Towards Solving the Challenge of Minimal Overhead Monitoring

D. Reichelt, Stefan Kühne, W. Hasselbring
{"title":"Towards Solving the Challenge of Minimal Overhead Monitoring","authors":"D. Reichelt, Stefan Kühne, W. Hasselbring","doi":"10.1145/3578245.3584851","DOIUrl":null,"url":null,"abstract":"The examination of performance changes or the performance behavior of a software requires the measurement of the performance. This is done via probes, i.e., pieces of code which obtain and process measurement data, and which are inserted into the examined application. The execution of those probes in a singular method creates overhead, which deteriorates performance measurements of calling methods and slows down the measurement process. Therefore, an important challenge for performance measurement is the reduction of the measurement overhead. To address this challenge, the overhead should be minimized. Based on an analysis of the sources of performance overhead, we derive the following four optimization options: (1) Source instrumentation instead of AspectJ instrumentation, (2) reduction of measurement data, (3) change of the queue and (4) aggregation of measurement data. We evaluate the effect of these optimization options using the MooBench benchmark. Thereby, we show that these optimizations options reduce the monitoring overhead of the monitoring framework Kieker. For MooBench, the execution duration could be reduced from 4.77 µs to 0.39 µs per method invocation on average.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3578245.3584851","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

The examination of performance changes or the performance behavior of a software requires the measurement of the performance. This is done via probes, i.e., pieces of code which obtain and process measurement data, and which are inserted into the examined application. The execution of those probes in a singular method creates overhead, which deteriorates performance measurements of calling methods and slows down the measurement process. Therefore, an important challenge for performance measurement is the reduction of the measurement overhead. To address this challenge, the overhead should be minimized. Based on an analysis of the sources of performance overhead, we derive the following four optimization options: (1) Source instrumentation instead of AspectJ instrumentation, (2) reduction of measurement data, (3) change of the queue and (4) aggregation of measurement data. We evaluate the effect of these optimization options using the MooBench benchmark. Thereby, we show that these optimizations options reduce the monitoring overhead of the monitoring framework Kieker. For MooBench, the execution duration could be reduced from 4.77 µs to 0.39 µs per method invocation on average.
解决最小开销监控的挑战
对软件性能变化或性能行为的检查需要对性能进行测量。这是通过探针完成的,即获取和处理测量数据的代码片段,并将其插入到被检查的应用程序中。在单个方法中执行这些探测会产生开销,这会降低调用方法的性能度量并减慢度量过程。因此,性能度量的一个重要挑战是减少度量开销。为了应对这一挑战,应该尽量减少开销。基于对性能开销来源的分析,我们得出了以下四种优化选项:(1)源插装而不是AspectJ插装,(2)减少测量数据,(3)改变队列,(4)测量数据聚合。我们使用MooBench基准测试来评估这些优化选项的效果。因此,我们展示了这些优化选项减少了监视框架Kieker的监视开销。对于moobbench,每次方法调用的执行时间平均从4.77µs减少到0.39µs。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书