Dependability and Survivability Evaluation of a Water Distribution Process with Arcade

Among others, drinking water belongs to the socalled critical infrastructures. To ensure that the water production meets current and future societal needs, a systematic and rigorous analysis is needed. In this paper, we report our first experience with dependability analysis of the last phase of a water treatment facility, namely the water distribution. We use the architectural language Arcade to model this facility and use the Arcade toolset to compute three relevant dependability measures: the availability of the water distribution, the reliability, i.e., the probability that the water distribution fails, and the survivability, that is, the ability to recover from disasters. Since survivability is not directly expressible in the Arcade formalism, we show how one can modify the toolchain for the analysis of survivability.\u

A Survey of Fault-Tolerance and Fault-Recovery Techniques in Parallel Systems

Supercomputing systems today often come in the form of large numbers of commodity systems linked together into a computing cluster. These systems, like any distributed system, can have large numbers of independent hardware components cooperating or collaborating on a computation. Unfortunately, any of this vast number of components can fail at any time, resulting in potentially erroneous output. In order to improve the robustness of supercomputing applications in the presence of failures, many techniques have been developed to provide resilience to these kinds of system faults. This survey provides an overview of these various fault-tolerance techniques.Comment: 11 page

Techniques for the Fast Simulation of Models of Highly dependable Systems

With the ever-increasing complexity and requirements of highly dependable systems, their evaluation during design and operation is becoming more crucial. Realistic models of such systems are often not amenable to analysis using conventional analytic or numerical methods. Therefore, analysts and designers turn to simulation to evaluate these models. However, accurate estimation of dependability measures of these models requires that the simulation frequently observes system failures, which are rare events in highly dependable systems. This renders ordinary Simulation impractical for evaluating such systems. To overcome this problem, simulation techniques based on importance sampling have been developed, and are very effective in certain settings. When importance sampling works well, simulation run lengths can be reduced by several orders of magnitude when estimating transient as well as steady-state dependability measures. This paper reviews some of the importance-sampling techniques that have been developed in recent years to estimate dependability measures efficiently in Markov and nonMarkov models of highly dependable system