(*Tiered Storage ARchitecture)

Transcription

1 TSAR*: A Two Tier Sensor Storage Architecture Using Interval Skip Graphs (*Tiered Storage ARchitecture) Peter Desnoyers, Deepak Ganesan, and Prashant Shenoy University of Massachusetts, Amherst Department of Computer Science University of Massachusetts, Amherst

2 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.

3 Existing storage and indexing approaches Streaming query systems TinyDB (Madden 2005), etc. Data storage and indexing is performed outside of network. Optimized for continuous queries. High energy cost if used for archival - data must be transmitted to central data store. In-network storage and indexing DCS, GHT (Ratnasamy 2002) Dimensions (Ganesan 2003) Directed Diffusion (Intangonwiwat 2000) Limited by lack of sufficient, energy-efficient storage and of communication and computation resources on current sensor platforms.

4 Technology Trends Radio µj/byte Flash µj/byte Max Flash size Mica MB MicaZ MB Telos MB UMass NAND 0.01 >1GB New flash technologies enable large storage systems on small energyconstrained sensors. 100x 1000x

5 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. Proxies Application Key challenge: Exploit proxy resources to perform intelligent search across data on resourceconstrained nodes. Sensors

6 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.

7 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. p 3. Local sensor data archive Stores detailed sensor information: e.g. images, events. Sensor node archive

8 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. Summarization function p 3. Local sensor data archive Stores detailed sensor information, e.g. images, events.

9 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. Distributed index p 3. Local sensor data archive Stores detailed sensor information, e.g. images, events.

10 Example - Camera Sensing Sensor archives information and transmits summary to proxy. Cyclops camera image p p Birds(t 1,t 2 )=1 summarize <id> Summary handle <id> Sensor node storage

11 Example - Indexing pp Birds(t 1,t 2)=1 Index <id> Birds t1,t2 1 <id> Network of proxies proxy Summary and location information are stored and indexed at proxy.

12 Example - Querying and Retrieval Cyclops camera p p summarize Birds in interval (t1,t2)? Birds t1,t2 1 <id> Cyclops camera p p summarize proxy Query is sent to any proxy.

13 Example - Querying and Retrieval Cyclops camera p p summarize Birds in interval (t1,t2)? Birds t1,t2 1 <id> Cyclops camera p p summarize <id> proxy Index is used to locate sensors holding matching records.

14 Example - Querying and Retrieval Cyclops camera p p summarize Birds t1,t2 1 <id> Cyclops camera p p <id> proxy Record is retrieved from storage and returned to application.

15 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

16 Goals of Index Structure 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. ( )? Distributed index insert( )

17 What is a Skip Graph? (Aspnes & Shah, 2003, Harvey et al. 2003) 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 Properties: Log(N) search and insert No single root -load balancing, robustness Single key and associated pointers

18 Interval search Query: x= Given intervals [low,high] and query X: 1 - order by low 2 - find first interval with high <= X 3 - search until low > X

19 Interval search 0 3 Query: x= Given intervals [low,high] and query X: 1 - order by low 2 - find first interval with high <= X 3 - search until low > X

20 Interval search 0 3 Query: x= Given intervals [low,high] and query X: 1 - order by low 2 - find first interval with high <= X 3 - search until low > X

21 Simple Interval Skip Graph Method: Index two increasing values: low, max Search on either as needed Interval keys: YES logn search: YES logn update: NO -(worst case O(N)) Derived from Interval Tree, Cormen et al. 1990

22 Sparse Interval Skip Graph Goal: efficient update of max(high) values in Interval Skip Graph. M proxies 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. Worst-case complexity: Search: O(logM) Update: O(M) N data items

23 Adaptive Summarization How accurately should the summary information represent the original data? updates Detailed summaries = more summaries, precise index queries Precise index = fewer wasted queries

24 Adaptive Summarization How accurately should the summary information represent the original data? updates Approximate summaries = fewer summaries, imprecise index queries?? imprecise index = more wasted queries

25 Adaptive Summarization Goal: balance update and query cost. Approach: adaptation. α = summarization (summaries / data) r = EWMA( wasted queries / data ) Target range: r 0 Decrease α if: Increase α if: r > r 0 + ε r < r 0 ε updates queries

26 Prototype and Experiments Software: Hardware: Network: Em* (proxy), TinyOS (sensor) Stargate Mica2 mote ad-hoc, multihop BMAC 11% Data: James Reserve [CENS] dataset 30s temperature readings 34 days For physical experiments, data stream was stored on sensor node and replayed.

27 Index performance How does the index performance scale with the number of proxies and size of dataset? Queries Interval skip graph index Tested in: Tasks: Em* emulation insert, query Variables: number of proxies (1-48) size of dataset Metric: proxy-to-proxy messages Sensor data

28 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.

29 Query performance What is the query performance on real hardware and real data? queries 4-proxy network Tested on: 4 Stargate proxies 12 Mica2 sensors in tree configuration Task: query Variables: size of dataset Metric: query latency (ms) 3-level multi-hop sensor field data

30 Query results Sensor link latency dominates Proxy link delay is negligible The sensor communication consists only of a query and a response - the minimal communication needed to retrieve the data. Validates the approach of using proxy resources to minimize the number of expensive sensor operations.

31 Adaptive Summarization How well does the adaptation mechanism track changes in conditions? 1/a Query/data = 0.2 Query/data = 0.1 Varied query rate Query/data =0.03 Tested in: Em*, EMTOSSIM emulation Task: data and queries Variables: query/data ratio Metric: summarization factor a Summary algorithm adapts to data and query dynamics. Summary rate adapts

32 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)

33 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: