Workshop on Software and Performance最新文献

Performance Antipattern Detection through fUML Model Library 通过uml模型库进行性能反模式检测

Workshop on Software and Performance Pub Date : 2015-01-31 DOI: 10.1145/2693561.2693565

Davide Arcelli, L. Berardinelli, Catia Trubiani

引用次数: 5

Software Performance Engineering Then and Now: A Position Paper 软件性能工程的过去和现在:一份意见书

Workshop on Software and Performance Pub Date : 2015-01-31 DOI: 10.1145/2693561.2693567

C. U. Smith

引用次数: 11

Challenges in Integrating the Analysis of Multiple Non-Functional Properties in Model-Driven Software Engineering 模型驱动软件工程中集成多种非功能特性分析的挑战

Workshop on Software and Performance Pub Date : 2015-01-31 DOI: 10.1145/2693561.2693566

D. Petriu

引用次数: 7

Integrating Formal Timing Analysis in the Real-Time Software Development Process 在实时软件开发过程中集成形式化时序分析

Workshop on Software and Performance Pub Date : 2015-01-31 DOI: 10.1145/2693561.2693562

R. Henia, L. Rioux, Nicolas Sordon, G. Garcia, Marco Panunzio

{"title":"Integrating Formal Timing Analysis in the Real-Time Software Development Process","authors":"R. Henia, L. Rioux, Nicolas Sordon, G. Garcia, Marco Panunzio","doi":"10.1145/2693561.2693562","DOIUrl":"https://doi.org/10.1145/2693561.2693562","url":null,"abstract":"When designing complex real-time software, it is very difficult to predict how design decisions may impact the system timing behavior. Usually, the industrial practices rely on the subjective judgment of experienced software architects and developers. This is however risky since eventual timing errors are only detected after implementation and integration, when the software execution can be tested on system level, under realistic conditions. At this stage, timing errors may be very costly and time consuming to correct. Therefore, to overcome this problem we need an efficient, reliable and automated timing estimation method applicable already at early design stages and continuing throughout the whole development cycle. Formal timing analysis appears at first sight to be the adequate candidate for this purpose. However, its use in the industry is conditioned by a smooth and seamless integration in the software development process. This is not an easy task due to the semantic mismatches between the design and analysis models but also due to the missing link between the analysis and the testing phase after code implementation. In this paper, we present a timing analysis framework we developed in the context of the industrial design of satellite on-board software, allowing an early integration and full automation of formal timing verification activities in the development process of real-time embedded software, as a mean to decrease the design time and reduce the risks of costly timing failures.","PeriodicalId":235512,"journal":{"name":"Workshop on Software and Performance","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117345485","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

Autoperf: Workflow Support for Performance Experiments autooperf:对性能实验的工作流支持

Workshop on Software and Performance Pub Date : 2015-01-31 DOI: 10.1145/2693561.2693569

Xiaoguang Dai, B. Norris, A. Malony

引用次数: 4

Runtime Performance Challenges in Big Data Systems 大数据系统中的运行时性能挑战

Workshop on Software and Performance Pub Date : 2015-01-31 DOI: 10.1145/2693561.2693563

John Klein, I. Gorton

{"title":"Runtime Performance Challenges in Big Data Systems","authors":"John Klein, I. Gorton","doi":"10.1145/2693561.2693563","DOIUrl":"https://doi.org/10.1145/2693561.2693563","url":null,"abstract":"Big data systems are becoming pervasive. They are distributed systems that include redundant processing nodes, replicated storage, and frequently execute on a shared 'cloud' infrastructure. For these systems, design-time predictions are insufficient to assure runtime performance in production. This is due to the scale of the deployed system, the continually evolving workloads, and the unpredictable quality of service of the shared infrastructure. Consequently, a solution for addressing performance requirements needs sophisticated runtime observability and measurement. Observability gives real-time insights into a system's health and status, both at the system and application level, and provides historical data repositories for forensic analysis, capacity planning, and predictive analytics. Due to the scale and heterogeneity of big data systems, significant challenges exist in the design, customization and operations of observability capabilities. These challenges include economical creation and insertion of monitors into hundreds or thousands of computation and data nodes, efficient, low overhead collection and storage of measurements (which is itself a big data problem), and application-aware aggregation and visualization. In this paper we propose a reference architecture to address these challenges, which uses a model-driven engineering toolkit to generate architecture-aware monitors and application-specific visualizations.","PeriodicalId":235512,"journal":{"name":"Workshop on Software and Performance","volume":"316 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116597131","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: 16

Towards a DevOps Approach for Software Quality Engineering 面向软件质量工程的DevOps方法

Workshop on Software and Performance Pub Date : 2015-01-31 DOI: 10.1145/2693561.2693564

Juan F. Pérez, Weikun Wang, G. Casale

引用次数: 23

Beyond Simulation: Composing Scalability, Elasticity, and Efficiency Analyses from Preexisting Analysis Results 超越模拟:从先前存在的分析结果组合可扩展性，弹性和效率分析

Workshop on Software and Performance Pub Date : 2015-01-31 DOI: 10.1145/2693561.2693568

Sebastian Lehrig, Steffen Becker

引用次数: 2

Adaptivity metric and performance for restart strategies in web services reliable messaging web服务可靠消息传递中重启策略的自适应度量和性能

Workshop on Software and Performance Pub Date : 2008-06-23 DOI: 10.1145/1383559.1383585

P. Reinecke, K. Wolter

引用次数: 14

Coupled model transformations 耦合模型转换

Workshop on Software and Performance Pub Date : 2008-06-23 DOI: 10.1145/1383559.1383573

Steffen Becker

{"title":"Coupled model transformations","authors":"Steffen Becker","doi":"10.1145/1383559.1383573","DOIUrl":"https://doi.org/10.1145/1383559.1383573","url":null,"abstract":"Model-driven performance prediction methods use abstract design models to predict the performance of the modelled system during early development stages. However, performance is an attribute of the running system and not its model. The system contains many implementation details not part of its model but still affecting the performance at run-time. Existing approaches neglect details of the implementation due to the abstraction underlying the design model. Completion components [26] deal with this problem, however, they have to be added manually to the prediction model. In this work, we assume that the system's implementation is generated by a chain of model transformations. In this scenario, the transformation rules determine the transformation result. By analysing these transformation rules, a second transformation can be derived which automatically adds details to the prediction model according to the encoded rules. We call this transformation a coupled transformation as it is coupled to an corresponding model-to-code transformation. It uses the knowledge on the output of the model-to-code transformation to increase performance prediction accuracy. The introduced coupled transformations method is validated in a case study in which a parametrised transformation maps abstract component connectors to realisations in different RPC calls. In this study, the corresponding coupled transformation captures the RPC's details with a prediction error of less than 5%.","PeriodicalId":235512,"journal":{"name":"Workshop on Software and Performance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129813298","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: 36