Inferring Netflix User Experience from Broadband Network Measurement

Madanapalli, Sharat Chandra; Gharakhieli, Hassan Habibi; Sivaraman, Vijay

doi:10.23919/tma.2019.8784609

Cited by 15 publications

(8 citation statements)

References 11 publications

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“…In real-time or streaming services, a five or one percent of the affected users might represent a significant number of users unsubscribing from those services, which will probably cause a severe drop in the overall company income. As an example, a well-known streaming platform, like Netflix, has around 180 million users, 63 where a five percent of these users represent nine million users having a bad user experience or degraded performance.…”

Section: Guestbook User Facing Applicationmentioning

confidence: 99%

Chaos as a Software Product Line—A platform for improving open hybrid‐cloud systems resiliency

et al. 2022

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Nowadays, cloud-native software architectures have a significant relevance due to the speed and agility they provide. These properties lead relevant organizations in different industries, like video streaming (Netflix), car-sharing (Uber, Cabify), banking (BBVA, HSBC), and governmental agencies (NASA, FBI, CERN, ESA) to heavily rely on cloud-native software to run their business-critical applications. Additionally, including fault injection actions in the production infrastructure allows companies to have consistent environments, to improve applications dependability against unexpected failures, to provide better user experience, and to improve the overall system quality. Thus, cloud computing technologies allow development teams to rapidly create complex systems and to continuously deploy them, at a global scale. This work describes Pystol, a novel fault injection platform-represented as aSoftware Product Line-to analyze the effects caused by a wide spectrum of adverse conditions. Pystol is designed to be executed on top of cloud-native environments, either in private or public clouds. The proposed architecture shows a way for representing feature models based on Unified Model Language (in short, UML) component diagrams. Furthermore, we present a thorough empirical study carried out in real-world environments, providing promising results.

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Section: Guestbook User Facing Applicationmentioning

confidence: 99%

Chaos as a Software Product Line—A platform for improving open hybrid‐cloud systems resiliency

et al. 2022

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“…Video QoE From Network: Recently, many researchers [15], [16], [17], [18], [19] have studied QoE metrics for video streaming services across providers such as YouTube, Netflix, Facebook, Bilibili and Amazon, particularly focusing on VoD. Among existing works, only [17] studied QoE for live streaming services (Twitch) by estimating only the resolution metric.…”

Section: Related Workmentioning

confidence: 99%

Modeling Live Video Streaming: Real-Time Classification, QoE Inference, and Field Evaluation

Madanapalli¹,

Mathai²,

Gharakheili³

et al. 2021

Preprint

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Social media, professional sports, and video games are driving rapid growth in live video streaming, on platforms such as Twitch and YouTube Live. Live streaming experience is very susceptible to short-time-scale network congestion since client playback buffers are often no more than a few seconds. Unfortunately, identifying such streams and measuring their QoE for network management is challenging, since content providers largely use the same delivery infrastructure for live and videoon-demand (VoD) streaming, and packet inspection techniques (including SNI/DNS query monitoring) cannot always distinguish between the two.In this paper, we design, build, and deploy ReCLive: a machine learning method for live video detection and QoE measurement based on network-level behavioral characteristics. Our contributions are four-fold: (1) We analyze about 23,000 video streams from Twitch and YouTube, and identify key features in their traffic profile that differentiate live and on-demand streaming. We release our traffic traces as open data to the public; (2) We develop an LSTM-based binary classifier model that distinguishes live from on-demand streams in real-time with over 95% accuracy across providers; (3) We develop a method that estimates QoE metrics of live streaming flows in terms of resolution and buffer stall events with overall accuracies of 93% and 90%, respectively; and (4) Finally, we prototype our solution, train it in the lab, and deploy it in a live ISP network serving more than 7,000 subscribers. Measurements from the field show that 99.8% of Twitch videos are streamed live, while this measure is only 2.3% for YouTube. Further, during peak hours as many as 15% of live video streams are played at low-definition resolution and about 7% of them experience a buffer stall. Our method provides ISPs with fine-grained visibility into live video streams, enabling them to measure and improve user experience.

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“…• Moving Averages: it has been shown [11], [20] that adjacent requests are not uncorrelated, and that information about recent history of the streaming session can give information on its future behavior. For this reason, we compute the moving average of both the inter-request Time between last packet of R i−1 received by the client and h i Inter-Packets Arrival Time distribution 25-th, 50-th and 75-th percentiles of IPA Time Inter-Packets Arrival Time Coefficient of Variation Ratio between standard deviation and mean of IPA Time Fig.…”

Section: Online Feature Extractionmentioning

confidence: 99%

Machine-Learning Based Prediction of Next HTTP Request Arrival Time in Adaptive Video Streaming

Pimpinella

Redondi

Loh

et al. 2021

2021 17th International Conference on Network and Service Management (CNSM)

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Continuously monitoring the network activity to proactively recognise possible problems and prevent users QoE degradation is a major concern for network operators, for both mobile radio and home networks. Considering video streaming applications, which generate the majority of overall Internet traffic, monitoring the chunk requests from the video client to the video server is of particular interest, as they not only indicate that a download burst is imminent, but their type (e.g., request of an audio or video chunk) and frequency also allow to estimate which and how much data will be downloaded to the client. In this work, we propose a machine-learning based video streaming traffic monitoring architecture able to i) predict when next uplink request will be issued by the video client and ii) classify the type of next uplink request. We evaluate the system performance on a dataset of more than 900 HTTP adaptive streaming sessions and 15,000 request-response exchanges, where both the predictor of the next request arrival and the request type classifier are fed with lightweight features extracted from encrypted traffic in an online fashion, both in the uplink and downlink directions of the traffic. Results show that i) the system is able to classify the type of a HAS uplink requests with an accuracy greater than 95 % and ii) pipe-lining request type classification and prediction of next request arrival time improves the final prediction performance.

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Inferring Netflix User Experience from Broadband Network Measurement

Cited by 15 publications

References 11 publications

Chaos as a Software Product Line—A platform for improving open hybrid‐cloud systems resiliency

Chaos as a Software Product Line—A platform for improving open hybrid‐cloud systems resiliency

Modeling Live Video Streaming: Real-Time Classification, QoE Inference, and Field Evaluation

Machine-Learning Based Prediction of Next HTTP Request Arrival Time in Adaptive Video Streaming

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