engleski

Statistical User Behavior Detection and QoE Evaluation for Thin Client Services

Remote desktop connection (RDC) services offer clients ac- cess to remote content and services, commonly used to access their work- ing 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 access- ing RDC services. We first identify different behavioral categories, and conduct traffic analysis to determine a feature set to be used for classi- fication 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.

user behaviour; remote desktop connection; traffic classifi- cation; machine learning

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