peer-assisted content distribution pablo rodriguez christos gkantsidis
TRANSCRIPT
Peer-Assisted Content DistributionPablo RodriguezChristos Gkantsidis
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Traditional Content Distribution
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
Server Farm
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Content Distribution Evolution
Hype
Realism
Growth
CachingIP Multicast
CDNsAkamai
EnterpriseCDNs
Layer-7 SwitchesSatellite CDNs
P2P
1999
2000
2001
2002
2003
2004
Disappointment
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Peer-Assisted Content Distribution
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Peer-Assisted Content DistributionDesktop 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!!
Server Farm
10
100
1000
10000
100000
1000000
10000000
Time (sec)
Nu
mb
er
of
Clien
ts S
erv
ed
Cooperative
Client/Server
4 MB file. Server 100 Mbps. Client 1 Mbps
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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
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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
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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
1 2 65
Server
3 4
1 5 6 2 4
1 2 3 4 5 6
3
[Rodriguez, Biersack, Infocom’00]
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1 2 65
The Challenge
Server
3 4
1 5 6 2 4
1 2 3 4 5 6
3
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
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Avalanche: Improving file swarming using Coding Techniques
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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
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Source
The Problem of Efficient Scheduling of Information
Node A Node B
Block 1Block 2
Node C
Block 1
Block 1, or 2, or 12?
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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
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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)].
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Avalanche Coding
B1 B2 Bn
Server
1 2
Client A
1 2 n
E1 E2
Client B
1 2
E3
[Chou et al., ’03]
• Content is encoded at the server• Clients can produce new encoded packets out of partial files
n
File
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Avalanche Robustness
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.
Typical file-swarming systems
Avalanche
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Avalanche Download Time
Fin
ish
Tim
es
Nodes (sorted by order of arrival)
AvalancheTypical swarming
Peers using typical file-swarming
techniques that did not finish.
=> Much lower and predictable download times
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No need for nodes to stay around…
• With Avalanche, there is no need for nodes to stay after they finish the download to help other nodes (the performance remains unchanged)
Nodes stay for ever
Nodes leave immediately
Nodes (sorted by order of arrival)
Fin
ish
Tim
es
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Minimum Server Requirements
Less than half the server requirements compared to systems based on current file-swarming techniques.
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Decoding Performance
Avalanche trades-off better speeds and less server load for more processing power at each node
File Size (MB) Blocks Time
10 100 5 sec
50 100 37 sec
100 100 2m 21 sec
200 100 3m 38 sec
Note: Pentium III, 650MHz, 512MB RAM.
Decoding time is less than 4% of the total download
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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