Abstract
Small credit-card-sized single-board computers, such as the Raspberry Pi, are becoming ever more popular in areas unrelated to the education of children, for which they were originally intended. So far, these computers have mainly been used in small-scale projects focusing very often on hardware aspects. We want to take single-board computer architectures a step further by showing how to deploy part of an orchestration platform (OpenStack Swift) on a Raspberry Pi cluster to make it a useful platform for more sophisticated data collection and analysis applications located at the edge of a cloud. Our results illustrate that this is indeed possible, but that there are still shortcomings in terms of performance. Nevertheless, with the next generation of small single-board computers that have been introduced recently, we believe that this is a viable approach for certain application domains, such as private clouds or edge computing in harsh environments.
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Notes
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Available at https://github.com/unibz-bobo.
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Swift distinguishes four different types of nodes: account, container, object, and proxy nodes.
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References
Abrahamsson, P., Helmer, S., Phaphoom, N., Nicolodi, L., Preda, N., Miori, L., Angriman, M., Rikkilä, J., Wang, X., Hamily, K., Bugoloni, S.: Affordable and energy-efficient cloud computing clusters: the Bolzano Raspberry Pi cloud cluster experiment. In: UNICO Workshop at CloudCom, Bristol (2013)
Basmadjian, R., De Meer, H., Lent, R., Giuliani, G.: Cloud computing and its interest in saving energy: the use case of a private cloud. JoCCASA 1(1), 1–25 (2012)
Bunch, C.: OpenStack Swift, Raspberry Pi, 23 USB keys - aka GhettoSAN v2 (2013). http://openstack.prov12n.com/openstack-swift-raspberry-pi-23-usb-keys-aka-ghettosan-v2/. Accessed Feb 2014
Cloutier, M.F., Paradis, C., Weaver, V.M.: Design and analysis of a 32-bit embedded high-performance cluster optimized for energy and performance. In: Co-HPC 2014, pp. 1–8, New Orleans (2014)
Cox, S.J., Cox, J.T., Boardman, R.P., Johnston, S.J., Scott, M., O’Brien, N.S.: Iridis-Pi: a low-cost, compact demonstration cluster. Clust. Comput. 17(2), 349–358 (2014)
Dickinson, J.: OpenStack Swift on Raspberry Pi (2013). http://programmerthoughts.com/openstack/swift-on-pi/. Accessed Feb 2014
Gropp, W., Lusk, E., Doss, N., Skjellum, A.: A high-performance, portable implementation of the MPI message passing interface standard. Parallel Comput. 22(6), 789–828 (1996)
Helmer, S., Pahl, C., Sanin, J., Miori, L., Brocanelli, S., Cardano, F., Gadler, D., Morandini, D., Piccoli, A., Salam, S., Sharear, A.M., Ventura, A., Abrahamsson, P., Oyetoyan, T.D.: Bringing the cloud to rural and remote areas via cloudlets. In: ACM DEV 2016, Nairobi (2016)
Merkel, D.: Docker: lightweight Linux containers for consistent development and deployment. Linux J. 2014(239) (2014)
Pahl, C., Lee, B.: Containers and clusters for edge cloud architectures - a technology review. In: FiCloud 2015, Rome, pp. 379–386, August 2015
Porter, J.H., Nagy, E., Kratz, T.K., Hanson, P., Collins, S.L., Arzberger, P.: New eyes on the world: advanced sensors for ecology. BioScience 59(5), 385–397 (2009)
Raspbian.org: Raspbian FAQ. http://www.raspbian.org/RaspbianFAQ. Accessed June 2013
Robinson, A., Cook, M.: Raspberry Pi Projects. Wiley, Chichester (2014)
Ruponen, S., Zidbeck, J.: Testbed for rural area networking - first steps towards a solution. In: AFRICOMM 2014, Yaounde, pp. 14–23, November 2012
Schot, N.: Feasibility of Raspberry Pi 2-based micro data centers in big data applications. In: 23rd Twente Student Conference on IT, Enschede, June 2015
Spillner, J., Beck, M., Schil, A., Bohnert, T.M.: Stealth databases: ensuring user-controlled queries in untrusted cloud environments. In: UCC 2015, Limassol, pp. 261–270, December 2015
SwiftStack: swiftstack/ssbench. https://github.com/swiftstack/ssbench. Accessed May 2014
Tso, P., White, D., Jouet, S., Singer, J., Pezaros, D.: The Glasgow Raspberry Pi cloud: a scale model for cloud computing infrastructures. In: CCRM 2013, Philadelphia (2013)
Wilcox, E., Jhunjhunwala, P., Gopavaram, K., Herrera, J.: Pi-crust: a Raspberry Pi cluster implementation. Technical report, Texas A&M University (2015)
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Miori, L., Sanin, J., Helmer, S. (2017). A Platform for Edge Computing Based on Raspberry Pi Clusters. In: Calì, A., Wood, P., Martin, N., Poulovassilis, A. (eds) Data Analytics. BICOD 2017. Lecture Notes in Computer Science(), vol 10365. Springer, Cham. https://doi.org/10.1007/978-3-319-60795-5_16
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DOI: https://doi.org/10.1007/978-3-319-60795-5_16
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