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Architectural Design Spaces for Feedback Control in Self-Adaptive Systems Concerns
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Transcript of Architectural Design Spaces for Feedback Control in Self-Adaptive Systems Concerns
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Architectural Design Spaces for Feedback Control Concerns in Self-Adaptive Systems
Sandro S. Andrade and Raimundo J. de A. Macêdo
Distributed Systems Laboratory (LaSiD)Department of Computer Science (DCC)
Federal University of Bahia (UFBa) - Brazil{sandros, macedo}@ufba.br
June/2013
Programa Multiinstitucional de Pós-Graduação em Ciência da Computação
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Context & Motivation
Self-AdaptiveSystems
More stringent demandsfor flexibility, dependability,energy-efficiency, ...
Applications thatweigh in at tens of
millions of lines of code
Complexity approachingthe limits of human
capability
Highly unpredictable andchanging operatingenvironments
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
State of the ArtMAPE-K Autonomic Manager(Kephart & Chess; 2003)
3-layer Architecture for Self-Adaptation(Kephart & Chess; 2003)
Middleware-level self-adaptation(e.g.: Rainbow – Garlan et al; 2004)
Explicit Modeling of Control Loops(Hebig, Giese & Becker; 2010)
FORMS Reference Model(Weyns, Malek & Andersson; 2012)
Megamodels@runtime(Vogel & Giese; 2012)
Uncertainty in Self-Adaptive Software Systems(Esfahani & Malek; 2012)
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Current Challenges
Highly specialized domain andlarge design/solution space
Current effective solutions arehighly tailored to specific problems
Control Theory mechanisms may notdirectly apply in computer systems
Architectural Styles/PatternsReference Architectures
Architecture Design Handbooks
It's hard to support earlyreasoning of properties and
well-informed trade-off analysis
Domain-Specific ADL'sFormal Reference Models/Frameworks
Qualitative Architecture Analysis MethodsQuantitative Architecture Analysis Methods
Model-Based SW Design & DevelopmentSearch-Based Software Engineering
Software Architecture Design Knowledge
Systematic Literature ReviewsModel-Predictive Control
Hybrid Systems Control
Current Approaches
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Current Challenges
It's hard to support earlyreasoning of properties and
well-informed trade-off analysis
Current effective solutions arehighly tailored to specific problems
Control Theory mechanisms may notdirectly apply in computer systems
Architectural Styles/PatternsReference Architectures
Architecture Design Handbooks
Domain-Specific ADL'sFormal Reference Models/Frameworks
Qualitative Architecture Analysis MethodsQuantitative Architecture Analysis Methods
Model-Based SW Design & DevelopmentSearch-Based Software Engineering
Software Architecture Design Knowledge
Systematic Literature ReviewsModel-Predictive Control
Hybrid Systems Control
Our Approach
Highly specialized domain andlarge design/solution space
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Our Approach
Systematic representation ofdomain-specific design spaces
DuSE=
+
+
Metrics for evaluation of automaticallygenerated candidate architectures
A multi-objective optimization approachto explicitly elicit design trade-offs
SA:DuSE is a particularDuSE instance whichenables automatedarchitecture design inthe Self-AdaptiveSystems domain
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Our ApproachAutonomic Manager
KnowledgeMonitor
Analyse Plan
Execute
Managed Element
SA:DuSE provides an automatedprocess for off-line design &analysis of Autonomic Managerarchitectures
Usually designed off-line, butmay be re-designed (adapted)at runtime by the Autonomic Manager
DuSE enables:● Manual design space exploration● Automated search for best candidates (multi-objective optimization)
An initial annotated UMLmodel is provided to SA:DuSE
A lot of degrees of freedom involved:control law, tuning approach, MAPE-K deployment, …
1
2
3
4
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
DuSE Metamodel
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
SA:DuSE Design SpaceVP11: ProportionalVP12: Proportional-IntegralVP13: Proportional-Integral-DerivativeVP14: Static State FeedbackVP15: Precompensated Static State FeedbackVP16: Dynamic State Feedback
DD1: Control Law
VP31: Fixed Gain (no adaptation)VP32: Gain SchedulingVP33: Model Identification Adaptation Control
DD3: Control Adaptation
VP21: Chien-Hrones-Reswick, 0 OS, Dist. RejectionVP22: Chien-Hrones-Reswick, 0 OS, Ref. TrackingVP23: Chien-Hrones-Reswick, 20 OS, Dist. RejectionVP24: Chien-Hrones-Reswick, 20 OS, Ref. TrackingVP25: Ziegler-NicholsVP26: Cohen-CoonVP27: Linear Quadratic Regulator
DD2: Tuning Approach
VP41: Global ControlVP42: Local Control + Shared ReferenceVP43: Local Control + Shared Error
DD4: MAPE Deployment
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
SA:DuSE Quality Metrics
M2: Average
Settling Time
ME2=allOwnedElements ()→ selectAsType(QParametricController )→sum(stime())
allOwnedElements()→selectAsType(QParametricController )→size ()
; where QParametricController : : stime()=−4
log(maxi∣p i∣);and max i∣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, VP41and 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 ()
; where QParametricController : : maxOS ()={0 ;real dominant pole p1≥0
∣p1∣;real dominant pole p1<0
r π/∣θ∣;dominant poles p1, p2=r.e±j. θ}
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
The DuSE Optimization ApproachA simple initial model with three loci of decision yields 54,010,152 candidates
With four loci of decision we get a space with 20,415,837,000 candidates
A search-based approach enables effective design space explorationand helps prevent false intuition and technology bias. The goal is to find
out a set of (locally) Pareto-optimal candidate architectures
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Case Study
Cloud-based mediaencoding service
Three loci of decision(controllable components)
Annotations from Qemu +Hadoop + CloudStackexperiments
Control goal: enforceencoding throughput
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Findings
Major trade-off betweenM
2 and M
3, but also M
2
and M4
M2/M
3 Pareto front:
● PI+CHR-20OS-DR● PID+CHR-0OS-DR● P+ZN
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Related Work
Automated Architecture Improvementfor Performance and other Attributes(Koziolek & Reussner; 2011)
Search-Based Software Architecture Design
A Meta-Framework for Design SpaceExploration(Saxena & Karsai; 2011)
+ Effective representation of architecture design knowledge- Limited extension to specific application domains
Explicit Modeling of Control Loops(Hebig, Giese & Becker; 2010)
FORMS Reference Model(Weyns, Malek & Andersson; 2012)
Megamodels@runtime(Vogel & Giese; 2012)
Actor-Based Adaptable Loops(Krikava et al; 2012)
Multiple Objective Self-Adaptation(Cheng, Garlan & Schmerl; 2006)
Software Engineering for Self-Adaptive Systems
+ Expressive constructs to model self- Adaptive aspects- No systematic knowledge representation- Low parsimony / No trade-off elicitation SA:DuSE
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Conclusion and Future Work
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 designknowledge in the field of Self-Adaptive Systems
A search-based approach for endowing architectureswith 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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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
DuSE-MT
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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Thank you !Questions ?
Sandro S. Andrade and Raimundo J. de A. Macêdo
Distributed Systems Laboratory (LaSiD)Department of Computer Science (DCC)
Federal University of Bahia (UFBa) - Brazil{sandros, macedo}@ufba.br
June/2013
Programa Multiinstitucional de Pós-Graduação em Ciência da Computação