面向大型动态体系结构的基于自适应规则的实用性驱动的高效自愈
Efficient Utility-Driven Self-Healing Employing Adaptation Rules for Large Dynamic Architectures
摘要：
自适应可以通过多种方式实现。基于规则的方法规定，如果系统或环境满足某些条件，并导致可扩展的解决方案，要执行的适应，然而，往往只有满足适应决策。相比之下，效用驱动的方法通过使用通常昂贵的优化步骤来确定最佳适应决策，这通常不能很好地扩展到更大的问题。我们提出了一种基于规则和效用驱动的方法，实现了这些方向的有益特性，使适应决策是最佳的，而计算保持可扩展性，因为可以避免昂贵的优化步骤。该方法可用于大型软件系统基于体系结构的自愈。我们定义的实用程序为大型动态架构的系统的基础上捕获的问题的模式的自我修复必须解决，我们使用patternbased适应规则来解决问题。定义的效用以及基于模式的自适应规则，使我们能够计算每个规则的应用程序对整体效用的影响，并实现增量和高效的效用驱动的自我修复。我们证明了我们的计划的效率和最优性的比较实验与静态的基于规则的计划作为基线和效用驱动的方法，使用约束求解器。
英文摘要：
Self-adaptation can be realized in various ways. Rule-based approaches prescribe the adaptation to be executed if the system or environment satisfy certain conditions and result in scalable solutions, however, with often only satisfying adaptation decisions. In contrast, utility-driven approaches determine optimal adaptation decisions by using an often costly optimization step, which typically does not scale well for larger problems. We propose a rule-based and utility-driven approach that achieves the beneficial properties of each of these directions such that the adaptation decisions are optimal while the computation remains scalable since an expensive optimization step can be avoided. The approach can be used for the architecture-based self-healing of large software systems. We define the utility for large dynamic architectures of such systems based on patterns capturing issues the self-healing must address and we use patternbased adaptation rules to resolve the issues. Defining the utility as well as the adaptation rules pattern-based 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. We demonstrate the efficiency and optimality of our scheme in comparative experiments with a static rule-based scheme as a baseline and a utility-driven approach using a constraint solver.
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