ACM Transactions on Modeling and Computer Simulation最新文献_第2页

Generating Hidden Markov Models from Process Models Through Nonnegative Tensor Factorization 通过非负张量因式分解从过程模型生成隐马尔可夫模型

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2024-06-10 DOI: 10.1145/3664813

Erik Skau, Andrew Hollis, Stephan Eidenbenz, Kim Rasmussen, Boian Alexandrov

{"title":"Generating Hidden Markov Models from Process Models Through Nonnegative Tensor Factorization","authors":"Erik Skau, Andrew Hollis, Stephan Eidenbenz, Kim Rasmussen, Boian Alexandrov","doi":"10.1145/3664813","DOIUrl":"https://doi.org/10.1145/3664813","url":null,"abstract":"Monitoring of industrial processes is a critical capability in industry and in government to ensure reliability of production cycles, quick emergency response, and national security. Process monitoring allows users to gauge the progress of an organization in an industrial process or predict the degradation or aging of machine parts in processes taking place at a remote location. Similar to many data science applications, we usually only have access to limited raw data, such as satellite imagery, short video clips, event logs, and signatures captured by a small set of sensors. To combat data scarcity, we leverage the knowledge of Subject Matter Experts (SMEs) who are familiar with the actions of interest. SMEs provide expert knowledge of the essential activities required for task completion and the resources necessary to carry out each of these activities. Various process mining techniques have been developed for this type of analysis; typically such approaches combine theoretical process models built based on domain expert insights with ad-hoc integration of available pieces of raw data. Here, we introduce a novel mathematically sound method that integrates theoretical process models (as proposed by SMEs) with interrelated minimal Hidden Markov Models (HMM), built via nonnegative tensor factorization. Our method consolidates: (a) theoretical process models, (b) HMMs, (c) coupled nonnegative matrix-tensor factorizations, and (d) custom model selection. To demonstrate our methodology and its abilities, we apply it on simple synthetic and real world process models.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507203","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

ENHANCE: Multilevel Heterogeneous Performance-Aware Re-Partitioning Algorithm For Microscopic Vehicle Traffic Simulation ENHANCE：用于微观车辆交通仿真的多级异构性能感知再分配算法

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2024-06-04 DOI: 10.1145/3670401

Anibal Siguenza-Torres, Alexander Wieder, Zhuoxiao Meng, Santiago Narvaez Rivas, Mingyue Gao, Margherita Grossi, Xiaorui Du, Stefano Bortoli, Wentong Cai, Alois Knoll

引用次数: 0

Computation Offloading and Band Selection for IoT Devices in Multi-Access Edge Computing 多接入边缘计算中物联网设备的计算卸载和频段选择

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2024-06-03 DOI: 10.1145/3670400

Kaustabha Ray, Ansuman Banerjee

{"title":"Computation Offloading and Band Selection for IoT Devices in Multi-Access Edge Computing","authors":"Kaustabha Ray, Ansuman Banerjee","doi":"10.1145/3670400","DOIUrl":"https://doi.org/10.1145/3670400","url":null,"abstract":"The advent of Multi-Access Edge Computing (MEC) has enabled service providers to mitigate high network latencies often encountered in accessing cloud services. The key idea of MEC involves service providers deploying containerized application services on MEC servers situated near Internet-of-Things (IoT) device users. The users access these services via wireless base stations with ultra low latency. Computation tasks of IoT devices can then either be executed locally on the devices or on the MEC servers. A key cornerstone of the MEC environment is an offloading policy utilized to determine whether to execute computation tasks on IoT devices or to offload the tasks to MEC servers for processing. In this work, we propose a two phase Probabilistic Model Checking based offloading policy catering to IoT device user preferences. The first stage evaluates the trade-offs between local vs server execution while the second stage evaluates the trade-offs between choice of wireless communication bands for offloaded tasks. We present experimental results in practical scenarios on data gathered from an IoT test-bed setup with benchmark applications to show the benefits of an adaptive preference-aware approach over conventional approaches in the MEC offloading context.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141259078","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

Comparing Statistical, Analytical, and Learning-Based Routing Approaches for Delay-Tolerant Networks 比较耐延迟网络的统计、分析和基于学习的路由选择方法

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2024-05-25 DOI: 10.1145/3665927

Pedro R. D'Argenio, Juan Fraire, Arnd Hartmanns, Fernando Raverta

引用次数: 0

Selection of the Best in the Presence of Subjective Stochastic Constraints 主观随机限制条件下的最佳选择

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2024-05-11 DOI: 10.1145/3664814

Yuwei Zhou, Sigrun Andradottir, Seong-Hee Kim

{"title":"Selection of the Best in the Presence of Subjective Stochastic Constraints","authors":"Yuwei Zhou, Sigrun Andradottir, Seong-Hee Kim","doi":"10.1145/3664814","DOIUrl":"https://doi.org/10.1145/3664814","url":null,"abstract":"We consider the problem of finding a system with the best primary performance measure among a finite number of simulated systems in the presence of subjective stochastic constraints on secondary performance measures. When no feasible system exists, the decision maker may be willing to relax some constraint thresholds. We take multiple threshold values for each constraint as a user’s input and propose indifference-zone procedures that perform the phases of feasibility check and selection-of-the-best sequentially or simultaneously. Given that there is no change in the underlying simulated systems, our procedures recycle simulation observations to conduct feasibility checks across all potential thresholds. We prove that the proposed procedures yield the best system in the most desirable feasible region possible with at least a pre-specified probability. Our experimental results show that our procedures perform well with respect to the number of observations required to make a decision, as compared with straight-forward procedures that repeatedly solve the problem for each set of constraint thresholds, and that our simultaneously-running procedure provides the best overall performance.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140936857","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

Rate Lifting for Stochastic Process Algebra by Transition Context Augmentation 通过转换上下文增强实现随机过程代数的速率提升

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2024-04-08 DOI: 10.1145/3656582

Amin Soltanieh, Markus Siegle

引用次数: 0

RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols RayNet：开发强化学习驱动网络协议的仿真平台

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2024-03-30 DOI: 10.1145/3653975

Luca Giacomoni, Basil Benny, George Parisis

{"title":"RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols","authors":"Luca Giacomoni, Basil Benny, George Parisis","doi":"10.1145/3653975","DOIUrl":"https://doi.org/10.1145/3653975","url":null,"abstract":"Reinforcement Learning (RL) has gained significant momentum in the development of network protocols. However, RL-based protocols are still in their infancy, and substantial research is required to build deployable solutions. Developing a protocol based on RL is a complex and challenging process that involves several model design decisions and requires significant training and evaluation in real and simulated network topologies. Network simulators offer an efficient training environment for RL-based protocols, because they are deterministic and can run in parallel. In this paper, we introduce RayNet, a scalable and adaptable simulation platform for the development of RL-based network protocols. RayNet integrates OMNeT++, a fully programmable network simulator, with Ray/RLlib, a scalable training platform for distributed RL. RayNet facilitates the methodical development of RL-based network protocols so that researchers can focus on the problem at hand and not on implementation details of the learning aspect of their research. We developed a simple RL-based congestion control approach as a proof of concept showcasing that RayNet can be a valuable platform for RL-based research in computer networks, enabling scalable training and evaluation. We compared RayNet with ns3-gym, a platform with similar objectives to RayNet, and showed that RayNet performs better in terms of how fast agents can collect experience in RL environments.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140600049","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

Overlapping Batch Confidence Intervals on Statistical Functionals Constructed from Time Series: Application to Quantiles, Optimization, and Estimation 从时间序列构建统计函数的重叠批量置信区间：定量、优化和估计的应用

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2024-03-14 DOI: 10.1145/3649437

Ziwei Su, Raghu Pasupathy, Yingchieh Yeh, Peter W. Glynn

{"title":"Overlapping Batch Confidence Intervals on Statistical Functionals Constructed from Time Series: Application to Quantiles, Optimization, and Estimation","authors":"Ziwei Su, Raghu Pasupathy, Yingchieh Yeh, Peter W. Glynn","doi":"10.1145/3649437","DOIUrl":"https://doi.org/10.1145/3649437","url":null,"abstract":"We propose a general purpose confidence interval procedure (CIP) for statistical functionals constructed using data from a stationary time series. The procedures we propose are based on derived distribution-free analogues of the χ2 and Student’s t random variables for the statistical functional context, and hence apply in a wide variety of settings including quantile estimation, gradient estimation, M-estimation, CVAR-estimation, and arrival process rate estimation, apart from more traditional statistical settings. Like the method of subsampling, we use overlapping batches of time series data to estimate the underlying variance parameter; unlike subsampling and the bootstrap, however, we assume that the implied point estimator of the statistical functional obeys a central limit theorem (CLT) to help identify the weak asymptotics (called OB-x limits, x=I,II,III) of batched Studentized statistics. The OB-x limits, certain functionals of the Wiener process parameterized by the size of the batches and the extent of their overlap, form the essential machinery for characterizing dependence, and consequently the correctness of the proposed CIPs. The message from extensive numerical experimentation is that in settings where a functional CLT on the point estimator is in effect, using large overlapping batches alongside OB-x critical values yields confidence intervals that are often of significantly higher quality than those obtained from more generic methods like subsampling or the bootstrap. We illustrate using examples from CVaR estimation, ARMA parameter estimation, and NHPP rate estimation; R and MATLAB code for OB-x critical values is available at <monospace>web.ics.purdue.edu/ ∼ pasupath</monospace>.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140124327","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

Performance Evaluation of Spintronic-Based Spiking Neural Networks Using Parallel Discrete-Event Simulation 利用并行离散事件仿真评估基于自旋电子的尖峰神经网络的性能

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2024-03-05 DOI: 10.1145/3649464

Elkin Cruz-Camacho, Siyuan Qian, Ankit Shukla, Neil McGlohon, Shaloo Rakheja, Christopher D. Carothers

{"title":"Performance Evaluation of Spintronic-Based Spiking Neural Networks Using Parallel Discrete-Event Simulation","authors":"Elkin Cruz-Camacho, Siyuan Qian, Ankit Shukla, Neil McGlohon, Shaloo Rakheja, Christopher D. Carothers","doi":"10.1145/3649464","DOIUrl":"https://doi.org/10.1145/3649464","url":null,"abstract":"Spintronics devices that use the spin of electrons as the information state variable have the potential to emulate neuro-synaptic dynamics and can be realized within a compact form-factor, while operating at ultra-low energy-delay point. In this paper, we benchmark the performance of a spintronics hardware platform designed for handling neuromorphic tasks. To explore the benefits of spintronics-based hardware on realistic neuromorphic workloads, we developed a Parallel Discrete-Event Simulation model called Doryta, which is further integrated with a materials-to-systems benchmarking framework. The benchmarking framework allows us to obtain quantitative metrics on the throughput and energy of spintronics-based neuromorphic computing and compare these against standard CMOS-based approaches. Although spintronics hardware offers significant energy and latency advantages, we find that for larger neuromorphic circuits, the performance is limited by the interconnection networks rather than the spintronics-based neurons and synapses. This limitation can be overcome by architectural changes to the network. Through Doryta we are also able to show the power of neuromorphic computing by simulating Conway’s Game of Life (GoL), thus showing that it is Turing complete. We show that Doryta obtains over 300 × speedup using 1,024 CPU cores when tested on a convolutional, sparse, neural architecture. When scaled-up 64 times, to a 200 million neuron model, the simulation ran in 3:42 minutes for a total of 2000 virtual clock steps. The conservative approach of execution was found to be faster in most cases than the optimistic approach, even when a tie-breaking mechanism to guarantee deterministic execution, was deactivated.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140047707","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

Projected Gaussian Markov Improvement Algorithm for High-dimensional Discrete Optimization via Simulation 通过模拟实现高维离散优化的投射高斯马尔可夫改进算法

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2024-03-01 DOI: 10.1145/3649463

Xinru Li, Eunhye Song

{"title":"Projected Gaussian Markov Improvement Algorithm for High-dimensional Discrete Optimization via Simulation","authors":"Xinru Li, Eunhye Song","doi":"10.1145/3649463","DOIUrl":"https://doi.org/10.1145/3649463","url":null,"abstract":"This paper considers a discrete optimization via simulation (DOvS) problem defined on a graph embedded in the high-dimensional integer grid. Several DOvS algorithms that model the responses at the solutions as a realization of a Gaussian Markov random field (GMRF) have been proposed exploiting its inferential power and computational benefits. However, the computational cost of inference increases exponentially in dimension. We propose the projected Gaussian Markov improvement algorithm (pGMIA), which projects the solution space onto a lower-dimensional space creating the region-layer graph to reduce the cost of inference. Each node on the region-layer graph can be mapped to a set of solutions projected to the node; these solutions form a lower-dimensional solution-layer graph. We define the response at each region-layer node to be the average of the responses within the corresponding solution-layer graph. From this relation, we derive the region-layer GMRF to model the region-layer responses. The pGMIA alternates between the two layers to make a sampling decision at each iteration; it first selects a region-layer node based on the lower-resolution inference provided by the region-layer GMRF, then makes a sampling decision among the solutions within the solution-layer graph of the node based on the higher-resolution inference from the solution-layer GMRF. To solve even higher-dimensional problems (e.g., 100 dimensions), we also propose the pGMIA+: a multi-layer extension of the pGMIA.We show that both pGMIA and pGMIA+ converge to the optimum almost surely asymptotically and empirically demonstrate their competitiveness against state-of-the-art high-dimensional Bayesian optimization algorithms.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002077","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