NDLI: TailGate: handling long-tail content with a little help from friends

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TailGate: handling long-tail content with a little help from friends

Content Provider	ACM Digital Library
Author	Laoutaris, Nikolaos Traverso, Stefano Trestian, Ionut Erramilli, Vijay Huguenin, Kévin Papagiannaki, Kostantina
Abstract	Distributing long-tail content is an inherently difficult task due to the low amortization of bandwidth transfer costs as such content has limited number of views. Two recent trends are making this problem harder. First, the increasing popularity of user-generated content (UGC) and online social networks (OSNs) create and reinforce such popularity distributions. Second, the recent trend of geo-replicating content across multiple PoPs spread around the world, done for improving quality of experience (QoE) for users and for redundancy reasons, can lead to unnecessary bandwidth costs. We build TailGate, a system that exploits social relationships, regularities in read access patterns, and time-zone differences to efficiently and selectively distribute long-tail content across PoPs. We evaluate TailGate using large traces from an OSN and show that it can decrease WAN bandwidth costs by as much as 80% as well as reduce latency, improving QoE. We deploy TailGate on PlanetLab and show that even in the case when imprecise social information is available, TailGate can still decrease the latency for accessing long-tail YouTube videos by a factor of 2.
Starting Page	151
Ending Page	160
Page Count	10
File Format	PDF
ISBN	9781450312295
DOI	10.1145/2187836.2187858
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-04-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Geo-replication Content distribution Long-tail Social networks
Content Type	Text
Resource Type	Article

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