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

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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

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Outline

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

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Generic Storage Schemes

• External Storage
• Local Storage
• Data-Centric Storage (DCS)

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Generic Storage Schemes

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

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Generic Storage Schemes

• Local Storage
• Overhead of flooding

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Generic Storage Schemes

• Data-Centric Storage CCR03
• Good to avoid hotspots and flooding overhead in
  some scenarios

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Motivation

• Routing Primitive for Data-Centric Storage vs
  Any-to-any Routing
• DCS doesnt 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. BVRNSDI05 and S4NSDI07
• Only a few any-to-any routing can be DCS routing
• E.g. VRR Sigcomm06, GEMSensys03

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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

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Outline

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

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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

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Voronoi Graph
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Hierarchical Coordinate

• Divide the network based on the hop distance to
  landmarks

Irregular borderline in realilty
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Hierarchical Coordinate

• Divide the network based on the hop distance to
  landmarks

In smallest region, nodes know each other
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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(mNlog N) messages
• Memory Usage
• Km O(mlog N)

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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
  Ljs 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
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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

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Outline

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

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Evaluation

• Simulation Setup
• C implementation
• Simple MAC without collision
• Unit disk graph model in 2D space (communication
  range 1)
• Baseline simulation
• 3200 nodes
• Density 3p neighbors in average
• Simulate HVGR, HVGR and VRRSigcomm06
• m 6 (number of landmarks splitting a region)
• Metrics
• Path stretch
• Load balancing CDF for visualization
• Route table size
• Initialization overhead
• Maintenance overhead

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Efficiency

• The stretch of HVGR doesnt increase as N
  increase.

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Scalability

• The route table size and initialization overhead
  increase logarithmically.

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Routing Load Balancing

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

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Storage Load Balancing

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

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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

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Thanks!

• QA?

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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

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Voronoi Graph
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Voronoi Graph

• Divide the regions based on the closest landmark
  rule.

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Number of Landmark (m) in Each Level

• m is not critical

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Number of Landmark (m) in Each Level

• The larger the m, the more overhead. We pick m6
  finally.

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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

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Outline

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

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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
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Hierarchical Coordinate

• Divide the network based on the hop distance to
  landmarks