This document discusses elements of self-adaptive architectures. It defines self-* systems and identifies five main adaptive architectural elements - alpha, beta, gamma, delta, and epsilon. It proposes that these elements can be composed using an "algebra" that expresses their relationships and allows autonomous systems to be built. Future work is suggested to develop a specific architectural language based on these concepts to support self-managing systems.

1. Elements of Self-Adaptive
Architectures
Carlos E. Cuesta
Rey Juan Carlos University (Spain)
M. Pilar Romay
St. Paul CEU University (Spain)
SOAR 2009 1
Cambridge, Sept. 14th, 2009

2. Contents
 Introduction
 Self-* Systems
 Basic Definitions
 Boundary Condition: Modularity
 Elements of Adaptive Architectures
 A Taxonomy of Adaptive Elements
 Compositionality of Adaptive Architectures
 A (sort of) “Algebra” of these Elements
 Conclusion
 The potential of a Reflective Architectural Approach
 On the Decentralized Approach
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3. Introduction
 Growing complexity of software
 An effort to automate some of its “internal” functions
• In principle, with no “human in the control loop”
 Giving rise to autonomic, self-managed and self-adaptive systems
• Generically, define a spectrum of self- * systems
 A great general impact on software engineering
 Many common issues with software architecture
• An architectural approach seems promising
• Self-* architecture: is it indeed feasible?
 Self-* Systems are obviously self-referential
 Implying (perhaps) a reflective approach
• Or even a reflective architectural approach?
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4. Self-* Systems (I): Attributes
 Those which manifest full (or partial) automatic control over
one (or several) special attributes
 Let them be named self-attributes (or ς-attributes)
• High-level and system-wide (but, considering subsystems too)
 A non-exhaustive (and somehow progressive) list:
• Self-monitoring (ςm)
• Self-tuning (ςt) and self-configuration (ςC)
• Self-optimization (ςO)
• Self-protection (ςp) and self-healing (ςH)
 But including also layouts and combinations
• Self-organization (ςo)
• Autonomy (ςa) and self-management (ςM)
• Self-adaptation (ςA)
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5. Self-* Systems (II): Basic Definitions
 Let’s define an (heuristic) measure function φ for the degree
of control of some ς-attribute
 Mainly theoretical, to unify the reasoning – though it could turn
practical when associated to some measurement method
 Additional definitions: thresholds
• Lower threshold (η) – the observed system has the ς-attribute
– I.e. φ [s, ςχ] ≥ η (for system s)
• Upper threshold (Ω) – the system has full control
– In the worst case, Ω = 1 at the very least
 Related definition: width (ω)
 The area where our self-control is observable
• Where it is between these two thresholds
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6. Self-* Systems (III): Modularity
 It can be stated that dynamism implies modularity
 Stable parts are “separated” from the rest by change itself
• Continuous evolution implies eventual (if partial) stability
• Two consequences of the Principle of Recursive System’s Construction, as
formulated by Heylighen (1992)
• Also similar to Morrison’s loci (2007)
 Change normally happens where interaction takes place
 Boundary Condition
 The evolutionary boundaries in a system tend to
converge with existing interfaces of components
 Then, the width ω of a ς-attribute defines
bounded subsystems
• This supports the architectural approach
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7. Elements of Adaptive Architectures (I)
 Five main roles identified
 Improved from a previous taxonomy for dynamic architectures
 Dummy names chosen to avoid referring to specific self-properties
 Alpha (α)
 Element in charge of itself: an autonomous element
α
 Most alphas would be composite – therefore autonomic systems
 Goal: the whole system as an alpha
 Beta (β) β
 Element partially in charge of itself: partial autonomic system
 Either partial scope (β|S) or partial behavior (βB|) of both
 Every alpha is also a beta (the limit is the upper threshold)
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8. Elements of Adaptive Architectures (II)
 Gamma (γ)
 An element in charge of another one: a controller γ
• Depending on the self-property: a monitor, manager, configuror…
 An external, now-internal control device
• An special, now-specific case of interaction
 Delta (δ) δ
 An element doing some self-activity, not controlling another
 An auxiliary element for autonomic subsystems
 Providing indirect control by interaction
• Needs not to know the role it plays
 Epsilon (ε) ε
 An element without self-management
 The topical “base” component in this taxonomy
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9. On Compositionality
 The architectural approach assumes a compositional nature
 This is, composition must provide several features
• Composite elements are managed just like atomic components
• That is the basis of the separation of concerns principle
 Algebraic vs. Predicative structure
 Compositional systems are algebraic (constructive)
 When internal structure must be considered: predicative
 Self-properties define a “loop” which seems to imply a predicative
structure, therefore compositionality could be lost
• Reflection does not always compromise compositionality
• Is then reasonable to use an architectural approach?
 To study this, we define an algebra with the five elements
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10. An “Algebra” of Adaptive Elements (I)
 Composition: identifying some common rules
 Basic compositional rule translates just to non-(self)-managed
ε ε ε
 Similar, but mostly irrelevant, for gammas and deltas
 Slightly different for betas
β α
β β
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11. An Algebra of Adaptive Elements (II)
 Principle of Autonomic Composition
 The union of autonomous elements (alphas) does not necessarily
provide an autonomic system (alpha)
α β
α
ε α
 An additional operator required (product)
 Autonomic Composition: Weak Rule
γ β ε
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12. An Algebra of Adaptive Elements (III)
 Autonomic Composition: Strong Rule
 Just when the gamma is specifically tailored for an epsilon
γ α ε
 Other compositional features are similarly considered
 Commutativity, associativity, etc.
• Though non-deterministic, it still seems to be an algebra
• Every condition seems to be algebraically expressible
 Some consequences can be explored using this basis
 If every property is algebraic, then the approach itself is compositional – architectural
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13. An Algebra of Adaptive Elements (IV)
 Case study: an insertion within an autonomic sub-system
 In principle results in a beta, but it might be an alpha
• How to explain that – without breaking boundaries?
• Scope extrusion effect (inspired by the pi-calculus)
 Also, must consider the threshold effect (the extent of control)
γ α γ ε ε
α β ε
 Conclusion: every useful abstraction seems to be
algebraically expressible
 Not proven, just a reasonable hypothesis
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14. Conclusions and Future Work
 The architectural approach for self-management is feasible
 Directly related to existing work on dynamic architectures
 A more challenging and perhaps practical approach
 A (generic) reflective approach is also logical
 Still a strongly self-referential nature, by definition
 It can be combined with the architectural approach
 The (existing) reflective ADL PiLar is able to
describe necessary abstractions
• Created for dynamic architectures, already used for
several other purposes (e.g. aspect-orientation)
 Future work: distilling of a specific self-*
architectural language, based on PiLar
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15. Thanks for your attention
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16. On Decentralization
 Implications of considering this in the context of a
decentralized setting
 I.e. there is no single point for managing the adaptations
 Our classification implies a decentralized setting
 Every gamma is an independent adaptation centre
• But, being architectural, it is quite “agnostic” in this sense
• Gammas (and deltas) can (and does) interact to each other
 The architectural approach provides a hierarchy
• A hierarchy of gammas can be described
• A hierarchy of controlled elements can be used
• But also possible to define a global gamma to act
over the entire architecture
 Also, depend on the kind of interaction
• E.g. interaction vs. reflection vs. superposition
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