Toward Systematic Conveying of
Architecture Design Knowledge for
Self-Adaptive Systems
Sandro S. Andrade
GSORT Distrib...
   
Pole
placement ?
Self-Adaptive
Systems
Effective
solutions highly
tailored to specific problems
Large design/solution
...
   
Root locus ?
Pole
placement ?
Self-Adaptive
Systems
Effective
solutions highly
tailored to specific problems
Large des...
   
What am I supposed
to do with all these
stuff ?
Designing
Self-Adaptive
Systems
Architecture Design
Handbooks
Referenc...
   
Domain-specific design spaces
DuSE
=
+
+
Quality metrics of automatically
generated candidate architectures
A multi-ob...
   
Domain-specific design spaces
DuSE
=
+
+
Quality metrics of automatically
generated candidate architectures
A multi-ob...
   
   
   
DuSE Metamodel
   
SA:DuSE Design Space
DD1
: Control Law
DD3
: Control Adaptation
DD2
: Tuning Approach
DD4
: MAPE Deployment
   
SA:DuSE Design Space
VP11: Proportional
VP12: Proportional-Integral
VP13: Proportional-Integral-Derivative
VP14: Stati...
   
SA:DuSE Quality Metrics
M2
: Average
Settling Time
M1
: Control
Overhead
M4
: Control
Robustness
M3
: Average
Maximum ...
   
SA:DuSE Quality Metrics
M2
: Average
Settling Time
ME2=
allOwnedElements()→selectAsType(QParametricController)→sum(sti...
   
DuSE Optimization
3 loci of decision -> 54,010,152 candidates
4 loci of decision -> 20,415,837,000 candidates
   
DuSE Optimization
3 loci of decision -> 54,010,152 candidates
4 loci of decision -> 20,415,837,000 candidates
A search...
   
Tool Support (DuSE-MT)
   
Case Study
Cloud-based media
encoding service
Three loci of decision
(controllable components)
Annotations from Qemu +...
   
Findings
   
Conclusion
Contributions Systematic gathering of architecture design
knowledge in the field of Self-Adaptive Systems
A...
   
Conclusion
Limitations Requires an initial annotated architectural model
No guaranteed optimality (local-optimal Paret...
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SASO2013 - PechaKucha version

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SASO2013 - PechaKucha version - September 2013 - Philadelphia/PA/EUA

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SASO2013 - PechaKucha version

  1. 1.     Toward Systematic Conveying of Architecture Design Knowledge for Self-Adaptive Systems Sandro S. Andrade GSORT Distributed Systems Group Federal Institute of Education, Science and Technology of Bahia (IFBa) Salvador - BA - Brazil sandros@ufba.br Raimundo José de A. Macêdo Distributed Systems Laboratory (LaSiD) Department of Computer Science Federal University of Bahia Salvador - BA - Brazil macedo@ufba.br The 7th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO'13) – Philadelpia/PA – USA – September/2013
  2. 2.     Pole placement ? Self-Adaptive Systems Effective solutions highly tailored to specific problems Large design/solution space Lack of support for well-informed trade-off analysis Highly specialized domain
  3. 3.     Root locus ? Pole placement ? Self-Adaptive Systems Effective solutions highly tailored to specific problems Large design/solution space Lack of support for well-informed trade-off analysis Highly specialized domain
  4. 4.     What am I supposed to do with all these stuff ? Designing Self-Adaptive Systems Architecture Design Handbooks Reference Architectures Architectural Styles Formal Reference Models / Frameworks Model-Based Software Design & Development
  5. 5.     Domain-specific design spaces DuSE = + + Quality metrics of automatically generated candidate architectures A multi-objective optimization approach
  6. 6.     Domain-specific design spaces DuSE = + + Quality metrics of automatically generated candidate architectures A multi-objective optimization approach SA:DuSE = DuSE instance (design space) for the Self-Adaptive Systems domain
  7. 7.    
  8. 8.    
  9. 9.     DuSE Metamodel
  10. 10.     SA:DuSE Design Space DD1 : Control Law DD3 : Control Adaptation DD2 : Tuning Approach DD4 : MAPE Deployment
  11. 11.     SA:DuSE Design Space VP11: Proportional VP12: Proportional-Integral VP13: Proportional-Integral-Derivative VP14: Static State Feedback VP15: Precompensated Static State Feedback VP16: Dynamic State Feedback DD1 : Control Law VP31: Fixed Gain (no adaptation) VP32: Gain Scheduling VP33: Model Identification Adaptation Control DD3 : Control Adaptation VP21: Chien-Hrones-Reswick, 0 OS, Dist. Rejection VP22: Chien-Hrones-Reswick, 0 OS, Ref. Tracking VP23: Chien-Hrones-Reswick, 20 OS, Dist. Rejection VP24: Chien-Hrones-Reswick, 20 OS, Ref. Tracking VP25: Ziegler-Nichols VP26: Cohen-Coon VP27: Linear Quadratic Regulator DD2 : Tuning Approach VP41: Global Control VP42: Local Control + Shared Reference VP43: Local Control + Shared Error DD4 : MAPE Deployment
  12. 12.     SA:DuSE Quality Metrics M2 : Average Settling Time M1 : Control Overhead M4 : Control Robustness M3 : Average Maximum Overshoot
  13. 13.     SA:DuSE Quality Metrics M2 : Average Settling Time ME2= allOwnedElements()→selectAsType(QParametricController)→sum(stime()) allOwnedElements()→selectAsType(QParametricController)→size() ; whereQParametricController:: stime()= −4 log(maxi∣pi∣) ;and maxi∣pi∣is themagnitude of thelargest closed−loop pole M1 : Control Overhead ME1= allOwnedElements()→ selectAsType(QController)→ collect(overhead())→sum () allOwnedElements()→selectAsType(QController)→size() ;QController :: overhead()increasingly penalizes VP32, VP33, VP41 and VP43 M4 : Control Robustness ME4= allOwnedElements ()→ selectAsType(QController)→collect (robustness())→sum () allOwnedElements()→selectAsType(QController)→size() ;QController ::robustness()increasingly penalizesVP31 andVP32 M3 : Average Maximum Overshoot ME3= allOwnedElements()→selectAsType(QParametricController)→sum (maxOS ()) allOwnedElements()→selectAsType(QParametricController)→size() ; whereQParametricController:: maxOS()= { 0 ;realdominant pole p1 ≥0 ∣p1∣;real dominant pole p1<0 rπ/∣θ∣ ;dominant poles p1, p2=r.e±j. θ}
  14. 14.     DuSE Optimization 3 loci of decision -> 54,010,152 candidates 4 loci of decision -> 20,415,837,000 candidates
  15. 15.     DuSE Optimization 3 loci of decision -> 54,010,152 candidates 4 loci of decision -> 20,415,837,000 candidates A search-based approach enables effective design space exploration and helps preventing false intuition and technology bias
  16. 16.     Tool Support (DuSE-MT)
  17. 17.     Case Study Cloud-based media encoding service Three loci of decision (controllable components) Annotations from Qemu + Hadoop + CloudStack experiments Control goal: enforce encoding throughput
  18. 18.     Findings
  19. 19.     Conclusion Contributions Systematic gathering of architecture design knowledge in the field of Self-Adaptive Systems A search-based approach for endowing architectures with self-adaptative behaviour and explicit support for well-informed design trade-off analysis A supporting tool (DuSE-MT)
  20. 20.     Conclusion Limitations Requires an initial annotated architectural model No guaranteed optimality (local-optimal Pareto fronts) Still requires a posteriori preference articulation Contributions Systematic gathering of architecture design knowledge in the field of Self-Adaptive Systems A search-based approach for endowing architectures with self-adaptative behaviour and explicit support for well-informed design trade-off analysis A supporting tool (DuSE-MT) Current & Future Work Second case study Resulting Parent front evaluation (indicators) From Design Spaces to Design Theories
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