The GreatSPN tool: recent enhancements

GreatSPN is a tool that supports the design and the qualitative and quantitative analysis of Generalized Stochastic Petri Nets (GSPN) and of Stochastic Well-Formed Nets (SWN). The very first version of GreatSPN saw the light in the late eighties of last century: since then two main releases where developed and widely distributed to the research community: GreatSPN1.7 [13], and GreatSPN2.0 [8]. This paper reviews the main functionalities of GreatSPN2.0 and presents some recently added features that significantly enhance the efficacy of the tool

Performance and Dependability Analysis of Fault-Tolerant Memory Mechanisms Using Stochastic Well-Formed Nets

This paper presents a performance and dependability study of a software fault-tolerant memory mechanism, namely the Distributed Memory (DM), which has been developed within a R&D European project. Relying on the UML specification (produced within the project), Stochastic Well-Formed Nets models of the DM are developed and analysed. Combinatorial methods are used in conjunction with state space based methods to study the impact of the mechanism configuration on its reliability and performance

Multiple abstraction levels in performance analysis of WSN monitoring systems

In this paper, we illustrate the use of different methods to support the design of a Wireless Sensor Network (WSN), by using as a case study a monitoring system that must track a moving object within a given area. The goal of the study is to find a good trade-off between the power consumption and the object tracking reliability. Power saving can be achieved by periodically powering off some of the nodes for a given time interval. Of course nodes can detect the moving object only when they are on, so that the power management strategy can affect the ability to accurately track the object movements. We propose two models and the corresponding analysis and simulation tools, that can be used in a synergistic way: the first model is based on the Markov Decision Well-formed Net (MDWN) formalism while the second one is based on the Stochastic Activity Network (SAN) formalism. The MDWN model is more abstract and is used to compute an optimal power management strategy by solving a Markov Decision Process (MDP); the SAN model is more detailed and is used to perform extensive simulation (using the Mobius tool) in order to analyze different performance indices, both when applying the power management policy derived from the first model and when using different policies

An application example of symbolic calculus for SWN structural relations

Structural analysis techniques allow system properties to be efficiently verified, and may significantly improve the effectiveness of state-space based analysis. Symbolic approaches have been proposed to extend in an effective way structural analysis results from ordinary Petri nets to high level Petri nets (e.g. Unary Regular nets). In the paper some salient points of a symbolic calculus of Stochastic Well-formed Nets structural relations are presented using an example

Parametric Fault-Tree for the Dependability Analysis of Redundant Systems and its High Level Petri Net Semantics

In order to cope efficiently with the dependability analysis of redundant systems with replicated units, a new, more compact fault-tree formalism, called Parametric Fault Tree (PFT), is defined. In a PFT formalism, replicated units are folded and indexed so that only one representative of the similar replicas is included in the model. From the PFT, a list of parametric cut sets can be derived, where only the relevant patterns leading to the system failure are evidenced regardless of the actual identity of the component in the cut set. The paper provides an algorithm to convert a PFT into a class of High-Level Petri Nets, called SWN. The purpose of this conversion is twofold: to exploit the modeling power and flexibility of the SWN formalism, allowing the analyst to include statistical dependencies that could not have been accommodated into the corresponding PFT and to exploit the capability of the SWN formalism to generate a lumped Markov chain, thus alleviating the state explosion problem. The search for the minimal cut sets (qualitative analysis) can be often performed by a structural T-invariant analysis on the generated SWN. The advantages that can be obtained from the translation of a PFT into a SWN are investigated considering a fault-tolerant multiprocessor system example

Efficient lumpability check in partially symmetric systems

State space based performance analysis of stochastic models may be impaired by the state space explosion but such problem can be mitigated in symmetrical behaving systems by aggregating equivalent states and transitions. An effective way of exploiting symmetries when the system is modeled using the stochastic well-formed net (SWN) formalism, is to generate the symbolic reachability graph (SRG) and automatically derive a lumped continuous time Markov chain (CTMC) of the same size as the SRG from it. For partially symmetric systems, the extended SRG (ESRG) can be used instead, but the derivation of a lumped CTMC in this case is not as direct as in the SRG case: in fact the ESRG structure might need a refinement to satisfy the lumpability conditions. In this paper a new efficient algorithm to derive a lumped CTMC from the ESRG is presented, and the results obtained by experimenting its implementation within the GreatSPN environment are discussed. The algorithm combines the Paige and Tarjan's partition refinement algorithm (extended to work with weighted arcs) and a previously proposed lumpability check algorithm (built specifically for the use with the ESRG) and outperforms both of them. The implementation of the algorithm within the GreatSPN environment would allow the several users that have chosen this package to apply the proposed technique