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

Generic Storage Schemes

• External Storage– Hotspot problem (if no need to

store all events )

Event

5

Generic Storage Schemes

• Local Storage– Overhead of flooding

Event

6

Generic Storage Schemes

• Data-Centric Storage [CCR03]– Good to avoid hotspots and flooding

overhead in some scenarios

Event

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 re

alilty

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

Name Based Routing

• S has an event E– Take a hash functi

on 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

sd

L1

L1,2

L1,2,3

L2

L3

a

Bypass landmark

s

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 (communicatio

n 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 pat

h routing)– Balanced load in both routing and st

orage• Future Work

– Theoretical analysis– Tinyos implementation

24

Thanks!

Q&A?

25

26

27

Backup

28

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 labe

l is the j th landmark

29

Voronoi Graph

30

Voronoi Graph

• Divide the regions based on the closest landmark rule.

31

Number of Landmark (m) in Each Level

• m is not critical

32

Number of Landmark (m) in Each Level

• The larger the m, the more overhead. We pick m=6 finally.

33

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

34

Outline

• Background and Motivation• Hierarchical Voronoi Graph based

Routing– Basic routing algorithm– Practical design issues

• Evaluation• Conclusions and Future Work

35

Region Oriented Routing

• From s to d with label (L1, L1,2, L1,2,3)

sd

L1

L1,2

L1,2,3

Bypass landmark

s

a

36

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