Stochastic Object-Based Graph Grammars

Authors:
Odorico M. Mendizabal;Fernando L. Dotti;Leila Ribeiro
Affiliations:
Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre - Brazil;Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre - Brazil;Universidade Federal do Rio Grande do Sul, Porto Alegre - Brazil
Venue:
Electronic Notes in Theoretical Computer Science (ENTCS)
Year:
2007

Citing 6
Cited 1

Computer networks

Computer networks
Graph processes

Fundamenta Informaticae - Special issue on graph transformations
On the stochastic structure of parallelism and synchronization models for distributed algorithms
Specification of mobile code system using graph grammars

Fourth International Conference on Formal methods for open object-based distributed systems IV
Numerical Evaluation of Stochastic Automata Networks

MASCOTS '95 Proceedings of the 3rd International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Verifying Object-Based Graph Grammars

Electronic Notes in Theoretical Computer Science (ENTCS)

Correct transformation: From object-based graph grammars to PROMELA

Science of Computer Programming

Quantified Score

Hi-index	0.01

Visualization

Abstract

Object-Based Graph Grammar (OBGG) is a formal visual language suited to the specification of asynchronous distributed systems based on message passing. Model-checking of OBGG models is currently supported and a series of case studies have been developed. However, in many situations one has to evaluate non-functional aspects like availability and performance of the system under consideration. In such cases, a stochastic analysis of the system is desired. This paper is a first contribution to the stochastic analysis of OBGG models. OBGG models with occurrence rates associated to rules are translated to Stochastic Automata Networks (SAN). SAN is a Markov Chain equivalent formalism having as advantage its modularity in terms of representation and a compact mathematical solution, allowing the analysis of models with larger state space.