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Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0003-4973-9255
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
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, Vol. 10232, 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. Vol. 10232, 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:bth-15490DOI: 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:bth-15490DiVA: diva2:1156958
Conference
International Conference on Green, Pervasive and Cloud Computing (GPC), Cetara, Amalfi Coast, Italy
Funder
Knowledge Foundation, 20140032
Available from: 2017-11-14 Created: 2017-11-14 Last updated: 2018-01-13Bibliographically approved
In thesis
1. Extraction and Energy Efficient Processing of Streaming Data
Open this publication in new window or tab >>Extraction and Energy Efficient Processing of Streaming Data
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The interest in machine learning algorithms is increasing, in parallel with the advancements in hardware and software required to mine large-scale datasets. Machine learning algorithms account for a significant amount of energy consumed in data centers, which impacts the global energy consumption. However, machine learning algorithms are optimized towards predictive performance and scalability. Algorithms with low energy consumption are necessary for embedded systems and other resource constrained devices; and desirable for platforms that require many computations, such as data centers. Data stream mining investigates how to process potentially infinite streams of data without the need to store all the data. This ability is particularly useful for companies that are generating data at a high rate, such as social networks.

This thesis investigates algorithms in the data stream mining domain from an energy efficiency perspective. The thesis comprises of two parts. The first part explores how to extract and analyze data from Twitter, with a pilot study that investigates a correlation between hashtags and followers. The second and main part investigates how energy is consumed and optimized in an online learning algorithm, suitable for data stream mining tasks.

The second part of the thesis focuses on analyzing, understanding, and reformulating the Very Fast Decision Tree (VFDT) algorithm, the original Hoeffding tree algorithm, into an energy efficient version. It presents three key contributions. First, it shows how energy varies in the VFDT from a high-level view by tuning different parameters. Second, it presents a methodology to identify energy bottlenecks in machine learning algorithms, by portraying the functions of the VFDT that consume the largest amount of energy. Third, it introduces dynamic parameter adaptation for Hoeffding trees, a method to dynamically adapt the parameters of Hoeffding trees to reduce their energy consumption. The results show an average energy reduction of 23% on the VFDT algorithm.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2017
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 3
Keyword
machine learning, green computing, data mining, data stream mining, green machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15532 (URN)
Presentation
2017-12-18, J1640, Blekinge Tekniska Högskola, 371 79, Karlskrona, 13:00 (English)
Opponent
Supervisors
Projects
Scalable resource-efficient systems for big data analytics
Funder
Knowledge Foundation, 20140032
Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2018-01-13Bibliographically approved

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García Martín, EvaLavesson, NiklasGrahn, Håkan

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