1. Optimizing P2P Networks: Lessons learned from social networking

a) Social Networksb) Lessons Learnedc) Are P2P Networks Social??d) Organizing P2P Networks

2. Peer Topologiesa) Centralized, Ring, Hierarchical & Decentralizedb) Hybrid:

o Centralized-Ring o Centralized-Centralizedo Centralized-Decentralized

c) Reflector Nodes

3. Gnutella Case Studiesa) 3 case studies

Scalability

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Scalability

“You can’t scale better than by utilising someone else’s computer.”

•Paul James1

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Limewire Gnutella Coding

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Social Networks

• Stanley Milgram (Harvard professor) – 1967 social networking experiment• How many ‘social hops’ would it take for messages to traverse through the US population (200 million)

• Posted 160 letters randomly chosen people in Omaha, Nebraska

Boston

Omaha

• Asked them to try to pass these letters to a stockbroker working in Boston, Massachusetts

• Rules:• use intermediacies whom they know on a first name basis• chosen intelligently• make a note at each hop

• 42 letters made it !!

• Average of 5.5 hops

• Demonstrated the ‘small world effect’

Proved that the social network of the United States is indeed connected with a path-length (number of hops) of around 6 – The 6 degrees of separation !

Does this mean that it takes 6 hops to traverse 200 million people?? 4

Lessons Learned from Milgrim’s Experiment

• Social circles are highly clustered

• A few members have wide-ranging connections• these form a bridge between far-flung social clusters • this bridging plays a critical role in bringing the network closer together

For example • A quarter of all letters passed through a local storekeeper• A half were mediated by just 3 people

Lessons Learned • These people acted as gateways or hubs between the source and the wider world• A small number of bridges dramatically reduces the number of hops

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From Social Networks toComputer Networks…

• There are a number of similarities to social networks • People = peers• Intermediaries = Hubs, Gateways or Rendezvous Nodes (JXTA speak...)• Number of intermediaries passed through = number of hops

Are P2P Networks Special then? • P2P networks are more like social networks than other types of computer network because they are often:

• Self Organizing• Ad-Hoc• Employ clustering techniques based on prior interactions (like we form relationships) • Decentralized discovery and communication (like we form neighbourhoods, villages, cities etc)

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• Problem: how do we organize peers within ad-hoc, multi-hop pervasive P2P networks?

• network of self-organizing peers organized in a decentralized fashion• such networks can rapidly expand from a few hundred peers to several thousand or even millions

Peer to Peer: What’s the problem?

• P2P Environment Recap:• Unreliable Environments• Peers connecting/disconnecting – network failures to participation• Random Failures e.g. power outages, Cable, DSL failure, hackers• Personal machines are much more vulnerable than servers• algorithms have to cope with this continuous restructuring of the network core.

• P2P systems need to treat failures as normal occurrences not freak exceptions

• must be designed in a way that promotes redundancy with the tradeoff of a degradation of performance 7

For P2P

• This does not mean abstract numerical benchmarks e.g. how many milliseconds will it take to compute this many millions of FFTs?

• Rather, it means asking question like:• How long will it take to retrieve this particular file? • How much bandwidth will this query consume?• How many hops will it take for my package to get to a peer on the far side of the network?•If I add/remove a peer to the network will the network still be fault tolerant?•Does the network scale as we add more peers. Such networks can rapidly expand from a few hundred peers to several thousand or even millions

So, how do we Organize Networks inOrder to Get Optimum Performance?

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3 main factors that make P2P networks more sensitive to performance issues:

Performance Issues in P2P Networks

1. Communication.• Fundamental necessity• Users connected via different connections speeds• Multi-hop

2. Searching• No central Control so more effort is needed• Each hop adds to total bandwidth – problems: time outs

3. Equal Peers• Free Riders – unbalance in the harmonicity of network• Degrades performance for others• Need to get this right to adjust accordingly

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• Core• Centralized• Ring• Hierarchical• Decentralized

• Hybrid• Centralized-Ring• Centralized-Centralized• Centralized-Decentralized

Peer Topologies

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Centralized

• Client/server• Web servers• Databases• Napster search• Instant Messaging• Popular Power

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Ring

• Fail-over clusters• Simple load balancing• Assumption

– Single owner

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Hierarchical

• Tree structure• DNS• Usenet (sort of)

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Decentralized

• Gnutella• Freenet• Internet routing

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Centralized + Ring

• Robust web applications• High availability of servers

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Centralized + Centralized

• N-tier apps• Database heavy systems• Web services gateways• Google.com uses this topology

to deliver their service

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Centralized + Decentralized

• New Wave of P2P• Clip2 Gnutella Reflector (next)• FastTrack

– KaZaA– Morpheus

• Email• Like Social Networks perhaps ?

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F1.mp3 – ID0:F1.mp3

…

C F1.mp3

F2.mp3

F3.mp3

0

1

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Reflector Nodes

• Known as ‘super peers’ – in JXTA these are Rendezvous peers• cache file list of connected users – maintain an index• When a query is issued, the Reflector does not retransmit it - it answers the query from its own memory

• Do they remind you of anything ?

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NapsterGnutella

User

Napster.com

Gnutella Super Peers:1. Natural??

2. Reflector (clip2.com)

=?

User

Napster

N2

N3

Napster Duplicated Servers

Napster = Gnutella?

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The figure below is a view of the topology of a Gnutella network as shown on the LimeWire web site, the popular Gnutella file-sharing client. Notice how the power-law or centralized-decentralized structure is demonstrated.

The Gnutella Network Today

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Another View of the Gnutella Network

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Gnutella Studies 1: Free Riding

E. Adar and B.A. Huberman (2000), “Free Riding on Gnutella,” First Monday 5(10),

http://firstmonday.org/issues/issue5_10/adar/index.html

Two types of free riding

1. download files but never provide any files for other to download

2. users that have undesirable content

• They found 22,084 of the 33,335 peers in the network (66%) of the peers share no files

• 24,347 or 73% share ten or less files

• top 1 percent (333 hosts) represent 37 percent of the total files shared

• 20 percent (6,667 hosts) sharing 98% of the files

shows - even without Gnutella Reflector nodes, the Gnutella network naturally converges into a centralized + decentralized topology with the top 20% of nodes acting as super peers or reflectors

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Gnutella Studies 2: Equal Peers

Study on Reflector Nodes [clip] www.clip2.com

Studied Gnutella for one month

• Noted an apparent scalability barrier when query rates went above 10 per second.

Why??

•Gnutella query = 560 bits long and queries make up approximately one quarter of traffic.

• Each peer is connect to three peers, so: 560 *10 * 3 = 16,800 bytes per second

• This is a quarter of the traffic so total traffic 67,200 bytes per second.

• a 56-K link cannot keep up with this amount of traffic

• one node connected in the incorrect place can grind the whole network to a halt.

• This is why P2P networks place slower nodes at the edges

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Gnutella Studies 3: Communication

Peer-to-Peer Architecture Case Study: Gnutella NetworkMatei Ripeanu, on-line at:

http://people.cs.uchicago.edu/~matei/PAPERS/P2P2001.pdf

Studied topology of Gnutella over several months & reported two findings: 1. Gnutella network shares the benefits and drawbacks of a power-law structure

- networks that organize themselves so that most nodes have a few links and a small number of nodes have many

- found to show an unexpected degree of robustness when facing random node failures.

- vulnerable to attacks e.g. by removing a few of the super nodes can have a massive effect on the function of the network as a whole.

2. Gnutella network topology does not match well with the underlying Internet topology leading to inefficient use of network bandwidth.

He gave 2 suggestions:

1. use an agent to monitor network and intervene by asking servents to drop/add links to keep the topology optimal.

2. replace the Gnutella flooding mechanism with a smarter routing and group communication mechanism.

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What about other topologies: The Future?

• Centralized + Hierarchical?– Back end tree of information– Caching architectures

• Decentralized + Ring?– P2P network of fail-over clusters

• More ??

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1. Summarya) Centralized + Decentralized – understand from the

original Gnutella to the new models

b) The role of Reflector nodes

2. Further Information: Distributed Hashtable Modelsa) Pastry: http://research.microsoft.com/~antr/pastry

b) Chord: http://www.pdos.lcs.mit.edu/chord/

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Closing Remarks