Presented at 25th International Conference on Software Engineering and Knowledge Engineering (SEKE 2013) - Boston/MA - EUA. A lot of current research efforts in self-adaptive systems community have been dedicated to the explicit modeling of architectural aspects related to system self-awareness and context-awareness. This paper presents a flexible and extensible representation of architectural design spaces for self-adaptation approaches based on feedback control loops. We have defined a generic representation for design spaces meta-modeling and have instantiated it in order to provide direct support for early reasoning and trade-off analysis of self-adaptation aspects with the aid of a set of feedback control metrics. The proposed approach has been fully implemented in a supporting tool and a case study with a distributed industrial data acquisition service has been undertaken. Whilst preliminary experiences with the proposed approach indicate useful reasoning support when comparing alternative design solutions for self-adaptation, further investigation regarding scalability aspects and automatic handling of conflicting goals has been identified as future work.

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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-Adaptive
Systems
More stringent demands
for flexibility, dependability,
energy-efficiency, ...
Applications that
weigh in at tens of
millions of lines of code
Complexity approaching
the limits of human
capability
Highly unpredictable and
changing operating
environments

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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
State of the Art
MAPE-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)

4.
The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Current Challenges
Highly specialized domain and
large design/solution space
Current effective solutions are
highly tailored to specific problems
Control Theory mechanisms may not
directly apply in computer systems
Architectural Styles/Patterns
Reference Architectures
Architecture Design Handbooks
It's hard to support early
reasoning of properties and
well-informed trade-off analysis
Domain-Specific ADL's
Formal Reference Models/Frameworks
Qualitative Architecture Analysis Methods
Quantitative Architecture Analysis Methods
Model-Based SW Design & Development
Search-Based Software Engineering
Software Architecture Design Knowledge
Systematic Literature Reviews
Model-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 early
reasoning of properties and
well-informed trade-off analysis
Current effective solutions are
highly tailored to specific problems
Control Theory mechanisms may not
directly apply in computer systems
Architectural Styles/Patterns
Reference Architectures
Architecture Design Handbooks
Domain-Specific ADL's
Formal Reference Models/Frameworks
Qualitative Architecture Analysis Methods
Quantitative Architecture Analysis Methods
Model-Based SW Design & Development
Search-Based Software Engineering
Software Architecture Design Knowledge
Systematic Literature Reviews
Model-Predictive Control
Hybrid Systems Control
Our Approach
Highly specialized domain and
large 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 of
domain-specific design spaces
DuSE
=
+
+
Metrics for evaluation of automatically
generated candidate architectures
A multi-objective optimization approach
to explicitly elicit design trade-offs
SA:DuSE is a particular
DuSE instance which
enables automated
architecture design in
the Self-Adaptive
Systems domain

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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Our Approach
Autonomic Manager
KnowledgeMonitor
Analyse Plan
Execute
Managed Element
SA:DuSE provides an automated
process for off-line design &
analysis of Autonomic Manager
architectures
Usually designed off-line, but
may 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 UML
model is provided to SA:DuSE
A lot of degrees of freedom involved:
control law, tuning approach,
MAPE-K deployment, …
1
2
3
4
5

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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 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

10.
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()
; 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}

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The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
The DuSE Optimization Approach
A 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 exploration
and helps prevent false intuition and technology bias. The goal is to find
out a set of (locally) Pareto-optimal candidate architectures

12.
The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Case Study
Cloud-based media
encoding service
Three loci of decision
(controllable components)
Annotations from Qemu +
Hadoop + CloudStack
experiments
Control goal: enforce
encoding 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 between
M2
and M3
, but also M2
and M4
M2
/M3
Pareto front:
● PI+CHR-20OS-DR
● PID+CHR-0OS-DR
● P+ZN

14.
The 25th International Conference on Software Engineering and Knowledge Engineering (SEKE'13) – Boston/MA – USA – June/2013
Related Work
Automated Architecture Improvement
for Performance and other Attributes
(Koziolek & Reussner; 2011)
Search-Based Software Architecture Design
A Meta-Framework for Design Space
Exploration
(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 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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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