ACM Transactions on Modeling and Computer Simulation最新文献_第6页

Learning to Simulate Sequentially Generated Data via Neural Networks and Wasserstein Training 学习通过神经网络和Wasserstein训练来模拟顺序生成的数据

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-04-03 DOI: https://dl.acm.org/doi/10.1145/3583070

Tingyu Zhu, Haoyu Liu, Zeyu Zheng

{"title":"Learning to Simulate Sequentially Generated Data via Neural Networks and Wasserstein Training","authors":"Tingyu Zhu, Haoyu Liu, Zeyu Zheng","doi":"https://dl.acm.org/doi/10.1145/3583070","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3583070","url":null,"abstract":"We propose a new framework of a neural network-assisted sequential structured simulator to model, estimate, and simulate a wide class of sequentially generated data. Neural networks are integrated into the sequentially structured simulators in order to capture potential nonlinear and complicated sequential structures. Given representative real data, the neural network parameters in the simulator are estimated and calibrated through a Wasserstein training process, without restrictive distributional assumptions. The target of Wasserstein training is to enforce the joint distribution of the simulated data to match the joint distribution of the real data in terms of Wasserstein distance. Moreover, the neural network-assisted sequential structured simulator can flexibly incorporate various kinds of elementary randomness and generate distributions with certain properties such as heavy-tail, without the need to redesign the estimation and training procedures. Further, regarding statistical properties, we provide results on consistency and convergence rate for the estimation procedure of the proposed simulator, which are the first set of results that allow the training data samples to be correlated. We then present numerical experiments with synthetic and real data sets to illustrate the performance of the proposed simulator and estimation procedure.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty-aware Simulation of Adaptive Systems 自适应系统的不确定性感知仿真

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-03-28 DOI: 10.1145/3589517

J. Jézéquel, Antonio Vallecillo

引用次数: 0

Efficient Simulation of Sparse Graphs of Point Processes 点过程稀疏图的高效模拟

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-02-28 DOI: https://dl.acm.org/doi/10.1145/3565809

Cyrille Mascart, David Hill, Alexandre Muzy, Patricia Reynaud-Bouret

引用次数: 0

Batching Adaptive Variance Reduction 批处理自适应方差减少

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-02-28 DOI: https://dl.acm.org/doi/10.1145/3573386

Chenxiao Song, Reiichiro Kawai

{"title":"Batching Adaptive Variance Reduction","authors":"Chenxiao Song, Reiichiro Kawai","doi":"https://dl.acm.org/doi/10.1145/3573386","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3573386","url":null,"abstract":"Adaptive Monte Carlo variance reduction is an effective framework for running a Monte Carlo simulation along with a parameter search algorithm for variance reduction, whereas an initialization step is required for preparing problem parameters in some instances. In spite of the effectiveness of adaptive variance reduction in various fields of application, the length of the preliminary phase has often been left unspecified for the user to determine on a case-by-case basis, much like in typical sequential frameworks. This uncertain element may possibly be even fatal in realistic finite-budget situations, since the pilot run may take most of the budget, or possibly use up all of it. To unnecessitate such an ad hoc initialization step, we develop a batching procedure in adaptive variance reduction, and provide an implementable formula of the learning rate in the parameter search which minimizes an upper bound of the theoretical variance of the empirical batch mean. We analyze decay rates of the minimized upper bound towards the minimal estimator variance with respect to the predetermined computing budget, and provide convergence results as the computing budget increases progressively when the batch size is fixed. Numerical examples are provided to support theoretical findings and illustrate the effectiveness of the proposed batching procedure.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulating the Impact of Dynamic Rerouting on Metropolitan-scale Traffic Systems 动态改道对大都市交通系统影响的模拟

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-02-28 DOI: https://dl.acm.org/doi/10.1145/3579842

Cy Chan, Anu Kuncheria, Jane Macfarlane

{"title":"Simulating the Impact of Dynamic Rerouting on Metropolitan-scale Traffic Systems","authors":"Cy Chan, Anu Kuncheria, Jane Macfarlane","doi":"https://dl.acm.org/doi/10.1145/3579842","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3579842","url":null,"abstract":"The rapid introduction of mobile navigation aides that use real-time road network information to suggest alternate routes to drivers is making it more difficult for researchers and government transportation agencies to understand and predict the dynamics of congested transportation systems. Computer simulation is a key capability for these organizations to analyze hypothetical scenarios; however, the complexity of transportation systems makes it challenging for them to simulate very large geographical regions, such as multi-city metropolitan areas. In this article, we describe enhancements to the Mobiliti parallel traffic simulator to model dynamic rerouting behavior with the addition of vehicle controller actors and vehicle-to-controller reroute requests. The simulator is designed to support distributed-memory parallel execution using discrete event simulation and be scalable on high-performance computing platforms. We demonstrate the potential of the simulator by analyzing the impact of varying the population penetration rate of dynamic rerouting on the San Francisco Bay Area road network. Using high-performance parallel computing, we can simulate a day in the San Francisco Bay Area with 19 million vehicle trips with 50 percent dynamic rerouting penetration over a road network with 0.5 million nodes and 1 million links in less than three minutes. We present a sensitivity study on the dynamic rerouting parameters, discuss the simulator’s parallel scalability, and analyze system-level impacts of changing the dynamic rerouting penetration. Furthermore, we examine the varying effects on different functional classes and geographical regions and present a validation of the simulation results compared to real-world data.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating Multiclass Service Demand Distributions Using Markovian Arrival Processes 利用马尔可夫到达过程估计多类服务需求分布

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-02-28 DOI: https://dl.acm.org/doi/10.1145/3570924

Runan Wang, Giuliano Casale, Antonio Filieri

{"title":"Estimating Multiclass Service Demand Distributions Using Markovian Arrival Processes","authors":"Runan Wang, Giuliano Casale, Antonio Filieri","doi":"https://dl.acm.org/doi/10.1145/3570924","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3570924","url":null,"abstract":"Building performance models for software services in DevOps is costly and error-prone. Accurate service demand distribution estimation is critical to precisely modeling queueing behaviors and performance prediction. However, current estimation methods focus on capturing the mean service demand, disregarding higher-order moments of the distribution that still can largely affect prediction accuracy. To address this limitation, we propose to estimate higher moments of the service demand distribution for a microservice from monitoring traces. We first generate a closed queueing model to abstract software performance and use it to model the departure process of requests completed by the software service as a Markovian arrival process (MAP). This allows formulating the estimation of service demand into an optimization problem, which aims to find the first multiple moments of the service demand distribution that maximize the likelihood of the MAP using generated the measured inter-departure times. We then estimate the service demand distribution for different classes of service with a maximum likelihood algorithm and novel heuristics to mitigate the computational cost of the optimization process for scalability. We apply our method to real traces from a microservice-based application and demonstrate that its estimations lead to greater prediction accuracy than exponential distributions assumed in traditional service demand estimation approaches for software services.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns 通过来源模式自动重用、适应和执行模拟实验

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-02-28 DOI: https://dl.acm.org/doi/10.1145/3564928

Pia Wilsdorf, Anja Wolpers, Jason Hilton, Fiete Haack, Adelinde Uhrmacher

{"title":"Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns","authors":"Pia Wilsdorf, Anja Wolpers, Jason Hilton, Fiete Haack, Adelinde Uhrmacher","doi":"https://dl.acm.org/doi/10.1145/3564928","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3564928","url":null,"abstract":"Simulation experiments are typically conducted repeatedly during the model development process, for example, to revalidate if a behavioral property still holds after several model changes. Approaches for automatically reusing and generating simulation experiments can support modelers in conducting simulation studies in a more systematic and effective manner. They rely on explicit experiment specifications and, so far, on user interaction for initiating the reuse. Thereby, they are constrained to support the reuse of simulation experiments in a specific setting. Our approach now goes one step further by automatically identifying and adapting the experiments to be reused for a variety of scenarios. To achieve this, we exploit provenance graphs of simulation studies, which provide valuable information about the previous modeling and experimenting activities, and contain meta-information about the different entities that were used or produced during the simulation study. We define provenance patterns and associate them with a semantics, which allows us to interpret the different activities and construct transformation rules for provenance graphs. Our approach is implemented in a Reuse and Adapt framework for Simulation Experiments (RASE), which can interface with various modeling and simulation tools. In the case studies, we demonstrate the utility of our framework for (1) the repeated sensitivity analysis of an agent-based model of migration routes and (2) the cross-validation of two models of a cell signaling pathway.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Personality-based Model of Emotional Contagion and Control in Crowd Queuing Simulations 人群排队模拟中基于人格的情绪感染与控制模型

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-02-28 DOI: https://dl.acm.org/doi/10.1145/3577589

Junxiao Xue, Mingchuang Zhang, Hui Yin

{"title":"A Personality-based Model of Emotional Contagion and Control in Crowd Queuing Simulations","authors":"Junxiao Xue, Mingchuang Zhang, Hui Yin","doi":"https://dl.acm.org/doi/10.1145/3577589","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3577589","url":null,"abstract":"Queuing is a frequent daily activity. However, long waiting lines equate to frustration and potential safety hazards. We present a novel, personality-based model of emotional contagion and control for simulating crowd queuing. Our model integrates the influence of individual personalities and interpersonal relationships. Through the interaction between the agents and the external environment parameters, the emotional contagion model based on well-known theories in psychology is used to complete the agents’ behavior planning and path planning function. We combine the epidemiological SIR model with the cellular automaton model to capture various emotional modelling for multi-agent simulations. The overall formulation involves different emotional parameters, such as patience, urgency, and friendliness, closely related to crowd queuing. In addition, to manage the order of the queue, governing agents are added to prevent the emotional outbreak. We perform qualitative and quantitative comparisons between our simulation results and real-world observations on various scenarios. Numerous experiments show that reasonably increasing the queue channel and adding governing agents can effectively improve the quality of queues.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Replication of Computational Results Report for “Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns” “通过来源模式自动重用、适应和执行模拟实验”计算结果报告的复制

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-02-28 DOI: https://dl.acm.org/doi/10.1145/3577007

Pierangelo Di Sanzo

引用次数: 0

Performance analysis of work stealing strategies in large scale multi-threaded computing 大规模多线程计算中工作窃取策略的性能分析

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-02-16 DOI: https://dl.acm.org/doi/10.1145/3584186

Grzegorz Kielanski, Benny Van Houdt

{"title":"Performance analysis of work stealing strategies in large scale multi-threaded computing","authors":"Grzegorz Kielanski, Benny Van Houdt","doi":"https://dl.acm.org/doi/10.1145/3584186","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3584186","url":null,"abstract":"Distributed systems use randomized work stealing to improve performance and resource utilization. In most prior analytical studies of randomized work stealing, jobs are considered to be sequential and are executed as a whole on a single server. In this paper we consider a homogeneous system of servers where parent jobs spawn child jobs that can feasibly be executed in parallel. When an idle server probes a busy server in an attempt to steal work, it may either steal a parent job or multiple child jobs. To approximate the performance of this system we introduce a Quasi-Birth-Death Markov chain and express the performance measures of interest via its unique steady state. We perform simulation experiments that suggest that the approximation error tends to zero as the number of servers in the system becomes large. To further support this observation we introduce a mean field model and show that its unique fixed point corresponds to the steady state of the QBD. Using numerical experiments we compare the performance of various simple stealing strategies as well as optimized strategies.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0