Advertisement

User Behavior Detection Based on Statistical Traffic Analysis for Thin Client Services

  • Mirko Suznjevic
  • Lea Skorin-Kapov
  • Iztok Humar
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 276)

Abstract

Remote desktop connection (RDC) services offer clients access to remote content and services, commonly used to access their working environment. With the advent of cloud-based services, an example use case is that of delivering virtual PCs to users in WAN environments. In this paper, we aim to analyze common user behavior when accessing RDC services. We first identify different behavioral categories, and conduct traffic analysis to determine a feature set to be used for classification purposes. We then propose a machine learning approach to be used for classifying behavior, and use this approach to classify a large number of real-world RDCs. Obtained results may be applied in the context of network resource planning, as well as in making Quality of Experience-driven resource allocation decisions.

Keywords

user behaviour remote desktop connection traffic classification machine learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lai, A.M., Nieh, J.: On the Performance of Wide-Area Thin-Client Computing. ACM Transactions on Computer Systems (TOCS) 24(2), 175–209 (2006)CrossRefGoogle Scholar
  2. 2.
    Casas, P., Seufert, M., Egger, S., Schatz, R.: Quality of Experience in Remote Virtual Desktop Services. In: 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), pp. 1352–1357. IEEE (2013)Google Scholar
  3. 3.
    Dusi, M., Napolitano, S., Niccolini, S., Longo, S.: A Closer Look at Thin-Client Connections: Statistical Application Identification for QoE Detection. IEEE Communications Magazine 50(11), 195–202 (2012)CrossRefGoogle Scholar
  4. 4.
    Staehle, B., Binzenhöfer, A., Schlosser, D., Boder, B.: Quantifying the Influence of Network Conditions on the Service Quality Experienced by a Thin Client User. In: 2008 14th GI/ITG Conference Measuring, Modelling and Evaluation of Computer and Communication Systems (MMB), VDE, pp. 1–15 (2008)Google Scholar
  5. 5.
    Sen, S., Spatscheck, O., Wang, D.: Accurate, Scalable In-Network Identification of P2P Traffic Using Application Signatures. In: Proceedings of the 13th International Conference on World Wide Web, pp. 512–521. ACM (2004)Google Scholar
  6. 6.
    Emmert, B., Binzenhöfer, A., Schlosser, D., Weiß, M.: Source Traffic Characterization for Thin Client Based Office Applications. In: Pras, A., van Sinderen, M. (eds.) EUNICE 2007. LNCS, vol. 4606, pp. 86–94. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Humar, I., Bester, J., Tomazic, S.: Characterizing Graphical Desktop Sharing System’s Workload in Collaborative Virtual Environments. In: Consumer Communications and Networking Conference, pp. 1–5. IEEE (2009)Google Scholar
  8. 8.
    Humar, I., Pustisek, M., Bester, J.: Evaluating Self-Similar Processes for Modeling Graphical Remote Desktop Systems’ Network Traffic. In: 10th International Conference on Telecommunications, pp. 243–248. IEEE (2009)Google Scholar
  9. 9.
    Nguyen, T.T., Armitage, G.: A Survey of Techniques for Internet Traffic Classification Using Machine Learning. IEEE Communications Surveys & Tutorials 10(4), 56–76 (2008)CrossRefGoogle Scholar
  10. 10.
    Park, B., Won, Y.J., Choi, M.J., Kim, M.S., Hong, J.W.: Empirical Analysis of Application-Level Traffic Classification Using Supervised Machine Learning. In: Ma, Y., Choi, D., Ata, S. (eds.) APNOMS 2008. LNCS, vol. 5297, pp. 474–477. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Tolia, N., Andersen, D.G., Satyanarayanan, M.: Quantifying Interactive User Experience on Thin Clients. Computer 39(3), 46–52 (2006)CrossRefGoogle Scholar
  12. 12.
    University of Waikato: WEKA - Waikato Environment for Knowledge Analysis, http://www.cs.waikato.ac.nz/ml/weka/
  13. 13.
    Arumaithurai, M., Seedorf, J., Dusi, M., Monticelli, E., Lo Cigno, R.: Quality-of-Experience driven Acceleration of Thin Client Connections. In: 12th IEEE International Symposium on Network Computing and Applications (NCA), pp. 203–210. IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mirko Suznjevic
    • 1
  • Lea Skorin-Kapov
    • 1
  • Iztok Humar
    • 2
  1. 1.Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia
  2. 2.Faculty of Electrical EngineeringUniversity of LjubljanaLjubljanaSlovenia

Personalised recommendations