Nishanth Sastry

King’s College London

Saving the Internet from On-demand Video Streaming

Content

Plan for the talk

• Content Delivery: a brief introduction

• Analysis of drawbacks of current systems

• The CD-GAIN take on content dlivery

http://bit.ly/cd-gain

Some statistics

• Cisco Visual Networking Index 2013-18:

– By 2018, videos will be 79% of all traffic

*Currently they comprise 66% of all traffic

• Netflix alone is 33% of peak time US traffic

• 44% of UK households using BBC iPlayer

Introducing BBC iPlayer

• 9 months: May 2013 – Jan 2014

• 1.9 Billion sessions

• In one representative month:– 32 Million users/devices

– 20 Million IP addresses

• In London alone, in one month:– 1.26 Million IPs

– 2.15 Million users/devices

How do Netflix/Youtube/iPlayer

scale?

?

If Internet connection is bottleneck,

bypass it by replicating!

Multihoming

Content Delivery Network

If Internet connection is bottleneck, bypass it by replicating!

Replication has fundamentally

changed the Internet’s structure

2007

Page 14 - Labovitz SIGCOMM 2010

Traditional Internet Model

Replication has fundamentally

changed the Internet’s structure

C. Labovitz et al., Internet Inter-domain Traffic.

Proc. SIGCOMM 2010

2009

Page 15 - Labovitz SIGCOMM 2010

A New Internet Model

Flatter and much more densely interconnected Internet

Disintermediation between content and “eyeball” networks

New commercial models between content, consumer and transit

Settlement Free

Pay for BW

Pay for access BW

Problem solved! But is solution right?

1. No longer an “Internet” of connected nets

– Have hyper-giants become “too big to fail”?

Problem solved! But is solution right?

2. Distributed systems hard to engineer:

– Consistency

– Failover

– …

Global scale distributed systems extremely hard!

3. Global replica infrastructure is expensive

– Content providers need to pay hyper-giants

– Or… be hyper-giants themselves

Problem solved! But is solution right?

4. Even CDNs don’t cover all over the globe:

performance and cost diverge by region

HH Liu et al., Optimizing cost and performance for content multihoming. SIGCOMM 2012

5. Misses opportunities for local sharing!

Problem solved! But is solution right?

Taking stock with

TV ContentHow did we consume content before?

How do we consume content now?

What can we learn from what we see?

How did we watch TV before?

http://www.watfordobserver.co.uk/nostalgia/memories/10099510.Coronation_treat_as_community_gathers_around_the_only_TV/

Today, TV is just another “app”

What changed: Push Pull

Superficially: audience to TV set ratio has decreased

At a fundamental level:

audience per “broadcast” is lower

“Broadcast” time is chosen by the consumer

Traditional mass media pushed content to consumer

Current dominant model has changed to pull

But people have not changed!

New Directions for

Content Delivery

1. Select few items become globally popular

Can we exploit redundancy using P2P?

2. …but individual users may have favourites

Can we predict user quirks/favourites and personalise content delivery?

3. What if we could in fact change users?

Can we “nudge” user behaviour and make content delivery cheaper for all?

1. Can we exploit

redundancy with peer-

assistance?P2P works at scale for Long Duration content such as TV

under “online while you watch” model.

P2P-assisted content delivery:

Looks good, but details important!

Simple model – augmenting traditional delivery:

Server-based content delivery as mainstay

Shift seamlessly to P2P as more users join

Peer availability offloads traffic from provider!

? Will there be enough peers in swarms?

• Peer arrivals may be asynchronous

• Peers may not participate in uploads

? Can P2P swarms be ISP-friendly & local?

• …and still work well?

Swarm fragmentation Factors

• ISP friendliness

• Bitrate stratification

• Partial participation

• Limited upload bandwidth

Taffic offloading gain as a function of

peer availability (swarm capacity, c)Model swarms as infinite-server queue (extending Menasche et al,. CoNEXT 2009)

• Server load increases with no. of users

• … until swarm has one user on average

• Subsequent increase in load decreasesserver traffic as swarm takes over!

Let’s test on real data from London

Gains in swarms fragmented by

ISP-locality & Bit-rate stratification

Why fragmentation does no harm?

Top 8 ISPs = 70% traffic Top 2 bitrates=70% sessions

“Online while you watch” model

critical for ensuring availability

ISP-friendly P2P is also greener

because of fewer hops to replica!

Carbon savings of P2P over CDN

for one ISP’s topology

2. Can we personalise

content delivery?Users are highly predictable.

Simple analytics can offload traffic and

decrease carbon footprint

Why iPlayer, not DVRs?

• DVRs have >50% penetration in US, UK

• Many (e.g. YouView) don’t need cable

• Could also use TV tuner and record on laptop

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

Because, people don’t remember to record!

Can we help users record

what they want to watch?

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

Speculative Content Offloading and

Recording Engine

Caching at-the-very-edge completely offloads traffic!

Which features to use?-I

• BBC proposes, consumer disposes!

• Serials:~50% of content corpus;

80% of watched content!

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

Which features to use?-II

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

Which features to use?-III

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

SCORE=predictor+optimiser

• Predict using user affinity for

• Serials: Episodes of same programme

• Favourite genres

• We can optimise for decreasing traffic or carbon footprint

• Decreasing carbon decreases traffic, but not vice versa

• Turns out we only take 5-15% hit by focusing on carbon

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

Performance evaluation

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

Compare SCORE wrt. Oracle knowing future requests

Oracle saves:

• Up to 97% of traffic

• Up to 74% of energy

• Savings relatively insensitive to choice

of energy model parameters

• SCORE: ~40-60% of Oracle savings

energy than traffic opt.

Not all of these savings come from

predicting popular content

• Indiscriminately recording top n shows can lead to

negative energy savings!

• Personalised approach necessary, despite

popularity of “prime time” content

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

3. ‘Nudging’ user

behaviourDecrease content delivery costs by

asking user to “go easy” on

infrastructure

What is ‘nudging’?

Current mindset: User is king

Operators/providers attempt to

satisfy all user accesses

Idea: ‘Nudge’ user to behaviours

better suited to network!

Passive nudgingGive users flexibility to choose: on-demand!

Active nudgingTime-shift users’ access pattern

E.g., lower price for off-peak access

Space-shift users’ accesses to different ISP

E.g., move smartphones from 3G Wifi

(Applying SCORE to smart phones)

Content-shifting: suggest alternate items for

users to watch, based on cache contents!

Digital Media Convergence:

Remember the hype?

Good News: it has happened

CD-GAIN: New directions for a

Content-centric Internet

1. Can we exploit redundancy using P2P?

– YES, but “online while you watch” is critical

2. Can we predict user quirks/favourites and personalise content delivery?

– YES, Speculative Content Offloading and Recording Engine (SCORE)

3. Can we “nudge” user behaviour and make content delivery cheaper for all?

Guiding principles

1. Cache as close to user as possible

2. Increase cache reuse by any means!

3. Decrease peak usage: infrastructure

can be provisioned for smaller load

• Can increase average use

(speculative traffic is fine!)

Saving the Internet from On-demand Video Streaming

Content

http://bit.ly/c

d-gainNishanth Sastry

King’s College London

Joint work with:Mustafa Al-Bassam, King’s College London

Jigna Chandaria, BBC R&D

Jon Crowcroft, U. Cambridge

Nick Feamster, Georgia Tech

Dmytro Karamshuk, King’s College London

Richard Mortier, Uni. Nottingham

Gianfranco Nencioni, Uni. Pisa

Andy Secker, BBC R&D

Gareth Tyson, Queen Mary London

Funding support from UK EPSRC