Statistical user behavior detection and QoE evaluation for thin client services

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.