1 / 10

MediaBroker: An Architecture for Pervasive Computing

Explore the MediaBroker architecture designed for managing streams in dynamic environments, enabling stream registry, discovery, sharing, and adaptable stream fusion/transformation. The system supports resource-constrained devices through efficient data brokerage and transformation capabilities.

inaa
Download Presentation

MediaBroker: An Architecture for Pervasive Computing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MediaBroker:An Architecture for Pervasive Computing Kishore Ramachandran (http://www.cc.gatech.edu/~rama) College of Computing Georgia Tech Presented by: Pouria Pirzadeh and Ronen Vaisenberg

  2. High connectivity Low connectivity / Wireless Cameras, sensor nodes High Performance Computing (HPC) resources Computing/Communication Continuum Sensor Network HPC resources Ambient Computing Infrastructure

  3. Application Characteristics • Physically distributed heterogeneous devices • Interfacing and integrating with the physical environment • Diverse stream types • Diverse computation, communication and power capabilities (from embedded sensors to clusters) • Stream fusion/transformation, with loadable code • Resource scarcities • Dynamicjoin/leave of application components

  4. MediaBroker • An architecture for stream management • A clearing house for sensors and actuators in a given space • Stream registry, discovery, sharing • Dynamic connection of sources (producers) and sinks (consumers) • Dynamic sharing of transformations and streams • Architecture Requirements • Scalability • Low latency/high throughput • adaptability

  5. Producer Consumer Producer Consumer Type Server • Elements • Type server: stores data types, relationships, and transformation code • Transformation engine: allow safe execution of injected code on cluster nodes • Scheduler: manages workload, and allows prioritizing transformation requests • Data brokers: manages connections between producers and consumers Transformation Engine Data Broker Data Broker Data Items Transformation Requests Transformation Code Transformation Engine Scheduler Transformation Engine

  6. Architecture Overview

  7. Architecture Workflow • New client connects to engine • MB establishes a command channel • Associates a listener to the channel • Client API • Sources/sinks instantiation/termination • New source instantiated • MB assigns it to a new data broker • New sink instantiated • MB assigns it to an existing data broker

  8. Data Broker • Core of MB engine • An execution context, containing 4 threads • Transport thread • From source to sink(s) • Command thread • Listens to channel for add/remove source/sink • Source-watch thread • Sink-watch thread • Tasks: • Transports data from one source to sink(s) • Type negotiation between source/sink(s)

  9. Data Types • Data broker • finds LUB of requested types • Communicates LUB with producer • It does LUB transformation to requests • Data types defined by a language for • Type description • Domain + attributes • Type relations • Through a Type map • Type transformations

  10. MediaBroker Vs. Satware • Code • C vs. Java • Types • C struct vs. tuples • Resource Discovery • Ldap vs. RMI • Messeging • Channel listeners vs. RMI RMI

More Related