Skip to main content

Scalability of IoT Systems: Do Execution Costs Predict the Quality of Service?

  • Conference paper
  • First Online:
Internet of Everything (IoECon 2022)

Abstract

Execution costs are broadly used in the evaluation of the scalability of IoT systems. A well-known concern in their use is the extent to which their scalability desiderata best predicts Quality of Service (QoS). At first, past studies did not ratify a relationship between the scalability approaches and QoS in IoT systems. More recently, however, the correlations between these have begun to emerge. In this paper, we extend those findings and open up new avenues to further research by proposing a statistical testing approach for scrutinizing this relationship. The initial findings delineate that there is a significant correlation between the scalability approach employed and QoS in IoT systems. Our results strengthen the use of execution costs in the scalability of IoT systems confirming that QoS can be successfully predicted.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.opensourceforu.com/2016/11/best-open-source-cloud-computing-simulators/ Last Accessed on 15/Jan/2022.

  2. 2.

    https://www.omantel.com/ Last Accessed on 10/Jan/2022.

References

  1. Gross, T.R., Hennessy, J.L., Przybylski, S.A., Rowen, C.: Measurement and evaluation of the MIPS architecture and processor. ACM Trans. Comput. Syst. 6(3), 229–257 (1988)

    Article  Google Scholar 

  2. Jain, R.: The Art of Computer Systems Performance Analysis. John Wiley & Sons, Hoboken (2008)

    Google Scholar 

  3. Sun, X., Ansari, N.: Edge IoT: mobile edge computing for the internet of things. IEEE Commun. Mag. 54(12), 22–29 (2016)

    Article  Google Scholar 

  4. Li, L., Li, S., Zhao, S.: QoS-aware scheduling of services-oriented internet of things. IEEE Trans. Ind. Inform. 10(2), 1497–1505 (2014)

    Article  Google Scholar 

  5. Michael, M., Moreira, J.E., Shiloach, D., Wisniewski, R.W.: Scale-up x scale-out: a case study using nutch/lucene. In: IEEE International Parallel and Distributed Processing Symposium, pp. 1–8 (2007)

    Google Scholar 

  6. Taniuchi, Y.: On-demand virtual system service. Fujitsu Sci. Tech. J 46(4), 420–426 (2010)

    Google Scholar 

  7. Misra, P.: Build a scalable platform for high-performance IoT applications. Technical report, TCS Experience Certainty (2016)

    Google Scholar 

  8. Sarkar, C., SN, A.U.N., Prasad, R.V., Rahim, A., Neisse, R., Baldini, G.:. DIAT: a scalable distributed architecture for IoT. IEEE Internet Things J.2(3), pp.230–239 (2014)

    Google Scholar 

  9. Arellanes, D., Lau, K.K.: Evaluating IoT service composition mechanisms for the scalability of IoT systems. Future Gener. Comput. Syst. 108, 827–848 (2020)

    Article  Google Scholar 

  10. Addisie, A., Bertacco, V.: Collaborative accelerators for in-memory mapreduce on scale-up machines. In: Proceedings of the 24th Asia and South Pacific Design Automation Conference, pp. 747–753 (2019)

    Google Scholar 

  11. White, G., Nallur, V., Clarke, S.: Quality of service approaches in IoT: a systematic mapping. J. Syst. Softw. 132, 186–203 (2017)

    Article  Google Scholar 

  12. Singh, M., Baranwal, G.: Quality of service (QOS) in internet of things. In: IEEE 3rd International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), pp. 1–6 (2018)

    Google Scholar 

  13. Snigdh, I., Gupta, N.: Quality of service metrics in wireless sensor networks: a survey. J. Inst. Eng. (India): Series B 97(1), 91–96 (2014). https://doi.org/10.1007/s40031-014-0160-6

    Article  Google Scholar 

  14. Staron, M., Meding, W.: A portfolio of internal quality metrics for software architects. In: Winkler, D., Biffl, S., Bergsmann, J. (eds.) SWQD 2017. LNBIP, vol. 269, pp. 57–69. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-49421-0_5

    Chapter  Google Scholar 

  15. Rahman, F.H., Au, T.W., Shah Newaz, S.H., Haji Suhaili, W.S.: A performance study of high-end fog and fog cluster in iFogSim. In: Omar, Saiful, Haji Suhaili, Wida Susanty, Phon-Amnuaisuk, Somnuk (eds.) CIIS 2018. AISC, vol. 888, pp. 87–96. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-03302-6_8

    Chapter  Google Scholar 

  16. Wickremasinghe, B., Calheiros, R.N., Buyya, R.: CloudAnalyst: a cloudsim-based visual modeller for analysing cloud computing environments and applications. In: IEEE 24thInternational Conference on Advanced Information Networking and Applications, pp. 446–452 (2010)

    Google Scholar 

  17. Turpin, A., Hersh, W.: User interface effects in past batch versus user experiments. In: 25th Annual International Conference ACM SIGIR Conference on Research and Development in Informational Retrieval, pp. 431–434 (2002)

    Google Scholar 

  18. Jena, S.R., Ahmed, Z.: Response time minimization of different load balancing algorithms in cloud computing environments. Int. J. Comput. Appl. 69(17), 22–27 (2013)

    Google Scholar 

  19. Luntovskyy, A., Globa, L.: Performance, reliability and scalability for IoT. In: IEEE International Conference on Information and Digital Technologies (IDT), pp. 316–321 (2019)

    Google Scholar 

  20. Bahwaireth, K., Tawalbeh, L., Benkhelifa, E., Jararweh, Y., Tawalbeh, M.A.: Experimental comparison of simulation tools for efficient cloud and mobile cloud computing applications. EURASIP J. Inf. Secur. 2016(1), 1–14 (2016). https://doi.org/10.1186/s13635-016-0039-y

    Article  Google Scholar 

  21. Karakus, M., Durresi, A.: A scalability metric for control planes in software defined networks (SDNs). In: IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), pp. 282–289 (2016)

    Google Scholar 

  22. Lilja, D.J.: Measuring Computer Performance: A Practitioner’s Guide. Cambridge University Press, Cambridge (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kennedy E. Ehimwenma .

Editor information

Editors and Affiliations

Appendix

Appendix

Default Configurations in Simulation.

Parameter

Value used

top1 (502,003 users)

4 GB total (from 1 small host modelled with 4 GB RAM)

top2 (304,050 users)

8 GB total (from 2 small hosts modelled with 4 GB RAM each)

top3 (415,952 users)

12 GB total (from 3 small hosts modelled with 4 GB RAM each)

top4 (360,534 users)

16 GB total (from 4 small hosts modelled with 4 GB RAM each)

top5 (400,453 users)

20 GB total (from 5 small hosts modelled with 4 GB RAM each)

top6 (298,952 users)

24 GB total (from 6 small hosts modelled with 4 GB RAM each)

top7 (370,119 users)

28 GB total (from 7 small hosts modelled with 4 GB RAM each)

top8 (360,017 users)

32 GB total (from 8 small hosts modelled with 4 GB RAM each)

Processing Speed

10000 MIPS (Host)

Transmission Rate

1.54 Mbps

Bandwidth (MB)

10000

Cloud latency

100 ms

Rights and permissions

Reprints and permissions

Copyright information

© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Al-Qasmi, A., Al Shuaily, H., Ehimwenma, K.E., Al Sharji, S. (2023). Scalability of IoT Systems: Do Execution Costs Predict the Quality of Service?. In: Pereira, T., Impagliazzo, J., Santos, H. (eds) Internet of Everything. IoECon 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 458. Springer, Cham. https://doi.org/10.1007/978-3-031-25222-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-25222-8_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25221-1

  • Online ISBN: 978-3-031-25222-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics