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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 in WAN 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.

Statistical user behavior detection and QoE evaluation for thin client services