Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Immune System and Search Technology Designing a Fast Search Algorithm

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation

Niloy Ganguly <[email protected]>

Immune System and Search Technology

Designing a Fast Search Algorithm for P2P Network using concepts from

Immune Systems



Overview of the Presentation● P2P Network

– Paradigm for Decentralised Computing

● Immune System Features

● Experimental Setup

● Simulation Results



Peer To Peer Network● Most Direct Method of Connecting Computers

– Simple

– Inexpensive

– No Boss

– No Regulation



Peer To Peer Network● PCs at the edge of the network are called “Peers”● Peers can retrieve objects directly from each other

Advantages of a P2P Network

A large collection of peers may be available for content distribution--sometimes millions!

User takes advantage of the network’s currently available resources.



Peer To Peer Network● Problem of Hugeness

– Emergence of Protocol

● Centralized Directory– Napster

● Decentralized Directory– KaZaA

● Query Flooding– Gnutella



P2P: Centralized Directory (Napster)When peer connects, it informs

central server:– IP address– content

Centralized

directory server

peers

Alice

Bob

1

1

1

1

3

Alice queries for

Das Wunder von Bern

Alice requests file from Bob

While file transfer is decentralized, locating content is highly centralized



P2P: Centralized Directory (Napster)● Fast ● Single point of failure

– Application crash● Performance bottleneck● Huge database to

maintain● Copyright infringement

– Legal proceedings may result in the company having to shut down directory server

Centralized

directory server

peers

Alice

Bob

1

1

1

1

3



P2P: Intermediate Arrangement (Kazaa)FeatureHas a centralized server that •maintains user registrations, •logs users into the systems to keep statistics, •provides downloads of client software.

Two client types are supported: Supernodes (fast cpus + high bandwidth connections)Nodes (slower cpus and/or connections)

Supernodes addresses are provided in the initial download. They also maintain searchable indexes and proxies search requests for users.

^



P2P: Totally Decentralized (Gnutella) Basic Feature● no hierarchy, peers have

similar responsibilities: no group leader

● no peer maintains directory info

● highly decentralized

Joining Algorithm ● use bootstrap node to

learn about others● Join message

^



P2P: Totally Decentralized (Gnutella) Message Query : ● Send query to neighbors● Neighbors forward query● If queried peer has object, it

sends message back to querying peer

● The queried peer forwards the query to its immediate neighbor.

● The resulting results are carried back to the user.

● A message Flooding occurs

^



P2P: Totally Decentralized (Gnutella) Pros : ● Totally Decentralized query ● Robust; Query doesn't stop

on break down of one of the nodes

● Fresh Results : No outdated Index

Cons ● Query radius: Query Radius

can be long● Excessive query traffic :

25% of the total traffic is query traffic

Courtesy : Limewire



P2P: Totally Decentralized (Gnutella) Challenges Ahead : ● Reduce Query time● Stop Flooding; use

Intelligent method for search to stop network congestion

Topology of Gnutella Network

Total Traffic in Gnutella Network is 1.7 Gbps1.7% of total traffic in US Internet Backbone



P2P: Totally Decentralized (Gnutella) Perspective● Introduce Intelligence in

the System through Bio-Inspired Techniques

● Ants, Immune System

Topology of Gnutella Network

Total Traffic in Gnutella Network is 1.7 Gbps1.7% of total traffic in US Internet Backbone



Artificial Immune System● Relatively new branch of computer science

– Using natural immune system as a metaphor for solving computational problems

– Not modelling the immune system

● Variety of applications so far …– Fault diagnosis (Ishida)– Computer security (Forrest, Kim)– Novelty detection (Dasgupta)– Robot behaviour (Lee)– Machine learning (Hunt, Timmis, de Castro)

– AIS are computational systems, inspired by theoretical immunology and observed immune functions, which are applied to complex problem domains (Timmis, 2001)



Why the Immune System?

● Recognition– Anomaly detection– Noise tolerance

● Robustness● Feature extraction● Diversity● Reinforcement learning● Memory● Distributed● Adaptive



Role of the Immune System

● Protect our bodies from infection

● Primary immune response– Launch a response to

invading pathogens● Secondary immune

response– Remember past

encounters– Faster response the

second time around

MHC protein Antigen

APC

Peptide

T-cell

Activated T - cell

B- cell

Lymphokines

Activated B -cell (plasma cell)

( I )

( III )

( IV )

( V )

( VI )

( VII )

( II )



Role of the Immune System

● Remembers encounters– No need to start from

scratch– Memory cells

Lymphatic vessels

Lymph nodes

Thymus

Spleen

Tonsils andadenoids

Bone marrow

Appendix

Peyer’s patches

Primary lymphoidorgans

Secondary lymphoidorgans

Epitopes

-B cell Receptors

Antigen

The immune recognition is based on the complementarily between the binding region of the receptor and a portion of the antigen called epitope.



Role of the Immune System● Antibodies present a single

type of receptor, antigens might present several epitopes.

● This means that different antibodies can recognize a single antigen

Lymphatic vessels

Lymph nodes

Thymus

Spleen

Tonsils andadenoids

Bone marrow

Appendix

Peyer’s patches

Primary lymphoidorgans

Secondary lymphoidorgans

Epitopes

-B cell Receptors

Antigen

The immune recognition is based on the complementarily between the binding region of the receptor and a portion of the antigen called epitope.



Clonal Selection (Burnet, 1978)

Foreign antigens

Proliferation

(Cloning)

Differentiation

Plasma cells

Memory cellsSelection

M

M

Antibody

Self-antigen

Self-antigen

Clonal deletion

(negative selection)

Clonal deletion

(negative selection)

● Elimination of self antigens

● Proliferation and differentiation on

contact of mature lymphocytes with

antigen

● Restriction of one pattern to one

differentiated cell and retention of

that pattern by clonal descendants

● Generation of new random genetic

changes, subsequently expressed as

diverse antibody patterns by a form

of accelerated somatic mutation



General Framework for AIS

Application Domain

Representation

Affinity Measures

Immune Algorithms

Solution

P2P Network Search

Search Item - Antigen

Similarity (message,search item)

ImmuneSearch Algorithm



Reiterating the Perspective

Solution

P2P Network Search

Search Item - Antigen

Similarity (message,search item)

ImmuneSearch Algorithm

Design Search Algorithm● Stop Flooding; ● Reduce Query Time



Modelling the Network

Design Search Algorithm● Stop Flooding; ● Reduce Query Time

Information Profile – Immune SystemSearch Profile – Fußball

User



Modelling the Network

Design Search Algorithm● Stop Flooding; ● Reduce Query Time Zipf Law

(Information and SearchProfile)

1

1

1

1

1

1

1

1

0

0

0

0

0

2

2

3



Search the Network – Flooding

Flooding essentially implies sending the message packet to all the neighboring nodes



Search the Network – Random Walk

A Message packet travels at its will



Search the Network – Immune Search

Algorithm Consists of two parts

1. The movement of Message Packets

2. Rearrangement of Topology

Proliferation

Mutation

High Concentration of Packets HomingAntibodies




Algorithm Consists of two parts

1. The movement of Message Packets

2. Rearrangement of Topology

Aim Cluster Similar Nodes (Similar in Information and Search Profile)

AlgorithmMove nodes similar to user node closer to the user




Movement Depends on1. The Distance from the

user node2. Amount of Matching3. Age

















No Movement



Experimental Results

Experiment : • Run for 100

generation, without changing the participating nodes

• Each Generation 100 searches by users selected randomly

Efficiency • No. Of Search Items

found in 50 time steps

100

100



Experimental Results (Clustering)

100

100




Experiment : Change 20 % of the node

100

100




Experiment : Change 5% of the node at

each generation

100

100



Amount of Change in Neighborhood




● Simulate the Results in Real Network

● Take into account the important concept of Network Traffic

● Test the algorithm with sophisticated Information Profile and Search Profile

● Building up mathematical framework through which the simulation results can be analytically justified

Future Work



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Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Immune System and Search Technology Designing a Fast Search Algorithm