1. Terminal QoS Alina Weffers-Albu
Quality of Service for In-Home Digital Networks PROGRESS PROJECT EES.5653
Terminal QoS
Alina Weffers-Albu
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

2. Contents Context Progress Project definition – Goals, Approach
Characterization of CS sequences
Stable State Theorem
Execution streaming chains - dependency on input.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

3. Context - QoS in IN-Home Digital Networks
Aim: provide guaranteed and optimised Quality of Service (QoS) for interconnected real-time embedded systems.
Network QoS:
Reliability,
Delay,
Jitter,
Bandwidth.
Terminal QoS:
Performance
Network
QoS= a collection of (QoS) parameters values related to functional and non-functional characteristics of the service in discussion,
and an assessment with respect to the degree of quality (unsatisfactory, good, excellent) derived from applying assessment rules
on the values of these QoS parameters.
Reliability & performance are 2 parameters that characterize the QoS of a teminal
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

4. Context - Description of Analyzed SystemsEQ FQ C1 C2 Cn …
Physical Platform …    
Empty Queue
Full Queue
Component
Processing
code 
Get Full Packet
Put Full Packet
Put Empty Packet
Get Empty Packet n m
System composed of (independent/dependent) streaming chains which are in turn composed of streaming components that communicate via buffers. Streaming components execute concurrently on a uni-processor platform.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

5. Context - Description of Analyzed Systems Components
Data driven. Execution determined by:
Availability of necessary input
Priority of component task
Time driven. Execution determined by:
Availability of necessary input. (Or NOT)
Priority
Periodicity.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

6. Context - Description of Analyzed Systems Components
Both types. Execution determined by:
Average computation time.
n->m 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.
Average suspension time (if task with execution deferral due to cooperation with hardware).
Computation time variable.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

7. Previous results Performed a literature survey on QoS work
Studied ways of estimating the overhead introduced by CS during the execution of streaming chains.
Provided a method for the calculation of the overhead introduced by CS.
Method based on an observation regarding the execution of streaming chains. Method tested on single case study.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

8. Progress
Expanded approach previously tested on particular case to a more general context - tests on other types of components, different priorities assignment.
Formulate “Stable Phase Theorem”, distinguished 7 separate cases of interest for proof.
=> Approach for control and optimization of performance parameters by formulating corollaries deduced from the proof.
Proof for first case, lemmas, corollaries.
Studied influence of input on the execution pattern of a streaming chain.
Defined goals and approach for PhD project.(not restricted to CPU, but also memory, bus – correlation of events sequences)
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

9. Goals Terminal QoS : Performance Predictability of the system Goals:
Prediction of performance quality parameters for a given system.
Control performance quality parameters - find good practices of design for the system so that its resources needs can be satisfied on the physical platform.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

10. Approach Study and model the dynamic behavior of a given system
=> prediction & control of performance quality parameters
Behavior characterization in terms of the events that occur during the execution of the system.
Events in our study:
Currently: buffer handling operations, context switches,
Future: memory accesses (to be extended).
Theoretical framework to model the sequences of events.
Derive characteristics of the sequences of events => meaningful abstractions.(Ex: repetitive patterns, bounds)
Identify conditions under which a sequence of events adopts a particular characteristic.
Identify correlations and dependencies between sequences of events (CS, memory accesses, events related to bus utilization).
First bullet – our approach is to understand the factors that determine the behavior of a system which will lead to prediction of perf parameters and subsequently control of perf. parameters.
Prediction is starting point for control.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

11. Performance Quality Parameters.
Buffer size
Packet size
Activation Times
Priority setting
Performance Quality Parameters
Resource Utilization (RU) for CPU, memory, bus – feasibility check on the physical platform at hand.
Activation Times (AT) – provide modeling basis for the sequence of context switches (CS).
Response Times (RT) – prediction/control of deadline misses.
Number of Context Switches (NCS) – overhead induced by the composed execution of components.
Required buffer space
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

12. Characterization of CS sequences.
Hypothesis
Let C1, C2, C3, …, Cn be a chain of components communicating through a set of
queues. The execution of the chain, after an initialization phase adopts a
repetitive pattern of execution.
Conditions under which the above statement holds in progress to be explored.
Examples:
input - constant rate and sufficiently long, components designed such that their execution in the chain does not lead to deadlock.
Hyperperiod
Initialization
Phase
Stable
Finalization
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

13. Two case studies FRead VDec SSE VO FRead C1-C8 C2
Data driven with execution deferral
VDec
Data driven
1->m, m variable
SSE
-Data driven
1->1 VO
Time driven
1->2 EQ FQ
FRead
VDec
SSE VO
P(FRead) > P(VDec) > P(SSE) > P(VO)
NCS Stable Phase
Calculated : 900;
Measured : 895;
FRead
Data driven with execution deferral
C1-C8
-Data driven
1->1 C2
-Data driven
2->3
NCS Stable Phase
Calculated : 245;
Measured : 245; P
FRead C1 C2 C3 C4 C5 C6 C7
Components
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

14. Stable State Theorem. Cases of interest for proof.
C1, C2, C3, …, CN chain of components communicating through a set of queues (slide 4):
N data-driven components (1-1).
N data-driven components (n-m).
C1 data-driven component with execution deferral (1-1), C2, C3, …, CN data-driven components (n-m).
C1, C2, C3, …, CN-1 data-driven components (n-m), CN time-driven component (n-m).
C1 time-driven component (n-m), C2, C3, …, CN data-driven components (n-m).
C1 time-driven component (n-m), C2, C3, …, CN-1 data-driven components (n-m), CN time-driven component (n-m).
C1 data-driven component with execution deferral (1-1), C2, C3, …, CN-1 data-driven components (n-m), CN time-driven component (n-m).
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

15. Stable State Theorem. 1-1 data-driven components.
Let C1, C2, C3, …, CN be a chain of data-driven components communicating
through a set of queues (slide 4). The relation between input and output for all
components is 1-1. The execution of the chain, after a finite initialization phase
adopts a repetitive pattern of execution.
Conditions: input - constant rate and sufficiently long, components designed such that their execution in the chain does not lead to deadlock.
Lemma 1: At stable state the execution of all components is dependent on the execution of the component with the lowest priority. (The component with the lowest priority in the chain is driving component).
Lemma 2: If Cm is the driving component in the chain then  i: 1 ≤ i < m, L(FQi) = S(FQi) Λ  i: m ≤ i < N, L(FQi) = 0.
Corollary 1: The minimum buffer length necessary to ensure the repetitive execution is 1.
Corollary 2: The NCS can be reduced by assigning priorities in a descending order from left to right.
Corollary 3: The length of the initialization time can be reduced by reducing the buffers length.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

16. Characterization of CS sequences.
Initialization phase:
C1: executes until output FQ is filled
=> C1 - Blocked (b).
Chain:
- N data driven components
- n->m: 1->1
- priorities in descending order.
C2(p)C1(b), C2(p)C1(b), …, until C2(b) (FQ filled, EQ empty)C1(b), EQ FQ C1 C2 CN
P(C1) > P(C2) > …> P(CN) …
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), C1 C2 CN …
Hyperperiod
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

17. Influence of input on the execution of a chain
Correlation between pattern in MPEG input and pattern of execution.
Characterization of input stream
Guidelines for intelligently choosing the size of the packets in order to increase predictability for components with variable output.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

18. Other activities Papers: Cooperations: Presenting my work:
NCS Calculation Method for Streaming Applications. Proceedings of the 5th PROGRESS Symposium on Embedded Systems
A Characterization of Streaming Applications Executions (submitted to the Design, Automation, and Test in Europe 2005 Conference)
In process of writing paper with Radu Dobrin – University of Malardalen Sweden
Cooperations:
Malardalen University, Sweden - Gerhard Fohler, Radu Dobrin
Carnegie Mellon, SEI – Kurt Wallnau, Mark Klein
Presenting my work:
Poster 5th PROGRESS Symposium on Embedded Systems, October 2004
Presentations for SAN group(TU/e), OASIS cluster (Philips Research), Carnegie Mellon SEI, Gerhard Fohler (Malardalen University)
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019

19. Other activities Presenting my work:
Liesbeth Steffens - Philips Research Laboratories
Reinder Bril - Eindhoven University of Technology
Clara Otero-Perez - Philips Research Laboratories
Laurentiu Papalau - Philips Research Laboratories
Giel van Doren - Philips Research Laboratories
Dietwig Lowet - Philips Research Laboratories
Sjir van Loo -  Philips Research Laboratories
Jan van der Wal - Eindhoven University of Technology
Clemens Wust -  Philips Research Laboratories
Marco Bekooij -  Philips Research Laboratories
Jeffrey Kang - Philips Research Laboratories
Saianath Karlapalem - Singapore
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
23 April 2019