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