A Public Dataset for YouTube's Mobile Streaming Client

Karagkioules, Theodoros; Tsilimantos, Dimitrios; Valentin, Stefan; Wamser, Florian; Zeidler, Bernd; Seufert, Michael; Loh, Frank; Tran-Gia, Phuoc

doi:10.23919/tma.2018.8506503

Cited by 15 publications

(11 citation statements)

References 12 publications

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“…Datasets/File types are used interchangeably throughout the study. Datasets are public and manually developed collected from UCI, YouTube and own sources [98]- [99]. In all cases, same quantity of training and testing is maintained.…”

Section: Methodsmentioning

confidence: 99%

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

et al. 2020

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Maintaining accuracy in load balancing using metaheuristics is a difficult task even with the help of recent hybrid approaches. In the existing literature, various optimized metaheuristic approaches are being used to achieve their combined benefits for proper load balancing in the cloud. These approaches often adopt multi-objective QoS metrics, such as reduced SLA violations, reduced makespan, high throughput, low overload, low energy consumption, high optimization, minimum migrations, and higher response time. The cloud applications are generally computation-intensive and can grow exponentially in memory with the increase in size if no proper effective and efficient load balancing technique is adopted resulting in poor quality solutions. To provide a better load balancing solution in cloud computing, with extensive data, a new hybrid model is being proposed that performs classification on the number of files present in the cloud using file type formatting. The classification is performed using Support Vector Machine (SVM) considering various file formats such as audio, video, text maps, and images in the cloud. The resultant data class provides high classification accuracy which is further fed into a metaheuristic algorithm namely Ant Colony Optimization (ACO) using File Type Formatting FTF for better load balancing in the cloud. Frequently used QoS metrics, such as SLA violations, migration time, throughput time, overhead time, and optimization time are evaluated in the cloud environment and comparative analysis is performed with recent metaheuristics, such as Ant Colony Optimization-Particle Swarm Optimization (ACOPS), Chaotic Particle Swarm Optimization (CPSO), Q-learning Modified Particle Swarm Optimization (QMPSO), Cat Swarm Optimization (CSO) and D-ACOELB. The proposed algorithm outperforms them and provides good performance with scalability and robustness.

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Section: Methodsmentioning

confidence: 99%

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

et al. 2020

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“…For each video streaming session, packet sizes and arrival times are collected, as well as DNS lookup responses to obtain a mapping between IP addresses and domain names serving YouTube contents. Finally, also the recently published open dataset [3] was considered, which consists of YouTube video streaming network and application measurements, related to the usage of the native Android YouTube app.…”

Section: Methodology and Evaluation Datasetmentioning

confidence: 99%

Let me Decrypt your Beauty: Real-time Prediction of Video Resolution and Bitrate for Encrypted Video Streaming

Wassermann

Seufert

Casas

et al. 2019

2019 Network Traffic Measurement and Analysis Conference (TMA)

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The dynamic adaptation of the video quality induced by HTTP Adaptive Streaming (HAS) technology introduces new Quality of Experience (QoE) metrics beyond re-buffering. In this work we address the problem of real-time QoE monitoring of HAS, focusing on the continuous prediction of video resolution and average video bitrate, for the particular case of YouTube. Through empirical evaluations over a large video dataset, we demonstrate that it is possible to accurately predict the specific video resolution, as well as the average video bitrate, both in real time, and using a time granularity as small as one new prediction every second, not achieved by other proposals in the literature.

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“…Nowadays, several challenges must be resolved for in-depth quality estimation in video streaming: First, data must be measured on a large scale. This is already being done for several platforms, including, among others, YouTube [ 20 ], Netflix and Hulu [ 21 ], and the Amazon Echo Show [ 22 ]. Afterwards, a flow separation and detection is essential to receive only the correct video flow for further prediction.…”

Section: Related Workmentioning

confidence: 99%

Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming

Loh

Poignée

Wamser

et al. 2021

Sensors

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Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.

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A Public Dataset for YouTube's Mobile Streaming Client

Cited by 15 publications

References 12 publications

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

Let me Decrypt your Beauty: Real-time Prediction of Video Resolution and Bitrate for Encrypted Video Streaming

Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming

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