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YouTube traffic dynamics and its interplay with a tier-1 ISP: an ISP perspective

Published: 01 November 2010 Publication History

Abstract

In this paper we conduct an extensive and in-depth study of traffic exchanged between YouTube data centers and its users, as seen from the perspective of a tier-1 ISP in Spring 2008 after YouTube was acquired by Google but before Google did any major restructuring of YouTube. Using flow-level data collected at multiple PoPs of the ISP, we first infer where the YouTube data centers are located and where they are connected to the ISP. We then deduce the load balancing strategy used by YouTube to service user requests, and investigate how load balancing strategies and routing policies affect the traffic dynamics across YouTube and the tier-1 ISP.
The major contributions of the paper are four-fold: (1) we discover the surprising fact that YouTube does not consider the geographic locations of its users at all while serving video content. Instead, it employs a location-agnostic, proportional load balancing strategy among its data centers to service user requests from all geographies; (2) we perform in-depth analysis of the PoP-level YouTube traffic matrix as seen by the ISP, and investigate how it is shaped by the YouTube load balancing strategy and routing policies utilized by both YouTube and the ISP; (3) with such knowledge, we develop a novel method to estimate unseen traffic (i.e. traffic that is carried outside the ISP network) so as to "complete" the traffic matrix between YouTube data centers and users from the customer ASes of the ISP; and 4) we explore "what if" scenarios by assessing the pros and cons of alternative load balancing and routing policies. Our study sheds light on the interesting and important interplay between large content providers and ISPs in today's Internet.

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  • (2022)Software‐defined content delivery network at the edge for adaptive video streamingInternational Journal of Network Management10.1002/nem.221032:6Online publication date: Aug-2022
  • (2021)Optimal server selection for competitive service providers in network virtualization contextTelecommunication Systems10.1007/s11235-021-00764-3Online publication date: 9-Mar-2021
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cover image ACM Conferences
IMC '10: Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
November 2010
496 pages
ISBN:9781450304832
DOI:10.1145/1879141
  • Program Chair:
  • Mark Allman
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2010

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Author Tags

  1. YouTube
  2. proportional load balancing
  3. unseen traffic estimation

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  • Research-article

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IMC '10
IMC '10: Internet Measurement Conference
November 1 - 30, 2010
Melbourne, Australia

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Overall Acceptance Rate 277 of 1,083 submissions, 26%

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Cited By

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  • (2023)Where is the Traffic Going? A Comparative Study of Clouds Following Different DesignsIEEE Transactions on Services Computing10.1109/TSC.2022.318204716:2(1473-1484)Online publication date: 1-Mar-2023
  • (2022)Software‐defined content delivery network at the edge for adaptive video streamingInternational Journal of Network Management10.1002/nem.221032:6Online publication date: Aug-2022
  • (2021)Optimal server selection for competitive service providers in network virtualization contextTelecommunication Systems10.1007/s11235-021-00764-3Online publication date: 9-Mar-2021
  • (2020)Delay-Sensitive Multicast in Inter-Datacenter WAN Using Compressive Latency MonitoringIEEE Transactions on Cloud Computing10.1109/TCC.2017.27690808:1(86-96)Online publication date: 1-Jan-2020
  • (2020)End-to-End Latency Prediction for General-Topology Cut-Through Switching NetworksIEEE Access10.1109/ACCESS.2020.29661398(13806-13820)Online publication date: 2020
  • (2020)Dissecting the performance of YouTube video streaming in mobile networksInternational Journal of Network Management10.1002/nem.205830:3Online publication date: 14-May-2020
  • (2019)Distributed Data Center Bandwidth Allocation for Cloud-Based StreamingIEEE Transactions on Sustainable Computing10.1109/TSUSC.2017.27224274:2(263-276)Online publication date: 1-Apr-2019
  • (2019)Tapping the Knowledge of Dynamic Traffic Demands for Optimal CDN DesignIEEE/ACM Transactions on Networking10.1109/TNET.2018.288116927:1(98-111)Online publication date: 1-Feb-2019
  • (2019)Coupled Tensor Decomposition for User Clustering in Mobile Internet Traffic Interaction PatternIEEE Access10.1109/ACCESS.2019.28942677(18113-18124)Online publication date: 2019
  • (2018)Five years at the edgeProceedings of the 14th International Conference on emerging Networking EXperiments and Technologies10.1145/3281411.3281433(1-12)Online publication date: 4-Dec-2018
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