Scalability Lecture

1
Scalability Lecture

• Optimizing P2P Networks Lessons learned from
  social networking
• Social Networks
• Lessons Learned
• Are P2P Networks Social??
• Organizing P2P Networks
• Peer Topologies
• Centralized, Ring, Hierarchical Decentralized
• Hybrid
• Centralized-Ring
• Centralized-Centralized
• Centralized-Decentralized
• Reflector Nodes
• Gnutella Case Studies
• 3 case studies

2
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

• 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??
3
Lessons Learned from Milgrims 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

4
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)

5
Peer to Peer Whats the problem?

• 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

• 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

6
So, how do we Organize Networks inOrder to Get
Optimum Performance?

• 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

7
Performance Issues in P2P Networks
3 main factors that make P2P networks more
sensitive to performance issues

• 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

8
Peer Topologies

• Core
• Centralized
• Ring
• Hierarchical
• Decentralized
• Hybrid
• Centralized-Ring
• Centralized-Centralized
• Centralized-Decentralized

9
Centralized

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

10
Ring

• Fail-over clusters
• Simple load balancing
• Assumption
• Single owner

11
Hierarchical

• Tree structure
• DNS
• Usenet (sort of)

12
Decentralized

• Gnutella
• Freenet
• Internet routing

13
Centralized Ring

• Robust web applications
• High availability of servers

14
Centralized Centralized

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

15
Centralized Decentralized

• New Wave of P2P
• Clip2 Gnutella Reflector (next)
• FastTrack
• KaZaA
• Morpheus
• Email
• Like Social Networks perhaps ?

16
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 ?

17
Napster Gnutella?
Gnutella
Napster
User
Napster.com
?
1. Natural??
2. Reflector (clip2.com)
18
The Gnutella Network Today
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-decentrali
zed structure is demonstrated.
19
Another View of the Gnutella Network
20
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/inde
x.html
Two types of free riding

• download files but never provide any files for
  other to download
• 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
21
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

22
Gnutella Studies 3 Communication
Peer-to-Peer Architecture Case Study Gnutella
Network Matei Ripeanu, on-line at
http//people.cs.uchicago.edu/matei/PAPERS/P2P200
1.pdf
Studied topology of Gnutella over several months
reported two findings

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

• He gave 2 suggestions
• use an agent to monitor network and intervene by
  asking servents to drop/add links to keep the
  topology optimal.
• replace the Gnutella flooding mechanism with a
  smarter routing and group communication
  mechanism.

23
What about other topologies The Future?

• Centralized Hierarchical?
• Back end tree of information
• Caching architectures
• Decentralized Ring?
• P2P network of fail-over clusters
• More ??

24
Closing Remarks

• Summary
• Centralized Decentralized understand from the
  original Gnutella to the new models
• The role of Reflector nodes
• Further Information Distributed Hashtable Models
• Pastry http//research.microsoft.com/antr/pastry
  
• Chord http//www.pdos.lcs.mit.edu/chord/