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P2P Overlay Networks

By

Behzad Akbari Spring 2011

These slidesin some parts are based on the slides of J. Kurose (UMASS) and Sanjay Rao ()

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OutlineOutline

Overview of P2P overlay networksOverview of P2P overlay networks Applications of overlay networksApplications of overlay networks Unstructured overlay networksUnstructured overlay networks Structured overlay networksStructured overlay networks Overlay multicast networksOverlay multicast networks P2P media streaming networksP2P media streaming networks

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Overview of P2P overlay Overview of P2P overlay networksnetworks What is a P2P system?What is a P2P system?

P2P refers to applications that P2P refers to applications that take advantage of resourcestake advantage of resources (storage, cycles, content, bandwidth, human presence) (storage, cycles, content, bandwidth, human presence) available at the available at the end systems ofend systems of the internet. the internet.

What is an overlay network?What is an overlay network? Overlay networks refer to networks that are constructed on Overlay networks refer to networks that are constructed on

top of another network (e.g. IP).top of another network (e.g. IP). What is a P2P overlay network?What is a P2P overlay network?

Any overlay network that is constructed by the Internet Any overlay network that is constructed by the Internet peers in the application layer on top of the IP network.peers in the application layer on top of the IP network.

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Overview of P2P overlay Overview of P2P overlay networksnetworks P2P overlay network propertiesP2P overlay network properties

Efficient use of resourcesEfficient use of resources Self-organizingSelf-organizing

All peers organize themselves into an application layer network on top of All peers organize themselves into an application layer network on top of IP.IP.

ScalabilityScalability Consumers of resources also donate resourcesConsumers of resources also donate resources Aggregate resources grow naturally with utilizationAggregate resources grow naturally with utilization

ReliabilityReliability No single point of failureNo single point of failure Redundant overlay links between the peersRedundant overlay links between the peers Redundant data source Redundant data source

Ease of deployment and administrationEase of deployment and administration The nodes are self-organizedThe nodes are self-organized No need to deploy servers to satisfy demand.No need to deploy servers to satisfy demand. Built-in fault tolerance, replication, and load balancingBuilt-in fault tolerance, replication, and load balancing No need any change in underlay IP networksNo need any change in underlay IP networks

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Applications of P2P overlay Applications of P2P overlay networksnetworks P2P file sharingP2P file sharing

Napster, Gnutella, Kaza, Emule, Edonkey, Bittorent, etc.Napster, Gnutella, Kaza, Emule, Edonkey, Bittorent, etc. Application layer multicastingApplication layer multicasting P2P media streamingP2P media streaming Content distributionContent distribution Distributed cachingDistributed caching Distributed storageDistributed storage Distributed backup systemsDistributed backup systems Grid computingGrid computing

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Classification of P2P overlay Classification of P2P overlay networksnetworks Unstructured overlay networksUnstructured overlay networks

The overlay networks organize peers in a random graph in flat or The overlay networks organize peers in a random graph in flat or hierarchical manners.hierarchical manners.

Structured overlay networksStructured overlay networks Are based on Distributed Hash Tables (DHT)Are based on Distributed Hash Tables (DHT) the overlay network assigns keys to data items and organizes its the overlay network assigns keys to data items and organizes its

peers into a graph that maps each data key to a peer peers into a graph that maps each data key to a peer Overlay multicast networksOverlay multicast networks

The peers organize themselves into an overlay tree for The peers organize themselves into an overlay tree for multicasting.multicasting.

P2P media streaming networksP2P media streaming networks Used for large scale media streaming applications (e.g IPTV) Used for large scale media streaming applications (e.g IPTV)

over the Internet.over the Internet.

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Unstructured P2P File Sharing Unstructured P2P File Sharing NetworksNetworks Centralized Directory based P2P systemsCentralized Directory based P2P systems Pure P2P systemsPure P2P systems Hybrid P2P systemsHybrid P2P systems

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Unstructured P2P File Sharing Unstructured P2P File Sharing Networks (…)Networks (…) Centralized Directory based P2P systemsCentralized Directory based P2P systems

All peers are connected to central entityAll peers are connected to central entity Peers establish connections between each other on Peers establish connections between each other on

demand to exchange user data (e.g. mp3 demand to exchange user data (e.g. mp3 compressed data)compressed data)

Central entity is necessary to provide the service Central entity is necessary to provide the service Central entity is some kind of index/group databaseCentral entity is some kind of index/group database Central entity is lookup/routing tableCentral entity is lookup/routing table Examples:Examples: Napster, Bittorent Napster, Bittorent

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Unstructured P2P File Sharing Unstructured P2P File Sharing Networks(…)Networks(…) Pure P2P systemsPure P2P systems

Any terminal entity can be removed Any terminal entity can be removed without loss of functionalitywithout loss of functionality

No central entities employed in the No central entities employed in the overlayoverlay

Peers establish connections between Peers establish connections between each other randomlyeach other randomly To route request and response messagesTo route request and response messages To insert request messages into the overlayTo insert request messages into the overlay

Examples:Examples: Gnutella, FreeNet Gnutella, FreeNet

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Unstructured P2P File Sharing Unstructured P2P File Sharing Networks(…)Networks(…)

Hybrid P2P systemsHybrid P2P systems Main characteristic, Main characteristic,

compared to pure P2P: compared to pure P2P: Introduction of another Introduction of another dynamic hierarchical layerdynamic hierarchical layer

Election process to select Election process to select an assign Super peersan assign Super peers

Super peers: high degree Super peers: high degree (degree>>20, depending on (degree>>20, depending on network size)network size)

Leaf nodes: connected to Leaf nodes: connected to one or more Super peers one or more Super peers (degree<7(degree<7))

Example:Example: KaZaAKaZaAleafnode

Superpeer

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Centralized Directory based P2P systems : Napster

Napster history: the rise January 1999: Napster version 1.0 May 1999: company founded December 1999: first lawsuits 2000: 80 million users

Napster history: the fall Mid 2001: out of business due to lawsuits Mid 2001: dozens of P2P alternatives that were

harder to touch, though these have gradually been constrained

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Napster Technology: Directory Service User installing the software

Download the client program Register name, password, local

directory, etc. Client contacts Napster (via TCP)

Provides a list of music files it will share

… and Napster’s central server updates the directory

Client searches on a title or performer Napster identifies online clients with

the file … and provides IP addresses

Client requests the file from the chosen supplier Supplier transmits the file to the client Both client and supplier report status to

Napster

centralizeddirectory server

peers

Alice

Bob

1

1

1

12

3

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Napster Technology: Properties Server’s directory continually updated

Always know what music is currently available Point of vulnerability for legal action

Peer-to-peer file transfer No load on the server Plausible deniability for legal action (but not enough)

Proprietary protocol Login, search, upload, download, and status operations No security: clear text passwords and other vulnerability

Bandwidth issues Suppliers ranked by apparent bandwidth & response time

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Napster: Limitations of Central Directory Single point of failure Performance bottleneck Copyright infringement

So, later P2P systems were more distributed Gnutella went to the other extreme…

File transfer is decentralized, but locating content is highly centralized

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Pure P2P system: Gnutella

Gnutella history 2000: J. Frankel &

T. Pepper released Gnutella

Soon after: many other clients (e.g., Morpheus, Limewire, Bearshare)

2001: protocol enhancements, e.g., “ultrapeers”

Query flooding Join: contact a few

nodes to become neighbors

Publish: no need! Search: ask neighbors,

who ask their neighbors Fetch: get file directly

from another node

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Gnutella: Query Flooding

Fully distributed No central server

Public domain protocol

Many Gnutella clients implementing protocol

Overlay network: graph Edge between peer X

and Y if there’s a TCP connection

All active peers and edges is overlay net

Given peer will typically be connected with < 10 overlay neighbors

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Gnutella: Protocol

Query message sent over existing TCPconnections

Peers forwardQuery message

QueryHit sent over reverse path

Query

QueryHit

Query

Query

QueryHit

Query

Query

Query

Hit

File transfer:HTTP

Scalability:limited scopeflooding

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Gnutella: Peer Joining

Joining peer X must find some other peers Start with a list of candidate peers X sequentially attempts TCP connections with

peers on list until connection setup with Y X sends Ping message to Y

Y forwards Ping message. All peers receiving Ping message respond with

Pong message X receives many Pong messages

X can then set up additional TCP connections

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Gnutella: Pros and Cons

Advantages Fully decentralized Search cost distributed Processing per node permits powerful search

semantics Disadvantages

Search scope may be quite large Search time may be quite long High overhead, and nodes come and go often

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Hybrid P2P system: KaAzA

KaZaA history2001: created by Dutch company (Kazaa BV)

Smart query flooding Join: on start, the client contacts a super-node (and may later

become one) Publish: client sends list of files to its super-node Search: send query to super-node, and the super-nodes flood

queries among themselves Fetch: get file directly from peer(s); can fetch from multiple

peers at once

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KaZaA: Exploiting Heterogeneity Each peer is either a group

leader or assigned to a group leader TCP connection between

peer and its group leader TCP connections between

some pairs of group leaders

Group leader tracks the content in all its children ordinary peer

group-leader peer

neighoring re la tionshipsin overlay network

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KaZaA: Motivation for Super-Nodes Query consolidation

Many connected nodes may have only a few files Propagating query to a sub-node may take more

time than for the super-node to answer itself Stability

Super-node selection favors nodes with high up-time

How long you’ve been on is a good predictor of how long you’ll be around in the future

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P2P Case study: Skype inherently P2P: pairs of

users communicate. proprietary application-

layer protocol (inferred via reverse engineering)

hierarchical overlay with SNs

Index maps usernames to IP addresses; distributed over SNs

Skype clients (SC)

Supernode (SN)

Skype login server

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Peers as relays

Problem when both Alice and Bob are behind “NATs”. NAT prevents an outside peer

from initiating a call to insider peer

Solution: Using Alice’s and Bob’s SNs,

Relay is chosen Each peer initiates session with

relay. Peers can now communicate

through NATs via relay

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BitTorrent

BitTorrent history and motivation 2002: B. Cohen debuted BitTorrent Key motivation: popular content

Popularity exhibits temporal locality (Flash Crowds)

Focused on efficient fetching, not searching Distribute same file to many peers Single publisher, many downloaders

Preventing free-loading

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BitTorrent: Simultaneous Downloading Divide large file into many pieces

Replicate different pieces on different peers A peer with a complete piece can trade with other

peers Peer can (hopefully) assemble the entire file

Allows simultaneous downloading Retrieving different parts of the file from different

peers at the same time And uploading parts of the file to peers Important for very large files

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BitTorrent: Tracker

Infrastructure node Keeps track of peers participating in the torrent

Peers register with the tracker Peer registers when it arrives Peer periodically informs tracker it is still there

Tracker selects peers for downloading Returns a random set of peers Including their IP addresses So the new peer knows who to contact for data

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BitTorrent: Chunks

Large file divided into smaller pieces Fixed-sized chunks Typical chunk size of 256 Kbytes

Allows simultaneous transfers Downloading chunks from different neighbors Uploading chunks to other neighbors

Learning what chunks your neighbors have Periodically asking them for a list

File done when all chunks are downloaded

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BitTorrent: Overall Architecture

Web page with link to .torrent

A

B

C

Peer

[Leech]

Downloader

“US”

Peer

[Seed]

Peer

[Leech]

TrackerWeb Server

.torr

ent

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BitTorrent: Overall Architecture

Web page with link to .torrent

A

B

C

Peer

[Leech]

Downloader

“US”

Peer

[Seed]

Peer

[Leech]

Tracker

Get-announce

Web Server

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BitTorrent: Overall Architecture

Web page with link to .torrent

A

B

C

Peer

[Leech]

Downloader

“US”

Peer

[Seed]

Peer

[Leech]

Tracker

Response-peer list

Web Server

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BitTorrent: Overall Architecture

Web page with link to .torrent

A

B

C

Peer

[Leech]

Downloader

“US”

Peer

[Seed]

Peer

[Leech]

Tracker

Shake-hand

Web Server

Shake-hand

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BitTorrent: Overall Architecture

Web page with link to .torrent

A

B

C

Peer

[Leech]

Downloader

“US”

Peer

[Seed]

Peer

[Leech]

Tracker

pieces

pieces

Web Server

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BitTorrent: Overall Architecture

Web page with link to .torrent

A

B

C

Peer

[Leech]

Downloader

“US”

Peer

[Seed]

Peer

[Leech]

Tracker

piecespieces

pieces

Web Server

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BitTorrent: Overall Architecture

Web page with link to .torrent

A

B

C

Peer

[Leech]

Downloader

“US”

Peer

[Seed]

Peer

[Leech]

Tracker

Get-announce

Response-peer list

piecespieces

pieces

Web Server

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BitTorrent: Chunk Request Order Which chunks to request?

Could download in order Like an HTTP client does

Problem: many peers have the early chunks Peers have little to share with each other Limiting the scalability of the system

Problem: eventually nobody has rare chunks E.g., the chunks need the end of the file Limiting the ability to complete a download

Solutions: random selection and rarest first

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BitTorrent: Rarest Chunk First Which chunks to request first?

The chunk with the fewest available copies I.e., the rarest chunk first

Benefits to the peer Avoid starvation when some peers depart

Benefits to the system Avoid starvation across all peers wanting a file Balance load by equalizing # of copies of chunks

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Free-Riding Problem in P2P Networks Vast majority of users are free-riders

Most share no files and answer no queries Others limit # of connections or upload speed

A few “peers” essentially act as servers A few individuals contributing to the public good Making them hubs that basically act as a server

BitTorrent prevent free riding Allow the fastest peers to download from you Occasionally let some free loaders download

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Bit-Torrent: Preventing Free-Riding Peer has limited upload bandwidth

And must share it among multiple peers Prioritizing the upload bandwidth: tit for tat

Favor neighbors that are uploading at highest rate

Rewarding the top four neighbors Measure download bit rates from each neighbor Reciprocates by sending to the top four peers Recompute and reallocate every 10 seconds

Optimistic unchoking Randomly try a new neighbor every 30 seconds So new neighbor has a chance to be a better partner

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BitTorrent Today

Significant fraction of Internet traffic Estimated at 30% Though this is hard to measure

Problem of incomplete downloads Peers leave the system when done Many file downloads never complete Especially a problem for less popular content

Still lots of legal questions remains Further need for incentives

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Structured overlay networksStructured overlay networks

Overlay topology construction is based on NodeID’s Overlay topology construction is based on NodeID’s that are generated by using Distributed Hash Tables that are generated by using Distributed Hash Tables (DHT)(DHT)..

In this category, the overlay network assigns keys to In this category, the overlay network assigns keys to data items and organizes its peers into a graph that data items and organizes its peers into a graph that maps each data key to a peer.maps each data key to a peer.

This structured graph enables efficient discovery of This structured graph enables efficient discovery of data items using the given keys. data items using the given keys.

It Guarantees object detection in O(log n) hops.It Guarantees object detection in O(log n) hops. Examples:Examples: Content Addressable Network (CAN), Content Addressable Network (CAN),

Chord, PastryChord, Pastry..

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Structured-DHT-based P2P overlay networks

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CAN: Content Addressable Network

Each key maps to one point in the d-dimensional space

Each node responsible for all the keys in its zone.

Divide the space into zones.

A B

C D E

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CAN(…)

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Pastry

Prefix-based Route to node with shared

prefix (with the key) of ID at least one digit more than this node.

Neighbor set, leaf set and routing table.

65a1fc

d13da3

d4213f

d462bad467c4

d471f1

d46a1c

Route(d46a1c)

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Pastry (…)

Routing table, leaf set and neighbor set example in a Pastry node b=2 and l=8

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Overlay Multicasting

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The key decision behind IP Multicast

“Smart Network”

Berkeley

Gatech Stanford

Per-group Router State

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Internet Design Philosophy Dumb routers , smart end systems ....

Minimal functionality in routers Push functionality to end systems as much as possible Key reason attributed to success and scaling of Internet

IP

Application/Transport

Internet architecture

Physical/Link layer

“Thin Waist”

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Significance of IP Multicast

First major functionality added to routers since original design

Per-group state implies: Routing tables with potentially millions of entries! Today’s routers have only tens of thousands of

entries in routing tables though there are millions of end systems!

Potential scaling concerns Per-group (per-flow) based solutions typically

find slow acceptance in the Internet

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Other concerns with IP Multicast Slow deployment

Very difficult to change network infrastructure and routers

Difficult to support higher layer functionality IP Multicast: Best effort delivery Model complicates support for reliability and

congestion control

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Congestion control with IP Multicast

Network with

IP Multicast

Purdue

Stanford, LAN

Stanford, modem

What rate must the source send at ?

Berkeley1

China

London

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Back to the drawing board…

IP

Application

Internet architecture

Physical/Link

?

?

Which is the right layer to implement multicast functionality?

IP Multicast: IP layer, efficient

Naïve Unicast: Application layer, inefficient

Can we do it efficiently at the application level ?

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Overlay Multicast

Purdue

Stan1

Stan2

Berk2

Overlay Tree

Stanford

Berkeley

Dumb Network

Gatech

Gatech

Berk1

Stan1

Stan2

Berk1

Berk2

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No per-group state in routers Easy to deploy: No change to network infrastructure Can simplify support for congestion control etc.

Overlay Multicast: Benefits

Unicast congestion

control

Purdue

Stan-Modem

Gatech

Leverage computation and storage

(e.g. Transcoding)Stan-LAN

Berk1

Berk2

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Overlay Performance Even a well-designed overlay cannot be as efficient as IP

Mulitcast But performance penalty can be kept low Trade-off some performance for other benefits

Increased Delay

Dumb Network

GatechDuplicate Packets:

Bandwidth Wastage

Stanford

Berkeley

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Architectural SpectrumPurely application

end-pointInfrastructure-Centric

+Instantaneous Deployment

+Low setup overhead, low cost

+Uses bandwidth resources at end systems

+Can scale to support millions of groups

+ Security

+ Robustness

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Router-Based(IP Multicast)

No Router Support

Infrastructure-Centric(CDNs, e.g. Akamai)

Application End-points Only, End-System Only

Application End-pointsor end-systems with infrastructure support

End-System, Application-Level, Overlay or Peer-to-Peer Multicast

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Applications

File Download Vs. Video Conferencing/Broadcasting Bandwidth demanding : hundreds or thousands of Kbps Real-time constraints: Continuous streaming delivery

Broadcasting Vs. Conferencing Conferencing: smaller scale, anyone can be source. Broadcasting: single source, tens or hundreds of thousands

of simultaneous participants

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Design Issues

Construct efficient overlays High bandwidth, low latency to receivers

Low Overheads Fault tolerant Self-organizing, Self-Improving Honor per-node access bandwidth constraints System Issues: NATs etc.

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What overlay to construct?

•No formal structure

•Each data pkt send using

epidemic algorithms

Overlay with a structure

Stan-LANNJ

Purdue

Berk2

Stan-Modem

Berk1Purdue

Stan-LAN

Stan-Modem

Berk2

Stan-LAN

Berk1

Tree Multi-Tree Mesh

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Inefficient Overlay Trees

-Poor network usage

-Potential congestion near Purdue

Purdue

High latency

Berk2

Gatech

Stan2

Stan1-Modem

Berk1Berk1

Berk2

Gatech

Stan2 Purdue

Stan1-Modem

Gatech

Purdue

Berk2

Stan-Modem

Stan-LAN

Berk1

Poor bandwidth

to members

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Efficient overlay trees

Gatech

Purdue

Berk2

Stan-LAN

Stan-Modem

Berk1 Gatech

Berk2Purdue

Stan-Modem

Berk1

Stan-LAN

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Self-Organizing Protocols

Construct efficient overlays in a distributed fashion Members may have limited knowledge of the

Internet Adapt to dynamic join/leave/death of members Adapt to network dynamics and congestion

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Example

Purdue

Stan-Lan joins

Berk1

Stan-Modem

Berk2

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Example

Purdue

Stan-Lan

Berk1

Stan-Modem

Berk2

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Example

Purdue

Stan-Lan

Berk1

Stan-Modem

Berk2

Bad Bw Perf!

Switch!

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Example

Purdue

Stan-Lan

Berk1

Stan-Modem

Berk2

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Example

Purdue

Stan-Lan

Berk1

Stan-Modem

Berk2

More “clustering” if

Stan-Modem moves to Stan-Lan

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Example

Purdue

Stan-Lan

Berk1

Stan-Modem

Berk2

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Example

Purdue

Berk1

Stan-Modem

Berk2

Stan-Modem is disconnected:

Back to Berk1

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Key Components of Protocol

Overlay Management: How many people does a member know? How is this knowledge maintained?

Overlay Optimization: Constructing efficient overlay among members Distributed heuristics

No entity with global knowledge of hosts and network

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Group Management Build separate control structure decoupled from tree

Each member knows small random subset of group members Knowledge maintained using gossip-like algorithm

Members also maintain path from source

Other design alternatives possible: Example: More hierarchical structure No clear winner between design alternatives

S

A

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Bootstrap

AsiaUS2

Euro2Euro3

Euro2, Euro3, ...

Source (US)

US1

Modem1

Asia

Euro1US2

US4, …US

Node that joins:

–Gets a subset of group membership from source

–Finds parent using parent selection algorithm

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Other causes for parent switch

Euro2, Euro3, ...

Modem1

Source (US)

US1Asia

Euro1US2

US4, …

Modem1

Euro3, ...

Source (US)

US1Asia

Euro2US2

US4, …

Modem1

Euro3, ...

Source (US)

US1Asia

Euro2US2

US4, …

Member Leave/Death Congestion/ poor bandwidth

Euro4

Better Clustering

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Parent Selection

Modem1X

–X sends PROBE_REQ to subset of members it knows

–Waits for 1 second for the responses

–Evaluates remote nodes and chooses a candidate parent

Euro2, Euro3, ...

Source (US)

US1Asia

Euro1US2

US4, …

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Factors in Parent Selection Filter out P if it is a descendant of X Performance of P

Application throughput P received over last few seconds Delay of path from S to P

Saturation level of P Performance of link P-X

Delay of link P-X TCP bandwidth of link P-X

S

PX

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Multiple Overlay Trees

Source

Peer A

Peer C

S/3 S/3S/3

With support from MDC, split into T-equally sized stripes T trees, each distributes a single stripe of size S/T Overall quality depends on the number of stripes received Number of trees node i is entitled to =

S Kbps

Tree 1 Tree 3

TS

ri/

Tree 2

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Mesh-Based Approach Tree-based:

Involves “push” Well-known structure

Mesh-based: Each node maintains a set of partners Exchange data availability info with partners Pull data from partner if do not possess it already

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Mesh-Based Streaming Vs. BitTorrent Clever Scheduling Algorithm regarding which

segments to fetch

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Deployments

Research ESM Broadcasting system (2002-) CoolStreaming (2004-)

Report peak sizes >25000 users

Industrial (last 2-3 yrs) PPLive

Reports even larger peak sizes Zattoo GridMedia

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Research Challenges/Open Issues Tree Vs. Mesh Approaches, Hybrids Access-bandwidth scarce regimes Incentives and fairness Extreme peer dynamics, flash crowd Support for heterogeneous receivers System Issues

NATs/Firewalls Transport Protocol Start-up delay/buffer interaction

Security Challenges

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U.S. East Coast

U.S. Central

U.S. West Coast

Europe

Asia

Unknown

Source

Snapshot from Sigcomm Broadcast