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

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":"107 1","pages":""},"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":"14 1","pages":""},"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":"127 1-4","pages":""},"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":"70 1","pages":""},"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

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: 10.1145/3584186

Grzegorz Kielanski, B. V. Houdt

引用次数: 1

Introduction to the Special Section on PADS 2021 关于PADS 2021的特别部分介绍

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/10.1145/3579840

Saikou Y. Diallo, Andreas Tolk

引用次数: 0

SEH: Size Estimate Hedging Scheduling of Queues SEH：队列的大小估计套期调度

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-01-17 DOI: 10.1145/3580491

Maryam Akbari-Moghaddam, D. Down

引用次数: 2

SEH: Size Estimate Hedging Scheduling of Queues SEH:队列的大小估计对冲调度

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-01-17 DOI: https://dl.acm.org/doi/10.1145/3580491

Maryam Akbari-Moghaddam, Douglas G. Down

引用次数: 0

Towards Differentiable Agent-Based Simulation 基于可微主体的仿真研究

IF 0.9 4区计算机科学

ACM Transactions on Modeling and Computer Simulation Pub Date : 2023-01-11 DOI: https://dl.acm.org/doi/10.1145/3565810

Philipp Andelfinger

{"title":"Towards Differentiable Agent-Based Simulation","authors":"Philipp Andelfinger","doi":"https://dl.acm.org/doi/10.1145/3565810","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3565810","url":null,"abstract":"Simulation-based optimization using agent-based models is typically carried out under the assumption that the gradient describing the sensitivity of the simulation output to the input cannot be evaluated directly. To still apply gradient-based optimization methods, which efficiently steer the optimization towards a local optimum, gradient estimation methods can be employed. However, many simulation runs are needed to obtain accurate estimates if the input dimension is large. Automatic differentiation (AD) is a family of techniques to compute gradients of general programs directly. Here, we explore the use of AD in the context of time-driven agent-based simulations. By substituting common discrete model elements such as conditional branching with smooth approximations, we obtain gradient information across discontinuities in the model logic. On the examples of a synthetic grid-based model, an epidemics model, and a microscopic traffic model, we study the fidelity and overhead of the differentiable simulations as well as the convergence speed and solution quality achieved by gradient-based optimization compared with gradient-free methods. In traffic signal timing optimization problems with high input dimension, the gradient-based methods exhibit substantially superior performance. A further increase in optimization progress is achieved by combining gradient-free and gradient-based methods. We demonstrate that the approach enables gradient-based training of neural network-controlled simulation entities embedded in the model logic. Finally, we show that the performance overhead of differentiable agent-based simulations can be reduced substantially by exploiting sparsity in the model logic.","PeriodicalId":50943,"journal":{"name":"ACM Transactions on Modeling and Computer Simulation","volume":"209 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138523744","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