Stampede Overview Joint research between HP CRL and Georgia Tech (*) Kishore Ramachandran (*) Jim Rehg(*), Phil Hutto(*), Ken Mackenzie(*), Irfan Essa(*), Kath Knobe, Jamey.
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Transcript Stampede Overview Joint research between HP CRL and Georgia Tech (*) Kishore Ramachandran (*) Jim Rehg(*), Phil Hutto(*), Ken Mackenzie(*), Irfan Essa(*), Kath Knobe, Jamey.
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
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
Augmented reality applications
viruses, pollution, …
animals and birds in natural habitats
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
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
Aspect-oriented programming support
STAGES language and compiler
Dynamic multi-cluster implementation
D-Stampede Web Service
resource naming and discovery
data fusion (fusion channels)
asynchronous notification
.NET implementation
Models for reasoning about failures
Security and privacy issues