论文标题
具有历史记录的内存效率运行时模型的可扩展查询方案
A Scalable Querying Scheme for Memory-efficient Runtime Models with History
论文作者
论文摘要
运行时型号可在所需的抽象水平下运行时的系统快照。通过与建模系统的因果关系并采用模型驱动的工程技术，运行时模型支持（运行时）适应方案，其中以前的快照的数据有助于更明智的决定。然而，尽管运行时模型和基于模型的适应技术一直是广泛研究的重点，但随着时间的流逝，作为一流公民的发展方案，最近才受到关注。因此，对于具有历史的此类运行时模型缺乏复杂的技术。 我们提出了一个查询方案，其中将时间需求与增量模型查询集成可以为具有历史记录的运行时模型提供可扩展的查询。此外，我们的方案提供了此类模型的记忆效率存储。通过将这两个功能集成到适应循环中，我们可以通过运行时模型启用有效的历史自我适应，我们提出了实现。
Runtime models provide a snapshot of a system at runtime at a desired level of abstraction. Via a causal connection to the modeled system and by employing model-driven engineering techniques, runtime models support schemes for (runtime) adaptation where data from previous snapshots facilitates more informed decisions. Nevertheless, although runtime models and model-based adaptation techniques have been the focus of extensive research, schemes that treat the evolution of the model over time as a first-class citizen have only lately received attention. Consequently, there is a lack of sophisticated technology for such runtime models with history. We present a querying scheme where the integration of temporal requirements with incremental model queries enables scalable querying for runtime models with history. Moreover, our scheme provides for a memory-efficient storage of such models. By integrating these two features into an adaptation loop, we enable efficient history-aware self-adaptation via runtime models, of which we present an implementation.
