1. MobiQuitous 2007 Towards Scalable and Robust Service Discovery in Ubiquitous Computing Environments via Multi-hop Clustering Wei Gao

2. Service discovery Users search for their desired network services Examples of network services: remote printers, scanners, data sources, and webpages…… Service discovery in ubiquitous computing environments needs to be: Scalable to search for matching services quickly and efficiently Robust against unpredictable network topology changes

3. Service discovery Components of a service discovery system: Service requesters Service providers Service repositories A service discovery process include: Dissemination of service discovery messages Matchmaking between the services requested and provided

4. Our focus Service discovery: partial match Services provided rarely match the requests perfectly An acceptable deviation between the services provided and requested is used in matchmaking The searching scope is fixed: Global An efficient network architecture is needed Ensures that every service request finds all the matching services on the network

5. Current solutions Solutions with fixed Internet infrastructure Jini, UPnP Not suitable for mobile environments Global DHT-based p2p network Too expensive and instable in dynamic network topology Partial match cannot be handled Cluster-based approaches Clusters are constructed with low stability and flexibility

6. Cluster-based Architecture for Service Discovery (CASD) For scalability The network is organized as multi-hop clusters based on the Neighborhood Benchmark (NB) scores of mobile nodes A virtual backbone is used for disseminating service discovery messages

7. Cluster-based Architecture for Service Discovery (CASD) For robustness Each cluster is constructed to be an extended local Content-Addressable Network (CAN) storing service indicesCAN Controllable redundancy of service indices on multiple service repositories in each cluster

8. The overall picture Service discovery message Multi-hop cluster (Local CAN) Virtual backbone Announcement: store service indices Request: local matchmaking A hybrid push-pull approach

9. Distinguished features Reduce the communication overhead of service discovery Achieve robust service discovery in cases of bounded numbers of node failures A service request is guaranteed to find all the matching services in the network

10. Neighborhood Benchmark (NB) The NB score of a node N i indicates the qualification of this node to be the clusterhead d i : the neighbor degree of N i LF i : the number of link failures N i encountered in unit time NB-based clustering ensures the efficiency and stability of the clusters constructed d i : the connectivity of Ni’s neighborhood LF i : the link stability of N i ’s neighborhood

11. Multi-hop clustering 1. Network initializationNetwork initialization Initialization by hello beaconing NB scores of mobile nodes are calculated and disseminated 2. Autonomous clusterhead selectionAutonomous clusterhead selection The node with the highest NB score in the R- hop neighborhood is selected to be the clusterhead

12. Multi-hop clustering 3. Handshake with clusterheads Possible inconsistency during the clusterhead selection and handshake process is solved Possible inconsistency All the clusters are connected ones

13. Construction of local CANs Constructed along with the handshake process Clusterheads allocate spaces for the cluster members Node join: the largest zone is split Node leave: the smallest neighbor zone takes over Area deviation among different zones is minimized N2 N6 N10 N3

14. Service discovery Service discovery messages are disseminated among different clusters via the virtual backbone Local CANs store service indices with controllable redundancy Every node is a service repository Every service discovery message is forwarded to multiple destinations in a local CAN

15. Multiple forwarding destinations for service announcements Redundancy degree d r in a local CAN The proportion of the forwarding destinations to the total number of nodes in the clusters Bounded by the radius R 1 of a circle in the virtual coordinate space:, where

16. Multiple forwarding destinations for service announcements Every service announcement falls into a zone in the virtual coordinate space All the nodes whose virtual zone overlaps with the circle With the radius R 1 S N2N3 N6N8 N12

17. Multiple forwarding destinations for service requests A service request is forwarded to all the nodes that possibly contains the matching services R 2 : The acceptable deviation range in service matchmaking A forwarding process consists of two steps

18. Multiple forwarding destinations for service requests 1. All the nodes within the acceptable deviation range Radius R 2 2. For each recipient in the 1st step, to its redundancy range Radius R 1 S N2N3N4 N7N8N6 N12 N9 N5 N13

19. Simulation settings The performance of CASD is compared with service discovery in flat network architecture Service discovery functionality is implemented on ad-hoc routing protocols: AODV and DSR Every node periodically announces a service and requests for another service Different variations of CASD Local CANs are used: CASD-1 Without local CANs: CASD-2

20. Simulation results Successful ratio in different mobility settings

21. Simulation results Overhead in different mobility settings

22. Simulation results Scalability: overhead in different network scales

23. Simulation results Robustness: successful ratio in cases of node failures

24. Effects of different cluster radius Multi-hop clusters achieves higher performance Multi-hop clusters are also more expensive to construct Cluster radius should be chosen appropriately according to application requirements and network conditions

25. Conclusions Scalable and robust service discovery cannot be achieved in flat network architecture A service discovery architecture based on multi-hop clustering is presented Communication overhead of service discovery is reduced A service request is ensured to find all the matching services in cases of bounded numbers of node failures

26. Future work Theoretical analysis on the communication overhead of service discovery in our approach Comparison with other service discovery approaches based on clustering and “remote contacts”

27. Thank you! Questions?

28. Content-Addressable Network (CAN) DHT-based p2p network A multi-dimensional Cartesian coordinate space Each node is allocated a rectangular zone Data items are mapped to virtual points in the space Back

29. Network initialization Network initialization by hello beaconing Hello beacons: {IP i, NBS i, Head_IP i, HEAD_NBS i, hop i } Interval of hello beaconing: T H The NB scores of mobile nodes are calculated d i is updated on each node by discovering its 1-hop neighborhood If a neighbor record has not been updated for long than 2 T H, a link failure is counted LF i is updated iteratively in each round of hello beaconing: Back

30. Autonomous clusterhead selection Mobile nodes exchange their selected clusterheads with their neighbors via hello beaconing Clusterhead selection consists of R consecutive rounds In every round, a node puts the clusterheads of itself and its 1-hop neighbors into a selection pool The one in the pool with the highest NB score is selected Hello beaconing ensures that in the kth round, the selected clusterhead are with the highest NB score in the k-hop neighborhood Back

31. Solving the possible inconsistency An example of constructing 2-hop clusters 1. N4 selects N3 N7 is notified about N3 2. N4 hears N6 from N5 N4 transfers to N6 N7 does not know that! N7 select N3 A disconnected cluster Solution: nodes only forwards the handshake messages if they have the same clusterhead Back