ملخص: As video traffic dominates the Internet, it is important for operators to detect video quality of experience (QoE) to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection--based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for RE al-time QU ality of experience metric detection for E ncrypted T raffic- Requet -which is suitable for network middlebox deployment. Requet uses a detection algorithm that we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a machine learning algorithm to predict QoE metrics, specifically buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi and LTE network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12×, 1.53×, and 3.14×, respectively.

لغة: إنجليزية