Capabilities of Raspberry Pi 2 for Big Data and Video Streaming Applications in Data Centres
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
Micro data centre Raspberry Pi 2 Benchmarking Hadoop Big data Video streaming Cloud computingNotes
Acknowledgements
The authors would like to thank Marijn Jongerden and Boudewijn Haverkort (both from University of Twente) for their constructive feedback.
References
- 1.Abrahamsson, P., Helmer, S., Phaphoom, N., Nicolodi, L., Preda, N., Miori, L., Angriman, M., Rikkila, J., Wang, X., Hamily, K., Bugoloni, S.: Affordable and energy-efficient cloud computing clusters: the Bolzano Raspberry Pi cloud cluster experiment. In: Proceedings of 5th International Conference on Cloud Computing Technology and Science, vol. 2, pp. 170–175. IEEE (2013)Google Scholar
- 2.Adhikari, V., Guo, Y., Hao, F., Varvello, M., Hilt, V., Steiner, M., Zhang, Z.L.: Unreeling netflix: understanding and improvingmulti-CDN movie delivery. In: INFOCOM, 2012 Proceedings IEEE, pp. 1620–1628 (2012)Google Scholar
- 3.Apache Software Foundation: Apache JMeter (2015). http://jmeter.apache.org/
- 4.Arregoces, M., Portolani, M.: Data Center Fundamentals. Cisco Press, Indianapolis (2003)Google Scholar
- 5.Arutyunyan, R.: NGINX-based Media Streaming Server (2015). https://github.com/arut/nginx-rtmp-module
- 6.Cassandra: Welcome to Apache Cassandra (2015). http://cassandra.apache.org/
- 7.Cox, S.J., Cox, J.T., Boardman, R.P., Johnston, S.J., Scott, M., OBrien, N.S.: Iridis-pi: a low-cost, compact demonstration cluster. Cluster Comput. 17(2), 349–358 (2013)CrossRefGoogle Scholar
- 8.Knight, D.: DietPi for Raspberry Pi’s (2014). http://fuzon.co.uk/phpbb/viewtopic.php?f=8&t=6
- 9.Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107 (2008)CrossRefGoogle Scholar
- 10.ESnet: iperf/iperf3 (2015). http://fasterdata.es.net/performance-testing/network-troubleshooting-tools/iperf-and-iperf3/
- 11.FFmpeg: FFmpeg (2015). https://www.ffmpeg.org/
- 12.Google: Google Datacenters (2015). http://www.google.com/about/datacenters/efficiency/internal/#temperature
- 13.Huang, S., Huang, J., Liu, Y., Yi, L., Dai, J.: HiBench: a representative and comprehensive hadoop benchmark suite. In: Proceedings of 26th International Conference on Data Engineering Workshops (2010)Google Scholar
- 14.Innovation First, inc: 19-inch rack (EIA-310) (2007). https://www.server-racks.com/eia-310.html
- 15.Innovation First, inc: Rack Mounting Depth (2007). https://www.server-racks.com/rack-mount-depth.html
- 16.Clark, J.: Raising Data Center Power Density (2013). http://www.datacenterjournal.com/raising-data-center-power-density/
- 17.Kiepert, J.: Creating a Raspberry Pi-Based Beowulf Cluster, May 2013. http://coen.boisestate.edu/ece/files/2013/05/Creating.a.Raspberry.Pi-Based.Beowulf.Cluster_v2.pdf
- 18.JW Player: JW PLayer (2015). http://www.jwplayer.com/
- 19.Kopytov, A.: SysBench benchmark suite (2015). https://github.com/akopytov/sysbench
- 20.Leigh, K., Ranganathan, P., Subhlok, J.: General-purpose blade infrastructure for configurable system architectures. Distrib. Parallel Databases 21(2–3), 115–144 (2007)CrossRefGoogle Scholar
- 21.Meisner, D., Gold, B.T., Wenisch, T.F.: Powernap: eliminating server idle power. ACM SIGARCH Comput. Archit. News 37(1), 205–216 (2009)CrossRefGoogle Scholar
- 22.Microchip: LAN9514-JZX (2012). http://ww1.microchip.com/downloads/en/DeviceDoc/9514.pdf
- 23.Nginx: NGINX (2015). http://nginx.com/
- 24.Raspberry Pi Foundation: Raspberry Pi 2 Model B (2015). https://www.raspberrypi.org/products/raspberry-pi-2-model-b/
- 25.The Apache software foundation: Welcome to Apache Hadoop! (2015). https://hadoop.apache.org/
- 26.Tso, F.P., White, D.R., Jouet, S., Singer, J., Pezaros, D.P.: The Glasgow Raspberry Pi cloud: a scale model for cloud computing infrastructures. In: Proceedings of 33rd International Conference on Distributed Computing Systems Workshops, pp. 108–112. IEEE (2013)Google Scholar
- 27.Uptime Institute: Designing Netflixs Content Delivery Network.Uptime Institute Symposium (2014). https://journal.uptimeinstitute.com/designing-netflixs-content-delivery-network/