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Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0003-4973-9255
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0002-0535-1761
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
2017 (English)In: GPC 2017: Green, Pervasive, and Cloud Computing / [ed] Au M., Castiglione A., Choo KK., Palmieri F., Li KC., Cham, Switzerland: Springer , 2017, 267-281 p.Conference paper, Published paper (Refereed)
Abstract [en]

Large-scale data centers account for a significant share of the energy consumption in many countries. Machine learning technology requires intensive workloads and thus drives requirements for lots of power and cooling capacity in data centers. It is time to explore green machine learning. The aim of this paper is to profile a machine learning algorithm with respect to its energy consumption and to determine the causes behind this consumption. The first scalable machine learning algorithm able to handle large volumes of streaming data is the Very Fast Decision Tree (VFDT), which outputs competitive results in comparison to algorithms that analyze data from static datasets. Our objectives are to: (i) establish a methodology that profiles the energy consumption of decision trees at the function level, (ii) apply this methodology in an experiment to obtain the energy consumption of the VFDT, (iii) conduct a fine-grained analysis of the functions that consume most of the energy, providing an understanding of that consumption, (iv) analyze how different parameter settings can significantly reduce the energy consumption. The results show that by addressing the most energy intensive part of the VFDT, the energy consumption can be reduced up to a 74.3%.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer , 2017. 267-281 p.
Series
Lecture Notes in Computer Science
Keyword [en]
Machine learning, Big data, Very Fast Decision Tree, Green machine learning, Data mining, Data stream mining
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-37926DOI: 10.1007/978-3-319-57186-7_21ISBN: 978-3-319-57185-0 (print)ISBN: 978-3-319-57186-7 (electronic)OAI: oai:DiVA.org:hj-37926DiVA: diva2:1160023
Conference
GPC 2017 : The 12th International Conference on Green, Pervasive and Cloud Computing, Cetara, Amalfi Coast, Italy
Funder
Knowledge Foundation, 20140032]
Available from: 2017-11-14 Created: 2017-11-24 Last updated: 2018-01-13Bibliographically approved

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Lavesson, Niklas

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