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Stampede Overview
Joint research between HP CRL and Georgia Tech (*)
Kishore Ramachandran (*) Jim Rehg(*), Phil Hutto(*), Ken
Mackenzie(*), Irfan Essa(*), Kath Knobe, Jamey Hicks
Students (*):Sameer Adhikari, Arnab Paul, Bikash Agarwalla,
Matt Wolenetz, Nissim Harel, Hasnain Mandviwala, Yavor Angelov, Junsuk Shin, Rajnish Kumar,
Ilya Bagrak, Martin Modahl, David Hilley
Hardware Model sensors, actuators, embedded processors,
PDAs, laptops, clusters…
“OCTOPUS” DIAGRAM
head / arms / tentacles
Skiff
Skiff
camera
camera
Data Aggregators
Sensors
Actuators Unix / Linux / NT cluster
Channels / queuesChannels / queues
SensorFusion
Distributed Ubiquitous Computing
Killer App?
Application context distributed sensors with varying capabilities control loop involving sensors, actuators rapid response time at computational
perception speeds
Application Scenarios Mobile robots Smart vehicles Aware homes Real-life emergencies
natural and man-made disaster response earthquakes, twisters, fire, terrorist situations
Environmental monitoring viruses, pollution, … animals and birds in natural habitats
Augmented reality applications training for hazardous situations battlefield management
Interactive animation
Application Characteristics
Physically distributed heterogeneous devices Distributed mobile sensing and actuation Interfacing and integrating with the physical
environment Information acquisition, processing, synthesis,
and correlation streaming high BW data such as audio and video low BW data such as from a haptic sensor time-sequenced data
Dynamic computation continuum from low end device-level filtering to high end inference
Research Issues
Stream-oriented and time-sequenced data
Heterogeneity of Components Resource management High Availability Clients leave and join arbitrarily Security and Privacy
Stampede Project
Theme seamless programming system spanning
sensors and backend servers d-stampede: common programming paradigm across
widely varying architectures [ICDCS 2002] supports development of pervasive computing
applications
Stampede computational model:
a dynamic thread-channel graph
thread
Channel
thread
thread
threadthread
Channel
Channel
Channel
i_conn
o_conn
•many to many connections
•time sequenced data
•correlation of streams
•automatic GC
•put(ts, item)
•get(ts, item)
•consume(ts)
Experiences with Stampede
Color-based people tracker for SmartKiosk (Jim Rehg)
ChangeDetection
Model 1Location
DigitizerVideoFrame
Histogram
MotionMask
TargetDetection
TargetDetection
HistogramModel
Model 2Location
Model 1 Model 2
Color-Based Tracking Example
Video Textures (Irfan Essa)
Generate an infinite video sequence from a finite setof video frames-embarrassingly parallel (comparison of images)-data distribution from source the main challenge-breaking image into strips to fit the computation in caches secondary challenge
Cluster
skiff
Stampedeclient (C)
StampedeApplication(C)
skiff
Stampedeclient (C)
STM
STM
STM
.
.
Multipoint video/audio capture
Multipoint Video Demo
Ongoing Work
Media broker architecture resource naming and discovery data fusion (fusion channels) asynchronous notification
Aspect-oriented programming support STAGES language and compiler
Dynamic multi-cluster implementation D-Stampede Web Service
.NET implementation Models for reasoning about failures Security and privacy issues