Wei Gao

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

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 indices • Controllable redundancy of service indices on multiple service repositories in each cluster

8. The overall picture Multi-hop cluster (Local CAN) Service discovery message Announcement: store service indices Request: local matchmaking Virtual backbone 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 Ni indicates the qualification of this node to be the clusterhead • di : the neighbor degree of Ni • LFi : the number of link failures Ni encountered in unit time • NB-based clustering ensures the efficiency and stability of the clusters constructed di: the connectivity of Ni’s neighborhood LFi: the link stability of Ni’s neighborhood

11. Multi-hop clustering • 1. Network initialization • Initialization by hello beaconing • NB scores of mobile nodes are calculated and disseminated • 2. Autonomous 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 • 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 N2 N3 N6 N10

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 dr in a local CAN • The proportion of the forwarding destinations to the total number of nodes in the clusters • Bounded by the radius R1 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 R1 N2 N3 N6 N8 S N12

17. Multiple forwarding destinations for service requests • A service request is forwarded to all the nodes that possibly contains the matching services • R2: 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 R2 • 2. For each recipient in the 1st step, to its redundancy range • Radius R1 N2 N3 N4 N5 S N6 N7 N8 N9 N12 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: {IPi, NBSi, Head_IPi, HEAD_NBSi, hopi} • Interval of hello beaconing: TH • The NB scores of mobile nodes are calculated • di is updated on each node by discovering its 1-hop neighborhood • If a neighbor record has not been updated for long than 2TH, a link failure is counted • LFi 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