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Analysis of a Time-driven Chain of Dependent Components. M.A. Weffers-Albu, J.J. Lukkien, P.D.V. v.d. Stok. Contents. Goal & Approach Analyzed Systems Characterization of behavior QoS Requirement. First solution Stable Phase Theory Practical Applications Future work. Goal & Approach.

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analysis of a time driven chain of dependent components

Analysis of a Time-driven Chain of Dependent Components

M.A. Weffers-Albu, J.J. Lukkien, P.D.V. v.d. Stok

Alina Weffers-Albu, [email protected]

TU/e Computer Science, System Architecture and Networking

Philips Research Laboratories Eindhoven

contents
Contents
  • Goal & Approach
  • Analyzed Systems
  • Characterization of behavior
  • QoS Requirement. First solution
  • Stable Phase Theory
  • Practical Applications
  • Future work

Alina Weffers-Albu, [email protected]

TU/e Computer Science, System Architecture and Networking

Philips Research Laboratories Eindhoven

goal approach
Goal & Approach
  • The goal of our work is the prediction and optimization of performance parameters to provide guaranteed and optimized Quality of Service (QoS) for real-time streaming applications.
  • Approach:
  • provide a characterization of streaming applications execution to determine performance parameters and provide insight into best design practices for optimising these attributes.
  • Performance parameters:
      • Response Time of tasks and chain (RT),
      • Resource utilization (RU) for CPU, memory.
      • Number of Context Switches (NCS)

Alina Weffers-Albu, [email protected]

TU/e Computer Science, System Architecture and Networking

Philips Research Laboratories Eindhoven

slide4

Physical Platform

Media Processing Applications

Alina Weffers-Albu, [email protected]

TU/e Computer Science, System Architecture and Networking

Philips Research Laboratories Eindhoven

slide5

Media Processing Applications

Physical Platform

Alina Weffers-Albu, [email protected]

TU/e Computer Science, System Architecture and Networking

Philips Research Laboratories Eindhoven

slide6

Media Processing Applications

Physical Platform

Alina Weffers-Albu, [email protected]

TU/e Computer Science, System Architecture and Networking

Philips Research Laboratories Eindhoven

slide7

Get Full Packet

Put Full Packet

fqi-1?

fqi!

Physical Platform

eqi-1!

eqi?

Put Empty Packet

Get Empty Packet

Component

Processing

code(ci)

fq1

fq2

fqN-1

C2

CN

C1

eq1

eq2

eqN-1

Media Processing Applications

Alina Weffers-Albu, [email protected]

TU/e Computer Science, System Architecture and Networking

Philips Research Laboratories Eindhoven

characterization of chain behaviour

arbitrary interleavings

channel consistent traces

schedule consistent traces

priority consistent traces

Characterization of chain behaviour
  • Express behavior as:
  • traces,
  • time assignment (schedule) associated with each trace

Impose

predicates

until

obtain

trace and schedule

that specify

the system behavior.

Unique trace ρ, eager scheduleeager

Alina Weffers-Albu, [email protected]

TU/e Computer Science, System Architecture and Networking

Philips Research Laboratories Eindhoven

qos requirement
QoS Requirement

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

First step solution non-optimal, pessimistic – implies sum of max computation times of components actions must be smaller than TN.

Alina Weffers-Albu, [email protected]

TU/e Computer Science, System Architecture and Networking

Philips Research Laboratories Eindhoven

stable phase theorem

C1

CN

Stable Phase Theorem

Stable Phase Theorem - Provided that PRkN-1. TN , k  N, the pipeline system assumes a repetitive, 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.

Execution in cascade of sub-chain

- all backward queues empty.

Alina Weffers-Albu, [email protected]

TU/e Computer Science, System Architecture and Networking

Philips Research Laboratories Eindhoven

stable phase theorem1

TN

idle time

idle time

idle time

tinit

Stable Phase Theorem

Stable Phase Theorem - Provided that PRkN-1. TN , k  N, the pipeline system assumes a repetitive, 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.

Alina Weffers-Albu, [email protected]

TU/e Computer Science, System Architecture and Networking

Philips Research Laboratories Eindhoven

practical applications
Practical applications
  • 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.
  • CN has the same influence as a minimum priority data-driven component.
  • Minimum Queue Capacity: 1
  • Chain RT cannot be optimized
  • Minimum NCS at Stable Phase:
  • P(C1)<…<P(CN-1) Cap(fqi) = 2 for all i, 1  i < N−1.

Alina Weffers-Albu, [email protected]

TU/e Computer Science, System Architecture and Networking

Philips Research Laboratories Eindhoven

on going work
On-going work
  • Optimal solution for satisfying QoS requirement
  • Study chains containing a time driven component at each end (realistic surveillance application)
  • Study chains that include video/audio decoding components, execution depends on input stream(realistic video/audio decoding chains).

Alina Weffers-Albu, [email protected]

TU/e Computer Science, System Architecture and Networking

Philips Research Laboratories Eindhoven

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