一句话：提出 Thallium，用结构化可达性与最优效用边界剪枝，缩减运行时自适应决策空间。
自适应系统常利用动态规划在运行时选择最优适应方案，但面临维数灾难，决策成本随候选方案增加而急剧上升。本文提出 Thallium，结合自动形式化建模技术：一方面分析系统结构模型，确定配置之间的可达关系；另一方面在有限时域内计算最优适应在最好与最坏情况下的效用边界。基于这些优化边界构造三角可能性值，自动比较每个配置的相邻适应方案，仅保留潜在结果范围最佳的候选。实验表明，Thallium 能显著减少每次适应决策需考虑的状态数量，从而释放运行时关键资源。
Self-adaptive systems often employ dynamic programming or similar techniques to select optimal adaptations at run-time. These techniques suffer from the "curse of dimensionality", increasing the cost of run-time adaptation decisions. We propose a novel approach that improves upon the state-of-the-art proactive self-adaptation techniques to reduce the number of possible adaptations that need be considered for each run-time adaptation decision. The approach, realized in a tool called Thallium, employs a combination of automated formal modeling techniques to (i) analyze a structural model of the system showing which configurations are reachable from other configurations and (ii) compute the utility that can be generated by the optimal adaptation over a bounded horizon in both the best- and worst-case scenarios. It then constructs triangular possibility values using those optimized bounds to automatically compare adjacent adaptations for each configuration, keeping only the alternatives with the best range of potential results. The experimental results corroborate Thallium's ability to significantly reduce the number of states that need to be considered with each adaptation decision, freeing up vital resources at run-time.
DOI 原文 ·
@inproceedings{paperbot5583,
title = {Reducing run-time adaptation space via analysis of possible utility bounds},
author = {Clay Stevens and Hamid Bagheri},
booktitle = {ICSE},
year = {2020},
doi = {10.1145/3377811.3380365}
}