Lorenzo Pagliari, Mirko D'Angelo, M. Caporuscio, R. Mirandola, Catia Trubiani
{"title":"Performance Modelling of Intelligent Transportation Systems: Experience Report","authors":"Lorenzo Pagliari, Mirko D'Angelo, M. Caporuscio, R. Mirandola, Catia Trubiani","doi":"10.1145/3447545.3451205","DOIUrl":null,"url":null,"abstract":"Modern information systems connecting software, physical systems and people, are usually characterized by high dynamism. These dynamics introduce uncertainties, which in turn may harm the quality of systems and lead to incomplete, inaccurate, and unreliable results. To deal with this issue, in this paper we report our incremental experience on the usage of different performance modelling notations while analyzing Intelligent Transportation Systems. More specifically, Queueing Networks and Petri Nets have been adopted and interesting insights are derived.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"45 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3447545.3451205","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Modern information systems connecting software, physical systems and people, are usually characterized by high dynamism. These dynamics introduce uncertainties, which in turn may harm the quality of systems and lead to incomplete, inaccurate, and unreliable results. To deal with this issue, in this paper we report our incremental experience on the usage of different performance modelling notations while analyzing Intelligent Transportation Systems. More specifically, Queueing Networks and Petri Nets have been adopted and interesting insights are derived.