Abstract
Maintaining a satisfactory customer Quality of Experience (QoE) is of vital importance for video service providers such as Netflix or Amazon Prime Video. Network faults degrade QoE and must therefore be detected, isolated, and fixed. However, this is difficult because each part of the end-to-end path belongs to a different autonomous system (AS) that is typically owned by a different entity, such as the video streaming provider, the internet service provider (ISP), and the client's local network operator. Although the video service provider (VSP) is usually blamed by the customer when there is poor QoE, the VSP does not have access to many parts of the network to localize the issue. In this paper, we show that with the aid of AI, it is possible for the VSP to localize the network fault without having access to the faulty part and using only QoE metrics. We collected a dataset from an actual video streaming testbed, where multiple videos are streamed from a video server through a simplified ISP network to a client network. Actual faults were generated in both the ISP and the client networks. Using only the QoE metrics measured at the client side, we use the deep learning methods of multi-layer perceptron (MLP) and long-short-term memory (LSTM) to detect and localize the fault with an accuracy of 93–97%, depending on the situation. Impact Statement —Technologically, our work impacts video/game streaming service providers such as Netflix, YouTube, Amazon Prime, Google Stadia, Sony PlayStation Now, Nvidia GeForce Now, and videoconferencing providers such as Zoom and Skype. Our work enables these providers to train similar AI systems that can localize network problems using only the video quality of experience (QoE) recorded by their client software. They can then take an appropriate action, such as rerouting traffic using Open Connect Appliances (OCA) if available, using another network provider if they have contracts with more than one, or informing the owner of the network segment with the fault, so they can fix the problem and maintain their customers’ QoE at a satisfactory level. Economically, our work can contribute to the market expansion of any video streaming solution because it will lead to better QoE, which is synonymous with more customers.
Highlights
I T IS safe to say that video has already taken over the internet, when considering that video traffic constituted 75% of all IP traffic in 2017, and will constitute 82% of all IP traffic by 2022 [1]
Quality of Experience (QoE) is a subjective measure of how satisfied the user is with the video viewing experience
video quality metrics (VQM) and similar metrics take into account factors such as the frequency of video bitrate changes, the video player’s buffer level, which is an indication of rebuffering events—one of the most significant negative influences on the user’s QoE, the frequency of rebuffering events, and other factors
Summary
I T IS safe to say that video has already taken over the internet, when considering that video traffic constituted 75% of all IP traffic in 2017, and will constitute 82% of all IP traffic by 2022 [1]. To the best of our knowledge, this is the first work that has studied the feasibility of using only the video player’s actual (not predicted) QoE metrics with ML to detect and isolate faults in networks To this end, we employed an industry testbed at Ciena Corp., presented, to collect QoS and QoE metrics, the former used only for verification as ground truth, not as features in our dataset. In the proactive approach [8], the system uses QoS parameters measured directly from network monitoring and inputs them into an approximation function or a trained AI method to predict the end users’ QoE If this QoE starts to degrade, it tries to detect, isolate, and remedy the fault. Our method during operation requires neither QoS metrics nor network topology, and uses only the QoE metrics measured at the client end
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