Stampede overview
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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,

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Stampede Overview

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Stampede overview

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

camera

Channels / queues

Channels / queues

camera

Skiff

Skiff

Sensor

Fusion

Sensors

Actuators

Unix / Linux / NT cluster

Data Aggregators

Distributed Ubiquitous Computing

  • Hardware Model

    • sensors, actuators, embedded processors, PDAs, laptops, clusters…

“OCTOPUS” DIAGRAM

head / arms / tentacles


Killer app

Killer App?

  • Application context

    • distributed sensors with varying capabilities

    • control loop involving sensors, actuators

    • rapid response time at computational perception speeds


Application scenarios

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

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

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

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

thread

thread

Channel

i_conn

Channel

thread

Channel

Stampede computational model:a dynamic thread-channel graph

  • put(ts, item)

  • get(ts, item)

  • consume(ts)

  • many to many connections

  • time sequenced data

  • correlation of streams

  • automatic GC


Experiences with stampede

Change

Detection

Motion

Mask

Target

Detection

Model 1

Location

Video

Frame

Digitizer

Target

Detection

Histogram

Model

Model 2

Location

Histogram

Experiences with Stampede

  • Color-based people tracker for SmartKiosk (Jim Rehg)


Stampede overview

Model 1

Model 2


Color based tracking example

Color-Based Tracking Example


Stampede overview

  • 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


Stampede overview

skiff

skiff

  • Multipoint video/audio capture

Cluster

STM

.

.

Stampede

client (C)

STM

Stampede

client (C)

Stampede

Application

(C)

STM


Multipoint video demo

Multipoint Video Demo


Ongoing work

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


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