Simulation Modelling Practice and Theory最新文献_第9页

Peridynamics simulation method and application of hydro-mechanical coupling in fractured rock masses under dynamic disturbance 动态扰动下裂隙岩体水-力耦合动力学模拟方法及应用

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-07-17 DOI: 10.1016/j.simpat.2025.103187

Daosheng Zhang , Zongqing Zhou , Liping Li , Chenglu Gao , Minghao Li , Panpan Gai , Xiaochu Chen

The hydro-mechanical coupling failure mechanism of fractured rock masses under dynamic disturbance remains a significant challenge in the field of rock mechanics. To address this, a peridynamics-based simulation method is developed to scientifically characterise the water pressure variations within water-bearing fractures subjected to dynamic disturbance and the associated rock mass damage. First, a governing equation for fracture seepage in the peridynamics framework is proposed. Building on this, a three-dimensional hydro-mechanical coupled peridynamics model for fractured rock masses is established, based on the principle of effective stress. Next, the response characteristics of fracture water pressure under dynamic disturbance (e.g., blasting impact) are analysed, and a mathematical expression describing the relationship between fracture deformation and water pressure variation is formulated. A three-dimensional peridynamics simulation method is then constructed to capture the coupled stress–seepage behaviour of fractured rock masses under dynamic loading. The evolution of water pressure and the dynamic propagation mechanisms of water-bearing fractures are subsequently investigated. The accuracy of the proposed method is validated through a series of numerical examples. Finally, the method is utilised to assess the stability of the surrounding rock during subsea tunnel construction using the drilling–blasting method. The influence of dynamic disturbance on tunnel stability is revealed, offering valuable insights for the safe construction of subsea tunnels.

裂隙岩体在动力扰动作用下的水-力耦合破坏机制一直是岩石力学领域的一个重大挑战。为了解决这一问题，开发了一种基于周动力学的模拟方法，以科学地表征受动力扰动和伴生岩体破坏的含水裂缝内的水压变化。首先，提出了围动力框架下的裂缝渗流控制方程。在此基础上，基于有效应力原理，建立了裂隙岩体三维水-力耦合动力学模型。其次，分析了裂隙水压在动力扰动（如爆破冲击）作用下的响应特征，建立了裂隙变形与水压变化关系的数学表达式。在此基础上，建立了裂隙岩体在动力加载作用下的三维围动力模拟方法。在此基础上，研究了水压力的演化和含水裂缝的动态扩展机制。通过一系列数值算例验证了该方法的准确性。最后，将该方法应用于钻爆法海底隧道施工过程中围岩稳定性的评估。揭示了动力扰动对隧道稳定性的影响，为海底隧道的安全施工提供了有价值的见解。

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引用次数: 0

JaGW: A hybrid meta-heuristic algorithm for IoT workflow placement in fog computing environment JaGW：用于雾计算环境下物联网工作流放置的混合元启发式算法

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-06-17 DOI: 10.1016/j.simpat.2025.103163

Hemant Kumar Apat , Bibhudatta Sahoo

In recent years, applications of the Internet of Things (IoT) have experienced rapid growth, driven by the widespread adoption of IoT devices in various sectors. However, these devices are typically resource-constrained in terms of computing power and storage capacity. As a result, they often offload the generated data and tasks to nearby edge devices or fog computing layers for further processing and execution. The fog computing layer is located in close vicinity of the IoT devices and comprises a set of heterogeneous fog computing nodes to supplement the capacities of resource-constrained IoT devices. The fog computing nodes often pose computational challenges for various computation-intensive tasks such as image processing application, comprises various machine learning and artificial intelligence enabled tasks. In such a scenario, finding the effective task placement for dynamic and heterogeneous applications is computationally hard. In this work, we formulate the IoT application workflow placement problem as a multi-objective optimization problem formulated as Integer Linear Programming (ILP) model with the objective of minimizing the makespan, cost of execution, and energy consumption. A hybrid metaheuristic approach is proposed that combines the strengths of the Jaya algorithm (JA) and Grey Wolf Optimization (GWO) named as JaGW to derive a sub-optimal solution. The proposed JaGW is compared with conventional GWO and other state of the art algorithms such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) using the Montage scientific workflow dataset. The simulation results demonstrate that the proposed algorithm achieves an average reduction in energy consumption of 24.84% compared to JAYA, 14.67% compared to ACO, 14.65% compared to PSO, and 8.78% compared to GWO, thereby exemplifying its superior performance over other metaheuristic algorithms.

近年来，由于物联网设备在各个领域的广泛采用，物联网（IoT）的应用经历了快速增长。然而，这些设备在计算能力和存储容量方面通常受到资源限制。因此，他们经常将生成的数据和任务卸载到附近的边缘设备或雾计算层，以便进一步处理和执行。雾计算层位于物联网设备附近，由一组异构雾计算节点组成，以补充资源受限的物联网设备的能力。雾计算节点通常为各种计算密集型任务（如图像处理应用）带来计算挑战，包括各种机器学习和人工智能支持的任务。在这种情况下，为动态和异构应用程序找到有效的任务布局在计算上是困难的。在这项工作中，我们将物联网应用工作流放置问题制定为一个多目标优化问题，该问题制定为整数线性规划（ILP）模型，目标是最小化完工时间、执行成本和能耗。提出了一种混合元启发式方法，将Jaya算法（JA）和灰狼优化（GWO）的优点结合起来，称为JaGW，以获得次优解。使用蒙太奇科学工作流数据集，将提出的JaGW与传统的GWO和其他最先进的算法（如蚁群优化（ACO）和粒子群优化（PSO））进行比较。仿真结果表明，该算法与JAYA相比平均能耗降低24.84%，与ACO相比平均能耗降低14.67%，与PSO相比平均能耗降低14.65%，与GWO相比平均能耗降低8.78%，证明了其优于其他元启发式算法的性能。

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引用次数: 0

Development of floor field cellular automaton model for pedestrian dynamics: Incorporating empirical acceleration mechanisms 行人动力学的层场元胞自动机模型的发展：结合经验加速机制

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-08-06 DOI: 10.1016/j.simpat.2025.103197

Xingwen Xiong , Lin Luo , Yujing Feng , Zhijian Fu , Jian Ma

Pedestrian movement during emergency evacuations involves frequent and rapid speed changes. However, most existing simulation models – including the widely used Floor Field Cellular Automaton (FFCA) – do not realistically account for acceleration and deceleration. These models often assume an instantaneous transition from rest to maximum speed within a single timestep. This simplification reduces their accuracy in high-speed or high-density situations. To address this limitation, we propose a fine-discrete FFCA model that explicitly integrates empirically derived acceleration mechanisms. Controlled experiments were conducted to identify triggers for acceleration and deceleration, collecting data across a broad range of pedestrian speeds. These behaviors were integrated into the FFCA framework through dynamic rules governing movement initiation, adjustment, and interaction. The model was validated by comparison with the classic FFCA model and empirical data from the 2008 Wenchuan Earthquake evacuation, as well as conducted bottleneck evacuation experiments. In validation using earthquake evacuation data, the developed model more accurately replicates pedestrian dynamics, producing smooth acceleration/deceleration profiles and flow rates consistent with empirical observations. Notably, it reduced the root mean standard error of cumulative passing interval distribution by 77.6%. In the controlled experiment validation, the model predictions closely matched experimental results in evacuation timing, pedestrian trajectories, and spatial speed distributions. These improvements significantly enhance the FFCA model’s applicability in emergency evacuation simulations and supporting more effective safety assessments.

在紧急疏散过程中，行人运动涉及频繁和快速的速度变化。然而，大多数现有的仿真模型——包括广泛使用的地板场元胞自动机（FFCA）——并不能真实地考虑加速和减速。这些模型通常假设在一个时间步长内从静止到最大速度的瞬时过渡。这种简化降低了它们在高速或高密度情况下的准确性。为了解决这一限制，我们提出了一个精细离散的FFCA模型，明确地集成了经验推导的加速机制。研究人员进行了对照实验，以确定加速和减速的触发因素，并收集了广泛的行人速度范围内的数据。这些行为通过控制运动启动、调整和互动的动态规则集成到FFCA框架中。通过与经典FFCA模型和2008年汶川地震疏散的实证数据进行对比验证，并进行瓶颈疏散实验。在使用地震疏散数据进行验证时，所开发的模型更准确地复制了行人动态，产生了与经验观察一致的平滑加/减速曲线和流量。值得注意的是，它使累计通过区间分布的均方根标准误差降低了77.6%。在控制实验验证中，模型预测在疏散时间、行人轨迹和空间速度分布方面与实验结果非常吻合。这些改进大大提高了FFCA模型在紧急疏散模拟中的适用性，并支持更有效的安全评估。

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引用次数: 0

Mesoscopic deformation mechanisms and engineering applications of soil-rock mixtures: effects of varying stone content 土石混合体的细观变形机制和工程应用：不同石料含量的影响

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-07-17 DOI: 10.1016/j.simpat.2025.103186

Yucheng Wu , Jie Yang , Chunhui Ma , Li Ran , Lin Cheng

This study explores the meso-mechanical characteristics and structural evolution of soil-rock mixtures (SRM) under complex stress conditions using six triaxial test models with varying stone content. Experimental simulations revealed that stone content significantly affects peak stress. A three-dimensional mesoscale structural fabric was employed to analyze the spatial distribution of normal and tangential contact forces, while a two-dimensional fabric tensor analysis highlighted the dominant role of stone content in mesoscale mechanical behavior. Results showed that increasing stone content leads to higher normal and tangential contact forces. Further analysis revealed a strong correlation between rock crushing and overall deformation, with higher rock content enhancing the anti-slip capability of SRMs. To extend these findings, a discrete element model was developed to simulate the rolling construction process of the anti-seepage core wall in a dam. This model evaluated the effects of varying stone content on coordination number, settlement rate, and porosity, as well as the influence of gravel mixing strategies on dam deformation. This research provides insights into the meso-deformation mechanisms of SRMs and the role of stone content in dam construction, offering valuable guidance for improving the design and performance of soil-rock structures in engineering applications.

采用6个不同含石量的三轴试验模型，探讨了复杂应力条件下土石混合体的细观力学特征和结构演化。实验模拟表明，岩石含量对峰值应力有显著影响。采用三维中尺度结构织物分析了法向和切向接触力的空间分布，而二维织物张量分析强调了石材含量对中尺度力学行为的主导作用。结果表明，随着石材含量的增加，法向接触力和切向接触力增大。进一步分析表明，岩石破碎与整体变形之间存在很强的相关性，岩石含量越高，srm的抗滑能力越强。为了推广这些发现，建立了一个离散元模型来模拟大坝防渗心墙的滚动施工过程。该模型评估了不同含石量对配位数、沉降速率和孔隙率的影响，以及碎石混合策略对大坝变形的影响。本研究揭示了土石结构细观变形机制和含石量在大坝建设中的作用，为工程应用中改进土石结构的设计和性能提供了有价值的指导。

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引用次数: 0

GPU-accelerated cloud computing services and performance evaluation gpu加速云计算服务和性能评估

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-07-11 DOI: 10.1016/j.simpat.2025.103181

Zakery Collins, Gennaro De Luca, Yinong Chen

This paper explores the feasibility of replacing traditional CPU-based cloud computing with Graphic Processing Unit GPU-accelerated services. Using NVIDIA’s CUDA GPU-accelerated C/C++ and Python libraries, we benchmark the performance of GPU computing against multithreaded CPU computing across several domains, including machine learning and large-scale image processing. A novel contribution of this work is an intelligent autoscaling system that maximizes single-GPU resource utilization before scaling to additional GPUs, improving efficiency in cloud-based deployments. Our simulation experiments demonstrate significant performance gains for GPU-accelerated computing and highlight the impact of optimized resource allocation in cloud environments. For example, in a machine learning experiment, using a dataset with 8.790 entries, the execution of a GeForce 3060 ti GPU is 3.42 times faster than a 16-thread CPU computer. Compared with the same 16-thread CPU, Tesla K80 GPU is 4.17 times faster. Furthermore, we provide an analysis of GPU performance optimization strategies, including memory management, concurrency techniques, and workload distribution methodologies, offering insights into the long-term scalability and cost-effectiveness of GPU-accelerated cloud infrastructure.

本文探讨了用图形处理单元gpu加速服务取代传统基于cpu的云计算的可行性。使用NVIDIA的CUDA GPU加速C/ c++和Python库，我们将GPU计算的性能与多个领域的多线程CPU计算进行基准测试，包括机器学习和大规模图像处理。这项工作的一个新颖贡献是一个智能自动扩展系统，在扩展到其他gpu之前最大限度地利用单个gpu资源，提高基于云的部署的效率。我们的模拟实验证明了gpu加速计算的显著性能提升，并突出了云环境中优化资源分配的影响。例如，在机器学习实验中，使用具有8.790个条目的数据集，GeForce 3060 ti GPU的执行速度比16线程CPU计算机快3.42倍。与相同的16线程CPU相比，Tesla K80 GPU的速度提高了4.17倍。此外，我们还提供了GPU性能优化策略的分析，包括内存管理、并发技术和工作负载分配方法，提供了对GPU加速云基础设施的长期可扩展性和成本效益的见解。

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引用次数: 0

Comparing Control Theory and Deep Reinforcement Learning techniques for decentralized task offloading in the edge–cloud continuum 边缘云连续体中分散任务卸载的控制理论与深度强化学习技术比较

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-07-03 DOI: 10.1016/j.simpat.2025.103170

Gorka Nieto , Neco Villegas , Luis Diez , Idoia de la Iglesia , Unai Lopez-Novoa , Cristina Perfecto , Ramón Agüero

With the increasingly demanding requirements of Internet-of-Things (IoT) applications in terms of latency, energy efficiency, and computational resources, among others, task offloading has become crucial to optimize performance across edge and cloud infrastructures. Thus, optimizing the offloading to reduce latency as well as energy consumption and, ultimately, to guarantee appropriate service levels and enhance performance has become an important area of research. There are many approaches to guide the offloading of tasks in a distributed environment, and, in this work, we present a comprehensive comparison of three of them: A Control Theory (CT) Lyapunov optimization method, 3 Deep Reinforcement Learning (DRL) based strategies and traditional solutions, like Round-Robin or static schedulers. This comparison has been conducted using ITSASO, an in-house developed simulation platform for evaluating decentralized task offloading strategies in a three-layer computing hierarchy comprising IoT, fog, and cloud nodes. The platform models service generation in the IoT layer using a configurable distribution, enabling each IoT node to decide whether to autonomously execute tasks (locally), offload them to the fog layer, or send them to the cloud server. Our approach aims to minimize the energy consumption of devices while meeting tasks’ latency requirements. Our simulation results reveal that Lyapunov optimization excels in static environments, while DRL approaches prove to be more effective in dynamic settings, by better adapting to changing requirements and workloads. This study offers an analysis of the trade-offs between these solutions, highlighting the scenarios in which each scheduling approach is most suitable, thereby contributing valuable theoretical insights into the effectiveness of various offloading strategies in different environments. The source code of ITSASO is publicly available.

随着物联网（IoT）应用程序在延迟、能效和计算资源等方面的要求越来越高，任务卸载对于优化跨边缘和云基础设施的性能变得至关重要。因此，优化卸载以减少延迟和能耗，并最终保证适当的服务水平和提高性能已成为一个重要的研究领域。有许多方法可以指导分布式环境中的任务卸载，在这项工作中，我们对其中三种进行了全面的比较：一种控制理论（CT） Lyapunov优化方法，3种基于深度强化学习（DRL）的策略和传统的解决方案，如轮询或静态调度程序。这项比较是使用ITSASO进行的，ITSASO是一个内部开发的仿真平台，用于评估由物联网、雾和云节点组成的三层计算层次结构中的分散任务卸载策略。该平台使用可配置的分布对物联网层中的服务生成进行建模，使每个物联网节点能够决定是自主执行任务（本地），将它们卸载到雾层，还是将它们发送到云服务器。我们的方法旨在最大限度地减少设备的能耗，同时满足任务的延迟要求。我们的模拟结果显示，Lyapunov优化在静态环境中表现出色，而DRL方法在动态环境中更有效，可以更好地适应不断变化的需求和工作负载。本研究提供了这些解决方案之间的权衡分析，突出了每种调度方法最适合的场景，从而为不同环境下各种卸载策略的有效性提供了有价值的理论见解。ITSASO的源代码是公开的。

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引用次数: 0

Mastering the complexity: An enhanced cellular automata-based framework for simulating resilience of hospital Power-Water-Firefighting-Space nexus system 控制复杂性：基于增强元胞自动机的医院动力-水-消防-空间联系系统弹性模拟框架

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-07-08 DOI: 10.1016/j.simpat.2025.103177

Renlong Wang , Lingzhi Li , Wenjie Lin , Endong Wang , Jingfeng Yuan

Modeling the resilience of hospital Power-Water-Firefighting-Space (PWFS) nexus systems is a complex, dynamic, and nonlinear challenge characterized by high uncertainty. Existing methods, mainly agent-based and network-based models, face difficulties in balancing detailed component-level behaviors with broader system-level interdependencies and neglect the impact of external disruptions, such as surges in service demand during the COVID-19 pandemic, on hospital PWFS system resilience. To address this, the study proposes an enhanced cellular automata (CA)-based framework for simulating hospital PWFS system resilience. The PWFS system is modeled as a seven-tuple CA, incorporating cell structure, state, space, neighborhood, transition rules, and time, facilitating the integration of micro-level component behavior with macro-level interdependencies. A set of resilience metrics, including robustness, rapidity, performance loss, and an integrated resilience index, are introduced, based on the system performance curve, which includes normality, connectivity, resource transfer efficiency, and space functionality. The model enables scalable, polynomial-time simulations of cascading failures, resource redistribution, and spatial–temporal recovery across interconnected PWFS subsystems. A real-world outpatient building case study demonstrates the applicability and validity of the enhanced CA model. The findings emphasize the importance of modeling intra-system interdependencies and provide actionable insights for infrastructure design and emergency preparedness. Overall, the enhanced CA framework offers a systematic, scalable, and computationally efficient approach to resilience assessment, bridging theoretical modeling with practical infrastructure planning.

医院电力-水-消防-空间（PWFS）联结系统的弹性建模是一个复杂的、动态的、非线性的、具有高度不确定性的挑战。现有方法（主要是基于代理和基于网络的模型）在平衡详细的组件级行为与更广泛的系统级相互依赖关系方面面临困难，并且忽略了外部中断（例如COVID-19大流行期间服务需求激增）对医院PWFS系统弹性的影响。为了解决这个问题，该研究提出了一个增强的基于元胞自动机（CA）的框架来模拟医院PWFS系统的弹性。PWFS系统被建模为一个七元组CA，包含单元结构、状态、空间、邻域、转换规则和时间，促进了微观级组件行为与宏观级相互依赖关系的集成。基于系统性能曲线，包括正态性、连通性、资源转移效率和空间功能，引入了一套弹性指标，包括鲁棒性、快速性、性能损失和综合弹性指标。该模型支持级联故障、资源再分配和跨互联PWFS子系统的时空恢复的可扩展、多项式时间模拟。一个实际的门诊建筑案例研究证明了增强CA模型的适用性和有效性。研究结果强调了系统内部相互依赖关系建模的重要性，并为基础设施设计和应急准备提供了可操作的见解。总体而言，增强的CA框架为弹性评估提供了一种系统的、可扩展的、计算效率高的方法，将理论建模与实际基础设施规划联系起来。

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引用次数: 0

Analyzing herding, stylized facts, and information cascades via self-organized criticality in an agent-based speculation game 在基于代理的投机博弈中，通过自组织临界性分析羊群、程式化事实和信息级联

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-07-29 DOI: 10.1016/j.simpat.2025.103190

Sawar Sagwal , Parthajit Kayal , Kavita Vemuri

This study advances Kai Katahira’s Speculation Game, an agent-based model (ABM) for financial markets, by addressing its limitation in capturing order flow imbalance, a critical indicator of herd behavior. Although the original model successfully replicated key stylized facts of financial markets, it did not account for the persistence of order imbalance observed in real-world trading. Through a comprehensive analysis of two decades of BSE Sensex data, we establish the prevalence of order imbalance and its correlation with price fluctuations. To bridge this gap, we propose an extended model, Speculation Game with Information Cascade (SGIC), which integrates Self-Organized Criticality (SOC) through a sand-pile model, enabling agents to interact within a small-world network. Our proposed model not only reproduces the stylized facts captured by the original Speculation Game, but also successfully generates the additional stylized fact of order flow imbalance. These advances enhance the realism of ABMs in financial markets, providing deeper insights into the mechanisms driving herding and market fluctuations.

本研究通过解决基于主体的金融市场投机博弈模型（ABM）在捕捉羊群行为的关键指标——订单流不平衡方面的局限性，进一步推进了Kai Katahira的投机博弈模型。尽管最初的模型成功地复制了金融市场的关键风格化事实，但它并没有考虑到在现实世界的交易中观察到的持续的订单不平衡。通过对二十年来BSE Sensex数据的综合分析，我们建立了订单失衡的普遍性及其与价格波动的相关性。为了弥补这一差距，我们提出了一个扩展模型，信息级联投机博弈（SGIC），它通过沙堆模型集成了自组织临界性（SOC），使代理能够在小世界网络中进行交互。我们提出的模型不仅再现了原始投机博弈捕获的风格化事实，而且成功地生成了额外的订单流不平衡的风格化事实。这些进展增强了金融市场中ABMs的真实性，为推动羊群效应和市场波动的机制提供了更深入的见解。

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引用次数: 0

GrC-VMM: An intelligent framework for virtual machine migration optimization using granular computing GrC-VMM：一个使用颗粒计算优化虚拟机迁移的智能框架

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-06-25 DOI: 10.1016/j.simpat.2025.103169

Seyyed Meysam Rozehkhani, Farnaz Mahan

Virtual Machine Migration (VMM) is a critical component in cloud computing environments, enabling dynamic resource management and system optimization. However, existing approaches often face challenges such as increased downtime, excessive resource consumption, and complex decision-making processes in heterogeneous environments. This paper presents a novel framework based on Granular Computing (GrC) principles to address these challenges through systematic VM categorization and prioritization. The proposed framework employs a three-stage approach: (1) feature extraction and granule formation, converting VM attributes such as workload, downtime sensitivity, and resource utilization into meaningful information granules; (2) granule-based decision rule generation using formal GrC methodologies; and (3) priority-based classification using weighted membership functions. Experimental evaluations conducted using CloudSim 5.0 demonstrate the framework’s effectiveness across multiple performance dimensions. The results show 92. 1% classification accuracy, 83. 7% resource utilization and reduced migration downtime of 1.9 s. The framework exhibits linear computational complexity O(N), confirming its scalability for large-scale deployments. Additionally, performance analysis under various workload patterns (resource-intensive, service-oriented, and mixed) validates the framework’s robustness and adaptability. These results suggest that the proposed GrC-based approach offers a promising solution to optimize VM migration in cloud environments while maintaining operational efficiency and service quality.

虚拟机迁移（VMM）是云计算环境中的一个关键组件，可以实现动态资源管理和系统优化。然而，现有的方法经常面临挑战，例如在异构环境中增加停机时间、过度的资源消耗和复杂的决策过程。本文提出了一个基于颗粒计算（GrC）原则的新框架，通过系统的VM分类和优先级来解决这些挑战。该框架采用三阶段方法：(1)特征提取和颗粒形成，将虚拟机属性（如工作负载、停机灵敏度和资源利用率）转换为有意义的信息颗粒；(2)采用形式化GrC方法生成基于颗粒的决策规则；(3)基于优先级的加权隶属函数分类。使用CloudSim 5.0进行的实验评估证明了该框架在多个性能维度上的有效性。结果显示为92。1%的分类准确率，83。7%的资源利用率和减少1.9秒的迁移停机时间。该框架显示出线性计算复杂度O(N)，证实了其大规模部署的可伸缩性。此外，各种工作负载模式（资源密集型、面向服务的和混合的）下的性能分析验证了框架的健壮性和适应性。这些结果表明，提出的基于grc的方法提供了一个有前途的解决方案，可以在保持运营效率和服务质量的同时优化云环境中的VM迁移。

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引用次数: 0

Modeling and functional verification of autonomous emergency braking systems based on extended colored hybrid petri nets 基于扩展彩色混合petri网的自主紧急制动系统建模与功能验证

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-06-24 DOI: 10.1016/j.simpat.2025.103149

Haijing Ning , Herong Zhu , Yisheng An , Naiqi Wu , Yupeng Cao , Xiangmo Zhao

The autonomous emergency braking (AEB) system constitutes a critical safety function within advanced driver assistance systems (ADAS). Verifying its functionality is essential to ensure its operational correctness and reliability. Currently, AEB systems developed by different vendors employ diverse algorithms and lack a unified simulation, verification, and fault-detection framework. To bridge these gaps, this paper proposes a comprehensive modeling and functional verification framework for AEB systems. First, we establish a basic model using extended colored hybrid Petri nets (ECHPN). Next, we enhance this model by incorporating fault observation points to form an FD-ECHPN, thereby enabling fault detection and localization. Furthermore, this paper develops a universal simulation and testing approach to verify the functionality of AEB systems from various vendors by transforming the FD-ECHPN model into a Simulink/Stateflow model. The simulation results demonstrate that the proposed method can accurately assess the functionality of an AEB system and effectively identify and localize faults during model execution. Finally, we examine the state evolution and formal properties of the FD-ECHPN model to verify its correctness.

自动紧急制动（AEB）系统是高级驾驶辅助系统（ADAS）中的一项关键安全功能。验证其功能是确保其操作正确性和可靠性的必要条件。目前，不同厂商开发的AEB系统采用不同的算法，缺乏统一的仿真、验证和故障检测框架。为了弥补这些差距，本文提出了一个全面的AEB系统建模和功能验证框架。首先，利用扩展的彩色混合Petri网（echnn）建立了一个基本模型。接下来，我们通过将故障观测点合并到FD-ECHPN中来增强该模型，从而实现故障检测和定位。此外，本文开发了一种通用的仿真和测试方法，通过将fd - ecpn模型转换为Simulink/Stateflow模型来验证来自不同供应商的AEB系统的功能。仿真结果表明，该方法能够准确地评估AEB系统的功能，有效地识别和定位模型执行过程中的故障。最后，我们检验了fd - echnn模型的状态演化和形式性质，以验证其正确性。

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引用次数: 0