Load Balance and Efficient Hierarchical Data-Centric Storage in Sensor Networks

1. Load Balance and Efficient Hierarchical Data-Centric Storage in Sensor Networks Yao Zhao,List Lab, Northwestern Univ Yan Chen, List Lab, Northwestern Univ Sylvia Ratnasamy, Intel Research

2. Outline • Background and Motivation • Hierarchical Voronoi Graph based Routing • Basic routing algorithm • Practical design issues • Evaluation • Conclusions and Future Work

3. Generic Storage Schemes • External Storage • Local Storage • Data-Centric Storage (DCS)

4. Event Generic Storage Schemes • External Storage • Hotspot problem (if no need to store all events )

5. Event Generic Storage Schemes • Local Storage • Overhead of flooding

6. Event Generic Storage Schemes • Data-Centric Storage [CCR03] • Good to avoid hotspots and flooding overhead in some scenarios

7. Motivation • Routing Primitive for Data-Centric Storage vs Any-to-any Routing • DCS doesn’t require any-to-any routing • E.g. in pathDCS [NSDI06], not all nodes are routable • Any-to-any routing may not be suitable for DCS • E.g. BVR[NSDI05] and S4[NSDI07] • Only a few any-to-any routing can be DCS routing • E.g. VRR [Sigcomm06], GEM[Sensys03]

8. Motivation • Routing Primitive for Data-Centric Storage vs Any-to-any Routing • Desirable Properties of DCS Routing • No GPS (or other location device) • Scalability • Efficiency • Path stretch (routing path length / shortest path length) • Load Balancing • In routing (forwarding overhead) • In Storage • Our Goal • Design routing primitive for DCS with the above properties

9. Outline • Background and Motivation • Hierarchical Voronoi Graph based Routing • Basic routing algorithm • Practical design issues • Evaluation • Conclusions and Future Work

10. Hierarchical Voronoi Graph based Routing • Basic Routing Algorithm • Hierarchical coordinate • Region oriented routing • Name based routing for DCS • Practical Issues • Landmark selection • Path stretch reduction • Handling dynamic changes

11. Voronoi Graph

12. Hierarchical Coordinate • Divide the network based on the hop distance to landmarks Irregular borderline in realilty

13. Hierarchical Coordinate • Divide the network based on the hop distance to landmarks In smallest region, nodes know each other

14. Overhead of Building Coordinate • Initialization Overhead • Each Layer • O(mN) messages where m is the number landmarks splitting a region, and N is the number of nodes • K Layers • K ~ O(log N) • Total Overhead • O(mN·log N) messages • Memory Usage • Km ~ O(m·log N)

15. d Name Based Routing Bypass landmarks • S has an event E • Take a hash function H1 and get j = H1(E)%3 • S sends E to the jth 1st level landmark and enter Lj’s region via node a • Node a compute H2(E)%3 to determine the next landmark L2 L1,2 s L1,2,3 a L1 L3

16. Load Balancing in Storage • Load Balancing Problem • In naïve name based routing, non-uniform division of regions causes non-uniform storage distribution • To divide regions uniformly is very hard • Our Approach: Non-uniform Hash Function • Collect the number of nodes in each region • Hashed value is proportional to the population of possible sub-regions

17. Outline • Background and Motivation • Hierarchical Voronoi Graph based Routing • Basic routing algorithm • Practical design issues • Evaluation • Conclusions and Future Work

18. Evaluation • Simulation Setup • C++ implementation • Simple MAC without collision • Unit disk graph model in 2D space (communication range 1) • Baseline simulation • 3200 nodes • Density: 3π neighbors in average • Simulate HVGR, HVGR+ and VRR[Sigcomm06] • m = 6 (number of landmarks splitting a region) • Metrics • Path stretch • Load balancing: CDF for visualization • Route table size • Initialization overhead • Maintenance overhead

19. Efficiency • The stretch of HVGR doesn’t increase as N increase.

20. Scalability • The route table size and initialization overhead increase logarithmically.

21. Routing Load Balancing • The routing load balancing feature of HVGR is close to that of shortest path routing.

22. Storage Load Balancing • The storage load balancing feature of HVGR is close to that of ideal hash based storage.

23. Conclusion • Design HVGR/HVGR+ • Topology based routing (No GPS) • Good scalability (log N memory) • High efficiency (close to shortest path routing) • Balanced load in both routing and storage • Future Work • Theoretical analysis • Tinyos implementation

24. Thanks! Q&A?

25. Name Based Routing for DCS • Convert Name to Label • Event name S • A series of hash functions Hi • Order the m landmarks • Let j = Hi(S) mod m, the ith level label is the j th landmark

26. Voronoi Graph

27. Voronoi Graph • Divide the regions based on the closest landmark rule.

28. Number of Landmark (m) in Each Level • m is not critical

29. Number of Landmark (m) in Each Level • The larger the m, the more overhead. We pick m=6 finally.

30. Desirable Properties of DCS • DCS without Location Information • No GPS or other location devices • Scalability • Memory usage • Control message overhead • Efficiency • Path stretch (routing path length / shortest path length) • Load Balancing • In routing (forwarding overhead) • In Storage

31. Outline • Background and Motivation • Hierarchical Voronoi Graph based Routing • Basic routing algorithm • Practical design issues • Evaluation • Conclusions and Future Work

32. Region Oriented Routing • From s to d with label (L1, L1,2, L1,2,3) Bypass landmarks L1,2 s d L1,2,3 a L1

33. Hierarchical Coordinate • Divide the network based on the hop distance to landmarks