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
29 April 2019

2. Contents Context Project definition – Goals, Approach
Previous results, progress
Results
Future work
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
29 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:
Dealing with Artefacts
Adaptiveness
Terminal QoS:
Performance
RU CPU,mem
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
29 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
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
29 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
29 April 2019

6. Context - Description of Analyzed Systems Components
Both types. Execution determined by:
Execution scenario of component (fixed/variable)
Computation time.
Nature of input stream.
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
29 April 2019

7. Goals Performance System Predictability & Optimization Goals Approach
1. Prediction of resource use through a set of performance quality parameters for a given system.
2. Control performance quality parameters - find good practices of design for the system.
Approach
Study and model the dynamic behavior of a given system
=> prediction & control of performance quality parameters
Predictability so you know how much resources you need to allocate for your system
Optimization so that your system needs to use less resources.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
29 April 2019

8. 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) – cost 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
29 April 2019

9. Stable Phase Approach. Pattern Initialization Phase Stable
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 was supposed to be explored.(slides 5,6)
Pattern
Initialization
Phase
Stable
Finalization
How did we do this? Well we observed a number of streaming applications and after observations we formulated the following hypothesis namely that the execution of … after an initialization time it stabilizes and adopts a repetitive pattern of execution
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
29 April 2019

10. Previous results
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 cases of interest for analysis, proof.
=> Approach for control and optimization of performance parameters by formulating corollaries deduced from the proof.
Analysis of first case - lemmas, corollaries.
Studied influence of input on the execution pattern of a streaming chain.
Defined goals and approach for PhD project.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
29 April 2019

11. Stable Phase Theorem. Cases of interest for analysis.
C1, C2, C3, …, CN chain of components communicating through a set of queues:
N data-driven components (1-1).
C1 data-driven component with execution deferral (1-1), C2, C3, …, CN data-driven components (1-1).
C1, C2, C3, …, CN-1 data-driven components (1-1), CN time-driven component.
C1 data-driven component with execution deferral (1-1), C2, C3, …, CN-1 data-driven components (1-1), CN time-driven component.
C1 time-driven component, C2, C3, …, CN data-driven components (1-1).
C1 time-driven component, C2, C3, …, CN-1 data-driven components (1-1), CN time-driven component.
C1 data-driven component with execution deferral (1-1), C2 data-driven component (n-m), C3, …, CN-1 data-driven components (1-1), CN time-driven component.
Building up progressively towards realistic cases.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
29 April 2019

12. Progress All cases afore-mentioned have been analyzed
technical note, full paper containing mathematical modeling / argumentation of the results in writing.
Results exposure:
Presented results for industry clients of MRM:
Ruud Derwig (Philips Semiconductors)
Presented results in other groups of Natlab, applicability in other projects:
S. Balakrishnan (CoolCat – Performance Analysis Based Power Management)
M. Bekooij (Hydra)
Liesbeth Steffens (Betsy)
Papers:
M.A. Weffers-Albu, J.J. Lukkien, P.D.V. v.d. Stok, "A Characterization of Streaming Applications Execution", Proceedings RM4NES 2005, Eindhoven
M.A. Weffers-Albu, J.J. Lukkien, P.D.V. v.d. Stok, "Towards A Characterization of Real-Time Streaming Systems", ECRTS WIP 2005, Palma de Mallorca
Posters:
M.A. Weffers-Albu, J.J. Lukkien, P.D.V. v.d. Stok, "Towards A Characterization of Real-Time Streaming Systems", ECRTS WIP 2005
M.A. Weffers-Albu, J.J. Lukkien, P.D.V. v.d. Stok, "Towards A Characterization of Real-Time Streaming Systems", SEES Workshop 2005
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
29 April 2019

13. Data-driven vs. Time-driven
N data-driven components (1-1)
Stable phase theorem, corollaries:
- driving component minimum in priority
- shortening initialization phase
- minimum necessary of memory
- prediction, optimization of NCS, RT, AT
Chain including time-driven component(s):
Stable phase theorem, corollaries – driving component(s) time-driven component(s) regardless of the priority assignment to components in the chain.
- prediction, optimization of NCS, RT
Time beats priority
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
29 April 2019

14. Video decoding chain. FQ EQ C2 FQ EQ C3 FQ FQ C1 … CN EQ EQ
Data driven,
Execution deferral(1-1)
Data driven (n-m)
Data driven (1-1)
Time driven (1-1) FQ EQ C2 FQ EQ C3 FQ FQ C1 … CN EQ EQ
Calculated parameterized pattern taking into account:
chain execution dependency on the input stream - variation in execution scenario of C2,
timing properties of some components w.r.t.
Periodicity (ex: video renderer)
Variable computation time (ex: video decoder)
Applicability of results in practice:
power saving – prediction of idle time at run time.
knowledge on when/how to change the quality level of decoding for a video decoder.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
29 April 2019

15. Video decoding chain. Calculated patterns, validation.
1. C2 “1-m” data-driven component, variable m.
E(CN), E(CN-1), …, E(C2, C1), (idle time until the end of 2T(CN)), (m-1) *{ E(CN), E(CN-1), …, E(C2), (idle time until the end of 2T(CN))}
2. C2 “n-1” data-driven component, variable n.
E(CN), E(CN-1), …, n * E(C2, C1), (idle time until the end of 2T(CN)).
m = 5
m = 3
m = 2
m = 7
n = 3
n = 4
n = 2
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
29 April 2019

16. Surveillance application.
C1 time-driven component, C2, C3, …, CN-1 data-driven components (1-1), CN time-driven component.
Previous approach:
introduce in the middle of the chain a long buffer that would split the chain into 2 sub-chains with different dependencies between tasks: in the first sub-chain all components would be driven by C1 while all components in the second sub-chain would be driven by CN.
Our result:
Pattern anyway – inherent to this type of chains – middle buffer not needed for predictability.
Optimization of performance parameters achievable by:
controlling the phasing between the activations of the two time driven components.
imposing at design time that sum of computation times of data-drive components smaller than period of time-driven components.
Advantage:
Memory use much diminished – no middle buffer is used.
Analysis of this case is very complex, introduces a lot of variables, I think best if you want details you should consult the draft of my report, I’ll present here the main result, and the advantage of this result compared with previous approach
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
29 April 2019

17. Future work. References.
Work in progress:
Technical note - mathematical modeling of results
Full paper ECRTS 2006 to be submitted in December 2005.
Chains with branches
Future work:
Multiple chains
Extend analysis to multi-processor platforms
References:
John T. H. Lan. Philips Research USA - TN Date of Issue: Scalable MPEG Decoding with Graceful Degradation, and Its Applications and Implementation on TriMedia.
Sharon Peng. Technical Report. Scalable MPEG2 Decoder with Graceful Degradation.
Alina Weffers-Albu,
TU/e Computer Science, System Architecture and Networking
Philips Research Laboratories Eindhoven
29 April 2019