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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,

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Stampede Overview

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  1. 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

  2. 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

  3. Killer App? • Application context • distributed sensors with varying capabilities • control loop involving sensors, actuators • rapid response time at computational perception speeds

  4. 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

  5. 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

  6. Research Issues Stream-oriented and time-sequenced data Heterogeneity of Components Resource management High Availability Clients leave and join arbitrarily Security and Privacy

  7. 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

  8. 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

  9. 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)

  10. Model 1 Model 2

  11. Color-Based Tracking Example

  12. 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

  13. skiff skiff • Multipoint video/audio capture Cluster STM . . Stampede client (C) STM Stampede client (C) Stampede Application (C) STM

  14. Multipoint Video Demo

  15. 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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