I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Youtube traffic characterization: a view from the edge
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Analyzing video services in Web 2.0: a global perspective
Proceedings of the 18th International Workshop on Network and Operating Systems Support for Digital Audio and Video
On dominant characteristics of residential broadband internet traffic
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Challenging statistical classification for operational usage: the ADSL case
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Comparing DNS resolvers in the wild
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
YouTube traffic dynamics and its interplay with a tier-1 ISP: an ISP perspective
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
On profiling residential customers
TMA'11 Proceedings of the Third international conference on Traffic monitoring and analysis
Dissecting Video Server Selection Strategies in the YouTube CDN
ICDCS '11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems
A longitudinal view of HTTP video streaming performance
Proceedings of the 3rd Multimedia Systems Conference
Analyzing the impact of YouTube delivery policies on user experience
Proceedings of the 24th International Teletraffic Congress
Internet video delivery in youtube: from traffic measurements to quality of experience
DataTraffic Monitoring and Analysis
| Hi-index | 0.00 |
Online video services account for a major part of broadband traffic with streaming videos being one of the most popular video services. We focus on the user perceived quality of YouTube videos as it can serve as a general index for customer satisfaction. Our tool, Pytomo [1], is a tomography tool that is designed to measure the playback quality of videos as if they are being viewed by a user. We model the YouTube video player to estimate the playback interruptions as experienced by a user watching a YouTube video. We also examine topology and download statistics such as delay towards the server, download rates, and buffering duration. We aim to analyze different DNS resolvers to obtain the IP address of the video server. We study how the DNS resolution impacts the performance of the video download, and thus, the video playback quality. As the tool is intended to run on multiple ISPs, we have discovered some interesting results in YouTube distribution policies. These results can be applied to any content-delivery networks (CDN) architecture and should help users to better understand what are the key performance factors of video streaming.