Advertisement

IoT Workload Distribution Impact Between Edge and Cloud Computing in a Smart Grid Application

  • Otávio CarvalhoEmail author
  • Manuel Garcia
  • Eduardo Roloff
  • Emmanuell Diaz Carreño
  • Philippe O. A. Navaux
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 796)

Abstract

The advent of Internet of Things is now part of our reality. Increasing amounts of data are being continuously generated and monitored through widespread sensing technologies such as personal smartphones, large scale smart cities sensor deployments and smart electrical grids.

However, the ability to aggregate and act upon such data gathered by sensors is still a significant research and industrial challenge. Devices that are able to collect and act on data at network edges are bounded by the amount of data that can be sent over networks.

In this paper, we analyze the impact of workload distribution in a smart grid application, evaluating how we can increase processing rates by leveraging each time more powerful edge node processors.

Our results show that our test bed application, leveraging cloud nodes processing and processing windows, is able to achieve processing rates of approximately 800k measurements per second using four edge node processors and a single cloud node.

Notes

Acknowledgments

This research received partial funding from CYTED for the RICAP Project.

It has also received partial funding from the EU H2020 Programme and from MCTI/RNPBrazil under the HPC4E project, grant agreement no. 689772.

Additional funding was provided by FAPERGS in the context of the GreenCloud Project.

References

  1. 1.
    Abdelwahab, S., Hamdaoui, B., Guizani, M., Znati, T.: REPLISOM: disciplined tiny memory replication for massive IoT devices in LTE edge cloud. IEEE Internet Things J. 3(3), 327–338 (2016)CrossRefGoogle Scholar
  2. 2.
    Ali, A.B.M.S.: Smart Grids: Opportunities, Developments, and Trends. Springer Science & Business Media, Berlin (2013).  https://doi.org/10.1007/978-1-4471-5210-1 CrossRefGoogle Scholar
  3. 3.
    Bailis, P., Kingsbury, K.: The network is reliable. Queue 12(7), 20 (2014)Google Scholar
  4. 4.
    Banks, A., Gupta, R.: MQTT Version 3.1.1. OASIS standard (2014)Google Scholar
  5. 5.
    Belshe, M., Thomson, M., Peon, R.: Hypertext transfer protocol version 2 (HTTP/2). Internet Engineering Task Force (IETF) - RFC-7540 (2015)Google Scholar
  6. 6.
    Bormann, C., Castellani, A.P., Shelby, Z.: CoAP: an application protocol for billions of tiny internet nodes. IEEE Internet Comput. 16(2), 62–67 (2012)CrossRefGoogle Scholar
  7. 7.
    Brown, R.E.: Impact of smart grid on distribution system design. In: 2008 IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, pp. 1–4. IEEE (2008)Google Scholar
  8. 8.
    Burrows, M.: The Chubby lock service for loosely-coupled distributed systems. In: Proceedings of the 7th symposium on Operating systems design and implementation, pp. 335–350. USENIX Association (2006)Google Scholar
  9. 9.
    Buyya, R., Dastjerdi, A.V.: Internet of Things: Principles and paradigms. Elsevier, Amsterdam (2016)Google Scholar
  10. 10.
    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009)CrossRefGoogle Scholar
  11. 11.
    Bylander, T., Rosen, B.: A perceptron-like online algorithm for tracking the median. In: International Conference on Neural Networks, vol. 4, pp. 2219–2224. IEEE (1997)Google Scholar
  12. 12.
    Chowdhury, S.A., Sapra, V., Hindle, A.: Is HTTP/2 more energy efficient than HTTP/1.1 for mobile users? PeerJ PrePrints 3, e1280v1 (2015)Google Scholar
  13. 13.
    Dastjerdi, A.V., Buyya, R.: Fog computing: helping the internet of things realize its potential. Computer 49(8), 112–116 (2016)CrossRefGoogle Scholar
  14. 14.
    Dischinger, M., Haeberlen, A., Gummadi, K.P., Saroiu, S.: Characterizing residential broadband networks. In: Internet Measurement Conference, pp. 43–56 (2007)Google Scholar
  15. 15.
    Gedawy, H., Tariq, S., Mtibaa, A., Harras, K.: Cumulus: a distributed and flexible computing testbed for edge cloud computational offloading. In: Cloudification of the Internet of Things (CIoT), pp. 1–6. IEEE (2016)Google Scholar
  16. 16.
    Gligorić, N., Dejanović, I., Krčo, S.: Performance evaluation of compact binary XML representation for constrained devices. In: 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS), pp. 1–5. IEEE (2011)Google Scholar
  17. 17.
    Goel, U., Steiner, M., Wittie, M.P., Flack, M., Ludin, S.: HTTP/2 performance in cellular networks. In: ACM MobiCom (2016)Google Scholar
  18. 18.
    Google: gRPC Motivation and Design Principles (2015). http://www.grpc.io/blog/principles
  19. 19.
    Habak, K., Ammar, M., Harras, K.A., Zegura, E.: Femto clouds: leveraging mobile devices to provide cloud service at the edge. In: 2015 IEEE 8th International Conference on Cloud Computing (CLOUD), pp. 9–16. IEEE (2015)Google Scholar
  20. 20.
    Kyriakides, E., Polycarpou, M.: Short term electric load forecasting: a tutorial. In: Chen, K., Wang, L. (eds.) Trends in Neural Computation, vol. 35, pp. 391–418. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-36122-0_16 CrossRefGoogle Scholar
  21. 21.
    Lee, S., Kim, H., Hong, D.K., Ju, H.: Correlation analysis of MQTT loss and delay according to QoS level. In: 2013 International Conference on Information Networking (ICOIN), pp. 714–717. IEEE (2013)Google Scholar
  22. 22.
    Liu, P., Willis, D., Banerjee, S.: ParaDrop: enabling lightweight multi-tenancy at the network’s extreme edge. In: IEEE/ACM Symposium on Edge Computing (SEC), pp. 1–13. IEEE (2016)Google Scholar
  23. 23.
    Maas, M., Harris, T., Asanovic, K., Kubiatowicz, J.: Trash day: coordinating garbage collection in distributed systems. In: HotOS (2015)Google Scholar
  24. 24.
    Orsini, G., Bade, D., Lamersdorf, W.: CloudAware: a context-adaptive middleware for mobile edge and cloud computing applications. In: IEEE International Workshops on Foundations and Applications of Self* System, pp. 216–221. IEEE (2016)Google Scholar
  25. 25.
    Pan, J., Ma, L., Ravindran, R., TalebiFard, P.: HomeCloud: an edge cloud framework and testbed for new application delivery. In: 2016 23rd International Conference on Telecommunications (ICT), pp. 1–6. IEEE (2016)Google Scholar
  26. 26.
    Reuters: U.S. Smart Grid to Cost Billions, Save Trillions (2011). http://www.reuters.com/article/2011/05/24/us-utilities-smartgrid-epri-idUSTRE74N7O420110524
  27. 27.
    Richardson, L., Ruby, S.: RESTful web services. O’Reilly Media, Inc., Sebastopol (2008)Google Scholar
  28. 28.
    Roloff, E., Diener, M., Carissimi, A., Navaux, P.O.A.: High performance computing in the cloud: deployment, performance and cost efficiency. In: 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom), pp. 371–378. IEEE (2012)Google Scholar
  29. 29.
    Ruellan, H., Peon, R.: HPACK: Header Compression for HTTP/2. Internet Engineering Task Force (IETF) - RFC-7541 (2015)Google Scholar
  30. 30.
    Satyanarayanan, M.: The emergence of edge computing. Computer 50(1), 30–39 (2017)CrossRefGoogle Scholar
  31. 31.
    Sun, X., Ansari, N.: EdgeIoT: mobile edge computing for the internet of things. IEEE Commun. Mag. 54(12), 22–29 (2016)CrossRefGoogle Scholar
  32. 32.
    Thangavel, D., Ma, X., Valera, A., Tan, H.X., Tan, C.K.Y.: Performance evaluation of MQTT and CoAP via a common middleware. In: 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 1–6. IEEE (2014)Google Scholar
  33. 33.
    Tirumala, A., Qin, F., Dugan, J., Ferguson, J., Gibbs, K.: Iperf: the TCP/UDP bandwidth measurement tool (2005). http://iperf.fr
  34. 34.
    Togashi, N., Klyuev, V.: Concurrency in Go and Java: performance analysis. In: 2014 4th IEEE International Conference on Information Science and Technology (ICIST), pp. 213–216. IEEE (2014)Google Scholar
  35. 35.
    Ziekow, H., Jerzak, Z.: The DEBS 2014 grand challenge. In: Proceedings of the 8th ACM International Conference on Distributed Event-based Systems, DEBS, vol. 14 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Otávio Carvalho
    • 1
    Email author
  • Manuel Garcia
    • 1
  • Eduardo Roloff
    • 1
  • Emmanuell Diaz Carreño
    • 1
  • Philippe O. A. Navaux
    • 1
  1. 1.Informatics InstituteFederal University of Rio Grande do Sul (UFRGS)Porto AlegreBrazil

Personalised recommendations