Statistical User Behavior Detection and QoE Evaluation for Thin Client Services

Mirko Suznjevic1, Lea Skorin-Kapov1 and Iztok Humar2

  1. University of Zagreb, Faculty of Electrical Engineering and Computing
    Unska 3, 10000, Zagreb, Croatia
  2. University of Ljubljana, Faculty of Electrical Engineering
    Tržaška 25, 1000 Ljubljana, Slovenia


Remote desktop connection (RDC) services offer clients the ability to access remote content and services, commonly in the context of accessing their working environment.With the advent of cloud-based services, an example use case is that of delivering virtual PCs to users inWAN environments. In this paper, we aim to detect and analyze common user behavior when accessing RDC services, and use this as input for making Quality of Experience (QoE) estimations and subsequently providing input for effective QoE management mechanisms. We first identify different behavioral categories, and conduct traffic analysis to determine a feature set to be used for classification purposes. We propose a machine learning approach to be used for classifying behavior, and use this approach to classify a large number of real-world RDCs. We further conduct QoE evaluation studies to determine the relationship between different network conditions and subjective end user QoE for all identified behavioral categories. Results show an exponential relationship between QoE and delay and loss degradations, and a logarithmic relationship between QoE and bandwidth limitations. Obtained results may be applied in the context of network resource planning, as well as in making QoE-driven resource allocation decisions.

Key words

user behavior, remote desktop connection, traffic classification, machine learning, QoE

Digital Object Identifier (DOI)

Publication information

Volume 12, Issue 2 (June 2015)
Year of Publication: 2015
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Suznjevic, M., Skorin-Kapov, L., Humar, I.: Statistical User Behavior Detection and QoE Evaluation for Thin Client Services. Computer Science and Information Systems, Vol. 12, No. 2, 587-605. (2015),