Load balance and efficient hierarchical data centric storage in sensor networks
Sponsored Links
This presentation is the property of its rightful owner.
1 / 33

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


  • 85 Views
  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

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

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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

    • Practical design issues

  • Evaluation

  • Conclusions and Future Work


Generic Storage Schemes

  • External Storage

  • Local Storage

  • Data-Centric Storage (DCS)


Event

Generic Storage Schemes

  • External Storage

    • Hotspot problem (if no need to store all events )


Event

Generic Storage Schemes

  • Local Storage

    • Overhead of flooding


Event

Generic Storage Schemes

  • Data-Centric Storage [CCR03]

    • Good to avoid hotspots and flooding overhead in some scenarios


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]


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


Outline

  • Background and Motivation

  • Hierarchical Voronoi Graph based Routing

    • Basic routing algorithm

    • Practical design issues

  • Evaluation

  • Conclusions and Future Work


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


Voronoi Graph


Hierarchical Coordinate

  • Divide the network based on the hop distance to landmarks

Irregular borderline in realilty


Hierarchical Coordinate

  • Divide the network based on the hop distance to landmarks

In smallest region, nodes know each other


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)


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


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


Outline

  • Background and Motivation

  • Hierarchical Voronoi Graph based Routing

    • Basic routing algorithm

    • Practical design issues

  • Evaluation

  • Conclusions and Future Work


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


Efficiency

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


Scalability

  • The route table size and initialization overhead increase logarithmically.


Routing Load Balancing

  • The routing load balancing feature of HVGR is close to that of shortest path routing.


Storage Load Balancing

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


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


Thanks!

Q&A?


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


Voronoi Graph


Voronoi Graph

  • Divide the regions based on the closest landmark rule.


Number of Landmark (m) in Each Level

  • m is not critical


Number of Landmark (m) in Each Level

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


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


Outline

  • Background and Motivation

  • Hierarchical Voronoi Graph based Routing

    • Basic routing algorithm

    • Practical design issues

  • Evaluation

  • Conclusions and Future Work


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


Hierarchical Coordinate

  • Divide the network based on the hop distance to landmarks


  • Login