Estudo de viabilidade do uso de Raspberry PI na Névoa
Abstract
The need for reduced latencies in many applications, often to improve end-user usability, brought about the mist paradigm, which moves processing or preprocessing out of the cloud and closer to the data source. To address the requirements of low cost, low energy consumption, small size, and others that are common with this paradigm, an option is the use of low capacity, general purpose and widely available equipment. This work aims at investigating the Raspberry Pi 3 as a dispositive for mist applications, assessing its characteristics with the NDBench benchmark. Results of experiments with read/write operations in a NoSQL database indicate the Raspberry’s viability in scenarios with significant load (around up to 1.200 operations per second) while maintaining an average latency of 500ms. Such characteristics contemplate a vast amount of applications and suggest that the Raspberry Pi can be successfully used in mist and cloud environments.
References
Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. (2012). Fog computing and its role in the internet of things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, MCC ’12, pages 13–16, New York, NY, USA. ACM.
Byers, C. C. (2017). Architectural imperatives for fog computing: Use cases, requirements, and architectural techniques for fog-enabled iot networks. IEEE Communications Magazine, 55(8):14–20.
Cattell, R. (2011). Scalable SQL and NoSQL data stores. Acm Sigmod Record, 39(4):12–27.
Featherston, D. (2010). Cassandra: Principles and application. Department of Computer Science University of Illinois at Urbana-Champaign.
G. Souza, J. O. and Pilla, M. (2018). Comparação de desempenho do workload ycsb em raspberry pi b+ e 3. In XVIII Escola Regional de Alto Desempenho, pages 117–120, Porto Alegre/RS, Brasil.
Gerla, M., Lee, E., Pau, G., and Lee, U. (2014). Internet of vehicles: From intelligent grid to autonomous cars and vehicular clouds. In 2014 IEEE World Forum on Internet of Things (WF-IoT), pages 241–246.
Gubbi, J., Buyya, R., Marusic, S., and Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 29(7):1645–1660.
Huang, T., Lin, W., Li, Y., He, L., and Peng, S. (2019). A latency-aware multiple data replicas placement strategy for fog computing. Journal of Signal Processing Systems.
Lakshman, A. and Malik, P. (2010). Cassandra: A decentralized structured storage system. SIGOPS Oper. Syst. Rev., 44(2):35–40.
Maksimović, M., Vujović, V., Davidović, N., Milošević, V., and Perišić, B. (2014). Raspberry pi as internet of things hardware: performances and constraints. Design Issues, 3(8).
Mell, P. and Grance, T. (2011). The NIST definition of cloud computing. NIST Special Publication 800–145.
Papapanagiotou, I. and Chella, V. (2018). NDBench: Benchmarking microservices at scale. arXiv preprint arXiv:1807.10792.
R. Nakhkash, M., Nguyen gia, T., Azimi, I., Anzanpour, A., Rahmani, A. M., and Liljeberg, P. (2019). Analysis of performance and energy consumption of wearable devices and mobile gateways in iot applications. In International Conference on Omni-Layer Intelligent Systems – COINS ’19, pages 68–73.
Radanliev, P., De Roure, D. C., Nurse, J. R., Montalvo, R. M., and Burnap, P. (2019). The Industrial Internet-of-Things in the Industry 4.0 supply chains of small and medium sized enterprises. University of Oxford.
Van Heddeghem, W., Lambert, S., Lannoo, B., Colle, D., Pickavet, M., and Demeester, P. (2014). Trends in worldwide ict electricity consumption from 2007 to 2012. Comput. Commun., 50:64–76.
