Deterministic Stochastic Petri Nets Research Articles

Performance impact analysis of services under a time-based moving target defense mechanism

Moving target defense (MTD) is a promising proactive defense technique to enhance system or network security. One caveat in developing a time-based MTD-enabled system is the potential performance degradation due to MTD operations being triggered periodically. In this work, we present our developed stochastic models based on deterministic stochastic Petri net (DSPN) formalism to assess the performance degradation introduced by periodic MTD operations. In addition, we demonstrate the effect of triggering a time-based MTD operation on performance degradation (e.g., jobs dropped or response time) and quality-of-service (QoS) of the system. We conducted comparative performance analysis with five DSPN models implemented in software packages such as TimeNet and Mercury under five different system configuration scenarios. Our key findings from this study include the following: (1) the deployment of MTD with the switchover strategy can improve the performance of services; (2) the switchover strategy showed the best cost-effectiveness among all strategies considered in this work; and (3) the interval of triggering MTD operations introduced a modest impact on job completion probability.

Architecture-level hazard analysis using AADL

Software systems are becoming increasingly important in safety-critical areas. Designing safe software requires a significant emphasis on hazards in the early design phase of software development. In this paper, we propose a hazard analysis approach based on Architecture Analysis and Design Language (AADL). First, to make up the deficiencies of Error Model Annex (EMV2), we create Hazard Model Annex (HMA) to specify the hazard sources, hazards, hazard trigger mechanisms, and mishaps. By using HMA, a safety model can be built by annotating an architecture model with the error model and hazard model. Then, an architecture-level hazard analysis approach is proposed to automatically generate the hazard analysis table. The approach contains the model transformation from a safety model to a Deterministic Stochastic Petri Nets (DSPNs) model for calculating the occurrence probability of hazards and mishaps. In addition, we present the formal semantics for each constituent part of the safety model, define the model mapping rules, and verify the semantic preservation of the transformation. Finally, HMA is implemented to build safety models and two Eclipse plug-ins of our methodology are also implemented. A case study on a flight control software system has been employed to demonstrate the feasibility of our proposed technique.

Design and Identification of Stochastic and Deterministic Stochastic Petri Nets

In this paper, we consider the identification problem of stochastic and deterministic stochastic Petri nets (PNs). The approach herein proposed consists of inferring a PN structure and identifying its parameters. Hence, the first step leads to the synthesis of a PN structure with the measurable sequence of events and states. This approach determines the measurable part and estimates the nonmeasurable part of the PN to be established. Once both parts are obtained, the PN structure and the initial marking of the nonmeasurable places are obtained thanks to the integer linear programming technique. In the second step of this approach, the parameters of the obtained model are estimated. Stochastic and deterministic stochastic PNs with deterministic and exponentially distributed transition durations are considered. A systematic identification method is proposed based on event sequences that are recorded by supervision systems. This method is based on a Markov model whose state space is isomorphic to the reachability graph of the untimed PN model.

Identification of stochastic and deterministic stochastic Petri net models for reliability analysis

Reliability analysis for dynamical systems is often based on timed stochastic Petri net (PN) models. A priori knowledge about failure processes is difficult to obtain and, as a consequence, the model structure and parameters are mainly unknown. In that case, synthesis and identification methods based on analysis of collected event sequences are of great interest. The contribution of this paper concerns the identification of timed stochastic PN models. Stochastic and deterministic stochastic PNs with deterministic and exponentially distributed transition durations are considered. A systematic identification method is proposed according to event sequences that are recorded by supervision systems. This method is based on the idea that the dynamic behaviour of considered PN can be mapped into a Markov model with state space isomorphic to the reachability graph of the untimed PN model.

Modeling and Analysis of a Scheduled Maintenance System: a DSPN Approach

This paper describes a way to manage the modeling and analysis of Scheduled Maintenance Systems (SMS) within an analytically tractable context. We chose a significant case study having a variety of interesting features like a heavily redundant architecture and a test and maintenance policy whose execution is made on-line without halting the system. We apply a methodology we previously developed based on the Deterministic Stochastic Petri Net (DSPN) approach where the underlying stochastic process is Markov regenerative (MRGP) solved in our setting with efficient analytical solution method. The model construction and its analysis have been carried out with the help of a tool for the modeling and the dependability evaluation of the Phased Mission Systems (PMS). We exercise our methodology with such case study, to check whether it can master real and complex SMS problems and compare its efficacy with traditional approaches (fault trees). At the same time the paper investigates the problem of the optimal tuning of a maintenance program, giving a useful decision support tool to evaluate the system performance since the early design stage.