1. Elements of Self-Adaptive Architectures Carlos E. Cuesta Rey Juan Carlos University (Spain) M. Pilar Romay St. Paul CEU University (Spain)

2. I Workshop Self-Organizing Architectures (SOAR 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

3. I Workshop Self-Organizing Architectures (SOAR 2009) 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?

4. I Workshop Self-Organizing Architectures (SOAR 2009) 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 )

5. I Workshop Self-Organizing Architectures (SOAR 2009) 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

6. I Workshop Self-Organizing Architectures (SOAR 2009) 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

7. I Workshop Self-Organizing Architectures (SOAR 2009) 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  Most alphas would be composite – therefore autonomic systems  Goal: the whole system as an alpha  Beta ( β )  Element partially in charge of itself: partial autonomic system  Either partial scope ( β| S ) or partial behavior ( β B | ) of both  Every alpha is also a beta (the limit is the upper threshold) β α

8. I Workshop Self-Organizing Architectures (SOAR 2009) 8 Elements of Adaptive Architectures (II)  Gamma ( γ )  An element in charge of another one: a controller Depending on the self-property: a monitor, manager, configuror…  An external, now-internal control device An special, now-specific case of interaction  Delta ( 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 )  An element without self-management  The topical “base” component in this taxonomy ε γ δ

9. I Workshop Self-Organizing Architectures (SOAR 2009) 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

10. I Workshop Self-Organizing Architectures (SOAR 2009) 10 An “Algebra” of Adaptive Elements (I)  Composition: identifying some common rules  Basic compositional rule translates just to non-(self)-managed εε ε  Similar, but mostly irrelevant, for gammas and deltas  Slightly different for betas ββ βα

11. I Workshop Self-Organizing Architectures (SOAR 2009) 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)  An additional operator required (product)  Autonomic Composition: Weak Rule αααβε γε β

12. I Workshop Self-Organizing Architectures (SOAR 2009) 12 An Algebra of Adaptive Elements (III)  Autonomic Composition: Strong Rule  Just when the gamma is specifically tailored for an epsilon  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 γε α

13. I Workshop Self-Organizing Architectures (SOAR 2009) 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)  Conclusion: every useful abstraction seems to be algebraically expressible  Not proven, just a reasonable hypothesis εα γε β γεεγε α

14. I Workshop Self-Organizing Architectures (SOAR 2009) 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

15. I Workshop Self-Organizing Architectures (SOAR 2009) 15 Thanks for your attention

16. I Workshop Self-Organizing Architectures (SOAR 2009) 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