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DVD. audio rend. audio dec. Get Full Packet. Put Full Packet. read. dmux. dec. sharp enh. main: scalabale. buffer. . mixer. . digit. scaler. enc. task. fq i-1. fq i. data transfer. digitizer: non-scalable. pip: scalable. mixer : non-scalable. dec. scalable task.

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slide1

DVD

audio

rend

audio

dec.

Get Full Packet

Put Full Packet

read

dmux

dec.

sharp

enh.

main: scalabale

buffer

mixer

digit

scaler

enc.

task

fqi-1

fqi

data transfer

digitizer: non-scalable

pip: scalable

mixer : non-scalable

dec

scalable task

hw

scaler

enc.

writer

enc.

hierarchical task

connection to HW IO

disk : non-scalable

eqi-1

eqi

Put Empty Packet

Get Empty Packet

Component

Processing

function(ci)

arbitrary interleavings

channel consistent traces

schedule consistent traces

priority consistent traces

Courtesy of E.F.M. Steffens

TN

idle time

idle time

idle time

tinit

fq1

fq2

fqN-1

C2

CN

C1

eq1

eq2

eqN-1

Analysis of a Time-driven Chain of Dependent Components M.A. Weffers-Albu 1 , J.J. Lukkien 1 , P.D.V. v.d. Stok 1,2

3. Modeling chain execution

1. Introduction

The goal of our work is the prediction and optimization of performance attributes to provide guaranteed and optimized Quality of Service (QoS) for real-time streaming applications.

Impose

predicates

until

obtain

trace and schedule

which specify

the system behavior.

The approach we took was to provide a characterization of streaming applications execution to determine performance attributes and provide insight into best design practices for optimising these attributes.Performance attributes:Number of Context Switches (NCS), Response Time of tasks and chain (RT), Resources utilization (RU) for CPU and memory.

Unique trace ρ, eager scheduleeager

QoS Requirement – CN executes always strictly at rate TN.

First step solution - rate of production higher than rate of consumption for packets in fqN-1:

PRkN-1. TN, k  N

The systems we analyseare streaming applications using the TriMedia Streaming Software Architecture (TSSA) and executing on a TriMedia device.

Stable Phase Theorem - Provided that PRkN-1. TN , k  N, the pipeline system assumes a periodic behavior after a finite initial phase.The complete behavior is characterized the unique trace:

ρ= tinit (inc(i) fqN-1? eqN? cN eqN-1! tL fqN ! d(i *TN)) ω.

tinit – trace recording the initial phaseof the system execution.

tstable– stable phase: (inc(i) fqN-1? eqN? cN eqN-1! tL fqN ! d(i *TN)) ω

tL – subtrace recording the interleaved execution of C1..CN-1.

2. TriMedia Streaming Software Architecture

  • A TSSA media processing application - graph:
    • Nodes: - software components
    • Edges: - finite buffers (queues) that transport the data stream from one component to the next component in the graph.

Time-driven component

Data-driven components

Typical program of component Ci :

j=0;

while (true) do

{inc(j);

 receive( fqi-1, p);

 receive( eqi, q);

 process_func_ci(p, q);

 send( eqi-1, p);

 send( fqi, q);

delay( j*TN ); }

  • Corollaries
  • Given the computation times of components actions, ρ andeagercan be calculated at design time.
  • Hence NCS, task and chain RT, RU for CPU and memory can also be calculated.
  • Minimum Queue Capacity: 1
  • CN drives execution in the same way as a minimum priority data-driven component in a chain composed of only data-driven components.
  • Minimum NCS at Stable Phase: P(C1)<…<P(CN-1) and Cap(fqi)=2, i < N-1.
  • Chain RT cannot be optimized

4. Conclusions

Express behavior as traces:

Tr(Ci): { (fqi-1?, eqi?, ci, eqi-1!, fqi!) },

Tr(CN): {j = 0 (inc(j) fqN-1? eqN? cN eqN-1! fqN! d(j *TN)).}.

  • Results
  • Characterization of streaming applications execution
  • Prediction and optimisation of performance quality attributes.

Affiliation

1) Eindhoven University of Technology

Department of Mathematics and Computer Science

HG 6.57, P.O. Box 513, NL-5600 MB, Eindhoven, The Netherlands

2) Philips Research NatLab

Prof. Holstlaan 4, 5656AA Eindhoven, The Netherlands

About the Author

Alina Weffers-Albu received her P.D.Eng. in Software Technology from the Department of Mathematics and Computer Science of the Eindhoven University of Technology (TU/e) in 2003. In July 2003 Alina started a Ph.D. project within the SAN group of the same department, in collaboration with Philips Research Laboratories Eindhoven.

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