Department of Computer Science University of Massachusetts, Amherst TSAR*: A Two Tier Sensor Storage...

Department of Computer ScienceUniversity of Massachusetts, Amherst

TSAR*: A Two Tier Sensor Storage Architecture Using Interval Skip Graphs

Peter Desnoyers, Deepak Ganesan, and Prashant ShenoyUniversity of Massachusetts, Amherst

(*Tiered Storage ARchitecture)

UNIVERSITY OF MASSACHUSETTS, AMHERST

Why do we need archival storage?

Applications need historical sensor information. Why? Trigger events:

• Traffic monitoring - crash• Surveillance - break-in• Environmental monitoring - natural disaster

lead to requests for past information.

This requires archival storage.


Limited by lack of sufficient, energy-efficient storage and of communication and computation resources on current sensor platforms.

Optimized for continuous queries. High energy cost if used for archival - data must be transmitted to central data store.

Existing storage and indexing approaches

◊Streaming query systems TinyDB (Madden 2005), etc. Data storage and indexing is performed outside of network.

◊In-network storage and indexing DCS, GHT (Ratnasamy 2002) Dimensions (Ganesan 2003) Directed Diffusion (Intangonwiwat 2000)


Technology Trends

RadioJ/byte

Flash J/byte

Max Flash size

Mica2 30 4.5 0.5MB

MicaZ 3.4 4.5 0.5MB

Telos 3.4 1 1MB

UMassNAND

0.01 >1GB

1000x

100xNew flash technologies enable large storage systems on small energy-constrained sensors.


Hierarchical Storage and Indexing

Hierarchical deployments are being used to provide scaling:

• James Reserve (CENS)

Higher powered micro-servers are deployed alongside resource constrained sensor nodes.

Key challenge:• Exploit proxy resources to

perform intelligent search across data on resource-

constrained nodes.

Sensors

Proxies

Application


Key Ideas in TSAR

◊ Exploit storage trends for archival. Use cheap, low-power, high capacity flash memory in

preference to communication.

◊ Index at proxies and store at sensors. Exploit proxy resources to conserve sensor resources

and improve system performance.

◊ Extract key searchable attributes. Distill sensor data into concise attributes such as ranges

of time or value that may be used for location and retrieval but require less energy to transmit.


TSAR Architecture

1. Interval Skip Graph-based index between proxies.

• Exploit proxy resources to locate data stored on sensors in response to queries.

2. Summarization process

• Extracts identifying information: e.g. time period during which events were detected, range of event values, etc.

3. Local sensor data archive

• Stores detailed sensor information: e.g. images, events. Sensor node archive


TSAR Architecture

1. Interval Skip Graph-based index between proxies.



• Stores detailed sensor information, e.g. images, events.



Summarization function


TSAR Architecture




• Stores detailed sensor information, e.g. images, events.

Distributed index1. Interval Skip Graph-based

index between proxies



Example - Camera Sensing

storage

Cyclops camera

summarize

image

Sensor archives information and transmits summary to proxy.

Sensor node<id>

Summary

handle

Birds(t1,t2)=1

<id>


Example - Indexing

Index Network of proxies

Summary and location information are stored and indexed at proxy.

proxy

Birds(t1,t2)=1

<id> Birds t1,t2 1 <id>


Example - Querying and Retrieval

Birds in interval (t1,t2)?

proxy

Cyclops camera

summarize

Cyclops camera

summarize

Query is sent to any proxy.

Birds t1,t2 1 <id>



Birds in interval (t1,t2)?

proxy

Cyclops camera

summarize

Cyclops camera

summarize

Index is used to locate sensors holding matching records.

Birds t1,t2 1 <id>

<id>


Record is retrieved from storage and returned to application.


proxy

Cyclops camera

summarize

Cyclops camera

Birds t1,t2 1 <id>

<id>


Outline of Talk

◊ Introduction and Motivation◊ Architecture◊ Example◊ Design

Skip Graph Interval Search Interval and Sparse Interval Skip Graph

◊ Experimental Results◊ Related Work◊ Conclusion and Future Directions


The index should:

• support range queries over time or value,

• be fully distributed among proxies, and

• Support interval keys indicating a range in time or value.

Goals of Index Structure

insert(| |)

Distributed index

(| |)?


What is a Skip Graph?

2 3 5 6 9 12 18 19

Single key and associated pointers

Distributed extension of Skip Lists (Pugh ‘90):

Probabilistically balanced - no global rebalancing needed.

Ordered by key - provides efficient range queries.

Fully distributed - data is indexed in place.

(Aspnes & Shah, 2003, Harvey et al. 2003)

Log(N) search and insert

No single root - load balancing, robustness

Properties:


Interval search

Given intervals [low,high] and query X:1 - order by low2 - find first interval with high <= X3 - search until low > X

0 1 2 3 4 5 6 7 8 9 10

0 3

5 8

6 10

8 9

42

2 3

1 5

Query: x=4


Simple Interval Skip Graph

0-3 0-1 1-5 2-4 5-8 6-10 8-9 9-12

Derived from Interval Tree,

Cormen et al. 1990

3 3 5 5 8 10 10 12

Method:

Index two increasing values: low, maxSearch on either as needed.

Interval keys: YESlogN search: YESlogN update: NO - (worst case O(N))


Sparse Interval Skip Graph

Goal: efficient update of max(high) values in Interval Skip Graph.

Approach: take advantage of ratio of proxies (M) to data items (N)

Solution: eliminate redundant links and corresponding updates.

Before: complete search tree rooted at each data item. After: retain M trees, one rooted at each proxy, keeping robustness and load balancing properties.

M proxies

N data items

- - - - - - - - - -

Worst-case complexity:

Search: O(logM)Update: O(M)


Adaptive Summarization

updates

queries

How accurately should the summary information represent the original data?

Detailed summaries =more summaries,

precise index

Precise index =fewer wasted queries



updates

queries

How accurately should the summary information represent the original data?

Approximate summaries =fewer summaries,imprecise index

imprecise index =more wasted queries? ?


= summarization (summaries / data) r = EWMA( wasted queries / data )

Target range: r0

Decrease if: r > r0Increase if: r < r0


updates

queries

Goal: balance update and query cost.

Approach: adaptation.


Prototype and Experiments

◊ Software: Em* (proxy),TinyOS (sensor)

◊ Hardware: StargateMica2 mote

◊ Network: 802.11 ad-hoc,multihop BMAC 11%

◊ Data:James Reserve [CENS] dataset30s temperature readings34 days

For physical experiments, data stream was stored on sensor node and replayed.


Index performance

Queries

Sensor data

How does the index performance scale with the number of proxies and size of dataset?

Tested in: Em* emulation

Tasks: insert, query

Variables: number of proxies (1-48)size of dataset

Metric: proxy-to-proxy messages

Interval skip graph index


Index results

Sparse skip graph provides >2x decrease in message traffic for small numbers of proxies.

Sparse skip graph shows virtually flat message cost for larger index sizes.


Tested on: 4 Stargate proxies12 Mica2 sensors in tree configuration

Task: query

Variables: size of dataset

Metric: query latency (ms)

Query performance

data

queriesWhat is the query performance on real hardware and real data?

4-proxynetwork

3-level multi-hop

sensor field


Validates the approach of using proxy resources to minimize the number of expensive sensor operations.

Query results

Sensor link latency dominates

Proxy linkdelay is negligible

The sensor communication consists only of a query and a response - the minimal communication needed to retrieve the data.


Summary algorithm adapts to data and query dynamics.

Tested in: Em*, EMTOSSIMemulation

Task: data and queries

Variables: query/data ratio

Metric: summarization factor

Query/data = 0.2

Query/data=0.03

Query/data = 0.1

Adaptive SummarizationVaried query

rate

Summary rate adapts

How well does the adaptation mechanism track changes in conditions?

1/


Related Work

◊ In-network Storage: DCS (Ratnasamy 2002) Dimensions (Ganesan 2003) …

◊ In-network Indexing: GHT (Ratnasamy 2002) DIFS (Greenstein 2003) DIM (Li 2003) …

◊ Hierarchical Sensor Systems: Tenet (CENS, USC)

◊ Sensor Flash File Systems: ELF (Dai 2004) Matchbox (Hill et al. 2000)


Conclusions and Future Work

◊ Proposed novel Interval Skip Graph-based index structure and adaptive summarization mechanism for multi-tier sensor archival storage.

◊ Implemented these ideas in the TSAR system.◊ Demonstrated index scalability, query performance,

and adaptation of summarization factor, both in emulation and running on real hardware.

Future Work◊ Investigate other index structures.◊ Alternate interval- and non-interval-based summary

mechanisms.

For more information: http://presto.cs.umass.edu

Department of Computer Science University of Massachusetts, Amherst TSAR*: A Two Tier Sensor Storage...

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