{"title":"使用动态检测的分布式Java应用分析","authors":"Clovis Seragiotto, T. Fahringer","doi":"10.1109/CLUSTR.2005.347065","DOIUrl":null,"url":null,"abstract":"Although new Java virtual machines provide an API to obtain raw performance data, it is still the task of a skillful performance analysis tool to take all the strategic decisions for instrumentation and performance analysis of distributed Java programs. In this paper we demonstrate two new tools. Twilight and Aksum, which try to automatically instrument code regions, to determine what performance data to collect, to interpret performance data, and to relate the bottlenecks found back to source code. We present experiments with a widely distributed Java application running on a heterogeneous set of machines with different operating systems to demonstrate the efficacy of our tools","PeriodicalId":255312,"journal":{"name":"2005 IEEE International Conference on Cluster Computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Analysis of Distributed Java Applications Using Dynamic Instrumentation\",\"authors\":\"Clovis Seragiotto, T. Fahringer\",\"doi\":\"10.1109/CLUSTR.2005.347065\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Although new Java virtual machines provide an API to obtain raw performance data, it is still the task of a skillful performance analysis tool to take all the strategic decisions for instrumentation and performance analysis of distributed Java programs. In this paper we demonstrate two new tools. Twilight and Aksum, which try to automatically instrument code regions, to determine what performance data to collect, to interpret performance data, and to relate the bottlenecks found back to source code. We present experiments with a widely distributed Java application running on a heterogeneous set of machines with different operating systems to demonstrate the efficacy of our tools\",\"PeriodicalId\":255312,\"journal\":{\"name\":\"2005 IEEE International Conference on Cluster Computing\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 IEEE International Conference on Cluster Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CLUSTR.2005.347065\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 IEEE International Conference on Cluster Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CLUSTR.2005.347065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis of Distributed Java Applications Using Dynamic Instrumentation
Although new Java virtual machines provide an API to obtain raw performance data, it is still the task of a skillful performance analysis tool to take all the strategic decisions for instrumentation and performance analysis of distributed Java programs. In this paper we demonstrate two new tools. Twilight and Aksum, which try to automatically instrument code regions, to determine what performance data to collect, to interpret performance data, and to relate the bottlenecks found back to source code. We present experiments with a widely distributed Java application running on a heterogeneous set of machines with different operating systems to demonstrate the efficacy of our tools
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