Approximate Distributed Discrete Event Simulation using Semi-Conservative Look-Ahead Estimation

Desheng Fu, Marcus O'Connor, Matthias Becker, H. Szczerbicka
{"title":"Approximate Distributed Discrete Event Simulation using Semi-Conservative Look-Ahead Estimation","authors":"Desheng Fu, Marcus O'Connor, Matthias Becker, H. Szczerbicka","doi":"10.1109/DS-RT47707.2019.8958660","DOIUrl":null,"url":null,"abstract":"A novel way of distributed discrete event simulation, called approximate distributed discrete event simulation, is presented in this paper. Compared with the classic simulation, the models for approximate simulation give some kind of free margin to the simulator during the execution. This can be used in some cases to reduce the overhead of the simulation, especially the execution time. Since the margin can be adjusted arbitrarily in the range, a trade-off between the simulation precision and the execution time can be achieved this way. It’s well known that the execution time of distributed discrete event simulation can’t be reduced significantly compared with a sequential simulation when the logical processes are tightly coupled and the lookahead is very short. In this study, a framework of approximate distributed discrete event simulation with some novel algorithms is developed, which is aimed to provide a longer look-ahead and further the trade-off between the simulation precision and the execution time using the free margin provided by the model.","PeriodicalId":377914,"journal":{"name":"2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DS-RT47707.2019.8958660","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

A novel way of distributed discrete event simulation, called approximate distributed discrete event simulation, is presented in this paper. Compared with the classic simulation, the models for approximate simulation give some kind of free margin to the simulator during the execution. This can be used in some cases to reduce the overhead of the simulation, especially the execution time. Since the margin can be adjusted arbitrarily in the range, a trade-off between the simulation precision and the execution time can be achieved this way. It’s well known that the execution time of distributed discrete event simulation can’t be reduced significantly compared with a sequential simulation when the logical processes are tightly coupled and the lookahead is very short. In this study, a framework of approximate distributed discrete event simulation with some novel algorithms is developed, which is aimed to provide a longer look-ahead and further the trade-off between the simulation precision and the execution time using the free margin provided by the model.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于半保守预估的近似分布离散事件模拟
本文提出了一种新的分布式离散事件模拟方法——近似分布离散事件模拟。与经典仿真相比,近似仿真模型在仿真过程中给仿真器提供了一定的自由余地。这可以在某些情况下用于减少模拟的开销,特别是执行时间。由于余量可以在这个范围内任意调整,因此可以通过这种方式在模拟精度和执行时间之间进行权衡。众所周知,在逻辑过程紧密耦合且前瞻时间很短的情况下,分布式离散事件仿真的执行时间与序列仿真相比不能显著缩短。本文提出了一种基于新算法的近似分布离散事件仿真框架,该框架旨在利用模型提供的自由余量提供更长的前瞻性,并进一步权衡仿真精度和执行时间之间的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
How can Machine Learning Support the Practice of Modeling and Simulation? —A Review and Directions for Future Research Performance Gains in V2X Experiments Using Distributed Simulation in the Veins Framework Formal Modelling and Verification of Real-Time Self-Adaptive Systems [DS-RT 2019 Title Page] Modelling and Simulation of ElasticSearch using CloudSim
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1