Stochastic Petri Nets Research Articles

Evaluation of time-based virtual machine migration as moving target defense against host-based attacks

Moving Target Defense (MTD) consists of applying dynamic reconfiguration in the defensive side of the attack-defense cybersecurity game. Virtual Machine (VM) migration could be used as MTD against specific host-based attacks in the cloud computing environment by remapping the distribution of VMs in the existing physical hosts. This way, when the attacker’s VM is moved to a different machine, the attack has to be restarted. However, one significant gap here is how to select a proper VM migration-based MTD schedule to reach the desired levels of system protection. This paper develops a Stochastic Petri Net (SPN) model to address this issue. The model leverages empirical knowledge about the dynamics of the attack defense in a VM migration-enabled setup. First, we present the results of an experimental campaign to acquire knowledge about the system’s behavior. The experiments provide insights for the model design. Then, based on the model, we propose a tool named PyMTDEvaluator, which provides a graphical interface that serves as a wrapper for the simulation environment of the model. Finally, we exercise the tool using Multi-Criteria Decision-Making methods to aid the MTD policy selection. Hopefully, our results and methods will be helpful for system managers and cybersecurity professionals.

Production chain modeling based on learning flow stochastic petri nets

Production chain modeling based on learning flow stochastic petri nets

Kyrtos: A methodology for automatic deep analysis of graphic charts with curves in technical documents

Deep Understanding of Technical Documents (DUTD) has become a very attractive field with great potential due to large amounts of accumulated documents and the valuable knowledge contained in them. In addition, the holistic understanding of technical documents depends on the accurate analysis of its particular modalities, such as graphics, tables, diagrams, text, etc. and their associations. In this paper, we introduce the Kyrtos methodology for the automatic recognition and analysis of charts with curves in graphics images of technical documents. The recognition processing part adopts a clustering based approach to recognize middle-points that delimit the line-segments that construct the illustrated curves. The analysis processing part parses the extracted line-segments of curves to capture behavioral features such as direction, trend and etc. These associations assist the conversion of recognized segments’ relations into attributed graphs, for the preservation of the curves’ structural characteristics. The graph relations are also are expressed into natural language (NL) text sentences, enriching the document’s text and facilitating their conversion into Stochastic Petri-net (SPN) graphs, which depict the internal functionality represented in the chart image. Extensive evaluation results demonstrate the accuracy of Kyrtos’ recognition and analysis methods by measuring the structural similarity between input chart curves and the approximations generated by Kyrtos for charts with multiple functions.

Mobility‐aware modeling and evaluation of IoT systems using stochastic reward nets

SummaryThe frequent geographical changes of mobile nodes in Internet of Things (IoT) systems affect communication, activities, and behaviors. In such scenarios, it is crucial to establish a system model capable of evaluating quality of service (QoS) measures. However, the existing formal modeling techniques pose complexities in modeling mobility. To deal with these challenges, this study aims to propose a model that simplifies the process of modeling mobility within IoT systems. This paper presents a method for modeling mobility within IoT systems by leveraging a widely recognized extension of stochastic Petri nets known as stochastic reward nets (SRNs). The proposed method enhances the SRN model by incorporating the location concept, resulting in a novel extension called mobile SRN (MSRN). In this work, a case study utilizes the MSRN to evaluate the suggested features, examining various scenarios and investigating the impact of factors such as environmental conditions, sensor sampling rate, and the permissible distance of the node from the sink.

Performance evaluation of a video surveillance system using stochastic petri nets for license plate detection on highways

Performance evaluation of a video surveillance system using stochastic petri nets for license plate detection on highways

Analysis and optimization of a Stochastic Petri Net for air-rail intermodal transportation.

Air-rail intermodal transportation (ARIT) plays a crucial role in China's intermodal transportation system. This study aims to model and optimize issues such as inefficiency and complexity in China's ARIT freight transportation using Business Process Reengineering and Stochastic Petri Nets theories. The Petri Net (PN) model for incoming freight transport in ARIT is based on actual operations, employing a new method involving Stochastic Petri Nets and isomorphic Markov Chains theory for performance analysis. Performance analysis results help intuitively identify areas needing optimization. Based on optimization principles, elements such as railway container packaging are improved, resulting in an optimized PN model for ARIT. Finally, data analysis shows that the optimized ARIT model reduces total delay by 7.7% compared to the original. This demonstrates that the new method, combining Markov Chain performance analysis and optimization principles, is feasible and effective for ARIT optimization.

Assuring Autonomy of UAVs in Mission-critical Scenarios by Performability Modeling and Analysis

Uncrewed Aerial Vehicles (UAVs) have been used in mission-critical scenarios such as Search and Rescue (SAR) missions. In such a mission-critical scenario, flight autonomy is a key performance metric that quantifies how long the UAV can continue the flight with a given battery charge. In a UAV running multiple software applications, flight autonomy can also be impacted by faulty application processes that excessively consume energy. In this article, we propose Flight Autonomy Assurance as a framework to assure the autonomy of a UAV considering faulty application processes through performability modeling and analysis. The framework employs hierarchically configured stochastic Petri nets, evaluates the performability-related metrics, and guides the design of mitigation strategies to improve autonomy. We consider a SAR mission as a case study and evaluate the feasibility of the framework through extensive numerical experiments. The numerical results quantitatively show how autonomy is enhanced by offloading and restarting faulty application processes.

Research on High-performance Computing Network Search System based on Computer Big Data

An efficient computing system for massively parallel systems is established. The stochastic Petri net abstracts and models the high-performance computer work scheduling system. The IB switch is modeled. Stochastic Petri nets are used for performance analysis. Finally, the proposed method is combined with InfiniBand interconnection architecture to evaluate the system's delay. The experimental results prove the feasibility of this algorithm.

Enjoy the silence: Analysis of stochastic Petri nets with silent transitions

Capturing stochastic behaviour in business and work processes is essential to quantitatively understand how nondeterminism is resolved when taking decisions within the process. This is of special interest in process mining, where event data tracking the actual execution of the process are related to process models, and can then provide insights on frequencies and probabilities. Variants of stochastic Petri nets provide a natural formal basis to represent stochastic behaviour and support different data-driven and model-driven analysis tasks in this spectrum. However, when capturing business processes, such nets inherently need a labelling that maps between transitions and activities. In many state of the art process mining techniques, this labelling is not 1-on-1, leading to unlabelled transitions and activities represented by multiple transitions. At the same time, they have to be analysed in a finite-trace semantics, matching the fact that each process execution consists of finitely many steps. These two aspects impede the direct application of existing techniques for stochastic Petri nets, calling for a novel characterisation that incorporates labels and silent transitions in a finite-trace semantics. In this article, we provide such a characterisation starting from generalised stochastic Petri nets and obtaining the framework of labelled stochastic processes (LSPs). On top of this framework, we introduce different key analysis tasks on the traces of LSPs and their probabilities. We show that all such analysis tasks can be solved analytically, in particular reducing them to a single method that combines automata-based techniques to single out the behaviour of interest within an LSP, with techniques based on absorbing Markov chains to reason on their probabilities. Finally, we demonstrate the significance of how our approach in the context of stochastic conformance checking, illustrating practical feasibility through a proof-of-concept implementation and its application to different datasets.

Reliability and Performance Evaluation of Safety-Critical Instrumentation and Control Systems of Nuclear Power Plant

Instrumentation and control systems are nervous systems of nuclear power plant (NPP). These systems interact with several safety-critical components of the NPP, such as actuators, transformers, control valves, sensors, circuit breakers, signal processing units, controllers, and heat exchangers. Therefore, the failure of these systems could result in significant financial loss, harm to human resources, or environmental damage. As a result, these systems need to be highly reliable and accurate. In this article, we suggest a framework, based on the batch deterministic and stochastic Petri nets (BDSPNs) to measure the reliability and performance of safety-critical system. The framework consists of three phases. In the first phase, the system is modeled using the BDSPN to derive the transition rate among the system states. In phase 2, the transition rate matrix is utilized to compute the steady-state probability values, which help to evaluate the response time of the system using Little's law. The third phase uses a transition probability matrix to assess the reliability of the system. The technique is illustrated on shutdown system of NPP and is validated on the operational profile data. The obtained accuracy of 99.9905% in measurement of reliability validates the approach.

Refined Oil Loading and Unloading Process Risk Assessment using Stochastic Colored Petri Nets Integrated with Risk Factors

Refined Oil Loading and Unloading Process Risk Assessment using Stochastic Colored Petri Nets Integrated with Risk Factors

Fluidization of Stochastic Petri Nets via Continuous Petri Nets: Comparative Study

Fluidization of Stochastic Petri Nets via Continuous Petri Nets: Comparative Study

A maintenance strategy for hydraulic systems based on generalized stochastic Petri nets under epistemic uncertainty

A maintenance strategy for hydraulic systems based on generalized stochastic Petri nets under epistemic uncertainty

Calculation of the System Unavailability Measures of Component Importance Using the D2T2 Methodology of Fault Tree Analysis

A recent development in Fault Tree Analysis (FTA), known as Dynamic and Dependent Tree Theory (D2T2), accounts for dependencies between the basic events, making FTA more powerful. The method uses an integrated combination of Binary Decision Diagrams (BDDs), Stochastic Petri Nets (SPN) and Markov models. Current algorithms enable the prediction of the system failure probability and failure frequency. This paper proposes methods which extend the current capability of the D2T2 framework to calculate component importance measures. Birnbaum’s measure of importance, the Criticality measure of importance, the Risk Achievement Worth (RAW) measure of importance and the Risk Reduction Worth (RRW) measure of importance are considered. This adds a vital ability to the framework, enabling the influence that components have on system failure to be determined and the most effective means of improving system performance to be identified. The algorithms for calculating each measure of importance are described and demonstrated using a pressure vessel cooling system.

Improving Centralized Offshore Power Generation Design With Petri Net-Based Availability and Reliability Analysis

Abstract The offshore industry has actively sought technological solutions that reduce CO2 emissions from platform operations. One of the possible solutions being studied is the implementation of Power Hubs, which would generate electricity and distribute it to nearby platforms. Unlike the traditional approach, in which the electricity is generated in the platform for its operation, centralizing such generation via Power Hubs can make the process more efficient, reducing CO2 emissions. However, such a configuration increases the complexity of the operation and can impact the reliability and availability of platforms connected to the Power Hub. Therefore, this work aims to perform reliability and availability estimates of this type of operational configuration and compare it with the traditional offshore operation to quantify the difference between them. Various kinds of Power Hubs configurations were also analyzed to compare the results obtained. Such analyzes were performed using Generalized Stochastic Petri Nets (GSPNs) models. Results show that, depending on their configurations, Power Hubs can guarantee an average availability of energy generation close to 100% even in periods of higher demand for oil and gas production.

Research on inventory replenishment strategy of supply chain based on SPN

The warehousing and replenishment strategy of supply chain is comprehensively affected by various factors such as supply chain structure, demand change, supply source, inventory state. Supply chain warehousing replenishment is full of uncertain factors, in order to achieve the optimal replenishment process and the best replenishment strategy effect, this paper uses SPN(Stochastic Petri net) to build the supply chain warehousing replenishment model. Based on the basis of the traditional replenishment process, this paper optimizes the new supply chain warehousing management process, optimizes the priority management measures of goods in the process of supply chain warehousing replenishment process, designs the new replenishment strategy algorithm and designs the replenishment strategy function analysis. Using SPN model, this paper intuitively shows the warehousing management process, and describes the dynamic changes of warehousing replenishment in detail. Through the implementation of different warehousing replenishment strategies to solve the influencing factors of uncertainty, so as to avoid the problem of various supply chain warehousing shortage caused by poor warehousing management. Finally, by comparing the traditional warehouse management strategy and the SPN model optimization of the supply chain storage strategy, the research result shows that the SPN replenishment strategy model is more efficient than the traditional warehouse replenishment strategy model.

Stochastic and continuous Petri nets approximation of Markovian model

Stochastic and continuous Petri nets approximation of Markovian model

Incorporating cascading effects analysis in the maintenance policy assessment of torrent check dams against torrential floods

In mountainous regions, protection infrastructures designed to mitigate the impacts of torrential floods often consist of a complex system of several structural components (check dams). Over time, the efficacy of this system in protecting downstream assets diminishes as the structural components deteriorate. The extent of deterioration is influenced by the interdependencies between the failure modes of individual components, as well as those between multiple components of the system. Understanding and quantifying the chain of failure events, known as cascading effects, is a critical scientific challenge that remains largely unexplored. In this study, we propose a novel approach that employs physics-based models to examine the deterioration of a series of check dams over time, while considering failure dependencies and bidirectional interactions between consecutive dams. The results obtained from this approach reveals that the absence of a downstream dam accelerates the deterioration rate of upstream dams, while its presence serves to stabilize them. We further incorporate stochastic deterioration and maintenance processes using Stochastic Petri nets to support decision-making regarding maintenance actions for each dam, while also considering economic factors. Strategies involving minor operations achieved cost-effectiveness and prolonged satisfactory performance of the dams, with notable impacts from upstream and downstream dam presence on maintenance costs. We illustrate our approach using a case study of the Faucon torrent in France, where we model the deterioration of three consecutive check dams subjected to torrential floods over a period of 100 years.

Optimization of safety instrumented system performance and maintenance costs in Algerian oil and gas facilities

Gas processing industry is associated with high risks which have the potential to cause catastrophic accidents. Safety Instrumented Systems (SISs) are considered the most effective safety barriers in this sector aiming to prevent undesired events and mitigate their consequences. However, several factors can affect their performance including maintenance strategy in place. In this paper, a Stochastic Petri Net (SPN) model is proposed for evaluating maintenance strategies related to SIS. The model is applied to analyze the performance of an Emergency Shutdown System (ESD) in a Flared Gases Recovery Unit located at south Algerian field. Furthermore, the paper investigates the financial impact of proof tests, including direct costs (such as manpower, equipment, and transportation) and indirect costs (such as production losses and gas flaring tax). These costs can be effectively managed and reduced by optimizing proof test intervals and scheduling tests during planned plant shutdowns. The results demonstrate that the proposed Stochastic Petri Net (SPN) model successfully analyzes the impact of imperfect full and partial proof tests on PFD average values of Safety Instrumented Functions (SIFs). In addition, it has been shown through reliability analysis that the proposed model is able to minimize spare parts expenses leading to significant cost savings while maintaining the required safety integrity levels (SILs), about 60% benefit achieved within two years compared to the actual procurement process over same period.

Urban Advanced Mobility Dependability: A Model-Based Quantification on Vehicular Ad Hoc Networks with Virtual Machine Migration

In the rapidly evolving urban advanced mobility (UAM) sphere, Vehicular Ad Hoc Networks (VANETs) are crucial for robust communication and operational efficiency in future urban environments. This paper quantifies VANETs to improve their reliability and availability, essential for integrating UAM into urban infrastructures. It proposes a novel Stochastic Petri Nets (SPN) method for evaluating VANET-based Vehicle Communication and Control (VCC) architectures, crucial given the dynamic demands of UAM. The SPN model, incorporating virtual machine (VM) migration and Edge Computing, addresses VANET integration challenges with Edge Computing. It uses stochastic elements to mirror VANET scenarios, enhancing network robustness and dependability, vital for the operational integrity of UAM. Case studies using this model offer insights into system availability and reliability, guiding VANET optimizations for UAM. The paper also applies a Design of Experiments (DoE) approach for a sensitivity analysis of SPN components, identifying key parameters affecting system availability. This is critical for refining the model for UAM efficiency. This research is significant for monitoring UAM systems in future cities, presenting a cost-effective framework over traditional methods and advancing VANET reliability and availability in urban mobility contexts.