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Proceedings of the 3rd International Workshop on Views on Designing Complex Architectures (VODCA2008)
No full textA calculus for team automata
No full textTeam automata are a formalism for the component-based specification of reactive, distributed systems. Their main feature is a flexible technique for specifying coordination patterns among systems, thus extending I/O automata. Furthermore, for some patterns the language recognized by a team automaton can be specified via those languages recognized by its components.
We introduce a process calculus tailored over team automata. Each automaton is described by a process, such that its associated (fragment of a) labeled transition system is bisimilar to the original automaton. The mapping is moreover denotational, since the operators defined on processes are in a bijective correspondence with a chosen family of coordination patterns and that correspondence is preserved by the mapping.
We thus extend to team automata a few classical results on I/O automata and their representation by process calculi. Moreover, besides providing a language for expressing team automata, we widen the family of coordination patterns for which an equational characterization of the language associated to a composite automaton can be provided. The latter result is obtained by providing a set of axioms, in ACP-style, for capturing bisimilarity in our calculus
The Legacy of Stefania Gnesi: From Software Engineering to Formal Methods and Tools, and Back
No full textStefania Gnesi was born in Livorno in 1954. She studied Computer Science at the University of Pisa, where she graduated summa cum laude in 1978. During her studies at ISI, which was the University of Pisa’s Institute for Computer Science, a young discipline at that time, Stefania became interested in the continuing challenge associated with the production of software, namely to demonstrate that the developed software is actually doing what is expected to do, a challenge made harder in many cases by the fact that the expectations themselves are not precisely expressed. This has kept her busy ever since. To face this challenge her very first steps in research, towards the end of her university studies, of purely theoretical nature, proved very valuable. In a publication in the Journal of the ACM [63] (not bad for a first journal paper!), resulting from her thesis under the supervision of Prof. Ugo Montanari, it is shown that finding the solution of a dynamic programming problem in the form of polyadic functional equations is equivalent to searching a minimal cost path in an and/or graph with monotone cost functions. An important computational application of this result is that the solution of a system of functional equations can always be reduced to the problem of searching a minimal cost solution tree in an and/or graph
Space-Fluid Adaptive Sampling: A Field-Based, Self-organising Approach
Full text linkA recurrent task in coordinated systems is managing (estimating, predicting, or controlling) signals that vary in space, such as distributed sensed data or computation outcomes. Especially in large-scale settings, the problem can be addressed through decentralised and situated computing systems: nodes can locally sense, process, and act upon signals, and coordinate with neighbours to implement collective strategies. Accordingly, in this work we devise distributed coordination strategies for the estimation of a spatial phenomenon through collaborative adaptive sampling. Our design is based on the idea of dynamically partitioning space into regions that compete and grow/shrink to provide accurate aggregate sampling. Such regions hence define a sort of virtualised space that is “fluid”, since its structure adapts in response to pressure forces exerted by the underlying phenomenon. We provide an adaptive sampling algorithm in the field-based coordination framework. Finally, we verify by simulation that the proposed algorithm effectively carries out a spatially adaptive sampling
Towards Reinforcement Learning-based Aggregate Computing
Full text linkRecent trends in pervasive computing promote the vision of Collective Adaptive Systems (CASs): large-scale collections of relatively simple agents that act and coordinate with no central orchestrator to support distributed applications. Engineering global behaviour out of local activity and interaction, however, is a difficult task, typically addressed by try-and-error approaches in simulation environments. In the context of Aggregate Computing (AC), a prominent functional programming approach for CASs based on field-based coordination, this difficulty is reflected in the design of versatile algorithms preserving efficiency in a variety of environments. To deal with this complexity, in this work we propose to apply Machine Learning techniques to automatically devise local actions to improve over manually-defined AC algorithms specifications. Most specifically, we adopt a Reinforcement Learning-based approach to let a collective learn local policies to improve over the standard gradient algorithm—a cornerstone brick of several higher-level self-organisation algorithms. Our evaluation shows that the learned policies can speed up the self-stabilisation of the gradient to external perturbations
Oscillaties van carry-under in een interne-loop airlift reactor
No full textDe interne-loop airlift reactor bestaat uit twee gescheiden delen. Een riserdeel met opstijgende lucht en een downcomerdeel met neerwaartse waterstroom. Deze stroming is bij benadering te vergelijken met de stroming in een bellenkolom die niet-homogeen belucht wordt. In dit onderzoek is het stromingsgedrag van een symmetrische 2D-airlift reactor met twee downcomerbenen onderzocht. In deze airlift is het mogelijk dat er door de neerwaartse waterstroom bellen uit de riser meegesleept worden in de downcomer, 'carry-under'. Deze bellen bereiken een zogenaamde carry-underdiepte en stromen niet door tot de riserinlaat. De carry-underdiepte in de twee downcomerbenen oscilleert met tegengestelde fase. Deze oscillatie is direct gekoppeld aan een oscillerende waterstroom in de downcomer. De periode van de oscillatie is in de orde van 10 seconden. In dit verslag wordt deze oscillatie in kaart gebracht door metingen van de watersnelheid in de downcomer met de LDA meetmethode. De watersnelheid is gemeten als functie van de riserlengte, waterhoogte en de luchttoevoer. Tijdens dit onderzoek zijn ook andere waarnemingen verricht; de beluchte waterhoogte en de gemiddelde carry-underdiepte zijn gemeten. Verder zijn er metingen aan een instabiele pluim in een tweedimensionale waterbak verricht. Op grond van deze inzichten kan een fysische beschrijving van de stroming gemaakt worden. Bij lage luchtdebieten is een periodieke instabiliteit in de stroming waarneembaar, die ook bekend is bij de stroming van een luchtpluim in een waterbak. Deze instabiliteit kan benaderd worden met de Kelvin-Helmholtz instabiliteit. De frequentie van deze pluim is afhankelijk van de lineaire watersnelheden en is gebruikt als invoer bij de modellering van de dynamische carry-under. De carry-under oscillatie kan worden benaderd door een relaxatie-oscillatie, veroorzaakt door niet-lineaire verschijnselen in de tweefasenstroming. Deze verschijnselen kunnen niet verklaard worden met het oppervlaktegemiddelde drift-flux model. Het resulterende model leidt tot een kwalitatief juist beeld van de instabiele stroming. Een eigenfrequentie op basis van het model van een oscillerende, beluchte U-buis levert een bevredigende overeenkomst met de gemeten frequentie van de oscillatie.Kramers Laboratorium voor Fysische TechnologieApplied Science
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