{"title":"Issues Arising in Using Kernel Traces to Make a Performance Model","authors":"C. Woodside, S. Tjandra, Gabriel Seyoum","doi":"10.1145/3375555.3384937","DOIUrl":null,"url":null,"abstract":"This report is prompted by some recent experience with building performance models from kernel traces recorded by LTTng, a tracer that is part of Linux, and by observing other researchers who are analyzing performance issues directly from the traces. It briefly distinguishes the scope of the two approaches, regarding the model as an abstraction of the trace, and the model-building as a form of machine learning. For model building it then discusses how various limitations of the kernel trace information limit the model and its capabilities and how the limitations might be overcome by using additional information of different kinds. The overall perspective is a tradeoff between effort and model capability.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-04-20","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/10.1145/3375555.3384937","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
This report is prompted by some recent experience with building performance models from kernel traces recorded by LTTng, a tracer that is part of Linux, and by observing other researchers who are analyzing performance issues directly from the traces. It briefly distinguishes the scope of the two approaches, regarding the model as an abstraction of the trace, and the model-building as a form of machine learning. For model building it then discusses how various limitations of the kernel trace information limit the model and its capabilities and how the limitations might be overcome by using additional information of different kinds. The overall perspective is a tradeoff between effort and model capability.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
