ACM Transactions on Modeling and Computer Simulation最新文献_第4页

LN: A Flexible Algorithmic Framework for Layered Queueing Network Analysis 分层排队网络分析的灵活算法框架

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-11-21 DOI: 10.1145/3633457

Giuliano Casale, Yicheng Gao, Zifeng Niu, Lulai Zhu

{"title":"LN: A Flexible Algorithmic Framework for Layered Queueing Network Analysis","authors":"Giuliano Casale, Yicheng Gao, Zifeng Niu, Lulai Zhu","doi":"10.1145/3633457","DOIUrl":"https://doi.org/10.1145/3633457","url":null,"abstract":"Layered queueing networks (LQNs) are an extension of ordinary queueing networks useful to model simultaneous resource possession and stochastic call graphs in distributed systems. Existing computational algorithms for LQNs have primarily focused on mean-value analysis. However, other solution paradigms, such as normalizing constant analysis and mean-field approximation, can improve the computation of LQN mean and transient performance metrics, state probabilities, and response time distributions. Motivated by this observation, we propose the first LQN meta-solver, called LN, that allows for the dynamic selection of the performance analysis paradigm to be iteratively applied to the submodels arising from layer decomposition. We report experiments where this added flexibility helps us to reduce the LQN solution errors. We also demonstrate that the meta-solver approach eases the integration of LQNs with other formalisms, such as caching models, enabling the analysis of more general classes of layered stochastic networks. Additionally, to support the accurate evaluation of the LQN submodels, we develop novel algorithms for homogeneous queueing networks consisting of an infinite server node and a set of identical queueing stations. In particular, we propose an exact method of moment algorithms, integration techniques for normalizing constants, and a fast non-iterative mean-value analysis technique.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523755","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

Contextual Ranking and Selection with Gaussian Processes and OCBA 基于高斯过程和OCBA的上下文排序与选择

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-11-20 DOI: 10.1145/3633456

Sait Cakmak, Yuhao Wang, Siyang Gao, Enlu Zhou

引用次数: 0

A Prescriptive Simulation Framework with Realistic Behavioural Modelling for Emergency Evacuations 具有紧急疏散现实行为模型的规定性模拟框架

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-11-18 DOI: 10.1145/3633330

Md. Shalihin Othman, Gary Tan

{"title":"A Prescriptive Simulation Framework with Realistic Behavioural Modelling for Emergency Evacuations","authors":"Md. Shalihin Othman, Gary Tan","doi":"10.1145/3633330","DOIUrl":"https://doi.org/10.1145/3633330","url":null,"abstract":"Emergency and crisis simulations play a pivotal role in equipping authorities worldwide with the necessary tools to minimize the impact of catastrophic events. Various studies have explored the integration of intelligence into Multi-Agent Systems (MAS) for crisis simulation. This involves incorporating psychological behaviours from the social sciences and utilizing data-driven machine learning models with predictive capabilities. A recent advancement in behavioural modelling is the Conscious Movement Model (CMM), designed to modulate an agent’s movement patterns dynamically as the situation unfolds. Complementing this, the model incorporates a Conscious Movement Memory-Attention (CMMA) mechanism, enabling learnability through training on pedestrian trajectories extracted from video data. The CMMA facilitates mapping a pedestrian’s attention to their surroundings and understanding how their past decisions influence their subsequent actions. This study proposes an efficient framework that integrates the trained CMM into a simulation model specifically tailored for emergency evacuations, ensuring realistic outcomes. The resulting simulation framework automates strategy management and planning for diverse emergency evacuation scenarios. A single-objective method is presented for generating prescriptive analytics, offering effective strategy options based on predefined operational rules. To validate the framework’s efficacy, a case study of a theatre evacuation is conducted. In essence, this research establishes a robust simulation framework for crisis management, with a particular emphasis on modelling pedestrians during emergency evacuations. The framework generates prescriptive analytics to aid authorities in executing rescue and evacuation operations effectively.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523752","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

Stochastic Approximation for Estimating the Price of Stability in Stochastic Nash Games 随机纳什对策稳定性价格估计的随机逼近

4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-11-11 DOI: 10.1145/3632525

Afrooz Jalilzadeh, Farzad Yousefian, Mohammadjavad Ebrahimi

{"title":"Stochastic Approximation for Estimating the Price of Stability in Stochastic Nash Games","authors":"Afrooz Jalilzadeh, Farzad Yousefian, Mohammadjavad Ebrahimi","doi":"10.1145/3632525","DOIUrl":"https://doi.org/10.1145/3632525","url":null,"abstract":"The goal in this paper is to approximate the Price of Stability (PoS) in stochastic Nash games using stochastic approximation (SA) schemes. PoS is amongst the most popular metrics in game theory and provides an avenue for estimating the efficiency of Nash games. In particular, knowing the value of PoS can help with designing efficient networked systems, including transportation networks and power market mechanisms. Motivated by the absence of efficient methods for computing the PoS, first we consider stochastic optimization problems with a nonsmooth and merely convex objective function and a merely monotone stochastic variational inequality (SVI) constraint. This problem appears in the numerator of the PoS ratio. We develop a randomized block-coordinate stochastic extra-(sub)gradient method where we employ a novel iterative penalization scheme to account for the mapping of the SVI in each of the two gradient updates of the algorithm. We obtain an iteration complexity of the order ϵ − 4 that appears to be best known result for this class of constrained stochastic optimization problems, where ϵ denotes an arbitrary bound on suitably defined infeasibility and suboptimality metrics. Second, we develop an SA-based scheme for approximating the PoS and derive lower and upper bounds on the approximation error. To validate the theoretical findings, we provide preliminary simulation results on a networked stochastic Nash Cournot competition.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135042729","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: 2

An Improved Model of Wavelet Leader Covariance for Estimating Multifractal Properties 一种用于多重分形性质估计的改进小波导协方差模型

4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-11-03 DOI: 10.1145/3631522

Garry Jacyna, Damon Frezza, David M. Slater, James R. Thompson

{"title":"An Improved Model of Wavelet Leader Covariance for Estimating Multifractal Properties","authors":"Garry Jacyna, Damon Frezza, David M. Slater, James R. Thompson","doi":"10.1145/3631522","DOIUrl":"https://doi.org/10.1145/3631522","url":null,"abstract":"Complex systems often produce multifractal signals defined by stationary increments that exhibit power law scaling properties. The Legendre transform of the domain-dependent scaling function that defines the power law is known as the multifractal spectrum. The multifractal spectrum can also be defined by a power-series expansion of the scaling function and in practice the first two leading coefficients of that series are estimated from the discrete wavelet transform of the signal. To quantify, validate, and compare simulations of complex systems with data collected empirically from the actual system, practitioners require methods for approximating the variance associated with estimates of these coefficients. In this work, we generalize a previously developed semi-parametric statistical model for the values extracted from a discrete multi-scale wavelet transform to include both within scale and between scale covariance dependencies. We employ multiplicative cascades to simulate multifractals with known parameters to illustrate the necessity for this generalization and to test the precision of our improved model. The combined within and between scale model of covariance results in a more accurate estimate of the expected variance of the coefficients extracted from an empirical data set.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135818868","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

Introduction to the Special Issue on QEST 2021 QEST 2021 特刊简介

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-10-31 DOI: 10.1145/3631707

Alessandro Abate, Andrea Marin

{"title":"Introduction to the Special Issue on QEST 2021","authors":"Alessandro Abate, Andrea Marin","doi":"10.1145/3631707","DOIUrl":"https://doi.org/10.1145/3631707","url":null,"abstract":"The International Conference on Quantitative Evaluation of SysTems (QEST) is the leading forum on evaluation and verification of computer systems and networks, through stochastic models and measurements. QEST covers topics including classic measures involving performance and reliability, as well as quantification of properties that are classically qualitative, such as safety, correctness, and security. QEST welcomes measurement-based studies as well as analytic studies, diversity in the model formalisms and methodologies employed, as well as development of new formalisms and methodologies. In short, QEST aims to encourage all aspects of work centered around creating a sound methodological basis for assessing and designing systems using quantitative means. This special issue consists of five articles extending earlier versions presented at QEST 2021, the 18th edition of the conference, which was hosted in Paris but virtually held August 23 through 27, 2021. A selection of the top-ranked conference papers was chosen by the chairs, and the authors were invited to submit an extended version to this special issue. The journal review process included both members of the QEST program committee and additional reviewers who were not involved in the conference refereeing process. The resulting collection of articles comprises exciting developments in the areas of system verification and performance or reliability analysis. In the contribution titled “Optimizing Reachability Probabilities for a Restricted Class of SHA via Flowpipe Construction,” da Silva, Schupp, and Remke","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139307560","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

Divergence Reduction in Monte Carlo Neutron Transport with On-GPU Asynchronous Scheduling 基于gpu异步调度的蒙特卡罗中子传输散度减小

4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-10-19 DOI: 10.1145/3626957

Braxton Cuneo, Mike Bailey

{"title":"Divergence Reduction in Monte Carlo Neutron Transport with On-GPU Asynchronous Scheduling","authors":"Braxton Cuneo, Mike Bailey","doi":"10.1145/3626957","DOIUrl":"https://doi.org/10.1145/3626957","url":null,"abstract":"While Monte Carlo Neutron Transport (MCNT) is near-embarrasingly parallel, the effectively unpredictable lifetime of neutrons can lead to divergence when MCNT is evaluated on GPUs. Divergence is the phenomenon of adjacent threads in a warp executing different control flow paths; on GPUS, it reduces performance because each work group may only execute one path at a time. The process of Thread Data Remapping (TDR) resolves these discrepancies by moving data across hardware such that data in the same warp will be processed through similar paths. A common issue among prior implementations of TDR is the synchronous nature of its remapping and processing cycles, which exhaustively sort data produced by prior processing passes and exhaustively evaluate the sorted data. In another paper, we defined a method of remapping data through an asynchronous scheduler which allows for work to be stored in shared memory and deferred arbitrarily until that work is a viable option for low-divergence evaluation. This paper surveys a wider set of cases, with the goal of characterizing performance trends across a more comprehensive set of parameters. These parameters include cross sections of scattering/capturing/fission, use of implicit capture, source neutron counts, simulation time spans, and tuned memory allocations. Across these cases, we have recorded minimum and average execution times, as well as a heuristically-tuned near-optimal memory allocation size for both synchronous and asynchronous scheduling. Across the collected data, it is shown that the asynchronous method is faster and more memory efficient in the majority of cases, and that it requires less tuning to achieve competitive performance.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135728822","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

Using Cache or Credit for Parallel Ranking and Selection 使用缓存或信用进行并行排序和选择

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-09-04 DOI: 10.1145/3618299

Harun Avci, Barry L. Nelson, Eunhye Song, Andreas Wächter

{"title":"Using Cache or Credit for Parallel Ranking and Selection","authors":"Harun Avci, Barry L. Nelson, Eunhye Song, Andreas Wächter","doi":"10.1145/3618299","DOIUrl":"https://doi.org/10.1145/3618299","url":null,"abstract":"In this paper, we focus on ranking and selection procedures that sequentially allocate replications to systems by applying some acquisition function. We propose an acquisition function, called gCEI, which exploits the gradient of the complete expected improvement with respect to the number of replications. We prove that the gCEI procedure, which adopts gCEI as the acquisition function in a serial computing environment, achieves the asymptotically optimal static replication allocation of Glynn and Juneja in the limit under a normality assumption. We also propose two procedures, called caching and credit, that extend any acquisition-function-based procedure in a serial environment into both synchronous and asynchronous parallel environments. While allocating replications to systems, both procedures use persistence forecasts for the unavailable outputs of the currently running replications, but differ in usage of the available outputs. We prove that under certain assumptions, the caching procedure achieves the same asymptotic allocation as in the serial environment. A similar result holds for the credit procedure using gCEI as the acquisition function. In terms of efficiency and effectiveness, the credit procedure empirically performs as well as the caching procedure despite not carefully controlling the output history as the caching procedure does, and is faster than the serial version without any number-of-replications penalty due to using persistence forecasts. Both procedures are designed to solve small-to-medium-sized problems on computers with a modest number of processors, such as laptops and desktops as opposed to high-performance clusters, and are superior to state-of-the-art parallel procedures in this setting.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44825472","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: 1

Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data 流输入数据多周期仿真优化的随机逼近

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-08-29 DOI: 10.1145/3617595

Linyun He, U. Shanbhag, Eunhye Song

{"title":"Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data","authors":"Linyun He, U. Shanbhag, Eunhye Song","doi":"10.1145/3617595","DOIUrl":"https://doi.org/10.1145/3617595","url":null,"abstract":"We consider a continuous-valued simulation optimization (SO) problem, where a simulator is built to optimize an expected performance measure of a real-world system while parameters of the simulator are estimated from streaming data collected periodically from the system. At each period, a new batch of data is combined with the cumulative data and the parameters are re-estimated with higher precision. The system requires the decision variable to be selected in all periods. Therefore, it is sensible for the decision-maker to update the decision variable at each period by solving a more precise SO problem with the updated parameter estimate to reduce the performance loss with respect to the target system. We define this decision-making process as the multi-period SO problem and introduce a multi-period stochastic approximation (SA) framework that generates a sequence of solutions. Two algorithms are proposed: Re-start SA (ReSA) reinitializes the stepsize sequence in each period, whereas Warm-start SA (WaSA) carefully tunes the stepsizes, taking both fewer and shorter gradient-descent steps in later periods as parameter estimates become increasingly more precise. We show that under suitable strong convexity and regularity conditions, ReSA and WaSA achieve the best possible convergence rate in expected sub-optimality either when an unbiased or a simultaneous perturbation gradient estimator is employed, while WaSA accrues significantly lower computational cost as the number of periods increases. In addition, we present the regularized ReSA which obviates the need to know the strong convexity constant and achieves the same convergence rate at the expense of additional computation.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45117582","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

DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning – Extended Version DSMC评估阶段:在深度强化学习中培养稳健和安全的行为-扩展版

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-07-12 DOI: https://dl.acm.org/doi/10.1145/3607198

Timo P. Gros, Joschka Groß, Daniel Höller, Jörg Hoffmann, Michaela Klauck, Hendrik Meerkamp, Nicola J. Müller, Lukas Schaller, Verena Wolf

{"title":"DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning – Extended Version","authors":"Timo P. Gros, Joschka Groß, Daniel Höller, Jörg Hoffmann, Michaela Klauck, Hendrik Meerkamp, Nicola J. Müller, Lukas Schaller, Verena Wolf","doi":"https://dl.acm.org/doi/10.1145/3607198","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3607198","url":null,"abstract":"Neural networks (NN) are gaining importance in sequential decision-making. Deep reinforcement learning (DRL), in particular, is extremely successful in learning action policies in complex and dynamic environments. Despite this success, however, DRL technology is not without its failures, especially in safety-critical applications: (i) the training objective maximizes average rewards, which may disregard rare but critical situations and hence lack local robustness; (ii) optimization objectives targeting safety typically yield degenerated reward structures which for DRL to work must be replaced with proxy objectives. Here we introduce a methodology that can help to address both deficiencies. We incorporate evaluation stages (ES) into DRL, leveraging recent work on deep statistical model checking (DSMC), which verifies NN policies in Markov decision processes. Our ES apply DSMC at regular intervals to determine state space regions with weak performance. We adapt the subsequent DRL training priorities based on the outcome, (i) focusing DRL on critical situations, and (ii) allowing to foster arbitrary objectives. We run case studies on two benchmarks. One of them is the Racetrack, an abstraction of autonomous driving that requires navigating a map without crashing into a wall. The other is MiniGrid, a widely used benchmark in the AI community. Our results show that DSMC-based ES can significantly improve both (i) and (ii).","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523757","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