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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Tolia, N., Andersen, D.G., Satyanarayanan, M.: Quantifying Interactive User Experience on Thin Clients. Computer 39(3), 46–52 (2006)
University of Waikato: WEKA - Waikato Environment for Knowledge Analysis, http://www.cs.waikato.ac.nz/ml/weka/
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Suznjevic, M., Skorin-Kapov, L., Humar, I. (2014). User Behavior Detection Based on Statistical Traffic Analysis for Thin Client Services. In: Rocha, Á., Correia, A., Tan, F., Stroetmann, K. (eds) New Perspectives in Information Systems and Technologies, Volume 2. Advances in Intelligent Systems and Computing, vol 276. Springer, Cham. https://doi.org/10.1007/978-3-319-05948-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-05948-8_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05947-1
Online ISBN: 978-3-319-05948-8
eBook Packages: EngineeringEngineering (R0)