Load Balancing of In-Network Data-Centric Storage

Schemes in Sensor Networks

Mohamed Aly In collaboration with

Kirk Pruhs and Panos K. Chrysanthis

Advanced Data Management Technologies LabDept. of Computer Science

University of Pittsburgh

Load Balancing of In-Network DCS Schemes in Sensor Networks 2 Mohamed Aly

Why Data Centric Storage??

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Why Data Centric Storage??

Motivating application: Disaster management sensor networks

Sensors are deployed to monitor the disaster area. First responders moving in the area issue ad-hoc queries

to nearby sensors The sensor network is responsible of answering these

queries First responders use query results to improve the

decision making process in the management process of the disaster

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Data-Centric Storage

Quality of Data (QoD) of ad-hoc queries Define an event owner based on the event value Examples:

Distributed Hash Tables (DHT) [Shenker et. al., HotNets’03]

Geographic Hash Tables (GHT) [Ratnasamy et. al., WSNA’02]

Distributed Index for Multi-dimensional data (DIM)[Li et. al., SenSys’03] Greedy Perimeter Stateless Routing algorithm

(GPSR)[Karp & Kung, Mobicom’00]

Among the above schemes, DIM has been shown to exhibit the best performance

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DIM

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Problems of Current DCS Schemes

Storage Hot-Spots: A large percentage of events is mapped to few sensor

nodes Our Solutions

The Zone Sharing algorithm on top of DIM (ZS) [DMSN’05] The K-D Tree based DCS scheme (KDDCS) [submitted]

Query Hot-Spots: A large percentage of queries is targeting events

stored in few sensor nodes Our Solutions [MOBIQUITOUS’06, to appear]

The Zone Partitioning algorithm on top of DIM (ZP) The Zone Partial Replication algorithm on top of DIM (ZPR)

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1. Storage Hot-Spots in DCS Schemes

S1x є

[1,10]

S2x є

[10,20]

S3x є

[20,30]

S4x є

[30,40]

50%

40%

7%

3%

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K-D Tree Based DCS (KDDCS) Scheme

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K-D Tree Based DCS (KDDCS) Scheme

Abstracted Theoretical Problem: Weighted Split Median problem

Each sensor has an associated value Goal: sensors to agree on a split value V such that approximately half

of the values are larger than V and half of the values are smaller than V

Distributed Algorithm O(log n) times the network diameter O(1) times network diameter if the number of sensors is known a

priori within a constant factor KDDCS Components:

Distributed logical address assignment algorithm Based on the usage of “dynamic split points”

Event to bit-code mapping Using the split points stored locally in any node

Logical Stateless Routing (LSR) KDTR: K-D Tree Re-balancing algorithm

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2. Query Hot-Spots in DIM

A high percentage of queries accessing a small number of nodes

Existence of query hot-spots lead to: Increased node death Network Partitioning Reduced network lifetime Decreased Quality of Data (QoD)

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Zone Partitioning [MOBIQUITOUS’06]

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Zone Partial Replication [MOBIQUITOUS’06]

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Experimental Results: QoDResult Size of a 50% Query for a network with a (80%, 10%) Hot-Spot

0

200

400

600

800

1000

1200

1400

50 100 150 200 250 300 350 400 450 500

Network Size

Res

ult

of

a 50

% Q

uer

y DIM

KDDCS

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Experimental Results: Quality of Data

5% hot-spot

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Conclusions (1)

Storage Hot-Spots: Serious problem in current DCS schemes Contribution:

ZS: A storage hot-spots decomposition algorithm working on top of DIM

KDDCS: A DCS scheme avoiding the formation of storage hot-spots

KDDCS Advantages: Achieving a better data persistence by balancing storage

responsibility among nodes Increasing the QoD by distributing the storage hot-spot

events among a larger number of nodes Increasing the energy savings by achieving a well balanced

energy consumption overhead among sensor nodes

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Conclusions (2)

Query Hot-Spots: Another important problem in DCS schemes Contribution:

A query hot-spots decomposition scheme for DCS sensor nets, ZP/ZPR

Experimental validation of its practicality Increasing energy savings by balancing energy consumption

among sensors Increasing the network lifetime by reducing node deaths Increasing the QoD by partitioning the hot range among a large

number of sensors, thus, balancing the query load among sensors and keep them alive longer to answer more queries.

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Thank You

Questions ?

Advanced Data Management Technologies Labhttp://db.cs.pitt.edu