Facilities Computing Cluster Data Handling Technique Deep Learning Deep Neural Network Architecture Energy Conservation Efforts Energy-related Feature Acquisition Environmental Aspects of Computing Feature Selection Green Computing High Power Consumption Internet Software Learning in AI Modern Cloud Computing Neural Computing Technique Neural Nets Operating System Power Aware Computing Power Consumption Power Consumption Model Power Consumption Prediction
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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