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Peer-Assisted Content Distribution. Pablo Rodriguez Christos Gkantsidis. Traditional Content Distribution. Server Farm. Often, large content needs to be distributed to millions of clients: Currently: Huge server farms Infrastructure-based solutions (e.g. Akamai)

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peer assisted content distribution

Peer-Assisted Content Distribution

Pablo Rodriguez

Christos Gkantsidis

traditional content distribution
Traditional Content Distribution

Server Farm

Often, large content needs to be distributed to millions of clients:

  • Currently:
  • Huge server farms
  • Infrastructure-based solutions (e.g. Akamai)

slow, expensive, non scalable

content distribution evolution
Content Distribution Evolution

Layer-7 Switches

Satellite CDNs

CDNs

Akamai

Disappointment

Hype

P2P

Caching

IP Multicast

Enterprise

CDNs

Growth

Realism

1999

2000

2001

2002

2003

2004

peer assisted content distribution2

4 MB file. Server 100 Mbps. Client 1 Mbps

Peer-Assisted Content Distribution

Server Farm

Desktop PCs can help each other!

  • Clients become new servers
  • Capacity increases with the number of clients
  • Limitless scalability and fast speeds at extremely low cost!!
examples
Examples
  • Updates/Critical Patches
    • Adding large servers and egress capacity to absorb pick load is quite expensive
    • Alternative solution is to delay clients
      • Patches do not arrive on-time
  • Software Distribution
  • TV On-Demand. Movie/Music downloads
  • PodCasting
  • Enterprise content distribution
p2p content distribution
P2P Content Distribution
  • Benefits:
    • Dramatically improves speed
    • Limitless scalability
    • Minimum server requirements
    • Very cheap
  • Challenges:
    • Requires incentives for cooperation
    • Hard to ensure end2end full connectivity
    • Security
    • Manageability
    • Lack of locality increases transit costs for ISPs
    • Asymmetric links (traffic engineering)
    • Variable bandwidth, peers come and go
    • Need for more sophisticated distribution algorithms
p2p swarming

Server

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P2P Swarming
  • File is divided into many small pieces for distribution
  • Clients request different pieces from the server or from other clients
  • Clients become servers for those pieces downloaded
  • When all pieces are downloaded, clients can re-construct the whole file

4

1

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3

[Rodriguez, Biersack, Infocom’00]

the challenge

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The Challenge
  • If there are many users,
  • deciding which is the best piece to
  • download can be very hard!!
  • Incorrect decisions result in low throughput, nodes not able to finish, bandwidth wasted, etc.

Solutions that require to have full

knowledge of who has what are non-

scalable

Server

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slide11
Goal
  • Provide a very fast and robust Peer-Assisted solution for the distribution of legal content
  • Current problems in existing File Swarming solutions:
    • Rare-blocks are hard to obtain
    • Tit-for-tat incentive mechanisms decrease speeds
    • Arrival of new users slows down old users
    • Heterogeneous nodes do not interact well
    • Same information travels repeatedly over bottleneck links
    • Too much dependency from seeds
    • Sudden departures can prevent peers from finishing
the problem of efficient scheduling of information
The Problem of Efficient Scheduling of Information

Source

Block 1

Block 1

Block 2

Node C

Node A

Node B

Block 1, or 2, or 12?

the avalanche magic
The Avalanche Magic
  • To solve problems of existing P2P file distribution

solutions, Avalanche uses special encoding algorithms

  • Each encoded piece has the “DNA” of all pieces in the file.

=> A given encoded piece can be used by any peer in place of any piece

  • Encoded pieces are created using linear equations that involve all pieces in the file
  • Reconstructing the file requires collecting enough encoded pieces and solving the set of mathematical equations
coding in general
Coding in general
  • Assume file: F = [x1 x2], where xi is a block.
  • Define code Ei(ai,1, ai,2) = ai,1*x1+ ai,2*x2, where ai,1, ai,2 are numbers.
  • “Infinite” number of Ei’s.
  • Any two linearly independent Ei(ai,1, ai,2) can recover [x1 x2].
    • Similar as solving a system of linear equations.
  • Operations in finite fields [such as GF(216)].
avalanche coding
Avalanche Coding

File

B1

B2

Bn

Server

b1

b2

a2

a1

an

bn

Client A

E1

E2

w1

w2

Client B

E3

  • Content is encoded at the server
  • Clients can produce new encoded packets out of partial files

[Chou et al., ’03]

avalanche robustness
Avalanche Robustness

Avalanche

Typical file-swarming systems

If server suddenly goes down (after serving the full file one), all Avalanche users are able to complete the download. Only 10% of users using typical file-swarming techniques are able to complete.

avalanche download time
Avalanche Download Time

Finish Times

Avalanche

Typical swarming

Peers using typical file-swarming techniques that did not finish.

Nodes (sorted by order of arrival)

=> Much lower and predictable download times

no need for nodes to stay around
No need for nodes to stay around…

Finish Times

Nodes stay for ever

Nodes leave immediately

Nodes (sorted by order of arrival)

  • With Avalanche, there is no need for nodes to stay after they finish the download to help other nodes (the performance remains unchanged)
minimum server requirements
Minimum Server Requirements

Less than half the server requirements compared to systems based on current file-swarming techniques.

decoding performance
Decoding Performance

Avalanche trades-off better speeds and less server

load for more processing power at each node

Note: Pentium III, 650MHz, 512MB RAM.

Decoding time is less than 4% of the total download

summary
Summary
  • Adding resources in an arbitrary fashion is not efficient or cost effective
  • We are witnessing a new Revolution
      • Peer-Assisted solutions can be used by content providers to provide hugely scalable, and very fast distribution of legal content at low cost