29 research outputs found
MORPH: A Reference Architecture for Configuration and Behaviour Self-Adaptation
An architectural approach to self-adaptive systems involves runtime change of
system configuration (i.e., the system's components, their bindings and
operational parameters) and behaviour update (i.e., component orchestration).
Thus, dynamic reconfiguration and discrete event control theory are at the
heart of architectural adaptation. Although controlling configuration and
behaviour at runtime has been discussed and applied to architectural
adaptation, architectures for self-adaptive systems often compound these two
aspects reducing the potential for adaptability. In this paper we propose a
reference architecture that allows for coordinated yet transparent and
independent adaptation of system configuration and behaviour
Project Final Report Use and Dissemination of Foreground
This document is the final report on use and dissemination of foreground, part of the CONNECT final report. The document provides the lists of: publications, dissemination activities, and exploitable foregroun
The Standard Problem
Crafting, adhering to, and maintaining standards is an ongoing challenge.
This paper uses a framework based on common models to explore the standard
problem: the impossibility of creating, implementing or maintain definitive
common models in an open system. The problem arises from uncertainty driven by
variations in operating context, standard quality, differences in
implementation, and drift over time. Fitting work by conformance services
repairs these gaps between a standard and what is required for interoperation,
using several strategies: (a) Universal conformance (all agents access the same
standard); (b) Mediated conformance (an interoperability layer supports
heterogeneous agents) and (c) Localized conformance, (autonomous adaptive
agents manage their own needs). Conformance methods include incremental design,
modular design, adaptors, and creating interactive and adaptive agents. Machine
learning should have a major role in adaptive fitting. Choosing a conformance
service depends on the stability and homogeneity of shared tasks, and whether
common models are shared ahead of time or are adjusted at task time. This
analysis thus decouples interoperability and standardization. While standards
facilitate interoperability, interoperability is achievable without
standardization.Comment: Keywords: information standard, interoperability, machine learning,
technology evaluation 25 Pages Main text word Count: 5108 Abstract word
count: 206 Tables: 1 Figures: 7 Boxes: 2 Submitted to JAMI
Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checking
For exhaustive formal verification, industrial-scale cyber-physical systems
(CPSs) are often too large and complex, and lightweight alternatives (e.g.,
monitoring and testing) have attracted the attention of both industrial
practitioners and academic researchers. Falsification is one popular testing
method of CPSs utilizing stochastic optimization. In state-of-the-art
falsification methods, the result of the previous falsification trials is
discarded, and we always try to falsify without any prior knowledge. To
concisely memorize such prior information on the CPS model and exploit it, we
employ Black-box checking (BBC), which is a combination of automata learning
and model checking. Moreover, we enhance BBC using the robust semantics of STL
formulas, which is the essential gadget in falsification. Our experiment
results suggest that our robustness-guided BBC outperforms a state-of-the-art
falsification tool.Comment: Accepted to HSCC 202
From distributed coordination to field calculus and aggregate computing
open6siThis work has been partially supported by: EU Horizon 2020 project HyVar (www.hyvar-project .eu), GA No. 644298; ICT COST Action IC1402 ARVI
(www.cost -arvi .eu); Ateneo/CSP D16D15000360005 project RunVar (runvar-project.di.unito.it).Aggregate computing is an emerging approach to the engineering of complex coordination for distributed systems, based on viewing system interactions in terms of information propagating through collectives of devices, rather than in terms of individual devices and their interaction with their peers and environment. The foundation of this approach is the distillation of a number of prior approaches, both formal and pragmatic, proposed under the umbrella of field-based coordination, and culminating into the field calculus, a universal functional programming model for the specification and composition of collective behaviours with equivalent local and aggregate semantics. This foundation has been elaborated into a layered approach to engineering coordination of complex distributed systems, building up to pragmatic applications through intermediate layers encompassing reusable libraries of program components. Furthermore, some of these components are formally shown to satisfy formal properties like self-stabilisation, which transfer to whole application services by functional composition. In this survey, we trace the development and antecedents of field calculus, review the field calculus itself and the current state of aggregate computing theory and practice, and discuss a roadmap of current research directions with implications for the development of a broad range of distributed systems.embargoed_20210910Viroli, Mirko; Beal, Jacob; Damiani, Ferruccio; Audrito, Giorgio; Casadei, Roberto; Pianini, DaniloViroli, Mirko; Beal, Jacob; Damiani, Ferruccio; Audrito, Giorgio; Casadei, Roberto; Pianini, Danil
Modular Learning and Optimization for Planning of Discrete Event Systems
Optimization of industrial processes, such as manufacturing cells, can have great impact on their performance. Finding optimal solutions to these large-scale systems is, however, a complex problem. They typically include multiple subsystems, and the search space generally grows exponentially with each subsystem. This is usually referred to as the state explosion problem and is a well-known problem within the control and optimization of automation systems. This thesis proposes two main contributions to improve and to simplify the optimization of these systems. The first is a new method of solving these optimization problems using a compositional optimization approach. This integrates optimization with techniques from compositional supervisory control using modular formal models, dividing the optimization of subsystems into separate subproblems. The second is a modular learning approach that alleviates the need for prior knowledge of the systems and system experts when applying compositional optimization. The key to both techniques is the division of the large system into smaller subsystems and the identification of local behavior in these subsystems, i.e. behavior that is independent of all other subsystems. It is proven in this thesis that this local behavior can be partially optimized individually without affecting the global optimal solution. This is used to reduce the state space in each subsystem, and to construct the global optimal solution compositionally.The thesis also shows that the proposed techniques can be integrated to compute global optimal solutions to large-scale optimization problems, too big to solve based on traditional monolithic models