Run-time efficient probabilistic model checking

Abstract

Unpredictable changes continuously affect software systems and may have a severe impact on their quality of service, potentially jeopardizing the system's ability to meet the desired requirements. Changes may occur in critical components of the system, clients' operational profiles, requirements, or deployment environments.The adoption of software models and model checking techniques at run time may support automatic reasoning about such changes, detect harmful configurations, and potentially enable appropriate (self-)reactions. However, traditional model checking techniques and tools may not be simply applied as they are at run time, since they hardly meet the constraints imposed by on-the-fly analysis, in terms of execution time and memory occupation. This paper precisely addresses this issue and focuses on reliability models, given in terms of Discrete Time Markov Chains, and probabilistic model checking. It develops a mathematical framework for run-time probabilistic model checking that, given a reliability model and a set of requirements, statically generates a set of expressions, which can be efficiently used at run-time to verify system requirements. An experimental comparison of our approach with existing probabilistic model checkers shows its practical applicability in run-time verification.

Probabilistic Verification at Runtime for Self-Adaptive Systems

“…At runtime all that is needed to obtain the actual analysis response is to replace the symbolic variables with the actual values provided from modeling, as soon as they are discovered. The evaluation of a mathematical expression, is in general a much simpler task than model-checking, and can be performed in a very short time even on low power devices, as we shown in [12].…”

“…In [12] we introduced a computational approach entirely based on matrix operations that can be straightforwardly suitable for parallelization. In this paper we will instead focus on how to efficiently compute matrix B in a sequential environment.…”

“…We are interested in evaluating the execution time of just the design time phase. The runtime verification, being just the evaluation of polynomial forms, takes a very short time even for very large parametric formulae, as discussed in [12].…”

Advances in Quantitative Verification for Ubiquitous Computing

“…To improve efficiency, one can employ parametric approaches, e.g. [21], and specifically parameter synthesis [28]. We consider parametric probabilistic models, where probabilities are specified in terms of expressions over parameters, rather than concrete values.…”

Towards Communication-Based Steering of Complex Distributed Systems

“…In [26] a model checker executed from the current local state has been used to predict and prevent future inconsistencies in a distributed system. In the quantitative runtime setting, a number of approaches have been proposed for different types of models, to mention the autonomic approach of [2,1] for discrete-and continuous-time Markov chains, parametric techniques of [15,16] for discrete time Markov chains and the incremental approach of [21] for Markov decision processes. Partially observable Markov decision processes are known to be infeasible, but a promising partial approach to adversary generation was recently proposed in [18].…”