Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing
A synchronous scheduling service for distributed real-time Java
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Transcript of A synchronous scheduling service for distributed real-time Java
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A synchronous scheduling service (SSS)for distributed real-time Java
Pablo Basanta-Val, Iria Estévez-Ayres, Marisol García-Valls, and Luis Almeida
mailto:[email protected]
†Jornadas de Tiempo Real 2011- Madrid( ) Publicado en IEEE Transactions on Parallel and Distributed Systems
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Outline• Context and Motivation• FTT and DREQUIEMI integration• SSS (Synchronous Scheduling Service)
– Master Slave Model– Choreographies– Choreographies scheduling/scheduler– Architecture and examples– Performance
• Conclusion and ongoing work
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Context
• Java programmers may use two specifications for develop their real-time applications– RTSJ: The Real-Time Specification for Java– DRTSJ: The Distributed Real-Time Specification for Java
• DRTSJ has focused on remote object upcalling and abstractions (distributable threads). – But not in a predictable networks– Networks predictability is a requirement
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In this work
• We introduce time-triggered orientation in distributed real-time Java– Basic model used the FTT (Flexible Time-Triggered)
protocol– Supported as a new service in distributed real-time Java
• SSS (Synchronous Scheduling Service)
• We obtain a more predictable network management– Useful for instance in high-integrity applications
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FTT and DREQUIEMI integration (1/3)
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FTT and DREQUIEMI integration (2/3)
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FTT and DREQUIEMI integration (3/3)
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System overview
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Choreographies set
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T and S choreographies
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C and P choreographies
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Scheduling Choreographies• Each choreography is
modeled as non preemptive task– {O, T, C, D}
• The choreographies executed by the master- It runs a NPR-EDF
- Simple admission control (T=D)
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Implementation issues: Convergence Layer
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Example 1: real-time producer consumer (1/2)
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producerslave
consumerslave
Every 10 ms generates a sample
Process data coming from a producer
10 10
Maximum network delay: 20 ms
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Example real-time producer consumer (2/2)
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PC# Produce# producer # CC.dataO= 5msT= 10 msC= 2 msD= 10 ms
CC # Consume# consumer# O= 15 msT= 10 msC= 2 msD= 10 ms
producerslave
consumerslave
Every 10 ms generates a sample
Process data coming from a producer
10 10
Maximum network delay: 20 ms
master
NPR-EDF
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Experiments (1/2)End-to-End costs (us) over 796 MHz-100Mbps
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JTime
J2ME-RMIOP
TimesysOs
DREQUIEMI
Convergence Layer
Master-slave templates
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Experiments (2/2) End-to-End costs (bytes) over 796 MHz-100Mbps
JTime
J2ME-RMIOP
TimesysOs
DREQUIEMI
Convergence Layer
Master-slave templates
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Jitter [new]time vs. event triggered
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JTime
J2ME-RMIOP
TimesysOs
DREQUIEMI
Convergence Layer
Master-slave templates
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Conclusions
• Developed techniques to include time-triggered orientation in distributed real-time Java– Synchronous Scheduling Service (SSS)
• Empirical evidences showed better performance than an ET approach– Because TCP/IP stacks and OS are not fully preemptive
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Ongoing work
• Developing a minimum time-triggered implementation without DREQUIEMI– Ongoing master thesis
• Changes in the model– NPR-RMS model vs. NPR-EDF– One way choreographies
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