Alina Albu, m'a'albutue'nl

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Quality of Service for In-Home Digital Networks
PROGRESS PROJECT EES.5653

• Terminal QoS
• M.A. Weffers-Albu

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Contents

• Project goals
• Approach
• Description of analyzed systems
• Trimedia Streaming Software Architecture
• A characterization of streaming applications
  execution
• DVD player case study
• Future work

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QoS in IN-Home Digital Networks
Aim provide guaranteed and optimised Quality of
Service (QoS) for interconnected real-time
embedded systems.
Prediction Optimisation of Performance
Attributes AT, RT, NCS, RU.
Terminal QoS Reliability Performance
Predictability of system.
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Component-based Real-Time Embedded Systems

• Goals
• Nearly optimised design for optimal resource
  utilization of the subsystems involved (good
  performance).
• ?
• Interference limited - reduced to a minimum
  through good design practices (good reliability).
• ?
• Fast integration of pre-designed subsystems.

Physical Platform
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Component-based Real-Time Embedded Systems
Challenges Scarcity of resources ? Resource
sharing ? Low Predictability ? Non-guaranteed
Reliability Performance

Physical Platform
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Component-based Real-Time Embedded Systems
Approach Resource requirements ? Resource
reservations ? Virtual Platforms ? Guaranteed
resource availability while Resource usage
restricted to a configured maximum.
VP1
VP2

VPn-1
VPn
Physical Platform
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Component-based Real-Time Embedded Systems

• Approach
• Derive the VPs of the subsystems involved by
  predicting performance quality parameters (NCS,
  AT, RT, RU) for each of the subsystems.
  (Specifications in terms of behaviour and
  performance).
•  
• Control performance quality parameters - find
  good practices of design for the subsystems so
  that their resources needs can be satisfied on
  the physical platform.
•  
• Example
• Predict the number of context switches (NCS)
  the overhead occurring during the execution of a
  system.
• Give guidelines for priority assignment such that
  the NCS is minimized.

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Component-based Real-Time Embedded Systems

• Approach
• 3. Resources needs - strongly related to events
  that occur during the execution of a subsystem.
• ?
• Repetitive patterns of events gt Execution
  predictable
•  ?
• Identify the patterns of events during the
  execution of a subsystem
•          the conditions under which the events
  adopt repetitive patterns
•          the relations between the patterns of
  events.
•  

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TriMedia Streaming Software Architecture

• Typical execution scenario of a TSSA component
• - get 1 FP from input FQ,
• - get 1 EP from input EQ,
• - processing,
• - put 1EP in output EQ.
• ? - put 1FP in output FQ.

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TriMedia Streaming Software Architecture TSSA
components

• Data driven. Execution determined by
• Availability of necessary input
• Priority of component task
• Data driven with blocking due to communication
  with hardware. Execution determined by
• Availability of necessary input
• Average blocking time
• Time driven. Execution determined by
• Availability of necessary input. (Or NOT)
• Priority
• Periodicity.

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TriMedia Streaming Software Architecture TSSA
components

• All types. Execution determined by
• Average computation time.
• n-gtm relation between input and output.
• If m variable average m or distribution over
  time for the values of m.
• Average times needed to get each input FP/EP.
• Average times needed to produce each output
  FP/EP.

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A characterization of streaming applications
execution.

• Theorem
• Let C1, C2, C3, , Cn be a chain of components
  communicating through a set of queues as in
  Figure 1. Provided that the components are
  designed such that their execution in the chain
  does not lead to deadlock, and provided that the
  input is sufficiently long, the execution of the
  components in the chain will adopt a repetitive
  pattern after a finite number of steps.

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A characterization of streaming applications
execution.

• Definitions
• Initialization phase the interval of time until
  the execution of the components reaches a
  repetitive pattern.
• Stable phase - the interval of time during which
  the all components execute according to a
  repetitive pattern.
• Hyperperiod the interval of time needed for the
  execution of the repetitive pattern.
• Finalization phase interval of time following
  the stable phase. The finalization phase starts
  when the first component does not have input
  anymore and finishes its execution. During the
  finalization phase the last transactions in the
  queues are completed and all the components are
  stopped

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A characterization of streaming applications
execution.
Initialization phase C1 executes until output
FQ is filled gt C1 - Blocked (b).

• Chain
• - N data driven components
• - n-gtm 1-gt1
• - priorities in descending order.

C2(p)C1(b), C2(p)C1(b), , until C2(b) (FQ
filled, EQ empty)C1(b),
C3(p)C2(p)C1(b) C2(b), C3(p) C2(p)C1(b) C2(b)
C3(p) C2(p)C1(b) C2(b), C3(b)
CN(p)CN-1(p) C2(p)C1(b)C2(b)CN-1(b),
Stable phase CN(p)CN-1(p) C2(p)C1(b)C2(b)CN-1(b
),
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DVD player case study

• VO
• Time driven
• 1-gt2

• SSE
• -Data driven
• 1-gt1

• FRead
• Data driven with blocking

• VDec
• Data driven
• 1-gtm, m variable

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DVD player case study

• NCS_hyperperiod(FRead) 5,
• NCS_hyperperiod(VDec) 9,
• NCS_hyperperiod(SSE) 8,
• NCS_hyperperiod(VO) 8.
• the total NCS_hyperperiod 5988 30
• In order to validate our results we measure the
  NCS on a duration of the stable phase equal with
  30 hyperperiods.
•  
• NCS_StablePhaseMeasured 895
• NCS_StablePhaseCalculated 900

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Current Future Work

• Current Work
• Proof Stable State Theorem in a general case of
  components types combinations.
• Provide design guidelines for optimizing the NCS,
  RU
• Find patterns in the input stream that can be
  related to the pattern of execution.
• Future Work
• Analyze patterns of memory access, bus
  utilization
• Find relations between memory accesses, bus
  utilization and the pattern of execution.
• Provide ways for controlling the above patterns.
• Consider multi-processor platforms.