Discrete-time rewards model-checked

This paper presents a model-checking approach for analyzing discrete-time Markov reward models. For this purpose, the temporal logic probabilistic CTL is extended with reward constraints. This allows to formulate complex measures – involving expected as well as accumulated rewards – in a precise and succinct way. Algorithms to efficiently analyze such formulae are introduced. The approach is illustrated by model-checking a probabilistic cost model of the IPv4 zeroconf protocol for distributed address assignment in ad-hoc networks

Expressing and Computing Passage Time Measures of GSPN Models with HASL

International audiencePassage time measures specification and computation for Generalized Stochastic Petri Net models have been faced in the literature from different points of view. In particular three aspects have been developed: (1) how to select a specific token (called the tagged token) and measure the distribution of the time employed from an entry to an exit point in a subnet; (2) how to specify in a flexible way any condition on the paths of interest to be measured, (3) how to efficiently compute the required distribution. In this paper we focus on the last two points: the specification and computation of complex passage time measures in (Tagged) GSPNs using the Hybrid Automata Stochastic Logic (HASL) and the statistical model checker COSMOS. By considering GSPN models of two different systems (a flexible manufacturing system and a workflow), we identify a number of relevant performance measures (mainly passage-time distributions), formally express them in HASL terms and assess them by means of simulation in the COSMOS tool. The interest from the measures specification point of view is provided by the possibility of setting one or more timers along the paths, and setting the conditions for the paths selection, based on the measured values of such timers. With respect to other specification languages allowing to use timers in the specification of performance measures, HASL provides timers suspension, reactivation, and rate change along a path

Spatially variable CO2 degassing in the Main Ethiopian Rift: Implications for magma storage, volatile transport, and rift-related emissions

Deep carbon emissions from historically inactive volcanoes, hydrothermal, and tectonic structures are among the greatest unknowns in the long-term (∼Myr) carbon cycle. Recent estimates of diffuse CO2flux from the Eastern Rift of the East African Rift System (EARS) suggest this could equal emissions from the entire mid-ocean ridge system. We report new CO2surveys from the Main Ethiopian Rift (MER, northernmost EARS), and reassess the rift-related CO2flux. Since degassing in the MER is concentrated in discrete areas of volcanic and off-edifice activity, characterization of such areas is important for extrapolation to a rift-scale budget. Locations of hot springs and fumaroles along the rift show numerous geothermal areas away from volcanic edifices. With these new data, we estimate total CO2emissions from the central and northern MER as 0.52–4.36 Mt yr−1. Our extrapolated flux from the Eastern Rift is 3.9–32.7 Mt yr−1CO2, overlapping with lower end of the range presented in recent estimates. By scaling, we suggest that 6–18 Mt yr−1CO2flux can be accounted for by magmatic extension, which implies an important role for volatile-enriched lithosphere, crustal assimilation, and/or additional magmatic intrusion to account for the upper range of flux estimates. Our results also have implications for the nature of volcanism in the MER. Many geothermal areas are found >10 km from the nearest volcanic center, suggesting ongoing hazards associated with regional volcanism

Faster and symbolic CTMC model checking

This paper reports on the implementation and the experiments with symbolic model checking of continuous-time Markov chains using multi-terminal binary decision diagrams (MTBDDs). Properties are expressed in Continuous Stochastic Logic (CSL) [7] which includes the means to express both transient and steady-state performance measures. We show that all CSL operators can be treated using standard operations on MTBDDs, thus allowing a rather straightforward implementation of symbolic CSL model checking on existing MTBDD-based platforms such as the verifier PRISM. The main result of the paper is an improvement of O(N) in the time complexity of checking time-bounded until-formulas, where N is the number of states in the CTMC under consideration. This result yields a drastic speed-up in the verification time of model checking CTMCs, both in the symbolic and non-symbolic case

Automated Performance and Dependability Evaluation Using Model Checking

Abstract. Markov chains (and their extensions with rewards) have been widely used to determine performance, dependability and performability characteristics of computer communication systems, such as throughput, delay, mean time to failure, or the probability to accumulate at least a certain amount of reward in a given time. Due to the rapidly increasing size and complexity of systems, Markov chains and Markov reward models are difficult and cumbersome to specify by hand at the state-space level. Therefore, various specification formalisms, such as stochastic Petri nets and stochastic process algebras, have been developed to facilitate the specification of these models at a higher level of abstraction. Uptill now, however, the specification of the measure-of-interest is often done in an informal and relatively unstructured way. Furthermore, some measures-of-interest can not be expressed conveniently at all. In this tutorial paper, we present a logic-based specification technique to specify performance, dependability and performability measures-ofinterest and show how for a given finite Markov chain (or Markov reward model) such measures can be evaluated in a fully automated way. Particular emphasis will be given to so-called path-based measures and hierarchically-specified measures. For this purpose, we extend so-called model checking techniques to reason about discrete- and continuous-time Markov chains and their rewards. We also report on the use of techniques such as (compositional) model reduction and measure-driven state-space generation to combat the infamous state space explosion problem.