Characterization of IoT Workloads

  • Uma TadakamallaEmail author
  • Daniel A. MenascéEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11520)


Workload characterization is a fundamental step in carrying out performance and Quality of Service engineering studies. The workload of a system is defined as the set of all inputs received by the system from its environment during one or more time windows. The characterization of the workload entails determining the nature of its basic components as well as a quantitative and probabilistic description of the workload components in terms of both the arrival process, event counts, and service demands. Several workload characterization studies were presented for a variety of domains, except for IoT workloads. This is precisely the main contribution of this paper, which also presents a capacity planning study based on one of the workload characterizations presented here.


Workload characterization Internet of Things Capacity planning G/G/n queue Quality of Service in edge computing 


  1. 1.
    Chicago data portal.
  2. 2.
  3. 3.
    Abbas, N., Zhang, Y., Taherkordi, A., Skeie, T.: Mobile edge computing: a survey. IEEE Internet Things J. 5(1), 450–465 (2018)Google Scholar
  4. 4.
    Ahn, S., Gorlatova, M., Chiang, M.: Leveraging fog and cloud computing for efficient computational offloading. In: 2017 Undergraduate Research Technology Conference (URTC), IEEE MIT, pp. 1–4. IEEE (2017)Google Scholar
  5. 5.
    Akula, V., Menasce, D.: Two-level workload characterization of online auctions. Electron. Commer. Res. Appl. 6, 192–208 (2007)Google Scholar
  6. 6.
    Al-Shaer, E., Wei, J., Hamlen, K.W., Wang, C.: HONEYSCOPE: IoT device protection with deceptive network views. Autonomous Cyber Deception, pp. 167–181. Springer, Cham (2019). Scholar
  7. 7.
    Babou, C.S.M., Fall, D., Kashihara, S., Niang, I., Kadobayashi, Y.: Home edge computing (HEC): design of a new edge computing technology for achieving ultra-low latency. In: Liu, S., Tekinerdogan, B., Aoyama, M., Zhang, L.-J. (eds.) EDGE 2018. LNCS, vol. 10973, pp. 3–17. Springer, Cham (2018). Scholar
  8. 8.
    Barroso, L.A., Gharachorloo, K., Bugnion, E.: Memory system characterization of commercial workloads. In: Proceedings of 25th Annual International Symposium Computer Architecture, ISCA 1998, pp. 3–14. IEEE Computer Society, Washington, DC (1998)Google Scholar
  9. 9.
    Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the Internet of Things. In: Proceedings of MCC Workshop on Mobile Cloud Computing, MCC 2012, pp. 13–16, New York, NY, USA. ACM (2012)Google Scholar
  10. 10.
    Brogi, A., Forti, S.: QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J. 4(5), 1185–1192 (2017)Google Scholar
  11. 11.
    Calzarossa, M., Massari, L., Tessera, D.: Workload characterization issues and methodologies. In: Haring, G., Lindemann, C., Reiser, M. (eds.) Performance Evaluation: Origins and Directions. LNCS, vol. 1769, pp. 459–482. Springer, Heidelberg (2000). Scholar
  12. 12.
    Calzarossa, M., Serazzi, G.: Workload characterization. Proc. IEEE 81, 1136–1150 (1993)Google Scholar
  13. 13.
    da Cruz, M.A.A., Rodrigues, J.J.P.C., Al-Muhtadi, J., Korotaev, V.V., de Albuquerque, V.H.C.: A reference model for Internet of Things middleware. IEEE Internet Things J. 5(2), 871–883 (2018)Google Scholar
  14. 14.
    Di, S., Kondo, D., Cirne, W.: Characterization and comparison of cloud versus grid workloads. In: 2012 IEEE International Conference Cluster Computing, pp. 230–238, September 2012Google Scholar
  15. 15.
    Donovan, D., Work, D.B.: New york city taxi trip data (2010–2013) (2016)Google Scholar
  16. 16.
    Elnaffar, S., Martin, P., Horman, R.: Automatically classifying database workloads. In: Proceedings of 11th International Conference Information and Knowledge Management, CIKM 2002, pp. 622–624, New York, NY, USA. ACM (2002)Google Scholar
  17. 17.
    Fan, Q., Ansari, N.: Application aware workload allocation for edge computing-based IoT. IEEE Internet Things J. 5(3), 2146–2153 (2018)Google Scholar
  18. 18.
    Garcia Lopez, P., et al.: Edge-centric computing: vision and challenges. SIGCOMM Comput. Commun. Rev. 45(5), 37–42 (2015)Google Scholar
  19. 19.
    Gomes, L.H., Cazita, C., Almeida, J.M., Almeida, V., Meira, Jr., W.: Characterizing a spam traffic. In: Proceedings of 4th ACM SIGCOMM Conference Internet Measurement, IMC 2004, pp. 356–369, New York, NY, USA. ACM (2004)Google Scholar
  20. 20.
    Gorlatova, M., Sarik, J., Grebla, G., Cong, M., Kymissis, I., Zussman, G.: Movers and shakers: kinetic energy harvesting for the Internet of Things. In: The 2014 ACM International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2014, pp. 407–419, New York, NY, USA. ACM (2014)Google Scholar
  21. 21.
    Jain, R.: The Art of Computer Systems Performance Analysis. Wiley, Hoboken (1991)zbMATHGoogle Scholar
  22. 22.
    Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., Zhao, W.: A survey on Internet of Things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4(5), 1125–1142 (2017)Google Scholar
  23. 23.
    Magalhaes, D., Calheiros, R.N., Buyya, R., Gomes, D.G.: Workload modeling for resource usage analysis and simulation in cloud computing. Comput. Electr. Eng. 47, 69–81 (2015)Google Scholar
  24. 24.
    Menascé, D., Abrahao, B., Barbará, D., Almeida, V., Ribeiro, F.: Fractal characterization of web workloads. In: Eleventh International World Wide Web Conference, Honolulu, HI, pp. 7–11 (2002)Google Scholar
  25. 25.
    Menasce, D., Almeida, V., Fonseca, R., Mendes, M.: A methodology for workload characterization of e-commerce sites. In: Proceedings of 1st ACM Conference on Electronic Commerce, EC 1999, pp. 119–128, New York, NY, USA. ACM (1999)Google Scholar
  26. 26.
    Menasce, D.A., Almeida, V.A.F., Dowdy, L.W.: Performance by Design: Computer Capacity Planning by Example. Prentice Hall, Upper Saddle River (2004)Google Scholar
  27. 27.
    Metzger, F., Hofeld, T., Bauer, A., Kounev, S., Heegaard, P.E.: Modeling of aggregated IoT traffic and its application to an IoT cloud. Proc. IEEE 107(4), 679–694 (2019)Google Scholar
  28. 28.
    Nedyalkov, I., Stefanov, A., Georgiev, G.: Characterization of the traffic in IP-based communication networks. In: 2018 International Conference on High Technology for Sustainable Development (HiTech), pp. 1–4. IEEE (2018)Google Scholar
  29. 29.
    Ngu, A.H., Gutierrez, M., Metsis, V., Nepal, S., Sheng, Q.Z.: IoT middleware: a survey on issues and enabling technologies. IEEE Internet Things J. 4(1), 1–20 (2017)Google Scholar
  30. 30.
    Paxson, V., Floyd, S.: Wide area traffic: the failure of poisson modeling. IEEE/ACM Trans. Netw. 3(3), 226–244 (1995)Google Scholar
  31. 31.
    Pereira, C., Pinto, A., Ferreira, D., Aguiar, A.: Experimental characterization of mobile IoT application latency. IEEE Internet Things J. 4(4), 1082–1094 (2017)Google Scholar
  32. 32.
    Postema, B.F., Geuze, N.J., Haverkort, B.R.: Fitting realistic data centre workloads: a data science approach. In: Proceedings of the Ninth International Conference on Future Energy Systems, e-Energy 2018, pp. 486–491, New York, NY, USA. ACM (2018)Google Scholar
  33. 33.
    Ren, J., Guo, H., Xu, C., Zhang, Y.: Serving at the edge: a scalable IoT architecture based on transparent computing. IEEE Netw. 31(5), 96–105 (2017)Google Scholar
  34. 34.
    Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)Google Scholar
  35. 35.
    Siegel, J.E., Kumar, S., Sarma, S.E.: The future Internet of Things: secure, efficient, and model-based. IEEE Internet Things J. 5(4), 2386–2398 (2018)Google Scholar
  36. 36.
    Smirni, E., Reed, D.: Lessons from characterizing the input/output behavior of parallel scientific applications. Perform. Eval. 33(1), 27–44 (1998)Google Scholar
  37. 37.
    Tadakamalla, U., Menasce, D.A.: FogQN: an analytic model for fog/cloud computing. In: Proceedings of 1st Workshop on Managed Fog-to-Cloud (mF2C), joint with 11th IEEE/ACM International Conference on Utility and Cloud Computing. IEEE/ACM (2018).
  38. 38.
    Tadakamalla, U., Menasce, D.A.: Autonomic resource management using analytic models for fog/cloud computing. In: Proceedings of IEEE International Conference on Fog Computing. IEEE (2019)Google Scholar
  39. 39.
    Veloso, E., Almeida, V., Meira, W., Bestavros, A., Jin, S.: A hierarchical characterization of a live streaming media workload. In: Proceedings of 2nd ACM SIGCOMM Workshop on Internet Measurement, IMW 2002, pp. 117–130, New York, NY, USA. ACM (2002)Google Scholar
  40. 40.
    Yousefpour, A., Ishigaki, G., Gour, R., Jue, J.P.: On reducing IoT service delay via fog offloading. IEEE Internet Things J. 5(2), 998–1010 (2018)Google Scholar
  41. 41.
    Zheng, Y.: T-drive trajectory data sample, August 2011.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceGeorge Mason UniversityFairfaxUSA

Personalised recommendations