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Intelligent resource sharing to enable quality of service for network clients: the trade-off between accuracy and complexity

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Abstract

Wireless network traffic estimation is of paramount importance for commercial network operators. Accurate traffic forecasting impacts resource allocation that ultimately influences the quality of service perceived by network clients. This kind of forecasting is particularly challenging in real-time scenarios since this operation demands the implementation of advanced artificial intelligence algorithms, which may be prohibitively complex in some scenarios. This article analyzes the trade-off between traffic forecasting accuracy and the complexity of artificial intelligence algorithms applied to this end. Such analysis brings a consistent contribution since the results allow network operators to select the most suitable model for their needs, leading to a well-organized resource sharing initiative that can reduce the amount of investment demanded by these operators to provide a high quality of service to their clients.

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References

  1. Kunst R, Avila L, Pignaton E, Bampi S, Rochol J (2018) Improving network resources allocation in smart cities video surveillance. Comput Netw 134:228–244

    Article  Google Scholar 

  2. Kliks A, Musznicki B, Kowalik K, Kryszkiewicz P (2018) Perspectives for resource sharing in 5g networks. Telecommun Syst 68:605–619

    Article  Google Scholar 

  3. Zhang Y, Zhou Y, Liu Z, Barua B, Nguyen DHN (2019) Toward efficient network resource sharing: from one-sided market to two-sided market. IEEE Wirel Commun 27:1–7

  4. Al-Turjman F, Radwan A, Mumtaz S, Rodriguez J (2017) Mobile traffic modelling for wireless multimedia sensor networks in IoT 112:09

  5. Qi L, Yan S, Peng M (2018) Modeling and performance analysis in UAV assisted ultra dense networks. In: 2018 IEEE international conference on communications workshops (ICC workshops), pp 1–6

  6. Papadopouli M, Raftopoulos E, Shen H (2006) Evaluation of short-term traffic forecasting algorithms in wireless networks. In: 2006 2nd conference on next generation internet design and engineering, 2006. NGI ’06., pp 8–109

  7. Liu Y, Lee JYB (2014) An empirical study of throughput prediction in mobile data networks. In: 2015 IEEE global communications conference (GLOBECOM), pp 1–6

  8. Naimi S, Busson A, Vèque V, Slama LBH, Bouallegue R (2014) Anticipation of etx metric to manage mobility in ad hoc wireless networks. In: ADHOC-NOW

  9. Noulas A, Scellato S, Lathia N, Mascolo C (2012) Mining user mobility features for next place prediction in location-based services. In: 2012 IEEE 12th international conference on data mining, pp 1038–1043

  10. Strobl C, Malley J, Tutz G (2009) An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol methods 14(4):323–48

    Article  Google Scholar 

  11. Xu Q, Mehrotra S, Mao ZM, Li J (2013) Proteus: network performance forecast for real-time, interactive mobile applications. In: MobiSys

  12. Lye KW, Yuan XM, Cai TX (2009) A spectrum comparison method for demand forecasting. SIMTech Tech Rep 10(1):32–35

  13. Bengio Y, Courville AC, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35:1798–1828

    Article  Google Scholar 

  14. Khatib T, Mohamed A, Sopian K, Mahmoud M (2012) Assessment of artificial neural networks for hourly solar radiation prediction. Int J Photoenergy. https://doi.org/10.1155/2012/946890

  15. Cheepati KR, Prasad TN (2016) Performance comparison of short term load forecasting techniques. Int J Grid Distrib Comput 9(4):287–302

    Article  Google Scholar 

  16. Feldman V (2007) Hardness of proper learning (1988; pitt, valiant)

  17. Matinmikko M, Palola M, Saarnisaari H, Heikkilä M, Prokkola J, Kippola T, Hänninen T, Jokinen M, Yrjölä S (2013) Cognitive radio trial environment: first live authorized shared access-based spectrum-sharing demonstration. IEEE Veh Technol Mag 8(3):30–37

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Luis Antonio L. F. da Costa & Edison Pignaton de Freitas

  2. University of Rio dos Sinos Valley (UNISINOS), São Leopoldo, Brazil

    Rafael Kunst

Authors
  1. Luis Antonio L. F. da Costa
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  2. Rafael Kunst
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  3. Edison Pignaton de Freitas
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Correspondence to Luis Antonio L. F. da Costa.

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Costa, L.A.L.F.d., Kunst, R. & de Freitas, E.P. Intelligent resource sharing to enable quality of service for network clients: the trade-off between accuracy and complexity. Computing 104, 1219–1231 (2022). https://doi.org/10.1007/s00607-021-01042-5

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  • DOI: https://doi.org/10.1007/s00607-021-01042-5

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