DIANE Project

Michael Klein, Birgitta König-Ries

http://www.ipd.uni-karlsruhe.de/DIANE

Multi-Layer Clusters in Ad-hoc Networks -

An Approach to Service Discovery

Universität KarlsruheInstitute for Program Structures und Data Organization

Universität KarlsruheGERMANY

International Workshop on Peer-to-Peer Computingco-located with the NETWORKING 2002 Conference

May 24th, 2002 – Pisa, Italy

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Our Scenario

Anna

More on SQL?

Official SQL Slides1 - 2 - 4

Summary on 2PC

Exercise Sheet on UML

Exercise Sheet on SQL

Solution to SQL Sheet

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Problems with mobile Ad-hoc Networks

• Highly dynamic topology due to • node movement • node fluctuation• appearing obstacles Routing difficult

• No dedicated server, no physical infrastructure No central service directory

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How to search for services?

Product search in a shopping centre

• Similar products are fixedly placed in physical proximity• Search by exploring the places around a similar product

?

Product search in an ad-hoc network• No explicit corelation between semantical and

physical proximity• Temporal changes in service offers and location

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Our Approach: Multi-Layer Clusters

Idea• Build clusters of devices that locally combine

semantical and physical proximity• Build supercluster of clusters by relaxing

proximity demands

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Semantical Proximity by an Ontology (1)

• Use a common ontology as a measure for proximity

• Use only isSubTopicOf and isDescribedBy relations

• Assumption: Each device offers one document, which can be described by one leaf term of the ontology

database

object oriented model

relational model

isSubTopicOf

rel. algebra

SQL OQL

isSubTopicOf

isDescribedBy

Two services/clusters are semantically similar iff. they belong to the same ontological term

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Physical Proximity by Radio Reachability

Device a reaches Device b iff.a is currently able to send data to b directly

a

b

Cluster A reaches Cluster B iff.there is a member m1 in A and a member m2 in B such that m1 reaches m2 ( gateway nodes)

A B

m1m2

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Clustering (1)

Step 1Form a layer 1 cluster from devices thata) are semantically similar

(= are described by the same ontological term)

b) and are physically close(= form a connected reachability graph)

select.doc

sql1.ppt

sql3.ppt

projection.pdf

selection.pdf

division.doc

relAlgebra1.ppt

sql2.ppt

insert.doc

update.doc

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Clustering (2)

Step iForm a layer i cluster from layer (i-1) clusters thata) are semantically similar

(= share the same supertopic term in the ontology)

b) are physically close(= form a connected reachability graph)

SQL

SQL

Rel. Algebra

Relational Model

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Service Discovery

The goal is to have a function

Device findService(Service s)

which• searches for a Device offering Service s• can be called from an arbitrary device in the network• can be used to find an arbitrary Service s• can be implemented locally (not centrally)

But we have:• Very basic functions on devices:

• 1. check if service request s matches• 2. send message to a reachable device

• Clustering of the devices

IdeaLayer Architecture: Break down the complex functionality in several steps.

User view

System view

gap

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Layer Architecture

Device Layer 0

Cluster Layer 1

Cluster Layer 2

Root Layer n

Cluster Layer (n-1)

ViewSearch function

Small Clusters ofterms of Level 1

Single devices (only on the current device)

(only in the current cluster)

(only in the current cluster)

(only in the current cluster)

Device findService(Service s)

(everywhere)

Clusters ofterms of Level 2

Big Clusters ofterms of Level n-1

One cluster ofthe root term

Send function

(only to reachable clusters)

sendTo(Node n, Message m)

--

(only to reachable clusters)

(only to reachable clusters)

(only to reachable devices)

given

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ExampleThe Ontology

?

findService( )

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ExampleExampleThe Ontology

findService( )

findService( )

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ExampleThe Ontology

Different routing methods:• Flooding

• Cycling (Ring)

• Direct (Table)

findService( )

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ExampleExampleThe Ontology

sendMessage( )

findService( )

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ExampleThe Ontology

sendMessage( )

findService( )

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ExampleThe Ontology

findService( )

sendMessage( )

findService( )

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ExampleThe Ontology

sendMessage( )

sendMessage( )

findService( )

findService( )

findService( )

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ExampleThe Ontology

sendMessage( )

findService( )

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ExampleThe Ontology

sendMessage( )

findService( )

findService( )

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ExampleThe Ontology

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ExampleThe Ontology

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Advantages of the Approach

• Naturalness only semantical and phyisical proximity, no parameters

• Decentralization no central device

• Resource-Awareness searches local clusters before accessing distant ones

• Adaptability to local network stability dynamically adapts exploration strategy

• Fault Tolerance by changing exploration strategy

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Future Work

Some open questions:

• Management of administrative data(routing tables, ring predecessors and successors, border nodes, service descriptions etc.)

elect cluster head in each cluster replicate on all cluster members (lazy replication)

• Performance Implementation in simulator QualNet

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Thank you!

More information on our project web page:http://www.ipd.uni-karlsruhe.de/DIANE/en

Are there any questions?

Thank you for your attention!

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