Stochastic Petri Net Model Research Articles

Transactional dynamics in hyperledger fabric: A stochastic modeling and performance evaluation of permissioned blockchains

Blockchain, often integrated with distributed systems and security enhancements, has significant potential in various industries. However, environmental concerns and the efficiency of consortia-controlled permissioned networks remain critical issues. We use a Stochastic Petri Net model to analyze transaction flows in Hyperledger Fabric networks, achieving a 95% confidence interval for response times. This model enables administrators to assess the impact of system changes on resource utilization. Sensitivity analysis reveals major factors influencing response times and throughput. Our case studies demonstrate that block size can alter throughput and response times by up to 200%, underscoring the need for performance optimization with resource efficiency.

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.

DDS-P: Stochastic models based performance of IoT disaster detection systems across multiple geographic areas

Effective management of catastrophic events in high-risk zones necessitates a holistic technological approach to protect ecosystems, biodiversity, and native populations. Limitations in sensor range and connectivity hamper real-time data gathering in secluded areas, while financial and technical hurdles hinder the creation of cost-effective, automated systems. This study presents stochastic models, the LoRaW protocol, and cloud technology to enhance sensor deployment simulations. Wireless Sensor Networks and LoRa technology are crucial for extensive monitoring and communication infrastructures. Stochastic Petri Net models optimize system components by assessing crucial performance indicators, such as average response time and system utilization, thus improving disaster response and supporting research hypotheses.

A New Stochastic Petri Net Modeling Approach for the Evolution of Online Public Opinion on Emergencies: Based on Four Real-Life Cases

A New Stochastic Petri Net Modeling Approach for the Evolution of Online Public Opinion on Emergencies: Based on Four Real-Life Cases

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.

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.

A novel fault detection and diagnostic Petri net methodology for dynamic systems

Faults can have significant, negative impacts on the operation and performance of simple and complex dynamic systems. Based on the integration of Bayesian network diagnostic features with Petri net formalism, the existing Bayesian-supported Petri net tool has demonstrated the flexibility of using the Petri net approach for diagnosing failure scenario of a dynamic system. However, studies on using the proposed hybrid Petri net approach for condition monitoring and early detection and diagnosis of single and multiple failures in a dynamic system with feedback control loops are yet to be investigated. Thus, this paper presents a methodology to address this research gap using the operation of a water tank level control system as a case study. The method combines the constructed Generalised Stochastic Petri Net (GSPN) model of the system operation with its corresponding fault diagnostic Petri net model, created using the proposed modified Bayesian Stochastic Petri Net (mBSPN) formalism. The GSPN model establishes the causal relationships between the system’s components and/or subsystems. It further identifies deviations in the sensor measurements of the observable process variables characterising the system operation. The information provided by the sensors in the system model are then inputted into the mBSPN model to diagnose the root cause of the observed deviations. The obtained results demonstrated the capability of using the proposed integrated Petri net methodology for system condition monitoring, early fault detection and diagnosis of single and multiple failures in a dynamic system with feedback control loops.

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.

Reliability Analysis of CTCS-3 Train-Ground Communication System Based on 5G-R

The reliability of Chinese Train Control System Level 3 (CTCS-3) train-ground communication system is of great importance to ensure the safe operation of trains. The fifth Generation Mobile Communication for Railway (5G-R) is prospective for train-ground communication in the future. In this paper, the train-ground communication procedure of 5G-R (e.g., connection reestablishment, handover and train-ground data transmission) is tested by a semi-physical simulation platform. The test results, including the delay of different procedures together with the handover success rate, all can satisfy the quality of service (QoS) requirements. According to the communication procedure, deterministic and stochastic Petri net (DSPN) models of communication links and train-ground communication scenarios are established. Furthermore, the parameters of models are calibrated with the test data and protocol standards. We utilize <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pi$</tex-math></inline-formula> -tool to simulate and analyze the reliability of the train-ground communication system under the interaction of multiple fault factors. The results show that even in the worst handover scenario where the station distance is 1 km and the train speed is 400 km/h, the reliability of the train-ground communication system based on 5G-R can still reach 99.9963 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> . It not only meets the requirements of the train control services for the train-ground communication system, but also is better than the reliability of the GSM-R based communication system.

Advancing Fault Prediction: A Comparative Study between LSTM and Spiking Neural Networks

Predicting system faults is critical to improving productivity, reducing costs, and enforcing safety in industrial processes. Yet, traditional methodologies frequently falter due to the intricate nature of the task. This research presents a novel use of spiking neural networks (SNNs) in anticipating faults in syntactical time series, utilizing the generalized stochastic Petri net (GSPN) model. The inherent ability of SNNs to process both time and space aspects of data positions them as a prime instrument for this endeavor. A comparative evaluation with long short-term memory (LSTM) networks suggests that SNNs offer comparable robustness and performance.

Dynamic risk investigation of urban natural gas pipeline accidents using Stochastic Petri net approach

The safety of urban gas pipelines is challenged by a series of adverse factors, and the unexpected accidents may pose a catastrophic threat to humans, the environment and assets. The existing studies mainly focused on causation investigation of natural gas pipeline accident, the dynamic evolution process of urban natural gas pipeline accident is still challenging task for accident prevention. To find out the unfavorable factors that cause accidents, this study presents a dynamic risk modeling of urban natural gas pipeline accidents using Stochastic Petri net (SPN). An SPN model of an accident evolution process is constructed based on the discrete events in an accident flowchart, and the critical places and transitions are evaluated through this model. Considering the slow development of the early events leading to accidents, the delay time of the transitions at this stage cannot be determined, Bayesian theory is used to dynamically update SPN model. Critical accident nodes and their occurrence probabilities are estimated, which are used to support the efficient risk management strategies. The gas explosion accident in Shiyan, Hubei, as a representative case is investigated, and the results show that pipeline corrosion, ignition sources, inefficient information feedback and unreasonable solutions are critical accident causations. The probabilities of critical accident nodes increase over time, which means that these factors need to be managed efficiently to prevent the accidents. The application shows that the model can be used as a tool for gas companies and governments to investigate urban gas pipeline accidents.

Availability Model for Data Center Networks With Dynamic Migration and Multiple Traffic Flows

The evaluation of application availability is a crucial step in ensuring functionality of the data center network (DCN). The application of data center network is no more a chain of physical devices, but a chain of virtualized network function (VNF) dynamically installed in servers, i.e., the service function chain (SFC), due to the adaptation of network function virtualization (NFV). When dynamic migration is considered, the application availability evaluation would encounter problems since the state of VNF components is not independent. And in the application deployed in DCN, there is multiple traffic flow because of the serialization of 1+1 (active-standby) and N-way (load-sharing) redundant VNF, however only single flow is considered in current availability model. When dynamic migration and multiple traffic flows are considered, the application availability evaluation would encounter the following problems: 1) The application behavior is not described in the existing availability model; 2) The availability model could not quantify the influence of different traffic of each flow. This paper proposes an availability model, which consist of network evolution model (NEM) of DCN and the availability evaluation algorithm based on NEM (AEA-NEM), to evaluate the availability of different applications deployed on DCN. The NEM of DCN describes the dynamical process of migration and switch of three kinds of VNF (normal, 1+1 and N-way) in application, and the AEA-NEM evaluate the availability of application with multiple traffic flows. The simulation results show that, the steady state availability of the proposed model is consistent with it of generalized stochastic Petri net (GSPN) model. And the model could evaluate the application availability with dynamic migration and multiple traffic flows in DCN. Based on the current VNF placement and redundant strategy, the most available designing scheme is analyzed.

Petri net analysis of a queueing inventory system with orbital search by the server

Abstract In this article, a queueing inventory system with finite sources of demands, retrial demands, service time, lead time, ( s , S ) \left(s,S) replenishment policy, and demands search from the orbit was studied. When the lead time is exponentially distributed (resp. lead time is generally distributed), generalized stochastic Petri net (GSPN) (resp. Markov regenerative stochastic Petri net [MRSPN]) is proposed for this inventory system. The quantitative analysis of this stochastic Petri net model was obtained by continuous time Markov chain for the GSPN model (resp. the supplementary variable method for the MRSPN model). The probability distributions are obtained, witch allowed us to compute performance measures and the expected cost rate of the studied system.

Cholesterol Metabolism Pathways Disturbances in Atherosclerosis—Analyses Using Stochastic Petri Net-Based Model

Atherosclerosis is a multifactorial disease that affects large arteries and causes much morbidity and mortality worldwide. Despite ongoing research for several decades, it is still a global health problem that cannot be stopped and cured completely. Furthermore, the development of this disease is contributed to by various processes, primarily disturbances in cholesterol metabolism, local low-grade inflammation, and oxidative stress, resulting in the formation of atherosclerotic plaques. In this work, a stochastic Petri net model was constructed and subsequently analyzed to examine the impact of these factors on the development and progression of atherosclerosis. The use of knockout- and simulation-based analysis allowed for a comprehensive investigation of the studied phenomena. Our research has demonstrated that while cholesterol is a contributing factor in atherosclerosis, blocking its impact alone is insufficient in halting the progression of this disorder. Inhibition of oxidative stress is also important when blocking the impact of phosphoprotein phosphatase inhibitor-1 (PPI-1), microsomal triglyceride transfer protein (MTTP), and 3-hydroxy-3-methyl-glutaryl coenzyme A reductase (HMGCR), as our model shows that this action reduces the number of foam cells underlying atherosclerosis. The results obtained further support the previous observations that the combined treatment is significantly effective in enhancing therapeutic efficacy against atherosclerosis.

A new generalized stochastic Petri net modeling for energy‐harvesting‐wireless sensor network assessment

SummaryThis paper proposes an energy‐harvesting‐aware model that aims to assess the performances of wireless sensor networks. Our model uses generalized stochastic Petri nets to define a sensor–neighbors relationship abstraction. The novelty of the proposed formulation is taking into account several real‐life considerations such as battery‐over breakdowns, unavailability of neighbors, retrial attempts, and sleeping mechanism in a single model. We use TimeNet tool to simulate the network behavior in order to evaluate its performance throughout different formulas after it had reached its steady state. Finally, we present a case study featuring the different solar energy recovery capabilities of the vast Algerian territory. The aim is to show with the presented model how to determine the kind of resources to be acquired in order to cope with the sensor deployment project requirements. The proposed model allows us to ensure that the battery energy level of sensors deployed in Algiers province for example is almost equal to 80% for 100 messages per day and (1 min/2 min) for (awakening time/sleeping time) ratio.

A stochastic Petri Net‐based approach for operational performance estimation of quay cranes

AbstractReliability, availability, and maintainability (RAM) of quay cranes (QCs) are essential for an effective port operation. This study estimates operational RAM of QCs using the Stochastic Petri Net (SPN) modelling. Asset Performance Assessment (APA) was performed to conduct the study by a SPN model. Based on the operation and maintenance data, probability distributions of Mean Time Between Failure (MTBF) and Mean Time to Repair (MTTR) is determined by Goodness of Fit Test of Anderson Darling. Subsequently, these distributions are applied in APA to determine the frequencies of failure/breakdowns, duration of downtimes, availability, and reliability with the assistance of the SPN model and Monte Carlo Simulation. The availability of the analysed QC is 0.97 or 97%. The results of this work are verified by the comparison with historical data. The outcomes of this paper contribute to reliability, availability, maintainability, and risk management of QCs.

Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture

In urban air mobility (UAM), video streaming platforms have gained significant attention from media companies due to their growing necessity for on-demand video streaming services-as-you-go in flights. Video streaming services can provide constant data transactions in a huge amount, especially in its operational digital twin (ODT). As a result, the ability to provide a satisfactory user experience through video streaming platforms is critical and complex. This requires continuously operating services while handling numerous user requests for near real-time video streaming. At the same time, high-quality video with high resolution and minimal interruptions is often expected. Therefore, the availability and performance (i.e., performability) of the Back-End video streaming infrastructure are crucial parameters for these platforms. However, evaluating novel video-on-demand architectures in real-world scenarios can be costly due to the numerous parameters involved. Analytical models, such as stochastic Petri nets (SPNs), can serve as an alternative in this complex scenario as they can be used to analyze systems during the design process. In this study, we developed a set of SPN models to assess the performance of a video-on-demand system. These models were designed to illustrate and to evaluate a video-on-demand architecture while considering performance. We had a base SPN model as well as three enhanced variants available. The extended models were generated using the Design of Experiments (DoE) technique and sensitivity analysis results. The DoE identified the factors with the greatest impacts on performance, and the most significant factor interactions. Redundancy strategies were applied to the extended models to increase the availability of the most important components. This redundancy increased the availability of “9 s” from three to five. It is worth noting that this study can help the designers of video streaming systems, to plan and to optimize their ideas based on the provided models.

Research on anti-attack performance of a private cloud safety computer based on the Markov-Percopy dynamic heterogeneous redundancy structure

Abstract With the increasing computing demand of train operation control systems, the application of cloud computing technology on safety computer platforms of train control system has become a research hotspot in recent years. How to improve the safety and availability of private cloud safety computers is the key problem when applying cloud computing to train operation control systems. Because the cloud computing platform is in an open network environment, it can face many security loopholes and malicious network attacks. Therefore, it is necessary to change the existing safety computer platform structure to improve the attack resistance of the private cloud safety computer platform, thereby enhancing its safety and reliability. Firstly, a private cloud safety computer platform architecture based on dynamic heterogeneous redundant (DHR) structure is proposed, and a dynamic migration mechanism for heterogeneous executives is designed. Then, a generalized stochastic Petri net (GSPN) model of a private cloud safety computer platform based on DHR is established, and its steady-state probability is solved by using its isomorphism with the continuous-time Markov model (CTMC) to analyse the impact of different system structures and executive migration mechanisms on the system's anti-attack performance. Finally, through experimental verification, the system structure proposed in this paper can improve the anti-attack capability of the private cloud safety computer platform, thereby improving its safety and reliability.

Modeling and performance analysis of gas leakage emergency disposal process in gas transmission station based on Stochastic Petri nets

Efficient emergency disposal on site after gas leakage is crucial to avoid accident escalatio and expansion. However, current performance analysis studies of emerency disposal process are insufficient due to the uncertainty of rescue time prediction. In this paper, we aimed to explore the integration of triangular membership and regional center method to SPN for the modeling and evaluation of gas leakage emergency rescue process in gas transmission station. The stochastic Petri nets model was established by considering the interrelation of each execution stage of emergency disposal. Considering the subjectivity of expert analysis, triangular membership function and regional center method were combined to obtain the values of firing delays. Then isomorphic Markov chain was built from the SPN model and the stability probability linear equations were obtained. By calculating the transition utilization rate and place busy probability, the critical places and transitions were obtained. The list of critical emergency disposal stages and actions was generated, based on which countermeasures were presented for the risk reduction of gas leakage emergency disposal process in gas transmission stations.