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
Distributed Ubiquitous Computing

Hardware Model

sensors, actuators, embedded processors, PDAs,
laptops, clusters…
camera
Skiff
camera
Skiff
Sensors
Actuators
Sensor
Fusion
Data Aggregators
Unix / Linux / NT cluster
“OCTOPUS” DIAGRAM
head / arms / tentacles
Killer App?

Application context
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distributed sensors with varying capabilities
control loop involving sensors, actuators
rapid response time at computational
perception speeds
Application Scenarios

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Mobile robots
Smart vehicles
Aware homes
Real-life emergencies

natural and man-made disaster response
 earthquakes, twisters, fire, terrorist situations

Environmental monitoring

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Augmented reality applications

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viruses, pollution, …
animals and birds in natural habitats
training for hazardous situations
battlefield management
Interactive animation
Application Characteristics
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Physically distributed heterogeneous devices
Distributed mobile sensing and actuation
Interfacing and integrating with the physical
environment
Information acquisition, processing, synthesis, and
correlation

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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

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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
o_conn
thread
Channel
i_conn
thread
thread
Channel
•put(ts, item)
thread
Channel
•get(ts, item)
•consume(ts)
•many to many connections
•time sequenced data
•correlation of streams
•automatic GC
Experiences with Stampede

Color-based people tracker for SmartKiosk
(Jim Rehg)
Digitizer
Change
Detection
Motion
Mask
Target
Detection
Model 1
Location
Histogram
Histogram
Model
Target
Detection
Model 2
Location
Video
Frame
Model 1
Model 2
Color-Based Tracking Example

Video Textures (Irfan Essa)
Generate an infinite video sequence from a finite set
of 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

Multipoint video/audio capture
STM
.
.
skiff
Stampede
client (C)
skiff
Stampede
client (C)
STM
STM
Cluster
Stampede
Application
(C)
Multipoint Video Demo
Ongoing Work

Media broker architecture

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Aspect-oriented programming support

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STAGES language and compiler
Dynamic multi-cluster implementation
D-Stampede Web Service

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resource naming and discovery
data fusion (fusion channels)
asynchronous notification
.NET implementation
Models for reasoning about failures
Security and privacy issues