Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation最新文献_第2页

DeepSim: A Transformer Based Model For Fast Simulation And Exploring Computer System Design Space 基于变压器的快速仿真模型与计算机系统设计空间探索

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3593634

Hamed Najafi, Xiaoyang Lu

引用次数: 0

Efficient Execution for Domain Specific Languages: Comparing Two Approaches for Demography and Cellular Biology 领域特定语言的有效执行:人口学和细胞生物学两种方法的比较

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3593630

Till Köster, A. Uhrmacher

引用次数: 0

Reproducibility Report for the Paper: “Workload Interference Prevention with Intelligent Routing and Flexible Job Placement on Dragonfly” 论文重复性报告:“基于智能路径和灵活工作安排的蜻蜓工作量干扰预防”

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3596136

Till Köster

引用次数: 0

SymBChainSim: A Novel Simulation Tool for Dynamic and Adaptive Blockchain Management and its Trilemma Tradeoff SymBChainSim:一种用于动态和自适应区块链管理的新型仿真工具及其三难选择权衡

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3591121

G. Diamantopoulos, R. Bahsoon, Nikos Tziritas, G. Theodoropoulos

{"title":"SymBChainSim: A Novel Simulation Tool for Dynamic and Adaptive Blockchain Management and its Trilemma Tradeoff","authors":"G. Diamantopoulos, R. Bahsoon, Nikos Tziritas, G. Theodoropoulos","doi":"10.1145/3573900.3591121","DOIUrl":"https://doi.org/10.1145/3573900.3591121","url":null,"abstract":"Despite the recent increase in the popularity of blockchain, the technology suffers from the trilemma trade-off between security decentralisation and scalability prohibiting adoption, and limiting the efficiency and effectiveness of the induced system. Addressing the trilemma trade-off calls for dynamic management and configuration of the blockchain system. In particular, choosing an effective and efficient consensus protocol for balancing the trilemma trade-off when inducing the blockchain-based system is acknowledged to be a challenging problem given the dynamic and complex nature of the blockchain environment. DDDAS approaches are particularly suitable for this challenge, and in previous work, the authors presented a novel DDDAS-based blockchain architecture and demonstrated that it offers a promising approach for dynamically adjusting the parameters of a system and optimising for the trade-off. This paper presents a novel simulation tool that can support and satisfy the DDDAS requirements for a dynamically re-configurable blockchain system. The tool supports the simulation and the dynamic switching of consensus protocols, analysing their trilemma trade-off. The simulator design is modular and allows the implementation and analysis of a wide range of consensus protocols and their implementation scenarios, along with the ability for parallelization. The paper also presents a quantitative evaluation of the tool.","PeriodicalId":246048,"journal":{"name":"Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115137323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

From Explainable AI to Explainable Simulation: Using Machine Learning and XAI to understand System Robustness 从可解释的AI到可解释的仿真:使用机器学习和XAI来理解系统鲁棒性

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3591114

N. Feldkamp, S. Strassburger

{"title":"From Explainable AI to Explainable Simulation: Using Machine Learning and XAI to understand System Robustness","authors":"N. Feldkamp, S. Strassburger","doi":"10.1145/3573900.3591114","DOIUrl":"https://doi.org/10.1145/3573900.3591114","url":null,"abstract":"Evaluating robustness is an important goal in simulation-based analysis. Robustness is achieved when the controllable factors of a system are adjusted in such a way that any possible variance in uncontrollable factors (noise) has minimal impact on the variance of the desired output. The optimization of system robustness using simulation is a dedicated and well-established research direction. However, once a simulation model is available, there is a lot of potential to learn more about the inherent relationships in the system, especially regarding its robustness. Data farming offers the possibility to explore large design spaces using smart experiment design, high performance computing, automated analysis, and interactive visualization. Sophisticated machine learning methods excel at recognizing and modelling the relation between large amounts of simulation input and output data. However, investigating and analyzing this modelled relationship can be very difficult, since most modern machine learning methods like neural networks or random forests are opaque black boxes. Explainable Artificial Intelligence (XAI) can help to peak into this black box, helping us to explore and learn about relations between simulation input and output. In this paper, we introduce a concept for using Data Farming, machine learning and XAI to investigate and understand system robustness of a given simulation model.","PeriodicalId":246048,"journal":{"name":"Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120997071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Equipment-centric Data-driven Reliability Assessment of Complex Manufacturing Systems 以设备为中心的数据驱动的复杂制造系统可靠性评估

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3591111

J. Friederich, Wentong Cai, Boon-Ping Gan, S. Lazarova-Molnar

{"title":"Equipment-centric Data-driven Reliability Assessment of Complex Manufacturing Systems","authors":"J. Friederich, Wentong Cai, Boon-Ping Gan, S. Lazarova-Molnar","doi":"10.1145/3573900.3591111","DOIUrl":"https://doi.org/10.1145/3573900.3591111","url":null,"abstract":"Complex manufacturing systems produce highly engineered products with long product cycle times and are characterized by complex production process behaviors. Ensuring the reliability of these systems is critical to meet customer demands, improve product quality and minimize production losses. The collection and storage of data by sensors and information systems respectively enable the automatic generation and analysis of reliability models of complex manufacturing systems, reducing the need for expert knowledge of the processes. In this article, we propose a novel approach to generate data-driven reliability models of complex manufacturing systems using stochastic Petri nets as the modeling formalism. Our method extracts models from event logs that capture relevant events related to material flow in a system, and state logs, that capture operational state changes in a system’s production resources using process mining. We, furthermore, simulate the derived data-driven reliability models using discrete-event simulation and validate the models to ensure their robustness. We demonstrate the successful application of our method using a case study from the wafer fabrication domain. The results of our case study indicate that data-driven reliability assessment of complex manufacturing systems is feasible and can provide rapid insights into such systems. In addition, the extracted models can be used to support decisions related to maintenance planning, parts procurement and system configuration.","PeriodicalId":246048,"journal":{"name":"Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126283313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid PDES Simulation of HPC Networks Using Zombie Packets 基于僵尸包的HPC网络混合PDES仿真

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3591122

Elkin Cruz-Camacho, K. Brown, X. Wang, Xiongxiao Xu, Kai Shu, Z. Lan, R. Ross, C. Carothers

{"title":"Hybrid PDES Simulation of HPC Networks Using Zombie Packets","authors":"Elkin Cruz-Camacho, K. Brown, X. Wang, Xiongxiao Xu, Kai Shu, Z. Lan, R. Ross, C. Carothers","doi":"10.1145/3573900.3591122","DOIUrl":"https://doi.org/10.1145/3573900.3591122","url":null,"abstract":"High-fidelity network simulations provide insights into new realms for high-performance computing (HPC) architectures, although at a high cost. Surrogate models offer a significant reduction in runtime, yet they cannot serve as complete replacements and should be only used when appropriate. Thus the need for hybrid modeling, where high-fidelity simulation and surrogates run side-by-side. We present a surrogate model for HPC networks in which packets bypass the network, and the network state itself is suspended when switching to the surrogate. To bypass the network, every packet is scheduled to arrive at a predicted time in the future estimated from historical data; to suspend the network, all in-flight packets are delivered to their destinations, but they are kept in the system to awaken as zombies when switching back to high-fidelity. Speedup for a hybrid model is relative to the proportion of surrogate to high-fidelity. We obtained a 3 × speedup for a simulation where 70% of virtual time was spent in surrogate mode. When considering the surrogate portion only, the speedup jumps to nearly 20 × on a uniform random network traffic example. The accuracy of the overall simulation increased when the network state was suspended instead of ignored, which demonstrates the need for modeling the network state when transitioning from surrogate back to high-fidelity mode.","PeriodicalId":246048,"journal":{"name":"Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124767115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Storage System Trace Characterization, Compression, and Synthesis using Machine Learning – An Extended Abstract 使用机器学习的存储系统跟踪表征，压缩和合成-扩展摘要

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3593632

Pratik Poudel

引用次数: 0

Reproducibility Report for the Paper: “Hybrid Speculative Synchronisation for Parallel Discrete Event Simulation” 论文的再现性报告:“并行离散事件模拟的混合投机同步”

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3596137

Elsa Gonsiorowski

引用次数: 0

Combining Power Simulation and Programmable Network Emulation for Smart Grid Security Application Evaluation 电力仿真与可编程网络仿真相结合的智能电网安全应用评估

Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation Pub Date : 2023-06-21 DOI: 10.1145/3573900.3593633

Gong Chen, Zheng Hu, Y. Qu, Dong Jin

引用次数: 0