P2P Search COP5711. 2 P2P Search Techniques Centralized P2P systems e.g. Napster, Decentralized unstructured P2P systems e.g. Gnutella.

P2P SearchCOP5711

2

P2P Search Techniques Centralized P2P systems

e.g. Napster, SETI@home

Decentralized & unstructured P2P systems e.g. Gnutella

Hybrid - partially decentralized e.g., Freenet

Structured P2P systems DHT CAN

P2P Network P2P network is an overlay

network built on top of a real physical network (e.g., Internet)

In a P2P network, peers are network nodes connected by virtual or logical links

A logical link is a path through many physical links in the underlying network

3

4

Napster server(Central Catalog)

(xyz.mp3, 192.1.2.3)

192.1.2.3

Napster: Publish a File

Users upload their IP address and music titles they wish to share

5

Users search for peers to download desired files

xyz.mp3 ?

192.1.2.3192.1.2.3

Napster: Query for a File

Central Napster server

6

File transfer is P2P, using a proprietary protocol

192.1.2.3

xyz.mp3 ?

Napster: Transfer Requested File

Central Napster server

7

Disadvantage of Centralized Directory

Performance bottleneck

Single point of failure

Can we do it without a directory ?

8

Decentralized P2P - Gnutella No catalog

Pings network to locate Gnutella peers

File requests are broadcast to peers

Flooding or breadth-first research

When provider is located, the file is transferred via HTTP

9

Who are my neighbors ?

Gnutella: Join the Network

Peers areInternetedges

Special peer maintained by Gnutella

Pings network

to locate peers

10

xyz.mp3 ?

Gnutella: Broadcast Request to Peers

11

Gnutella: Flood the Request (Breadth-first research)

I have it.

12

xyz.mp3

Gnutella: Reply with the File(via HTTP)

I have it.

13

Gnutella - Disadvantages Network flooding - unnecessary

network traffic

Using TTL - some files might not be found

Alternatively, using ultranodes (or supernodes)using depth-first search, i.e., Freenet

14

Morpheus, KazaaFlooding only the Supernodes

Cluster

Cluster

Cluster

Center Index for its cluster

C

B

A

F

E

D

I

H

G

Query: “W

ho has

file X”

Reply: “Peer H

has

file X”

Download file X from Peer H

SupernodeLayer

15

Using Ultranodes Queries flood only the network of

ultranodes

Other peer nodes shielded from query traffic

Combine the benefits of centralized and decentralized search;

Take advantage of the heterogeneity in peer capabilities;

16

Freenet - Depth-First Search

A

B

D

C

E

Query: “Who has file X”

Peer D might have file X

Peer E might have file X

Reply: “I have file X”

Reply : “Peer E has file X”

Reply : “Peer E

has file X”

Download file X from Peer E

Peer C might

have file X

17

Freenet – File not Found

A

B

D

C

E

Peer D might have file X

Peer E might have file X

Peer C might

have file X F

NOT FOUND !

The requested file not found due to a poor routing decision made at peer D

In this case, query backs out of the dead-end, and tries another peer in depth-first manner

I havefile X

Using Distributed Directory Data objects are everywhere

Distribute subsets of the data directory among peers

If we can find the relevant sub-directory, we can locate the data object

18

DirectoryData

ObjectsSub-directory

19

How to Bound Search Space ?Basic Idea - Hashing

Hash key

Object “y”

Objects have hash keys

Peer “x”Peer nodes also have hash keys in the same hash space

P2P Network

y xH(y) H(x)

Join (H(x))Publish (H(y))

Place location information about an object at the peer with closest hash keys (i.e., a distributed directory)

20

Viewed as a Distributed Hash Table

Hash table0 2128-1

Peer nodes• Each peer node is responsible for a range of

the hash table, according to the peer hash key

• Location information about Objects are placed in the peer with the closest key (information redundancy)

21

How to Find an Object ?Looks for a peer /w the corresponding peer hash key

A peer knows its logical neighbors Find peer X based on multihop routing X knows who has the object

Hashtable

0 2128-1

Peernode X

Peer Y has the file

22

K V

K V

K V

K V

K V

K V

K V

K V

K V

K V

K V

Dynamic Hash Table (DHT) in action

23

K V

K V

K V

K V

K V

K V

K V

K V

K V

K V

K V

DHT in action

24

K V

K V

K V

K V

K V

K V

K V

K V

K V

K V

K V

DHT in action: put()

insert(K1,V1)

Operation: Route message, “I have the file,” to node holding key K1

Want to share a

file

25

(K1,V1)

K V

K VK V

K V

K V

K V

K V

K V

K V

K V

K V

DHT in action: put()

Operation: take key as input; route messages to node holding key

26

retrieve (K1)

K V

K VK V

K V

K V

K V

K V

K V

K V

K V

K V

DHT in action: get()

Operation: Retrieve message V1 at node holding key K1

27

K V

K V

K V

K V

K V

K V

K V

K V

K V

K V

K V

DHT in action

Retrieve file according to V1

28

Still Flooding

Still flood the network although intermediate nodes do not need to search

Can we avoid flooding ?

29

CAN – Content Addressable Network Each peer is

responsible for one zone, i.e., stores all (key, value) pairs of the zone

Each peer knows the neighbors of its zone

Random assignment of peers to zones at startup – split zone if not empty

Dimensional-ordered multihop routing

30

CAN: Object Publishing

node I::publish(K,V) I

31

(1) a = hx(K)

CAN: Object Publishingx = a

node I::publish(K,V) I

32

(1) a = hx(K) b = hy(K)

CAN: Object Publishingx = a

y = b

node I::publish(K,V) I

33

(1) a = hx(K) b = hy(K)

CAN: Object Publishing

(2) route (K,V) -> J

node I::publish(K,V) I

J

34

(2) route (K,V) -> J

(3) J stores (K,V)

CAN: Object Publishing

(K,V)

node I::publish(K,V) I

(1) a = hx(K) b = hy(K)

J

35

(2) route “retrieve(K)” to J that is in charge of (a,b)

(K,V)(1) a = hx(K) b = hy(K)

node I::retrieve(K)

I

CAN: Object Retrieval

J

36

Maintenance

Inform neighbors that you are alive at discrete time interval t

If your neighbor does not send alive message in time t, takeover its zone

P2P Benefits Efficient use of resources

Use unused bandwidth, storage, and processing power at the edge of the network

Scalability Consumers of resources also donate resources

Reliability Replicas, geographic distribution No single point of

failure Ease of administration

Self organized nodes Built-in reliability and load balancing

37

Some Prototypes at UCF iSEE (Internet-scale Sensor Exploration Environement)Publishing real-time sensor data

Browsing and querying real-time sensor data

P2P Video Streaming for VoD and Live Broadcast Applications

38