Transcript Modern Technologies

Decentralized Systems:
P2P
Networks
1
Agenda

Network Models

What are P2P Systems?

File Sharing Techniques

Content Delivery

PageRank
2
Network Models

Centralized: A centralized
network uses a server as a
central node.

Decentralized: A decentralized
network has no servers, only
clients.

People can exchange info directly,
without a central server.
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What are P2P Systems?

Allow a group of computer users
(employing the same networking software)
to connect with each other to share
resources.

Processing power, disk storage, network
bandwidth and files to be directly available
to other peers.

They behave in a distributed manner
without a central server.

Peers can act as both server and client
then they are also called servent, which is
different from the traditional client-server
model.
4
What are P2P Systems?

First, protocols are constructed at
the application layer of the network
protocol stack.

Second, in most designs peers have
a unique identifier, which is the peer
ID or peer address.

Third, many of the message types
defined in various P2P protocols are
similar.

Finally, the protocol supports some
type of message-routing capability.

That is, a message intended for one
peer can be transmitted via
intermediate peers to reach the
destination peer.
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What are P2P Systems?

An overlay network design that is consistent
with self-organization would not use a star
topology or a broadcast topology to operate
the peers or form the overlay.
 Instead the topology is likely to be
decentralized such that the
interconnectedness of any peer, referred to
as the degree, doesn’t dominate the overlay
graph.

Self-organization means that peers
cooperate in the formation and maintenance
of the overlay, with each peer using local
state and partial information about the
overlay.
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What are P2P Systems?

Each peer determines its capabilities
based on its own resources.

Each peer also determines when it joins the overlay,
what requests it makes to the overlay, and when it
leaves the overlay.

A peer that searches for an object and
doesn’t find it might not be able to
determine whether the object doesn’t exist
in the overlay or the peer storing the object
has left the overlay.

Peers may act to limit their resource
contribution to the overlay, for example, by
disconnecting from the overlay when not
using it.
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P2P File-Sharing Application

Peer-to-Peer technology is a way to share
music, video and documents, play games,
and facilitate online telephone conversations.


The technology enables computers using the same or
compatible P2P programs to form a network and share
digital files directly with other computers on the network.
User have digital media files that they want to
share with others.

These files are registered by the user using the local
application according to properties such as title, artist, date,
and format
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P2P File-Sharing Application
How are P2Ps Different From
Traditional File Transfer Methods?

File transfer using the FTP is
referred as Client-Server file
transfer.

The file is downloaded from a
single source (the file server) by
the destination/PC (a client).
http://propakistani.pk/2010/02/08/what-is-peer-to-peer-p2p-filesharing-and-how-does-it-work/
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P2P File-Sharing Application
How to Search for Files?

Users anywhere on the Internet can search
for these files by providing a query in terms of
some combination of the same attributes.


Peer that has local media files matching the
query will return information on how to
retrieve the files.


Query is sent to other online peers in the
network.
It may also forward the query to other peers.
Users may receive multiple successful
responses to their query and can then select
the files they want to retrieve.

The files are then downloaded from the remote
peer to the local machine.
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P2P File-Sharing Application
Challenging Issues

A large amount of polluted or corrupted content
has been published in file-sharing systems, and it
is difficult for a user to distinguish such content
from the original digital content they seek.

A free rider is a peer that uses the file-sharing
application to access content from others but does
not contribute content to the same degree to the
community of peers.
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P2P File-Sharing Application
Proxy Connection
 If the user hasn’t initiated any file searches or
selected local files for sharing, the P2P application is
most likely using the computer and its network
connection for other peers.



It may be responding to search requests from other peers
or acting as a bootstrap peer for newly joining peers.
It may be caching popular files on its disk drive to save
search time for other peers.
It could be proxying connections on behalf of peers
that are behind firewalls.
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P2P File-Sharing Application

Peer is connected to the overlay, it can exchange
messages with other peers in its neighbor list.


Over time it can add and remove peers from its neighbor list.
To refresh and update its neighbor list, it can send requests to
current neighbors asking for their neighbors.
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P2P File-Sharing Application

Random graphs and scale-free or power-law random
graphs are relevant to most unstructured topologies.
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File Sharing Techniques (Gnutella)

Gnutella was the first full P2P file-sharing system
and has remained one of the more popular systems
to date.

The earliest versions of the Gnutella protocol,
through version 0.4, used an unstructured overlay
with flooding for query routing.

After scalability became an apparent performance
issue, the most recent version of the Gnutella
protocol (version 0.6) adopted a superpeer
architecture in which the high-capacity peers are
superpeers and all queries are routed, using a
flooding mechanism, between superpeers.

Gnutella network has a power-law distribution and
what the peer lifetime distribution is.
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File Sharing Techniques (Napster)

Napster is a file-sharing system that used
a central server for storing and searching
the directory of files, but performed the file
transfer in a direct P2P fashion.

The majority of these designs used an
unstructured overlay mechanism.

Napster is not a full P2P system, it
popularized the P2P concept in the mass
media and influenced subsequent filesharing systems.
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File Sharing Techniques (Freenet)

Peer identifiers are called routing keys.

Each peer has a fixed-size routing table that stores links to other peers.

Each entry contains the routing key of the peer.

Freenet uses key-based routing for inserting and retrieving objects in the mesh.
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Requests are forwarded to peers with the closest matching routing key.

If a request along one hop fails, the peer will try the next closest routing key in its routing table.
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File Sharing Techniques

Most existing P2P indexing schemes can be
categorized into local, centralized, distributed,
or hybrid indexing types.
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File Sharing Techniques (Centralized
Indexing)

The index is kept on a centralized server.


Object lookup is done by searching over the index on the central server to
obtain the location of the target object.
Napster,190 the father of today’s P2P content distribution system, is a
classical example of centralized indexing.

Napster, peers share MP3 files stored locally on their hard drives.

Text-based content description, such as the title of a song, is then
generated, indexed, and stored by the Napster server.

Each peer in the Napster network uses the Napster client software to
connect to the centralized Napster server.

Peers connected to the Napster server can submit keyword-based queries
for a particular audio file.
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File Sharing Techniques (Centralized
Indexing)

A list of matching files along with the description and
location of the file is sent to the peer from the
server.

The peer then tries to connect to the peer with the
desired audio file and transfers the target content in
a P2P fashion.

The most noticeable drawbacks of a fully centralized
indexing system include vulnerability to attacks on
the server and the possibility of a bottleneck at the
server.
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File Sharing Techniques (Localized
Index)

Each peer keeps the directory of its own data objects locally.

The early Gnutella design is a typical example of local index-based P2P system.

Local object indexing can be used to support complex query search.



Compared to centralized indexing schemes, localized indexing can create more network
load since queries potentially have to be sent to many peers in the overlay.



When a peer generates a query, it conveys the query to peers in the network to locate the desired
object, most often through flooding or random walk.
Forwarding of query messages is stopped when the desired object is found or when the Time-to-Live
(TTL) value expires.
But since query processing is distributed across many peers in parallel, there is inherent scalability.
If any peer node is faulty, it affects the objects stored locally but not the overall search mechanism of
the network.
Localized indexing offers advantages over centralized schemes in terms of system
scalability and reliability. It has led to the development of many Gnutella-based systems
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File Sharing Techniques (Distributed
Index)

Distributes the index over all peers.

Pointers to a single object may reside in multiple indices located in
different peer nodes, most often in neighborhood peer nodes.

Freenet is one of the earliest P2P systems in this category.

It uses content hash keys to identify files.

Queries are forwarded to neighborhood peers based on a peer routing
table until the target object is found or the TTL threshold is reached.

One disadvantage posed by Freenet is its long startup time at peer
joining.
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File Sharing Techniques (Hybrid Index)

Hybrid indexing intends to take advantage of the
query efficiency of centralized indexing and the
scalability of localized and distributed indexing.

Superpeers (supernodes) maintain the indices in a
distributed manner.

A query from an ordinary peer is sent to its
superpeer to retrieve the location of the desired
object.

The superpeer forwards the query to other
superpeers if it does not find the desired object in
its own index.

Information about the desired object is sent back to
the ordinary peer via its superpeer.

Thereafter, the query issuer may directly contact
the peer with the desired object.
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References

John Buford, Heather Yu, Eng Keong Lua , P2P
Networking and Applications, Morgan Kaufmann Series
in Networking, December 26, 2008.

http://www.youtube.com/watch?feature=ends
creen&NR=1&v=i2m0qzRCs78
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Telecommunication
Technology and Management
Thank you for all information and pictures referred in this lecture.
www.att.com and www22.verizon.com
Agenda

Old Technology

Modern Technologies
 Digital TV
 Cell Phone: Calling Features
 Hi-speed Internet

Network Management
 Expert System
 Knowledge Discovery and Data Mining

Other Intelligent Applications
26
Old Technologies
http://www.supermegatrolled.com/just-nokia-3310-destroysthe-dinosaurs/
http://www.prattonline.com/ResponsePoint.htm
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Trend of Communication
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Trend of Communication
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Modern Technologies

Digital TV

TV Wireless Receiver

Requirements:

Wireless service from the Wireless Access Point to the
Wireless Receiver

Power outlet and connection of Wireless Receiver to TV
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31
Modern Technologies

Home DVR

Ability to schedule recordings and pause live TV

Record up to four shows at once on a single DVR and
play them back in any room

Pause, fast-forward and rewind live or recorded shows
on any TV or pause your recorded show in one room
and pick it up in another

Play the same recorded shows on different TVs at one
time and control them separately
32
Modern Technologies

Interactive Applications (between TV and smart
phone)

Browse available TV content for current or future viewing,
search for content, and remotely manage recordings of
shows and movies on DVR.

With qualifying TV plans, download select popular TV
episodes/series from the Mobile Library to select
smartphones for viewing on the go.

Once a show is downloaded, you can watch it anytime!
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Modern Technologies

TV Multiview

It allows you to choose your own Multiview
channels you want to watch.
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Modern Technologies

Phone Services on TV

We can see who's calling without leaving the
comfort of their couch!

Caller ID notifications and Message Waiting Indicators
(MWI) will display on their TV
35
Modern Technologies

Calling Features






Call Screening – Only accept calls from a list of
phone numbers you select.
Call Transfer – Send a call that’s already in
progress to a different phone number.
Locate Me - Provides simultaneous ringing on up
to four wireless/landline numbers when someone
calls your home phone.
Call History – View a list of your recent calls, by
date and time, either online or on your TV screen.
Click to Call – Return a phone call using your Uverse TV screen and remote.
Caller ID on TV – See Caller ID and Voice Mail
notifications on your TV screen.
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Modern Technologies

Online Photos and Music


Customers can now view the photos they've uploaded at
www.flickr.com on channel 91.
Share music and photos from networked Windows
PCs to TV
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Modern Technologies

TV for Xbox 360

Access your DVR recordings from your
existing Xbox 360.

Switch seamlessly from playing games to
watching TV—without switching video inputs
on your TV.

Access the U-verse TV Menu, Guide, and a
robust library of On Demand programming.

Chat: Know instantly when your friends are on
Xbox LIVE®.

Use Xbox IM and Chat while watching TV.
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Modern Technologies: Customer Support
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Modern Technologies

Mobile Phone
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Modern Technologies
Digital Voice: Calling Features
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Modern Technologies
Digital Voice: Calling Features
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Network Solution for Business
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Fiber Optic Technology!!
http://www.verizon.com/?lid=//global//residential
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Network Management

Telecommunication networks are
extremely complex systems requiring
 High reliability
 High availability

The effective management of networks is
a critical, but complex, task.
 Telecommunications industry has heavily
invested in intelligent technologies.

Telecommunication industry has relied
on intelligent solutions to help manage
telecommunication networks.
http://www.qbase.gr/en/node/124
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Network Management

Building intelligent applications involved acquiring
valuable telecommunication knowledge



Human experts
Applying this knowledge: an expert system.
This knowledge acquisition process is so timeconsuming that it is referred to as the “knowledge
acquisition bottleneck”.

Data mining techniques are now being applied to industrial
applications to break this bottleneck,

Replacing the manual knowledge acquisition process with
automated knowledge discovery.
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Data Mining
http://www.laits.utexas.edu/~anorman/BUS.FOR/course.mat/Alex/
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Expert Systems

Expert systems are programs which represent and
apply factual knowledge of specific areas of
expertise to solve problems



Require a knowledge engineer to acquire knowledge from
the domain experts
Encode knowledge in a rule-based expert system
These rules were very “ad-hoc” and as the number
of rules increased

Expert system became more difficult to understand and
modify
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Expert Systems

The design of telecommunication expert
systems

needs to recognize all telecommunication
equipment incorporates self-diagnostic
capabilities
http://www.nextnine.com
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Network Management Applications

Max & Opti-Max: Locating Problems in the Local
Loop

The Max (Maintenance administrator expert) system

diagnoses customer reported telephone problems in the local
loop, the final segment of the telephone network that connects
the customer to a central office

Max is a rule-based expert system



Diagnoses problems based on results of an electrical test on the
customer’s phone line,
Specific knowledge of the customer’s phone line and general
equipment knowledge.
Max determines where the trouble lies and selects the type of
technician to solve the problem.
50
Network Management Applications


Max & Opti-Max: Locating Problems in the Local Loop
 Problem of Max

its performance is affected by the local characteristics of each site
and thus numerous rule parameters must be tuned to optimize its
performance.

This tuning process is time consuming and for this reason a system
called Opti-Max was created to automatically tune these parameters
to appropriate values.
Opti-Max takes as input a set of training examples,
 Problem description and a diagnosis assigned by an expert,

Uses a hill-climbing search to find a set of parameter values
which perform well on these examples.

Opti-Max performs a type of automated knowledge discovery.
51
Network Management Applications

Trouble Locator: Locating Cable Network Troubles


It determines the location of troubles in a local telephone cable network
Data generated by a nightly automated test to help narrow down potential
cables or network equipment which may be faulty;


The Trouble Locator uses a Bayesian network and Bayesian inference
to solve this problem.


Test results are not sufficient to determine the exact cause.
The system begins by generating a local plant topology graph and then from
this generates a Bayesian network, where each node in the network contains
state information (belief of failure) of a plant component.
This system is used by preventative maintenance analysts as a
decision support system.
52
Network Management Applications

TASA: Finding Frequently Occurring Alarm
Episodes

The Telecommunication Network Alarm Sequence
Analyzer (TASA)


System for extracting knowledge about the behavior of the
network from a database of telecommunication network
alarms.
The goal of this system



To locate regularities in the alarm sequences in order to filter
redundant alarms
Locate problems in the network
Predict future faults
53
Network Management Applications

TASA operates in two phases


First phase: specialized algorithms are used to
find rules that describe frequently occurring alarm
episodes from the sequential alarm data
An example rule describing an alarm episode
is:

if alarms of types A and B occur within 5 seconds,
then an alarm of type C occurs within 60 seconds
with probability 0.7.
54
Network Management Applications

Second phase: collections of episodes are
interactively manipulated by the user


Interesting episodes from the original set can be
found
TASA supports this process by providing
operations to prune uninteresting episodes


Order the set of episodes
Group similar episodes
55
Network Management Applications

Scout: Identifying Network Faults via Data Mining

It operates by mining historical telecommunication data



Machine learning
Correlation techniques.
Scout identifies patterns of chronic problems directly
from the data by examining the network behavior
over periods of days and weeks.
56
Other Intelligent Applications

APRI: Predicting Uncollectible Debt

The Advanced Pattern Recognition and Identification
(APRI) system


The output of APRI is fed into a decision support
system which can take a variety of actions


To predict the probability of uncollectible debt based on
historical data, including data of past uncollectibles
Blocking a call from being completed.
APRI automatically constructs Bayesian network
models for classification problems using extremely
large databases.
57
Challenging Works

Existing Researches:


http://www.research.att.com/evergreen/what_we_do
/research.html?fbid=A_Kn38ajPF9#Computing and
Communications Foundations
Existing Software's:

http://www.research.att.com/export/sites/att_labs/sof
tware_tools/index.html?fbid=A_Kn38ajPF9
58
Telecommunication Union

ITU (International Telecommunication Union) is the
United Nations specialized agency for information and
communication technologies – ICTs.
 สหภาพโทรคมนาคมระหว่างประเทศ


http://www.itu.int/en/Pages/default.aspx
National Broadcasting and Telecommunications
Commission (NBTC)

คณะกรรมการกิจการกระจายเสี ยง กิจการโทรทัศน์และกิจการโทรคมนาคมแห่งชาติ

http://www.nbtc.go.th/wps/portal/NTC/eng
59
References



http://www.att.com/
Gary Weiss, John Eddy, Sholom Weiss, “INTELLIGENT
TELECOMMUNICATION TECHNOLOGIES”, AT&T Labs, AT&T
Corporation, United States
http://www22.verizon.com/home/aboutfios/

http://www.research.att.com/evergreen/what_we_do/research.html?f
bid=A_Kn38ajPF9#Computing and Communications Foundations

http://www.research.att.com/export/sites/att_labs/software_tools/ind
ex.html?fbid=A_Kn38ajPF9
60