Capabilities of Raspberry Pi 2 for Big Data and Video Streaming Applications in Data Centres

Nick J. Schot, Paul J.E. Velthuis, Björn Frits Postema

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
376 Downloads (Pure)

Abstract

Many new data centres have been built in recent years in order to keep up with the rising demand for server capacity. These data centres require a lot of electrical energy and cooling. Big data and video streaming are two heavily used applications in data centres. This paper experimentally investigates the possibilities and benefits of using cheap, low power and widely supported hardware in the form of a micro data centre with big data and video streaming as its main application area. For this purpose, multiple Raspberry Pi 2 Model B (RPi2)’s have been used in order to build a fully functional distributed Hadoop and video streaming setup that has acceptable performance and extends to new research opportunities. We experimentally validated the new setup to fit in a data centre environment by analysis of its performance, scalability, energy consumption, temperature and manageability. This paper proposes a high concurrency and low power setup in a small 1U form factor with an estimated number of 72 RPi2’s as an interesting alternative to traditional rack servers.
Original languageUndefined
Title of host publicationProceedings of the 18th International GI/ITG Conference on Measurement, Modelling and Evaluation of Dependable Computer and Communication Systems (MMB & DFT 2016)
EditorsAnne Katharina Ingrid Remke, A.K.I. Remke, Boudewijn R.H.M. Haverkort, B.R.H.M. Haverkort
Place of PublicationSwitzerland
PublisherSpringer
Pages183-198
Number of pages16
ISBN (Print)978-3-319-31558-4
DOIs
Publication statusPublished - Apr 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
Volume9629
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • EWI-26954
  • Benchmarking
  • Big data
  • Hadoop
  • Cloud computing
  • Video streaming
  • Raspberry Pi 2
  • METIS-316898
  • IR-100417
  • Micro data centre

Cite this

Schot, N. J., Velthuis, P. J. E., & Postema, B. F. (2016). Capabilities of Raspberry Pi 2 for Big Data and Video Streaming Applications in Data Centres. In A. K. I. Remke, A. K. I. Remke, B. R. H. M. Haverkort, & B. R. H. M. Haverkort (Eds.), Proceedings of the 18th International GI/ITG Conference on Measurement, Modelling and Evaluation of Dependable Computer and Communication Systems (MMB & DFT 2016) (pp. 183-198). (Lecture Notes in Computer Science; Vol. 9629). Switzerland: Springer. https://doi.org/10.1007/978-3-319-31559-1_15
Schot, Nick J. ; Velthuis, Paul J.E. ; Postema, Björn Frits. / Capabilities of Raspberry Pi 2 for Big Data and Video Streaming Applications in Data Centres. Proceedings of the 18th International GI/ITG Conference on Measurement, Modelling and Evaluation of Dependable Computer and Communication Systems (MMB & DFT 2016). editor / Anne Katharina Ingrid Remke ; A.K.I. Remke ; Boudewijn R.H.M. Haverkort ; B.R.H.M. Haverkort. Switzerland : Springer, 2016. pp. 183-198 (Lecture Notes in Computer Science).
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abstract = "Many new data centres have been built in recent years in order to keep up with the rising demand for server capacity. These data centres require a lot of electrical energy and cooling. Big data and video streaming are two heavily used applications in data centres. This paper experimentally investigates the possibilities and benefits of using cheap, low power and widely supported hardware in the form of a micro data centre with big data and video streaming as its main application area. For this purpose, multiple Raspberry Pi 2 Model B (RPi2)’s have been used in order to build a fully functional distributed Hadoop and video streaming setup that has acceptable performance and extends to new research opportunities. We experimentally validated the new setup to fit in a data centre environment by analysis of its performance, scalability, energy consumption, temperature and manageability. This paper proposes a high concurrency and low power setup in a small 1U form factor with an estimated number of 72 RPi2’s as an interesting alternative to traditional rack servers.",
keywords = "EWI-26954, Benchmarking, Big data, Hadoop, Cloud computing, Video streaming, Raspberry Pi 2, METIS-316898, IR-100417, Micro data centre",
author = "Schot, {Nick J.} and Velthuis, {Paul J.E.} and Postema, {Bj{\"o}rn Frits}",
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doi = "10.1007/978-3-319-31559-1_15",
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Schot, NJ, Velthuis, PJE & Postema, BF 2016, Capabilities of Raspberry Pi 2 for Big Data and Video Streaming Applications in Data Centres. in AKI Remke, AKI Remke, BRHM Haverkort & BRHM Haverkort (eds), Proceedings of the 18th International GI/ITG Conference on Measurement, Modelling and Evaluation of Dependable Computer and Communication Systems (MMB & DFT 2016). Lecture Notes in Computer Science, vol. 9629, Springer, Switzerland, pp. 183-198. https://doi.org/10.1007/978-3-319-31559-1_15

Capabilities of Raspberry Pi 2 for Big Data and Video Streaming Applications in Data Centres. / Schot, Nick J.; Velthuis, Paul J.E.; Postema, Björn Frits.

Proceedings of the 18th International GI/ITG Conference on Measurement, Modelling and Evaluation of Dependable Computer and Communication Systems (MMB & DFT 2016). ed. / Anne Katharina Ingrid Remke; A.K.I. Remke; Boudewijn R.H.M. Haverkort; B.R.H.M. Haverkort. Switzerland : Springer, 2016. p. 183-198 (Lecture Notes in Computer Science; Vol. 9629).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Schot NJ, Velthuis PJE, Postema BF. Capabilities of Raspberry Pi 2 for Big Data and Video Streaming Applications in Data Centres. In Remke AKI, Remke AKI, Haverkort BRHM, Haverkort BRHM, editors, Proceedings of the 18th International GI/ITG Conference on Measurement, Modelling and Evaluation of Dependable Computer and Communication Systems (MMB & DFT 2016). Switzerland: Springer. 2016. p. 183-198. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-31559-1_15