Incremental Runtime-generation of Optimisation Problems using RAG-controlled Rewriting
Incremental Runtime-generation of Optimisation Problems using RAG-controlled Rewriting
复制标题
使用 RAG 控制的重写增量运行时生成优化问题
DOI：
--
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Christoff Bürger
中科院分区：
文献类型：
--
作者：
René Schöne;Sebastian Götz;U. Assmann;Christoff Bürger
在物联网时代，软件系统需要与许多物理实体交互，并在运行时应对新的需求。自适应系统旨在解决这些挑战，通常使用能够更好地推理能力的运行时模型来表示它们的上下文。然而，由于大量物理实体不断变化，这些模型的大小迅速增长，需要频繁更新并进行微小更改。这种情况威胁到对这些模型的分析的有效性，因为它们缺乏对导致不必要的计算开销的那些变化的有效管理。我们建议在存在复杂模型的情况下将可伸缩的、增量的运行时模型更改管理应用于文本转换。给出了一个整数线性规划的代码生成实例，并对其进行了评价。在我们使用合成模型的案例研究中，与非增量方法相比，我们节省了35%-83%的处理时间。使用我们的方法，未来的自适应系统可以处理和分析大规模的运行时模型，即使它们经常变化。(更少)
In the era of Internet of Things, software systems need to interact with many physical entities and cope with new requirements at runtime. Self-Adaptive systems aim to tackle those challenges, often representing their context with a runtime model enabling better reasoning capabilities. However, those models quickly grow in size and need to be updated frequently with small changes due to a high number of physical entities changing constantly. This situation threatens the efficacy of analyses on such models, as they lack an efficient management of those changes leading to unnecessary computation overhead. We propose applying scalable, incremental change management of runtime models in the presence of a complex model to text transformation. In this paper, we present and evaluate an example of code generation of integer linear programs. In our case study using synthesized models, we saved 35 - 83% processing time compared to a non-incremental approach. Using our approach, future self-Adaptive systems can handle and analyze large-scale runtime models, even if they change frequently. (Less)
DOI：
10.1007/978-3-319-08915-7
发表时间：
2014-12
期刊：
--
影响因子：
--
作者：
N. Bencomo;B. Cheng;U. Assmann
通讯作者：
N. Bencomo;B. Cheng;U. Assmann