Peer-Assisted Content Distribution Networks: Techniques and Challenges

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Peer-Assisted Content Distribution Networks
Techniques and Challenges

• Pei Cao
• Stanford University

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Traditional Intra-Provider Content Distribution
Networks
National Center
Regional Center
. . .
Branch
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Users
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Peer-to-Peer Content Distribution
National Center
Regional Center
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Branch
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Users
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. . .
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P2P vs CDN

• P2P
• No infrastructure cost
• Supply grows linearly with demand
• Simple distributed, randomized algorithms
• No QoS
• CDN
• Initial infrastructure cost
• Centralized scheduling algorithms
• Network efficiency
• Capable of supporting QoS

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Combine P2P with CDN?

• Use P2P to complement CDN
• P2P reduces load on the CDN, covers areas where
  CDN is not installed
• Must be able to control, or shape, P2P traffic
• Use CDN to complement P2P
• CDN steps in when peer-based distribution is
  falling short, enabling QoS
• Must be able to detect when peers wont meet the
  delivery time guarantee

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Outline

• Review of BitTorrent
• Traffic-shaping BitTorrent biased neighbor
  selection
• QoS in BitTorrent delivery time prediction

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BitTorrent File Sharing Network

• Goal replicate K chunks of data among N nodes
• Form neighbor connection graph
• Neighbors exchange data

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BitTorrent Neighbor Selection
Tracker file.torrent
1
Seed
Whole file
4
3
2
5
A
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BitTorrent Piece Replication
Tracker file.torrent
1
Seed
Whole file
3
5
A
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BitTorrent Piece Replication Algorithms

• Tit-for-tat (choking/unchoking)
• Each peer only uploads to 7 other peers at a time
• 6 of these are chosen based on amount of data
  received from the neighbor in the last 20 seconds
• The last one is chosen randomly, with a 75 bias
  toward newcomers
• (Local) Rarest-first replication
• When peer 3 unchokes peer A, A selects which
  piece to download

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Analysis of BitTorrent

• Conclusion from modeling studies BitTorrent is
  nearly optimal in idealized, homogeneous networks
• Demonstrated by simulation studies
• Confirmed by theoretical modeling studies
• Intuition in a random graph,
• Prob(Peer As content is a subset of Peer Bs)
  50

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Traffic-Shaping BitTorrent
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Random Neighbor Graph

• Existing studies all assume random neighbor
  selection
• BitTorrent no longer optimal if nodes in the same
  ISP only connect to each other
• Random neighbor selection ? high cross-ISP traffic

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Difficulty in Traffic-Shaping P2P Applications

• ISPs
• Different links have different monetary costs
• Prefer clustering of traffic
• P2P Applications
• No knowledge of underlying ISP topology
• Use randomized algorithms that dont do well
  under clustering
• Current solution throttling ? users suffer

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A Network-Friendly BitTorrent?

• ISPs inform BitTorrent of its link preferences
• Algorithm of BitTorrent is adjusted such that
  both users and ISPs benefit
• Example Biased Neighbor Selection
• Works when cost function is transitive

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Biased Neighbor Selection

• Idea of N neighbors, choose N-k from peers in
  the same ISP, and choose k randomly from peers
  outside the ISP

ISP
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Implementing Biased Neighbor Selection

• By Tracker
• Need ISP affiliations of peers
• Peer to AS maps
• Public IP address ranges from ISPs
• Special X- HTTP header
• By traffic shaping devices
• Intercept peer ? tracker messages and
  manipulate responses
• No need to change tracker or client

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Evaluation Methodology

• Event-driven simulator
• Use actual client and tracker codes as much as
  possible
• Calculate bandwidth contention, assume perfect
  fair-share from TCP
• Network settings
• 14 ISPs, each with 50 peers, 100Kb/s upload,
  1Mb/s download
• Seed node, 400Kb/s upload
• Optional university nodes (1Mb/s upload)
• Optional ISP bottleneck to other ISPs

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Limitation of Throttling
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Throttling Cross-ISP Traffic
Redundancy Average of times a data chunk
enters the ISP
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Biased Neighbor Selection Download Times
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Biased Neighbor Selection Cross-ISP Traffic
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Importance of Rarest-First Replication

• Random piece replication performs badly
• Increases download time by 84 - 150
• Increase traffic redundancy from 3 to 14
• Biased neighbors Rarest-First ? More uniform
  progress of peers

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Presence of External High-Bandwidth Peers

• Biased neighbor selection alone
• Average download time same as regular BitTorrent
• Cross-ISP traffic increases as of university
  peers increase
• Result of tit-for-tat
• Biased neighbor selection Throttling
• Download time only increases by 12
• Most neighbors do not cross the bottleneck
• Traffic redundancy (i.e. cross-ISP traffic) same
  as the scenario without university peers

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Comparison with Simple Clustering

• Gateway peer only one peer connects to the peers
  outside the ISP, all other peers only connect to
  peers inside the ISP
• Gateway peer must have high bandwidth
• It is the seed for this ISP
• Ends up benefiting peers in other ISPs

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Combining Biased Neighbor Selection with Caches

• Under random neighbor selection
• bandwidth requirement of cache is high
• Under biased neighbor selection
• bandwidth needed from the cache is reduced by an
  order of magnitude

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Conclusions

• By choosing neighbors well, BitTorrent can
  achieve high peer performance without increasing
  ISP cost
• Biased neighbor selection choose initial set of
  neighbors well
• Can be combined with throttling and caching
• ? BitTorrents algorithm can be shaped!

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Delivery Time Prediction
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Motivation

• Provide delivery time guarantee under P2PCDN
• What contributes to delivery time of a download
  via BitTorrent?
• From simulations seed bandwidth and even
  replication of blocks
• Missing node join/leave dynamics, TCP effects,
  etc.

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Side-by-Side Live Experiments

• Two clients, running on the same machine,
  starting at the same time, downloading the same
• 13 experiments from Apr-May 2006
• File sizes 700MB 1.4GB
• Network size 1100 2100 peers
• Duration 10 hrs 2 days

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Results from Experiments

• Effective download rate 10 30KB/s
• Speed difference between the two peers 3 82
• What made the slower peer slow?

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Suspicion 1 Slower Neighbors?

• Calculate unweighted average of observed
  throughput at application level
• R1 average from all neighbors
• R2 average from neighbors uploading gt250KB of
  data
• R3 average from neighbors uploading gt2.5MB of
  data
• Low correlation between download-time ratio and
  neighbor-speed ratio
• 0.57 for R1, 0.43 for R2, 0.47 for R3
• Faster neighbors corresponds to slower downloads
  in 3 experiments

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Suspicion 2 Fewer Neighbors Uploading to the
Peer?

• Slot analysis calculate download concurrency
• Maximum number of neighbors 35
• Neighbors come and go ? align neighbors into 35
  slots
• Calculate time-average of number of concurrent
  slots with neighbors uploading
• Upload concurrency varies from 7 to 11
• Explains one of the download-time/neighbor-speed
  reversal case
• But doesnt explain the two others

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Close Neighbors

• 90 of data downloaded from 1-4 of neighbors
• Let F(p) and G(p) be the number of neighbors that
  provides p of data to peers F and G, then
• F(p) gt G(p) ? peer F is slower than G
• This holds for p 90, 75, and 50

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What makes a neighbor close?

• Not related to speed, or order of connection to
  peer, or order of unchoking by peer

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Cost of Departure of a Close Neighbor

• Departure cost if one close neighbor leaves,
  calculate the time until the earliest next close
  neighbor
• The average departure cost 30 min
• ? The convergence time of the tit-for-tat
  algorithm is slow

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Why Do Close Neighbors Leave

• Five possible reasons
• A Random disconnect
• B Finished downloading
• C Peer broke off the relationship
• D Neighbor broke off the relationship
• Results B is most common, followed by C/D, then A

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Conclusions

• Content delivery time in BitTorrent is determined
  by
• Neighbor upload speed
• Stability of neighbor relationship
• Disruption of the pairing leads to long delivery
  time
• Neighbors may leave due to random disconnection,
  completion of download, or finding faster
  neighbors

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Using CDN to Complement P2P

• Use nodes CDN as high-speed specially managed
  seeds
• Seeds are called to help whenever a node loses a
  close neighbor

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Summary

• A way to shape BitTorrent traffic
• Predicting BitTorrent performance by monitoring
  close peer relationship

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Related Work

• Many modeling studies of BitTorrent
• Simulation studies
• Measurements of real torrents

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Ongoing Work

• Live experiments with biased neighbor selections
• A k-regular graph algorithm with faster
  convergence
• Prototype implementation of P2PCDN