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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 Karlsruhe Institute for Program Structures und Data Organization Universität Karlsruhe GERMANY International Workshop on Peer-to-Peer Computing co-located with the NETWORKING 2002 Conference May 24 th, 2002 – Pisa, Italy 1/15
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Our Scenario Anna More on SQL? Official SQL Slides 1 - 2 - 4 Summary on 2PC Exercise Sheet on UML Exercise Sheet on SQL Solution to SQL Sheet 2/15
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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 3/15
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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 4/15
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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 5/15
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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 modelrelational model isSubTopicOf rel. algebraSQLOQL isSubTopicOf isDescribedBy Two services/clusters are semantically similar iff. they belong to the same ontological term 6/15
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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) AB m1 m2 7/15
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Clustering (1) Step 1 Form a layer 1 cluster from devices that a)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 8/15
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Clustering (2) Step i Form a layer i cluster from layer (i-1) clusters that a)are semantically similar (= share the same supertopic term in the ontology) b)are physically close (= form a connected reachability graph) SQL Rel. Algebra Relational Model 9/15
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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 Idea Layer Architecture: Break down the complex functionality in several steps. User view System view gap 10/15
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Layer Architecture Device Layer 0 Cluster Layer 1 Cluster Layer 2 Root Layer n Cluster Layer (n-1) View Search function Small Clusters of terms of Level 1 Single devices (only on the current device) (only in the current cluster) Device findService (Service s) (everywhere) Clusters of terms of Level 2 Big Clusters of terms of Level n-1 One cluster of the root term Send function (only to reachable clusters) sendTo(Node n, Message m) -- (only to reachable clusters) (only to reachable devices) given 11/15
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Example The Ontology ? findService( ) 12/15
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Example The Ontology findService( ) 12/15
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Example The Ontology Different routing methods: Flooding Cycling (Ring) Direct (Table) findService( ) 12/15
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Example The Ontology sendMessage( ) findService( ) 12/15
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Example The Ontology sendMessage( ) findService( ) 12/15
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Example The Ontology findService( ) sendMessage( ) findService( ) 12/15
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Example The Ontology sendMessage( ) findService( ) findService( ) 12/15
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Example The Ontology sendMessage( ) findService( ) 12/15
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Example The Ontology sendMessage( ) findService( ) findService( ) 12/15
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Example The Ontology 12/15
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Example The Ontology 12/15
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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 13/15
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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 14/15
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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! 15/15
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