Intermittently Connected Query Processing Yang Zhang, Bret Hull, Hari Balakrishnan, and Samuel Madden MIT Computer Science and AI Lab April 17, 2007

ICEDB Server (portal) Example Query Show me photos of traffic jams. No duplicates. Example Query Show me photos of traffic jams. No duplicates.

ICEDB Server (portal) wireless connection sensors + #!/usr/bin/perl while (true) { raw = read(serial); tuple = convert(raw); send(icedb, tuple); } #!/usr/bin/perl while (true) { raw = read(serial); tuple = convert(raw); send(icedb, tuple); } adapter schema data source

ADAPTERADAPTER ADAPTERADAPTER DB CQ Ad-hoc Query Processor Ad-hoc Query Processor Output Buffers Output Buffers CAFNETCAFNET CAFNETCAFNET

SELECT... EVERY n [SECONDS] BUFFER IN buffername

tuplesbuffer query DB

 PRIORITY rank,weight : inter-query (local)  DELIVERY ORDER BY : intra-query (local)  SUMMARIZE AS : global tuplesbuffer

FIFO Bisect

ICEDB Server

from central server to central server ICEDB Server latlonins_time :30pm :35pm ……… latlonins_timerank :35pm :30pm2 …………

 232 days of normal driving (07/05 – 07/06)  Boston and Seattle areas  260 distinct km of roads, 50% from 15 km  32,000 APs discovered, 2,000 open  Mean time between APs: 23 seconds  Mean association duration: 24 seconds  Median TCP upload: ~200 kbytes  Connectivity is equi-probable in [0,60] km/h

 Query workloads: uniform, hotspot  Camera data: 50KB  Metric: fraction query points satisfied  Prioritization schemes: FIFO, bisect, random, global  Cars: one, many query point

 FIFO: zero success  Random/bisect: ~0.25x success of global  Bottleneck: not query count, but total network capacity  Global: remote nodes and central server share data

Intermittently Connected Query Processing Yang Zhang, Bret Hull, Hari Balakrishnan, and Samuel Madden MIT Computer Science and AI Lab

SELECT... EVERY n [SECONDS] BUFFER IN buffername

ICEDB Server latlonins_time :30pm :35pm ……… latlonins_timerank :35pm :30pm2 …………

Trace: 1 trip all sensor data collected between ignition on to ignition off Trace: 1 trip all sensor data collected between ignition on to ignition off

 Trace-driven simulation

ICEDB Server

data source = ++ #!/usr/bin/perl while (true) { raw = read(serial); tuple = convert(raw); send(icedb, tuple); } #!/usr/bin/perl while (true) { raw = read(serial); tuple = convert(raw); send(icedb, tuple); }

 Trace-driven simulation  Query workloads: uniform, hotspot  Camera data: 50 KB/img  Metric: success ratio  Prioritization schemes: FIFO, bisect, random, global  Cars: one, many

 CarTel: mobile sensor platform  Opportunistic data delivery  >1 year, Boston/Seattle, 260 km