11,126 research outputs found
Compositional reliability analysis using probabilistic component automata
Compositionality is a key property in the development and analy- sis of component-based systems. In non-probabilistic formalisms such as Labelled Transition Systems (LTS) the functional behaviour of a system can be readily constructed from the behaviours of its parts. However, this is not true for probabilistic extensions of LTS, which are necessary to analyse non-functional properties such as reliability. We propose Probabilistic Component Automata (PCA) as a proba- bilistic extension to Interface Automata to automatically construct a system model by composing models of its sub-components. In par- ticular, we focus on modelling failure scenarios, failure handling and failure propagation. Additionally, we propose a novel algorithm based on Compositional Reachability Analysis to mitigate the well-known state-explosion problem associated with composable models. Both Probabilistic Component Automata and the reduction algorithm have been implemented in the LTSA tool
The compositional construction of Markov processes II
In an earlier paper we introduced a notion of Markov automaton, together with
parallel operations which permit the compositional description of Markov
processes. We illustrated by showing how to describe a system of n dining
philosophers, and we observed that Perron-Frobenius theory yields a proof that
the probability of reaching deadlock tends to one as the number of steps goes
to infinity. In this paper we add sequential operations to the algebra (and the
necessary structure to support them). The extra operations permit the
description of hierarchical systems, and ones with evolving geometry
Challenges for Efficient Query Evaluation on Structured Probabilistic Data
Query answering over probabilistic data is an important task but is generally
intractable. However, a new approach for this problem has recently been
proposed, based on structural decompositions of input databases, following,
e.g., tree decompositions. This paper presents a vision for a database
management system for probabilistic data built following this structural
approach. We review our existing and ongoing work on this topic and highlight
many theoretical and practical challenges that remain to be addressed.Comment: 9 pages, 1 figure, 23 references. Accepted for publication at SUM
201
Formal and Informal Methods for Multi-Core Design Space Exploration
We propose a tool-supported methodology for design-space exploration for
embedded systems. It provides means to define high-level models of applications
and multi-processor architectures and evaluate the performance of different
deployment (mapping, scheduling) strategies while taking uncertainty into
account. We argue that this extension of the scope of formal verification is
important for the viability of the domain.Comment: In Proceedings QAPL 2014, arXiv:1406.156
Interface Simulation Distances
The classical (boolean) notion of refinement for behavioral interfaces of
system components is the alternating refinement preorder. In this paper, we
define a distance for interfaces, called interface simulation distance. It
makes the alternating refinement preorder quantitative by, intuitively,
tolerating errors (while counting them) in the alternating simulation game. We
show that the interface simulation distance satisfies the triangle inequality,
that the distance between two interfaces does not increase under parallel
composition with a third interface, and that the distance between two
interfaces can be bounded from above and below by distances between
abstractions of the two interfaces. We illustrate the framework, and the
properties of the distances under composition of interfaces, with two case
studies.Comment: In Proceedings GandALF 2012, arXiv:1210.202
Modelling, reduction and analysis of Markov automata (extended version)
Markov automata (MA) constitute an expressive continuous-time compositional modelling formalism. They appear as semantic backbones for engineering frameworks including dynamic fault trees, Generalised Stochastic Petri Nets, and AADL. Their expressive power has thus far precluded them from effective analysis by probabilistic (and statistical) model checkers, stochastic game solvers, or analysis tools for Petri net-like formalisms. This paper presents the foundations and underlying algorithms for efficient MA modelling, reduction using static analysis, and most importantly, quantitative analysis. We also discuss implementation pragmatics of supporting tools and present several case studies demonstrating feasibility and usability of MA in practice
Analysis of Timed and Long-Run Objectives for Markov Automata
Markov automata (MAs) extend labelled transition systems with random delays
and probabilistic branching. Action-labelled transitions are instantaneous and
yield a distribution over states, whereas timed transitions impose a random
delay governed by an exponential distribution. MAs are thus a nondeterministic
variation of continuous-time Markov chains. MAs are compositional and are used
to provide a semantics for engineering frameworks such as (dynamic) fault
trees, (generalised) stochastic Petri nets, and the Architecture Analysis &
Design Language (AADL). This paper considers the quantitative analysis of MAs.
We consider three objectives: expected time, long-run average, and timed
(interval) reachability. Expected time objectives focus on determining the
minimal (or maximal) expected time to reach a set of states. Long-run
objectives determine the fraction of time to be in a set of states when
considering an infinite time horizon. Timed reachability objectives are about
computing the probability to reach a set of states within a given time
interval. This paper presents the foundations and details of the algorithms and
their correctness proofs. We report on several case studies conducted using a
prototypical tool implementation of the algorithms, driven by the MAPA
modelling language for efficiently generating MAs.Comment: arXiv admin note: substantial text overlap with arXiv:1305.705
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