An Efficient Symbolic Elimination Algorithm for the Stochastic Process Algebra Tool CASPA

Artificial Intelligence and Applications / 794: Modelling, Identification and Control / 795: Parallel and Distributed Computing

2013

Self Cite

We propose a novel modelling concept for stochastic control on systems which are hierarchically composed of subsystems with discrete states and stochastic continuous time. The global control structure (called decision tree) is based on the model hierarchy and can be defined by an interleaving of local control with concurrency. For model specification we review the language LARES which comprises an object-oriented modelling design in order to specify modular and hierarchically structured stochastic systems. In order to embed the control structure into the LARES framework we describe the language extension LARES.de. The main focus of the paper is a transformation to a Markov Decision Process induced by an agent-based view on the control structure. This defines the concrete language semantics and makes state-based system optimization accessible.

Section: The Lares Formalismmentioning

confidence: 99%

“…0 ) i n i t i a l l y a c t i v e I n s t a n c e C [ 3 ] o f Cont ( 3 . 0 ) i n i t i a l l y a c t i v e 5 C o n d i t i o n s y s F = C [ 1 ] . F & C [ 2 ] .…”

Section: The Lares Formalismunclassified

Section: The Lares Formalismmentioning

confidence: 99%

See 1 more Smart Citation

A Modular and Hierarchical Modelling Approach for Stochastic Control

Gouberman¹,

Riedl²,

Artificial Intelligence and Applications / 794: Modelling, Identification and Control / 795: Parallel and Distributed Computing

2013

Self Cite

“…Knowing the most likely paths from an initial, error-free state to the set of states where the system has failed, and knowing the associated probabilities and average durations of such paths, is very valuable for system designers and may assist them in debugging and improving their fault-tolerant system. This paper discusses algorithms for path-based analysis as implemented in the modelling tool CASPA [2][3][4], which is a tool for performance and dependability modelling and is based on a stochastic process algebra. CASPA is completely based on symbolic, i.e.…”

Section: Introductionmentioning

confidence: 99%

Path-based calculation of MTTFF, MTTFR, and asymptotic unavailability with the stochastic process algebra tool CASPA

Schuster

Proceedings of the Institution of Mechanical Engineers, Part O:

2011

CASPA is a stochastic process algebra tool for performance and dependability modelling, analysis, and verification. It is based entirely on the symbolic data structure of the multi-terminal binary decision diagram (MTBDD) which enables the tool to handle models with very large state space. This paper describes an extension of CASPA's solving engine for path-based approximation of the mean time to first failure, the mean time to first recovery, and asymptotic unavailability by MTBDD algorithms. A non-trivial case study illustrates the use of path-based analysis and comparisons between the path-based unavailability calculations and results obtained from standard Markovian analysis are presented.

“…[3,6], T can be converted into a pure transition rate matrix by eliminating all entries referring to vanishing states. This elimination can be done either at the level of the high-level model or at the level of the state space [2]. For simplicity we only consider high-level model descriptions with timed activities.…”

Section: High-level System Modellingmentioning

confidence: 99%

A Symbolic Approach to the Analysis of Multi-Formalism Markov Reward Models

Lampka

Advances in Systems Analysis, Software Engineering, and High Performance Computing

Self Cite

When modelling large systems, modularity is an important concept, as it aids modellers to master the complexity of their model. Moreover, employing different modelling formalisms within the same modelling project has the potential to ease the description of various parts or aspects of the overall system. In the area of performability modelling, formalisms such as stochastic reward nets, stochastic process algebras, stochastic automata, or stochastic UML state charts are often used, and several of these may be employed within one modelling project. This chapter presents an approach for efficiently constructing a symbolic representation in the form of a zero-suppressed Binary Decision Diagram (BDD), which represents the Markov Reward Model underlying a multi-formalism high-level model. In this approach, the interaction between the submodels may be established either by the sharing of state variables or by the synchronisation of common activities. It is shown that the Decision Diagram data structure and the associated algorithms enable highly efficient state space generation and different forms of analysis of the underlying Markov Reward Model (e.g. calculation of reward measures or asserting non-functional system properties by means of model checking techniques).