Characteristics of YouTube network traffic at a campus network - Measurements, models, and implications

By M. Zink, Kyoungwon Suh, Yu Gu, J. Kurose. Comput. Networks, 2009

392 citations recorded by the source corpus.

User-Generated Content has become very popular since new web services such as YouTube allow for the distribution of user-produced media content. YouTube-like services are different from existing traditional VoD services in that the service provider has only limited control over the creation of new content. We analyze how content distribution in YouTube is realized and then conduct a measurement study of YouTube traffic in a large university campus network. Based on these measurements, we analyzed the duration and the data rate of streaming sessions, the popularity of videos, and access patterns for video clips from the clients in the campus network. The analysis of the traffic shows that trace statistics are relatively stable over short-term periods while long-term trends can be observed. We demonstrate how synthetic traces can be generated from the measured traces and show how these synthetic traces can be used as inputs to trace-driven simulations. We also analyze the benefits of alternative distribution infrastructures to improve the performance of a YouTube-like VoD service. The results of these simulations show that P2P-based distribution and proxy caching can reduce network traffic significantly and allow for faster access to video clips.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/e7dbde3d5f530395c216f8223bcce593d9965ab1

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Citations in the corpus

YouTube everywhere: impact of device and infrastructure synergies on user experience (2011, neutral): “We investigate the users behavior and correlateit withthe systemperformance.”
UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities (2018, background): “, a flow that has been classified as Youtube by our classification component can either be a video streaming flow, browsing flows, or flows generated by redirections between various content servers of Google and Youtube [130], [131].”
FemtoCaching: Wireless Content Delivery Through Distributed Caching Helpers (2013, methodology): “For a more detailed experimental evaluation, using actual Youtube request traces from a campus [43], and taking the actual LTE scheduling into account in the downlink, we refer the reader to the papers [10], [11].”
Network characteristics of video streaming traffic (2011, background): “[26] study the viewing patterns in a campus network and suggest proxy caches for enhancing the user experience and reducing the network traffic.”
Confused, timid, and unstable: picking a video streaming rate is hard (2012, background): “Other work looks at network characteristics of video streaming traffic, rather than focusing on the client or viewer experiences [11, 19, 24].”
FemtoCaching: Wireless video content delivery through distributed caching helpers (2011, methodology): “We simulate using the user request trace of YouTube videos from the Amherst campus [5] [6].”