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Measuring Traffic Gains from Peer-assisted Content Delivery

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To effectively serve massive volumes of video traffic content delivery networks (CDNs) are turning to clients for assistance, creating hybrid peer-assisted content delivery systems. In this project we analyze how peer-assisted CDNs are affected by a number of design obstacles which include: the need of localizing peer-to-peer traffic within ISPs (isp-friendliness), reluctance of users to participate in redistributing the content (partial participation) and necessity to match users with similar bitrate requirements (bitrate stratification)

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Page 1: Measuring Traffic Gains from Peer-assisted Content Delivery

CD-GAIN: Measuring Traffic Gains from Peer-assisted Content Delivery

Dmytro Karamshuk1, Nishanth Sastry1, Jigna Chandaria2, Andy Secker2

1King's College London, 2BBC R&D

ESPRC project in collaboration with BBC R&D

Page 2: Measuring Traffic Gains from Peer-assisted Content Delivery

Video Delivery Network user

44% of UK' households

user user user

user user

user

Video traffic now accounts for 66% of all traffic on the Internet and is predicted to reach 79% by 2018

over 50% of all traffic for streaming TV

Appetite comes with Eating

BBC iPlayer

Page 3: Measuring Traffic Gains from Peer-assisted Content Delivery

Content is King

Video Delivery Network

user

user user

user

useruser

average of 5K users online every sec in the first day after release

5K duplicates

Solution: ask users for assistance

Page 4: Measuring Traffic Gains from Peer-assisted Content Delivery

Higher Demand means Higher Savings

ci=ui r iAverage number of peers online

G(c)=1−(c(1+ec c−m

(m Γ(m)−Γ(1+m , c)))m

+1)

−1

How much we can offload to peers

Model traffic swarms as infinite-server queue(extending Menasche et al., CoNext 2009)

The share of traffic which can be offloaded to peers is growing with the demand and the size of content

request ratecontent duration

Page 5: Measuring Traffic Gains from Peer-assisted Content Delivery

London, Sep. 2013

Users - 2.2 M IP address - 1.3 M Sessions - 15.9 M Traffic - 6.5 Gbps

Records in the Dataset:<network id, ISP, user id, request time, session duration,

bitrate, content id>

Page 6: Measuring Traffic Gains from Peer-assisted Content Delivery

So, How Much we can Offload?

theo

retic

al

● model is in a good agreement with simulations● significant traffic savings can be achieved despite constraints

Page 7: Measuring Traffic Gains from Peer-assisted Content Delivery

What do we do with the ObstaclesEight biggest ISPs account for

over 70% of trafficTwo main bitrate formats

dominate in over 70% of sessions

Fragmentation of traffic by ISP and bitrate formats yields several large content swarms

Page 8: Measuring Traffic Gains from Peer-assisted Content Delivery

Top-5% of the content corpus accounts for 80% of traffic

Why it works in the end?

● “online while watching” model is important

● most traffic is generated by a small amount of popular content

Page 9: Measuring Traffic Gains from Peer-assisted Content Delivery

Takeaways● online while watching model and long-duration content are crucial factors for feasibility of peer-assistant content delivery

● fragmentation of peer-to-peer swarms by ISP, quality format and content doesn't impose significant limitations on the system

● up to 80% of all traffic for the whole content corpus in iPlayer can be offloaded for the biggest ISPs

Page 10: Measuring Traffic Gains from Peer-assisted Content Delivery

Visit our website: http://bit.ly/cd-gain

Dmytro KaramshukKing's College London

follow me on Twitter: @karamshuk