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Improving Scalability and Reward of Utility-Driven Self-Healing for Large Dynamic Architectures
ACM Transactions on Autonomous and Adaptive Systems ( IF 2.7 ) Pub Date : 2020-02-25 , DOI: 10.1145/3380965
Sona Ghahremani 1 , Holger Giese 1 , Thomas Vogel 2
Affiliation  

Self-adaptation can be realized in various ways. Rule-based approaches prescribe the adaptation to be executed if the system or environment satisfies certain conditions. They result in scalable solutions but often with merely satisfying adaptation decisions. In contrast, utility-driven approaches determine optimal decisions by using an often costly optimization, which typically does not scale for large problems. We propose a rule-based and utility-driven adaptation scheme that achieves the benefits of both directions such that the adaptation decisions are optimal, whereas the computation scales by avoiding an expensive optimization. We use this adaptation scheme for architecture-based self-healing of large software systems. For this purpose, we define the utility for large dynamic architectures of such systems based on patterns that define issues the self-healing must address. Moreover, we use pattern-based adaptation rules to resolve these issues. Using a pattern-based scheme to define the utility and adaptation rules allows us to compute the impact of each rule application on the overall utility and to realize an incremental and efficient utility-driven self-healing. In addition to formally analyzing the computational effort and optimality of the proposed scheme, we thoroughly demonstrate its scalability and optimality in terms of reward in comparative experiments with a static rule-based approach as a baseline and a utility-driven approach using a constraint solver. These experiments are based on different failure profiles derived from real-world failure logs. We also investigate the impact of different failure profile characteristics on the scalability and reward to evaluate the robustness of the different approaches.

中文翻译:

提高大型动态架构的实用驱动自我修复的可扩展性和回报

自适应可以通过多种方式实现。如果系统或环境满足某些条件,基于规则的方法规定了要执行的适应。它们产生可扩展的解决方案,但通常只是令人满意的适应决策。相比之下,效用驱动的方法通过使用通常代价高昂的优化来确定最佳决策,这种优化通常不适用于大型问题。我们提出了一种基于规则和效用驱动的自适应方案,该方案实现了两个方向的好处,使得自适应决策是最优的,而计算则通过避免昂贵的优化来扩展。我们将这种适应方案用于大型软件系统的基于架构的自我修复。以此目的,我们根据定义自我修复必须解决的问题的模式来定义此类系统的大型动态架构的实用程序。此外,我们使用基于模式的适应规则来解决这些问题。使用基于模式的方案来定义效用和适应规则允许我们计算每个规则应用对整体效用的影响,并实现增量和高效的效用驱动自我修复。除了正式分析所提出方案的计算工作量和最优性之外,我们还在比较实验中彻底证明了它在奖励方面的可扩展性和最优性,其中以基于静态规则的方法作为基线和使用约束求解器的效用驱动方法。这些实验基于从真实世界的故障日志中得出的不同故障配置文件。
更新日期:2020-02-25
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