Resources in process algebra

The Algebra of Communicating Shared Resources (ACSR) is a timed process algebra which extends classical process algebras with the notion of a resource. It takes the view that the timing behavior of a real-time system depends not only on delays due to process synchronization, but also on the availability of shared resources. Thus, ACSR employs resources as a basic primitive and it represents a real-time system as a collection of concurrent processes which may communicate with each other by means of instantaneous events and compete for the usage of shared resources. Resources are used to model physical devices such as processors, memory modules, communication links, or any other reusable resource of limited capacity. Additionally, they provide a convenient abstraction mechanism for capturing a variety of aspects of system behavior. In this paper we give an overview of ACSR and its probabilistic extension, PACSR, where resources can fail with associated failure probabilities. We present associated analysis techniques for performing qualitative analysis (such as schedulability analysis) and quantitative analysis (such as resource utilization analysis) of process-algebraic descriptions. We also discuss mappings between probabilistic and non-probabilistic models, which allow us to use analysis techniques from one algebra on models from the other

Mathematical Abstraction, Conceptual Variation and Identity

One of the key features of modern mathematics is the adoption of the abstract method. Our goal in this paper is to propose an explication of that method that is rooted in the history of the subject

Mathematical Abstraction, Conceptual Variation and Identity

One of the key features of modern mathematics is the adoption of the abstract method. Our goal in this paper is to propose an explication of that method that is rooted in the history of the subject

When are Stochastic Transition Systems Tameable?

A decade ago, Abdulla, Ben Henda and Mayr introduced the elegant concept of decisiveness for denumerable Markov chains [1]. Roughly speaking, decisiveness allows one to lift most good properties from finite Markov chains to denumerable ones, and therefore to adapt existing verification algorithms to infinite-state models. Decisive Markov chains however do not encompass stochastic real-time systems, and general stochastic transition systems (STSs for short) are needed. In this article, we provide a framework to perform both the qualitative and the quantitative analysis of STSs. First, we define various notions of decisiveness (inherited from [1]), notions of fairness and of attractors for STSs, and make explicit the relationships between them. Then, we define a notion of abstraction, together with natural concepts of soundness and completeness, and we give general transfer properties, which will be central to several verification algorithms on STSs. We further design a generic construction which will be useful for the analysis of {\omega}-regular properties, when a finite attractor exists, either in the system (if it is denumerable), or in a sound denumerable abstraction of the system. We next provide algorithms for qualitative model-checking, and generic approximation procedures for quantitative model-checking. Finally, we instantiate our framework with stochastic timed automata (STA), generalized semi-Markov processes (GSMPs) and stochastic time Petri nets (STPNs), three models combining dense-time and probabilities. This allows us to derive decidability and approximability results for the verification of these models. Some of these results were known from the literature, but our generic approach permits to view them in a unified framework, and to obtain them with less effort. We also derive interesting new approximability results for STA, GSMPs and STPNs.Comment: 77 page

Language-based Abstractions for Dynamical Systems

Ordinary differential equations (ODEs) are the primary means to modelling dynamical systems in many natural and engineering sciences. The number of equations required to describe a system with high heterogeneity limits our capability of effectively performing analyses. This has motivated a large body of research, across many disciplines, into abstraction techniques that provide smaller ODE systems while preserving the original dynamics in some appropriate sense. In this paper we give an overview of a recently proposed computer-science perspective to this problem, where ODE reduction is recast to finding an appropriate equivalence relation over ODE variables, akin to classical models of computation based on labelled transition systems.Comment: In Proceedings QAPL 2017, arXiv:1707.0366