Companion of the 2019 ACM/SPEC International Conference on Performance Engineering最新文献_第3页

Concern-driven Reporting of Software Performance Analysis Results 关注驱动的软件性能分析结果报告

Companion of the 2019 ACM/SPEC International Conference on Performance Engineering Pub Date : 2019-03-27 DOI: 10.1145/3302541.3313103

Dusan Okanovic, A. Hoorn, C. Zorn, Fabian Beck, Vincenzo Ferme, J. Walter

{"title":"Concern-driven Reporting of Software Performance Analysis Results","authors":"Dusan Okanovic, A. Hoorn, C. Zorn, Fabian Beck, Vincenzo Ferme, J. Walter","doi":"10.1145/3302541.3313103","DOIUrl":"https://doi.org/10.1145/3302541.3313103","url":null,"abstract":"State-of-the-art approaches for reporting performance analysis results rely on charts providing insights on the performance of the system, often organized in dashboards. The insights are usually data-driven, i.e., not directly connected to the performance concern leading the users to execute the performance engineering activity, thus limiting the understandability of the provided result. A cause is that the data is presented without further explanations. To solve this problem, we propose a concern-driven approach for reporting of performance evaluation results, shaped around a performance concern stated by a stakeholder and captured by state-of-the-art declarative performance engineering specifications. Starting from the available performance analysis, the approach automatically generates a customized performance report providing a chart- and natural-language-based answer to the concern. In this paper, we introduce the general concept of concern-driven performance analysis reporting and present a first prototype implementation of the approach. We envision that, by applying our approach, reports tailored to user concerns reduce the effort to analyze performance evaluation results.","PeriodicalId":231712,"journal":{"name":"Companion of the 2019 ACM/SPEC International Conference on Performance Engineering","volume":"363 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122842317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Performance Benchmarking of Infrastructure-as-a-Service (IaaS) Clouds with Cloud WorkBench 使用云工作台对基础设施即服务(IaaS)云进行性能基准测试

Companion of the 2019 ACM/SPEC International Conference on Performance Engineering Pub Date : 2019-03-27 DOI: 10.1145/3302541.3310294

Joel Scheuner, P. Leitner

引用次数: 6

Model-based Performance Self-adaptation: A Tutorial 基于模型的性能自适应:教程

Companion of the 2019 ACM/SPEC International Conference on Performance Engineering Pub Date : 2019-03-27 DOI: 10.1145/3302541.3310293

Emilio Incerto, M. Tribastone

{"title":"Model-based Performance Self-adaptation: A Tutorial","authors":"Emilio Incerto, M. Tribastone","doi":"10.1145/3302541.3310293","DOIUrl":"https://doi.org/10.1145/3302541.3310293","url":null,"abstract":"This tutorial presents techniques for self-adaptive software systems that use performance models in order to achieve desired quality-of-service objectives. Main hindrances with the state of the art are the assumption of a steady-state regime to be able to use analytical solutions and the explosion of the state space which occurs when modeling software systems with stochastic processes such as Markov chains. This makes their online use difficult because the system under consideration may be in a transient regime, and the typically large cost of the analysis does not permit fast tracking of performance dynamics. We will introduce fluid models based on nonlinear ordinary differential equations as a key enabling technique to effectively approximate large-scale stochastic processes. This representation makes it possible to employ online optimization methods based on model-predictive control in order to find an assignment of the values of tunable parameters of the model steering the system toward a given performance goal. We will also show how, dually, the same techniques can be used for the online estimation of software service demands. In this tutorial we will focus on software performance models based on queuing networks, with applications to runtime auto-scaling in virtualized environments.","PeriodicalId":231712,"journal":{"name":"Companion of the 2019 ACM/SPEC International Conference on Performance Engineering","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132031140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Performance-influencing Factors for Parallel and Algorithmic Problems in Multicore Environments: Work-In-Progress Paper 多核环境中并行和算法问题的性能影响因素:工作进展论文

Companion of the 2019 ACM/SPEC International Conference on Performance Engineering Pub Date : 2019-03-27 DOI: 10.1145/3302541.3313099

Markus Frank, Steffen Becker, Angelika Kaplan, A. Koziolek

引用次数: 0

Companion of the 2019 ACM/SPEC International Conference on Performance Engineering 2019年ACM/SPEC性能工程国际会议指南

Companion of the 2019 ACM/SPEC International Conference on Performance Engineering Pub Date : 1900-01-01 DOI: 10.1145/3302541

引用次数: 0