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A P2P Semantic-Based Service Discovery Method for Pervasive Computing Environment

A P2P Semantic-Based Service Discovery Method for Pervasive Computing Environment. 2007.10.31 Shin Yongjin. Contents. Introduction Multi-level Structure Service Discovery Algorithm Contact Selection Mechanism Evaluation Conclusion. Introduction. Peer to Peer Search

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A P2P Semantic-Based Service Discovery Method for Pervasive Computing Environment

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  1. A P2P Semantic-Based Service Discovery Method for Pervasive Computing Environment 2007.10.31 Shin Yongjin

  2. Contents • Introduction • Multi-level Structure • Service Discovery Algorithm • Contact Selection Mechanism • Evaluation • Conclusion

  3. Introduction • Peer to Peer Search • Unstructured: flooding queries to all peers, use data replication algorithm. • Structured: using DHT • Semantic-based service discovery scheme • A portion of service attributes are registered with index • Service attributes are hierarchically organized. • Small world network

  4. Multi-level Structure Hierarchical Service Attribute Tree Function Class ( ) Location Information ( ) Invoking Interface( ) Regulation Parameter( ) Priority :

  5. Multi-level Structure • Service Discovery Network • Three Level of topology • Node level topology: physical communication relation among nodes • Location level topology: location information about the physical space • Service level topology: relation expression of service function class information  Enhance semantic connection degree of node.  Reduce the search space in order for lesser discovery time and lesser node disturbance.

  6. Service Discovery Algorithm • Service function class = service key of node • Local service function class – primary key • Non-local service function class – secondary key • Every node has two node sets. • Small world network • Distant nodes = contact • Local contact with small average length. • Small number of long range contacts with large clustering coefficient.

  7. Service Discovery Algorithm • Hybrid of proactive and reactive • Advertisement diameter is determined by node capability and mobility possibility • When receiving advertisement message, node can modify advertisement hop counts according to own capability • Node can specify the predicate of advertisement and forward

  8. Contact Selection Mechanism • Kindred-Based Contact Selection • Two considerations • Find the long contact node containing more service function class information and overlapping fewer dominant community of service request node • Find a long contact for establishing the kindred node topology to guarantee the higher reachability

  9. Contact Selection Mechanism • Three Steps • Frontier Node (FN) Selection From the center q, it starts finding FN. At first, q visits one node, and its tag becomes true, and it is set to FN. Visit next and changes tag to true and check the FN or not. u q_f FN, tag=true; FN, tag=true; d q FN, tag=true; tag=true; tag=true;

  10. Contact Selection Mechanism • Contact Discover Direction • Finding exterior neighbor node(EN) u and c are not belong to service community of q To forward contact discover message With EN, can select long contact discover direction for avoiding the unstable path. u c EN u EN u FN q_f FN u_f q

  11. Contact Selection Mechanism • Contact Node Selection • If long contact node is kindred node of query node, then, directly select this node to be long contact node. • If not, • Similarity check with RSI • RSI (Relative Semantic Information value) • Represents similarity between two nodes. • Ratio of the number of same service function class and total number of contact node.

  12. Contact Selection Mechanism • Location-Aware Contact Selection • Location level topology • Choose the node with different location information from service community of query node. • Optimal average hop in querying the service with specified location requirement.

  13. Evaluation • Balance capacity of discovery efficiency • Average overhead in discovering different popularity service • Predict the overhead proportion relation of service discovery method • Discover a service with different popularity = = Message overhead = average effect of discovery result = function about similarity between different service function class discovery

  14. Evaluation • For simulation, • Use 10 services • J-Sim Simulator • Service advertisement diameter = 3

  15. Conclusion • Contact based small world network • Low maintenance overhead • Efficient search performance • My opinion • Only for small size network. • It takes too much time for finding FN and EN • So, it is not appropriate to big size network.

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