Automatically RELAXing a Goal Model to Cope with Uncertainty
Automatically RELAXing a Goal Model to Cope with Uncertainty
复制标题
自动放松目标模型以应对不确定性
DOI：
--
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
B. Cheng
中科院分区：
文献类型：
--
作者：
A. J. Ramírez;Erik M. Fredericks;Adam C. Jensen;B. Cheng
动态自适应系统（DAS）必须科普不断变化的系统和环境条件，这些条件在开发过程中可能没有完全理解或预期。RELAX是一种基于模糊逻辑的规范语言，用于使DAS需求更能容忍意外的环境条件。本文介绍了AutoRELAX，一种方法，生成RELAXed目标模型，解决环境的不确定性，确定哪些目标RELAX，哪些RELAX运营商应用，以及定义目标满意度标准的模糊逻辑函数的形状。AutoRELAX搜索RELAXed目标模型，使DAS能够满足其功能要求，同时在最小化RELAXed目标数量和最小化由次要和不利环境条件触发的适应数量之间进行权衡。我们将AutoRELAX应用到行业提供的网络应用程序中，该应用程序可以自我重新配置以应对不利的环境条件，例如链路故障。
Dynamically adaptive systems (DAS) must cope with changing system and environmental conditions that may not have been fully understood or anticipated during development time. RELAX is a fuzzy logic-based specification language for making DAS requirements more tolerable to unanticipated environmental conditions. This paper presents AutoRELAX, an approach that generates RELAXed goal models that address environmental uncertainty by identifying which goals to RELAX, which RELAX operators to apply, and the shape of the fuzzy logic function that defines the goal satisfaction criteria. AutoRELAX searches for RELAXed goal models that enable a DAS to satisfy its functional requirements while balancing tradeoffs between minimizing the number of RELAXed goals and minimizing the number of adaptations triggered by minor and adverse environmental conditions. We apply AutoRELAX to an industry-provided network application that self-reconfigures in response to adverse environmental conditions, such as link failures.