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Thread Transparency in Information Flow Middleware

  • Rainer Koster
  • Andrew P. Black
  • Jie Huang
  • Jonathan Walpole
  • Calton Pu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2218)

Abstract

Applications that process continuous information flows are challenging to write because the application programmer must deal with flow-specific concurrency and timing requirements, necessitating the explicit management of threads, synchronization, scheduling and timing. We believe that middleware can ease this burden, but middleware that supports control-flow centric interaction models such as remote method invocation does not match the structure of these applications. Indeed, it abstracts away from the very things that the information-flow centric programmer must control

We are defining Infopipes as a high-level abstraction for information flow, and we are developing a middleware framework that supports this abstraction directly. Infopipes handle the complexities associated with control flow and multi-threading, relieving the programmer of this task. Starting from a high-level description of an information flow configuration, the framework determines which parts of a pipeline require separate threads or coroutines, and handles synchronization transparently to the application programmer. The framework also gives the programmer the freedom to write or reuse components in a passive style, even though the configuration will actually require the use of a thread or coroutine. Conversely, it is possible to write a component using a thread and know that the thread will be eliminated if it is not needed in a pipeline. This allows the most appropriate programming model to be chosen for a given task, and existing code to be reused irrespective of its activity model

Keywords

Control Event Pipeline Stage Remote Method Invocation Thread Creation Push Mode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© IFIP International Federation for Information Processing 2001

Authors and Affiliations

  • Rainer Koster
    • 1
  • Andrew P. Black
    • 2
  • Jie Huang
    • 2
  • Jonathan Walpole
    • 2
  • Calton Pu
    • 3
  1. 1.University of KaiserslauternUSA
  2. 2.OGI School of Science and EngineeringOregon Health and Science UniversityUSA
  3. 3.Georgia Institute of TechnologyUSA

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