Scalable design and performance analysis for long-living software families (DAPS2)

适用于长寿命软件系列的可扩展设计和性能分析 (DAPS2)

• 批准号: 221770164
• 负责人: Professorin Dr.-Ing. Ina Schaefer
• 金额: --
• 依托单位: KASTEL - Institut für Informationssicherheit und Verlässlichkeit
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2012
• 资助国家: 德国
• 起止时间: 2011-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/221770164?language=en
• 关键词: Scalable; design; performance; analysis; long

Long-living software systems are typically available in a rich set of variants to deal with differing customer requirements and application contexts. Furthermore, users are often given the possibility to change to a different configuration online to dynamically adapt to varying environmental conditions. In addition to satisfying functional requirements, such changes are to preserve existing service-level agreements. The focus of this project is to define a methodology for expressing system variability and its impact on performance. Motivated by its widespread use in certain domains, we consider a model-driven approach based on behavioral models, using notions of delta modeling and feature composition enriched with information needed to automatically derive a performance model. We are concerned with the usually very large number of variations in models of realistic systems, which impede naive approaches based on exhaustive exploration for predictive purposes, especially at runtime when requirements on execution times are stringent. We offer a symbiotic approach which harnesses a structure of variability inferred by deltas to efficiently analyzing the whole configuration space, for instance by pruning certain subspaces with provably inferior estimated performance. The approach will be practically applied to the dynamic throughput optimization of a software-controlled automated assembly line, chosen as a representative case study of variant-rich long-lived software systems where changes are ideally applied online to avoid costly interruptions.

长期使用的软件系统通常有丰富的变体可供使用，以处理不同的客户要求和应用环境。此外，用户通常可以在线更改为不同的配置，以动态适应变化的环境条件。除了满足功能要求外，这些变化是为了维护现有的服务级别协议。这个项目的重点是定义一种方法来表达系统的可变性及其对性能的影响。出于其在某些领域的广泛使用，我们认为一个模型驱动的方法的基础上的行为模型，使用的概念三角洲建模和功能组合丰富的信息需要自动推导出的性能模型。我们关注的是现实系统的模型中通常存在大量的变化，这阻碍了基于预测目的的穷举探索的天真方法，特别是在运行时对执行时间的要求非常严格。我们提供了一种共生的方法，利用三角洲推断的可变性结构，有效地分析整个配置空间，例如通过修剪某些子空间，可证明较低的估计性能。该方法将实际应用于软件控制的自动化装配线的动态吞吐量优化，选择作为一个代表性的案例研究丰富的变量的长期使用的软件系统，其中的变化是理想的在线应用，以避免昂贵的中断。