Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Immune System and Search Technology Designing a Fast Search Algorithm for P2P Network using concepts from Immune Systems

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Overview of the Presentation ● P2P Network – Paradigm for Decentralised Computing ● Immune System Features ● Experimental Setup ● Simulation Results

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Peer To Peer Network ● Most Direct Method of Connecting Computers – Simple – Inexpensive – No Boss – No Regulation

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly 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.

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Peer To Peer Network ● Problem of Hugeness – Emergence of Protocol ● Centralized Directory – Napster ● Decentralized Directory – KaZaA ● Query Flooding – Gnutella

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly P2P: Centralized Directory (Napster) When peer connects, it informs central server: – IP address – content Centralized directory server peers Alice Bob Alice queries for Das Wunder von Bern Alice requests file from Bob While file transfer is decentralized, locating content is highly centralized

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly 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

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly P2P: Intermediate Arrangement (Kazaa) Feature Has 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. ^

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly 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 ^

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly 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 ^

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly 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

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly 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 Gbps 1.7% of total traffic in US Internet Backbone

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly 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 Gbps 1.7% of total traffic in US Internet Backbone

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly 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)

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Why the Immune System? ● Recognition – Anomaly detection – Noise tolerance ● Robustness ● Feature extraction ● Diversity ● Reinforcement learning ● Memory ● Distributed ● Adaptive

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly 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 proteinAntigen APC Peptide T-cell Activated T-cell B- Lymphokines Activated B-cell (plasma cell) ( I ) ( III ) ( IV ) ( V ) ( VI ) ( VII ) ( II )

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Role of the Immune System ● Remembers encounters – No need to start from scratch – Memory cells Lymphatic vessels Lymph nodes Thymus Spleen Tonsils and adenoids Bone marrow Appendix Peyer’s patches Primary lymphoid organs Secondarylymphoid organs 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.

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly 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 and adenoids Bone marrow Appendix Peyer’s patches Primary lymphoid organs Secondarylymphoid organs 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.

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Clonal Selection (Burnet, 1978) ● 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

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly General Framework for AIS Application Domain Representation Affinity MeasuresImmune Algorithms Solution P2P Network Search Search Item - Antigen Similarity (message,search item) ImmuneSearch Algorithm

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Reiterating the Perspective Solution P2P Network Search Search Item - Antigen Similarity (message,search item) ImmuneSearch Algorithm Design Search Algorithm ● Stop Flooding; ● Reduce Query Time

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Modelling the Network Design Search Algorithm ● Stop Flooding; ● Reduce Query Time Information Profile – Immune System Search Profile – Fußball User

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Modelling the Network Design Search Algorithm ● Stop Flooding; ● Reduce Query Time Zipf Law (Information and SearchProfile)

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Search the Network – Flooding Flooding essentially implies sending the message packet to all the neighboring nodes

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Search the Network – Random Walk A Message packet travels at its will

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly 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 Homing Antibodies

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Search the Network – Immune Search 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) Algorithm Move nodes similar to user node closer to the user

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Search the Network – Immune Search Movement Depends on 1.The Distance from the user node 2.Amount of Matching 3.Age Aim Cluster Similar Nodes (Similar in Information and Search Profile) Algorithm Move nodes similar to user node closer to the user

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Search the Network – Immune Search Movement Depends on 1.The Distance from the user node 2.Amount of Matching 3.Age Aim Cluster Similar Nodes (Similar in Information and Search Profile) Algorithm Move nodes similar to user node closer to the user

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Search the Network – Immune Search Movement Depends on 1.The Distance from the user node 2.Amount of Matching 3.Age Aim Cluster Similar Nodes (Similar in Information and Search Profile) Algorithm Move nodes similar to user node closer to the user No Movement

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly 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

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Experimental Results (Clustering) 100

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Experimental Results Experiment : Change 20 % of the node 100

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Experimental Results Experiment : Change 5% of the node at each generation 100

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Amount of Change in Neighborhood Experimental Results

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly ● 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

Zentrum für Hochleistungsrechnen (ZHR) – A Bios Group Presentation Niloy Ganguly Fragen und Antworten