Decomposing Data-Centric Storage Query Hot-Spots in Sensor Networks

1. Decomposing Data-Centric Storage Query Hot-Spots in Sensor Networks Mohamed Aly In collaboration with Panos K. Chrysanthis and Kirk Pruhs Advanced Data Management Technologies Lab Dept. of Computer Science University of Pittsburgh

2. Motivating Application: Disaster Management

3. 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.

4. Data Storage Options in Sensor Networks • Base Station Storage: • Events are sent to base stations where queries are issued and evaluated. • Best suited for continuous queries. • In-Network Storage (INS): • Events are stored in the sensor nodes. • Best suited for ad-hoc queries. • All previous INS schemes were Data-Centric Storage (DCS) schemes.

5. Data-Centric Storage (DCS) • 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’02] • 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.

6. The DIM DCS Scheme

7. Problems of Current DCS Schemes • Storage Hot-Spots: • A large percentage of events is mapped to few sensor nodes. • Our Solutions • The Zone Sharing (ZS) algorithm on top of DIM [DMSN’05] • The K-D Tree based DCS scheme (KDDCS) [submitted] • Query Hot-Spots: • A large percentage of queries target events stored in few sensor nodes. • Our Solutions [MOBIQUITOUS’06] • The Zone Partitioning (ZP) algorithm • The Zone Partial Replication (ZPR) algorithm

8. Query Hot-Spots in DIM • Definition: A high percentage of queries accessing a “hot zone” stored by a small number of nodes. • Existence of query hot-spots leads to: • Increased node deaths • Network Partitioning • Reduced network lifetime • Decreased Quality of Data (QoD)

9. Query Hot-Spots Decomposition Algorithms • Uniform vs. skewed distribution of the number of accesses among the hot-zone events: • The Zone Partitioning (ZP) algorithm • The Zone Partial Replication (ZPR) algorithm • Basic Idea: • Each sensor keeps track of the Average Querying Frequency (AQF) of its stored events • Periodically compares its AQF to its neighbors’ AQFs • In case a large difference is detected, the node (donor) selects the Best neighbor (receiver) that can receive part of its responsibility range • Donor locally determines receiver • Partitioning Criterion (PC)

10. The Zone Partitioning (ZP) Algorithm

11. The Zone Partial Replication (ZPR) Algorithm

12. Query Hot-Spots Decomposition Algorithms • Uniform vs. skewed distribution of the number of accesses among the hot-zone events: • The Zone Partitioning (ZP) algorithm • The Zone Partial Replication (ZPR) algorithm • Basic Idea: • Each sensor keeps track of the Average Querying Frequency (AQF) of its stored events • Periodically compares its AQF to its neighbors’ AQFs • In case a large difference is detected, the node (donor) selects the Best neighbor (receiver) that can receive part of its responsibility range • Donor locally determines receiver • Partitioning Criterion (PC)

13. Query Hot-Spots Decomposition Algorithms • Uniform vs. skewed distribution of the number of accesses among the hot-zone events: • The Zone Partitioning (ZP) algorithm • The Zone Partial Replication (ZPR) algorithm • Basic Idea: • Each sensor keeps track of the Average Querying Frequency (AQF) of its stored events • Periodically compares its AQF to its neighbors’ AQFs • In case a large difference is detected, the node (donor) selects the Best neighbor (receiver) that can receive part of its responsibility range • Donor locally determines receiver • Partitioning Criterion (PC)

14. PC: Storage Safety Requirement • The sum of the pre-partitioning load of the receiver and the traded zone should be less than the receiver’s storage capacity • T + lreceiver ≤ S

15. PC: Energy Safety Requirement (1) • The energy consumed by the donor in the partitioning process should be much less than the total energy of the donor • T / edonor ≤ E1 • E1 ≤ 0.5

16. PC: Energy Safety Requirement (2) • The energy consumed by the receiver in the partitioning process should be much less than the total energy of the receiver • (T * re) / ereceiver ≤ E2 • E2 ≤ 0.5

17. PC: Access Frequency Requirement • The average access frequency of the donor is much larger than that of the receiver • AQF(donor) / AQF(receiver) ≥ Q1 • Q1 should be greater than 2 to avoid cyclic migrations

18. ZPR Initiation Requirements • In case all previous requirements are satisfied: • ZP initiated • If a hot sub range of small size exists within the hot range  ZPR initiated instead of ZP • AQF(hot sub range) / AQF(total range) ≥ Q2 • Q2 should be close to 1, for e.g. 0.9 • size(hot sub range) / size(total range) ≤ Q3 • Q3 should be close to 0, for e.g. 0.2

19. Partitioning Criterion (PC) • T + lreceiver ≤ S • T / edonor ≤ E1 • (T * re) / ereceiver ≤ E2 • AQF(donor) / AQF(receiver) ≥ Q1 • AQF(hot sub range) / AQF(total range) ≥ Q2 • size(hot sub range) / size(total range) ≤ Q3 1:4 satisfied  ZP initiated 1:6 satisfied  ZPR initiated

20. More about the Algorithms • Mechanism to lower messaging overhead • GPSR Modifications • Traded Zone List (TZL) • Coalescing Process • Insertion process in ZPR • Bound on the replication hops of ZPR

21. Roadmap • Background • Problem Statement: Query Hot-spots • Algorithms: ZP, ZPR • Experimental Results • Conclusions

22. Simulation Description • Compare: DIM, ZP/ZPR. • Simulator similar to the DIM’s [Li et. al., SenSys’03] • Two phases: insertion & query. • Insertion phase (to achieve a steady state of network storage) • Each sensor initiates 5 events • Events forwarded to owners • Query phase • Each sensor generates 20 single-event queries (worst case scenario)

23. Experimental Setup

24. Experimental Results: Quality of Data (QoD) 5% hot-spot

25. Experimental Results: Balancing Energy Consumption 200 nodes, 0.33% hot-spot

26. Experimental Results: ZP/ZPR Strengths • 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. • Increasing energy savings by balancing energy consumption among sensors. • Increasing the network lifetime by reducing node deaths.

27. Acknowledgment • This work is part of the “Secure CITI: A Secure Critical Information Technology Infrastructure for Disaster Management (S-CITI)” project funded through the ITR Medium Award ANI-0325353 from the National Science Foundation (NSF). • For more information, please visit: http://www.cs.pitt.edu/s-citi/

28. Conclusions and Extensions • Query Hot-Spots: An important problem in current DCS schemes. • Contribution: • A query hot-spots decomposition scheme for DCS sensor nets, ZP/ZPR, working on top of the DIM DCS scheme. • Experimental validation of the ZP/ZPR practicality • Work under submission: • KDDCS: A unified DCS scheme for load balancing storage and query loads.

29. Thank You Questions ? Advanced Data Management Technologies Lab http://db.cs.pitt.edu

30. Experimental Results: Load Balancing 0.05% hotspot

31. Experimental Results: Load Balancing 0.05% hot-spot