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Query Processing in Mobile P2P Databases

Query Processing in Mobile P2P Databases. IGERT Seminar Presentation Bo Xu joint work with Ouri Wolfson. Talk outline. Introduction System Model The MARKET Algorithm Evaluation Extension to CTS Conclusion and Future Work. Query Processing Environments. GPS receiver

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Query Processing in Mobile P2P Databases

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  1. Query Processing in Mobile P2P Databases IGERT Seminar Presentation Bo Xu joint work with Ouri Wolfson

  2. Talk outline • Introduction • System Model • The MARKET Algorithm • Evaluation • Extension to CTS • Conclusion and Future Work IGERT seminar

  3. Query Processing Environments GPS receiver chemical spill detector still/video camera vibration sensor acoustic detector Vehicular Sensor Network (VSN) Motivation: a general purpose query processing strategy mobile disconnected wireless ad-hoc networks IGERT seminar

  4. Store-and-forward to deal with sparseness QA A Q r Q q A qA A IGERT seminar

  5. Issues with Store-and-forward • How to manage limited memory, power, and bandwidth? • Which reports to save/transmit? IGERT seminar

  6. Difficulty of Store-and-forward Case: Each mobile node is interested in every data-item Assume that the trajectories of all nodes is known a priori at a central server. If memory, energy, and bandwidth are bounded at mobile nodes, then the problem of determining whether a set of data-items can be disseminated to all the mobile nodes is NP-complete. Mobile P2P: Trajectories unknown a priori; Heuristics needed IGERT seminar

  7. Talk outline • Introduction • System Model • The MARKET Algorithm • Evaluation • Extension to CTS • Conclusion and Future Work IGERT seminar

  8. Mobile P2P Database report 8 query C query A report 1 report 2 report 3 Local query query B report 4 report 5 Local database Pda’s, cell-phones, sensors, hotspots, vehicles, with short-range wireless capabilities C A B • Applications coexist • Variable report sizes • A peer can be a produce, consumer, and broker IGERT seminar

  9. Queries • A query Q maps each report R to a match degree: • Examples: • Top parking slots given my current location • Profile with expertise “children-periodontics” • Similarity between two images match(R,Q)=e-t-d IGERT seminar

  10. Query/report Dissemination • Two peers within transmission range exchange queries and reports • Least relevant reports that do not fit in local broker database are purged • Exchange not necessarily synchronous (periodic broadcast) IGERT seminar

  11. Talk outline • Introduction • System Model • The MARKET Algorithm • Evaluation • Extension to CTS • Conclusion and Future Work IGERT seminar

  12. Ranking Factors Rank of a report R is determined by • Demand: What fraction of peers are querying R • Probability that a peer is interested in R • Supply: What fraction of peers already have R • Probability that a peer has R • Size of R IGERT seminar

  13. Rank of a report reports benefit 0.7 0.7 0.5 0.5 0.4 0.5 0.8 0.3 reports database demand(R)*(1supply(R)) Rank(R)= size(R) expected benefit = demand(R)*(1supply(R)) IGERT seminar

  14. Report Ranking: sample demand Queries relation is FIFO maintained IGERT seminar

  15. Rank of Reports • Demand for R • Qi’s are the members of the queries relation • Size of the queries relation determined based on Hoeffding’s inequality E.g., if n=108, then with 95% chance the demand estimation error is smaller than 0.08 IGERT seminar

  16. How does peer O determine supply(R)? • A parametric formula giving the supply is beyond the state of the art • O machine-learns supply(R) based on meta-data of R: • Age of R • Number of times O sighted R from other peers • etc. IGERT seminar

  17. Computing Supply by Machine-learning MAchine LEarning based Novelty rAnking (MALENA) Reports database of O Report Report aro aro fin fin report report - - id id description description … … R1 R1 1 1 1 1 … … R4 R4 2 4 2 4 … … R2 R2 3 2 3 2 … … R7 R7 4 2 4 2 aro: The age rank order within O’s reports database fin: The number of times O has sighted the report from other peers IGERT seminar

  18. MALENA B B Examples created negative positive Request R2 IGERT seminar

  19. MALENA Implementation Considerations • Minimize overhead • No need to actually store examples • Model incrementally built • Bayesian learning a simple but effective method IGERT seminar

  20. Talk outline • Introduction • System Model • The MARKET Algorithm • Evaluation • Extension to CTS • Conclusion and Future Work IGERT seminar

  21. Comparison with RANDI (MDM’07) mobility model=random way point, average motion speed=1 mile/hour transmission range=100 meters, mean of reports database size=100Kbytes queries database size=100 queries report size uniformly distributed between 1K and 2K bytes 0.1 report produced per second 1 peer within transmission range 20 peers within transmission range MARKET half as good as ideal benchmark MARKET twice better than RANDI RANDI=MARKET-supply IGERT seminar

  22. Comparison with LRU and LFU mobility model=iMotes traces mean of reports database size=150Kbytes queries database size=10 queries report size uniformly distributed between 2K and 20K bytes 0.1 report produced per second, transmission size=100Kbytes throughput (matches/peer) response-time bound (second) (results obtained by Fatemeh Vafaee) IGERT seminar

  23. Evaluation of MALENA (TAAS’09) low-turn-over/low-injection low-turn-over/low-injection high-turn-over/high-injection turn-over: peers enter/exit system injection: number of peers that have a report initially mobility model=iMotes traces, reports database size=100 reports 2 reports produced per second, transmission size=10 reports MALENA always follows the best indicator IGERT seminar

  24. Application: K-nearest-neighbors query-point • Query: K-nearest-neighbors of a fixed location (query-point) • Reports: current locations of mobile sensors • match(Q,R): in reverse proportion to the distance from query point sink IGERT seminar

  25. Itinerary based KNN processing Phase I: Query delivered to the sensor closest to query point Phase II: Query traverses an itinerary to collect answers Phase III: Answers returned to sink IGERT seminar

  26. Simulation Results mobility model=random way point, average motion speed=1 mile/hour transmission range=100 meters report size=24 bytes, query size=16 bytes mean of reports database size=100 reports one location report produced at each sensor per second MARKET is especially suitable for sparse environments IGERT seminar

  27. Talk outline • Introduction • System Model • The MARKET Algorithm • Evaluation • Extension to CTS • Conclusion and Future Work IGERT seminar

  28. TrafficInfo: Disseminating Traffic Information in VANET’s IGERT seminar

  29. What does relevance mean in TrafficInfo B B A A A report is relevant if it changes the route IGERT seminar

  30. Which factors indicate relevance of report? • Distance to the reported road segment • Type of road segment • Speed variance • … IGERT seminar

  31. Conceptual Learning Procedure • An example is created for a received report • The example is labeled positive if the report changes route and negative otherwise • Individual vs. group • How to deal with aggregation? IGERT seminar

  32. Conclusion sensor-rich environment short-range wireless • Query processing + Mobile P2P  • Store-and-forward enables in-network processing in mobile disconnected networks • Ranking is important for dealing with memory, bandwidth, and energy constraints IGERT seminar

  33. Future Work • Multimedia reports • Utilization of metadata • Integration of stateless and stateful approaches • Starvation/fairness IGERT seminar

  34. Thanks! Questions? IGERT seminar

  35. 802.11 Basics • 3 modes: transmitting, receiving, listening (order of power consumption) • When listening: if detecting a message destined to host  receive-mode • Time divided into slots, 20microsecs each • Transmission: • Listen for 1 time slot • If channel free start broadcast (observe collision possible) • Broadcast may last for many time slots IGERT seminar

  36. Energy Efficiency of a Broadcast X successfully receive the broadcast from x Collisions occur at neighbor Throughput (Th) = (expected number of neighbors that successfully receive broadcast)  (broadcast size) Power efficiency (PE) = IGERT seminar

  37. Computation of Throughput X Y Conditions for successful reception at an arbitrary node Y • No green node inside starts to broadcast at the same time slot with X • No transmission from any purple node overlaps with that from X IGERT seminar

  38. Energy Constraints • Energy consumed by a 802.11 network interface for transmitting a message of size M bytes En=fM+g For 802.11 broadcast, g=26610-6 Joule, f=5.2710-6Joule/byte IGERT seminar

  39. Experimental MP2P Projects (Pedestrians) • 7DS – Columbia University (web pages) • iClouds – Darmstadt Univ. (incentives) • MoGATU – UMBC (specialized query processing, e.g., collaborative joins) • PeopleNet – NUS, IIS-Bangalore (Mobile commerce, information type  location baazar) • MoB – Wisconsin, Cambridge (incentives, information resources e.g. bandwidth) • Mobi-Dik – Univ. of Illinois, Chicago (brokering, physical resources, bandwidth/memory/power management) IGERT seminar

  40. Vehicular Projects • Inter-vehicle Communication and Intelligent Transportation: • CarTALK 2000 is a European project • VICS (The Vehicle Information and Control System) is a government-sponsored system in Japan with an 11-year track record • FleetNet, an inter-vehicle communications system, is being developed by a consortium of private companies and universities in Germany • IVI (Intelligent Vehicle Initiative) and VII (Vehicle Infrastructure Integration), the US DOT • MP2P provides data management capabilities on top of these communication systems • Grassroots, TrafficView, SOTIS, V3 – P2P dissemination of traffic info to reduce travel times IGERT seminar

  41. RANk-based DIssemination (RANDI) • Ranking of reports • Bandwidth/energy aware • Exchange enhances • Consumer functionality • Broker functionality • Consumer: Answer local query (pull) • Broker: Transmit reports most likely requested by future-encountered peers (push) • Transmission trigger: • Encounter • New reports IGERT seminar

  42. RANDI When two peers meet they conduct a two-phase exchange: local query Phase 1 answers satisfied as a consumer (pull) more reports Phase 2 enhanced as a broker (push) Phase 1: Exchange queries and receive answers (pull) Phase 2: Exchange more reports using available energy/bandwidth (push) • Combination of: • unicast (thin line) and • broadcast (thick lines) to enable overhearing. IGERT seminar

  43. RANDI (Cont’d) To solve problem with static peers: Two interaction modes which combine pull and push new reports • Query-response: triggered by discovery of new neighbors • Relay: triggered by receipt of new reports • Disseminate to existing neighbors IGERT seminar

  44. 7DS query query reports reports query query P2P mode: each node periodically broadcasts its query and receives reports from neighboring peers. No strategy to determine query frequency and transmission size. Cache management based on web-page expiration time. IGERT seminar

  45. PeopleNet Peer A Peer B Peer A Peer B random-spread before exchange after exchange Peer A Peer B Peer A Peer B random-swap before exchange after exchange Reports are randomly selected for exchanging and saving upon encountering. IGERT seminar

  46. 7DS query query reports reports query query Each peer periodically broadcasts its query and receives reports from neighboring peers. No strategy to determine query frequency and transmission size. Cache management based on web-page expiration time. IGERT seminar

  47. PeopleNet Reports are randomly selected for exchanging and saving upon encountering. Peer A Peer B Peer A Peer B before exchange after exchange IGERT seminar

  48. Mobile Local Search: Applications • transportation • Announce sudden stop, malfunctioning brake light, patch of ice • Floating car data • Dissemination of multi-media traffic information (picture, video, voice) • Search close-by taxi customer, parking slot, ride-share • social networking (wearable website) • Personal profile of interest at a convention • Singles matchmaking • Floating BBS • mobile electronic commerce • Sale on an item of interest at mall • Music-file exchange • emergency response • Search for victims in a rubble • asset management and tracking • Sensors on containers exchange security information => remote checkpoints • tourist and location-based-services • Closest ATM IGERT seminar

  49. Applications – Common features • Mobile/stationary peers • Resources of interest • in a limited geographic area • Short time duration • Can be solved by fixed servers, but • Unlikely solution • Proposed mp2p paradigm can enhance fixed solution (reliability, performance, coverage) IGERT seminar

  50. MARKET When two peers meet they conduct a two-phase exchange: Local query Phase 1 answers satisfied as a consumer (pull) more reports Phase 2 enhanced as a broker (push) Phase 1: Exchange subscriptions and receive answers (pull) Phase 2: Exchange more publications using available energy/bandwidth (push) • Combination of: • unicast (thin line) and • broadcast (thick lines) to enable overhearing. IGERT seminar

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