1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. Example The Ontology ? findService( ) 12/15

13. Example The Ontology findService( ) 12/15

14. Example The Ontology Different routing methods: Flooding Cycling (Ring) Direct (Table) findService( ) 12/15

15. Example The Ontology sendMessage( ) findService( ) 12/15

16. Example The Ontology sendMessage( ) findService( ) 12/15

17. Example The Ontology findService(  ) sendMessage( ) findService( ) 12/15

18. Example The Ontology sendMessage( ) findService(  ) findService( ) 12/15

19. Example The Ontology sendMessage( ) findService( ) 12/15

20. Example The Ontology sendMessage( ) findService( ) findService(  ) 12/15

21. Example The Ontology 12/15

22. Example The Ontology 12/15

23. 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

24. 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

25. 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