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

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. Outline. Background and Motivation Hierarchical Voronoi Graph based Routing Basic routing algorithm

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

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  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

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