Balancing Speedup and Accuracy in Smart City Parallel Applications

  • Carlo MastroianniEmail author
  • Eugenio Cesario
  • Andrea Giordano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10104)


Smart city and Internet of Things applications can benefit from the use of distributed computing architectures, due to the large number and pronounced territorial dispersion of the involved users and devices. In this context, a natural method to parallelize the computation is to consider the territory as partitioned into regions, e.g., city neighborhoods, and associate a computing entity with each region. The application considered in this paper is the prediction of the amount of internet traffic generated within a given region, which requires to consider not only the devices located in the region but also the mobile devices that are expected to enter the local region in the future. When setting the number of neighbor regions included in the computation, it must be considered that this parameter has opposite effects on two important objectives: increasing the number of neighbors tends to improve the accuracy of the prediction but slows down the computation because more computing entities need to synchronize among each other. Similar considerations apply when setting the size and number of regions that partition the territory. This paper offers an insight onto these important tradeoff issues.


Mobile Device Neighbor Node Smart City Pareto Frontier Internet Traffic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Altomare, A., Cesario, E., Talia, D.: Energy-aware migration of virtual machines driven by predictive data mining models. In: Proceedings of the 23rd Euromicro International Conference on Parallel, Distributed and Network-Based Computing (PDP 2015), Turku, Finland, pp. 549–553 (2015)Google Scholar
  2. 2.
    Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the 1st ACM MCC Workshop on Mobile Cloud Computing, pp. 13–16 (2012)Google Scholar
  3. 3.
    Botta, A., de Donato, W., Persico, V., Pescapé, A.: Integration of cloud computing and internet of things: a survey. Future Gen. Comput. Syst. 56, 684–700 (2016)CrossRefGoogle Scholar
  4. 4.
    Cicirelli, F., Forestiero, A., Giordano, A., Mastroianni, C., Spezzano, G.: Parallel execution of space-aware applications in a cloud environment. In: 24th Euromicro International Conference on Parallel, Distributed and Network-Based Computing (PDP 2016), Heraklion, Crete, Greece, February 2016Google Scholar
  5. 5.
    Ekanayake, J., Fox, G.: High performance parallel computing with clouds and cloud technologies. In: Avresky, D.R., Diaz, M., Bode, A., Ciciani, B., Dekel, E. (eds.) CloudComp 2009. LNICSSTE, vol. 34, pp. 20–38. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-12636-9_2 CrossRefGoogle Scholar
  6. 6.
    Göndör, S., Uzun, A., Rohrmann, T., Tan, J., Henniges, R.: Predicting user mobility in mobile radio networks to proactively anticipate traffic hotspots. In: Proceedings of the 2013 International Conference on Mobile Wireless MiddleWARE, Operating Systems, and Applications (Mobilware 2013), Bologna, Italy, pp. 120–129 (2013)Google Scholar
  7. 7.
    Hank, P., Müller, S., Vermesan, O., Van Den Keybus, J.: Automotive ethernet: in-vehicle networking and smart mobility. In: Proceedings of the Conference on Design, Automation and Test in Europe (DATE 2013), San Jose, CA, USA, pp. 1735–1739 (2013)Google Scholar
  8. 8.
    van Hee, K., Oanea, O., Post, R., Somers, L., van der Werf, J.M.: Yasper: a tool for workflow modeling and analysis. In: Proceedings of the Sixth International Conference on Application of Concurrency to System Design (ACSD 2006), pp. 279–282. IEEE Computer Society, Washington, DC (2006)Google Scholar
  9. 9.
    Krishnan, Y.N., Bhagwat, C.N., Utpat, A.P.: Fog computing- network based cloud computing. In: 2nd IEEE International Conference on Electronics and Communication Systems (ICECS), pp. 250–251 (2015)Google Scholar
  10. 10.
    Lee, I., Lee, K.: The internet of things (IoT): applications, investments, and challenges for enterprises. Bus. Horiz. 58(4), 431–440 (2015)CrossRefGoogle Scholar
  11. 11.
    Li, R., Zhao, Z., Zhou, X., Palicot, J., Zhang, H.: The prediction analysis of cellular radio access network traffic: from entropy theory to networking practice. IEEE Commun. Mag. 52(6), 234–240 (2014)CrossRefGoogle Scholar
  12. 12.
    Mitton, N., Papavassiliou, S., Puliafito, A., Trivedi, K.S.: Combining cloud and sensors in a smart city environment. EURASIP J. Wireless Commun. Netw. 2012(1), 1–10 (2012)CrossRefGoogle Scholar
  13. 13.
    Peterson, J.L.: Petri nets. ACM Comput. Surv. 9(3), 223–252 (1977)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Singh, R., Srinivasan, M., Murthy, C.: A learning based mobile user traffic characterization for efficient resource management in cellular networks. In: 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), pp. 304–309, January 2015Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Carlo Mastroianni
    • 1
    Email author
  • Eugenio Cesario
    • 1
  • Andrea Giordano
    • 1
  1. 1.ICAR-CNRRendeItaly

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