Model checking for survivability!

Abstract

Abstract-Business and social life have become increasingly dependent on large-scale communication and information systems. A partial or complete breakdown as a consequence of natural disasters or purposeful attacks might have severe impacts. Survivability refers to the ability of a system to recover from such disaster circumstances. Evaluating survivability should therefore be an important part of communication system design. In this paper we take a model checking approach toward assessing survivability. We use the logic CSL to phrase survivability in a precise manner. The system operation is modelled through a labelled CTMC. Model checking algorithms can then decide automatically whether the system is survivable. We illustrate our method by evaluating the survivability of the Google file system using stochastic Petri nets.

Quantitative Timed Analysis of Interactive Markov Chains

“…We study the practical feasibility of Imca's algorithms for expected time reachability and long-run averages on two case studies: A dependable workstation cluster [18] and a Google file system [10]. The experiments were conducted on a single core of a 2.8 GHz Intel Core i7 processor with 4GB RAM running Linux.…”

“…The model of [10] focuses on a replicated file system as used as part of the Google search engine. In the Google file system model, files are divided into chunks of equal size.…”

“…The set G of goal states characterizes the set of states that offer at least service level one. We consider a variant of the GSPN model in [10] in which the probability of a hardware or a software failure in the chunk server is unknown. This aspect was not addressed in [10].…”

“…We consider a variant of the GSPN model in [10] in which the probability of a hardware or a software failure in the chunk server is unknown. This aspect was not addressed in [10]. Table 2 summarizes the computation times for the analysis of the nondeterministic Google file system model.…”

“…Prototypical tool support for these analyses is presented that includes an implementation of [30]. The feasibility and scalability of our algorithms are illustrated on two examples: A dependable workstation cluster [18] and a Google file system [10]. Our Imca tool is a useful backend for the CADP toolbox, as well as for analysis tools for dynamic fault trees and AADL error models.…”