A closer look at thin-client connections: statistical application identification for QoE detection

Running desktop-as-a-service solutions in remote data centers is an emerging means of delivering virtual PCs in an inexpensive, secure, and easy-to-maintain way. The fact that such solutions rely on the presence of connectivity between users and their virtual PCs poses a challenging operational question: what is the quality of experience of the user when running a particular application inside the thin-client protocol? The challenge is to understand whether the path between the client and the server has enough resources to sustain the rendering of the specific application. To address this question, we propose a method that exploits statistical classification to infer on-the-fly the class of applications running inside a given thin-client connection. We then correlate such information with the key factor that limits thin-client performance (i.e., network latency) to obtain the current user's QoE. We evaluate how machine-learning techniques can robustly detect applications that exchange data over the Microsoft Remote Desktop Protocol, with promising results (over 80 percent of accuracy for multimedia content). To the best of our knowledge, this is the first attempt of using statistical techniques to monitor thin-client applications for QoE detection.