Self-adaptation improves the resilience of software-intensive systems, enabling them to adapt their structure and behavior to run-time changes (e.g., in workload and resource availability). Many of these approaches reason about the best way of adapting by synthesizing adaptation plans online via planning or model checking tools. This method enables the exploration of a rich solution space, but optimal solutions and other guarantees (e.g., constraint satisfaction) are computationally costly, resulting in long planning times during which changes may invalidate plans. An alternative to online planning involves selecting at run time the adaptation best suited to the current system and environment conditions from among a predefined repertoire of adaptation strategies that capture repair and optimization tasks. This method does not incur run-time overhead but requires additional effort from engineers, who have to specify strategies and lack support to systematically assess their quality. In this article, we present MOSAICO, an approach for offline synthesis of adaptation strategy repertoires that makes a novel use of discrete abstractions of the state space to flexibly adapt extra-functional behavior in a scalable manner. The approach supports making trade-offs: (i) among multiple extra-functional concerns, and (ii) between computation time and adaptation quality (varying abstraction resolution). Our results show a remarkable improvement on system qualities in contrast to manually-specified repertoires. More interestingly, moderate increments in abstraction resolution can lead to pronounced quality improvements, whereas high resolutions yield only negligible improvement over medium resolutions.

中文翻译：

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MOSAICO：具有灵活权衡的适应策略库的离线合成

自适应提高了软件密集型系统的弹性，使它们能够使它们的结构和行为适应运行时的变化（例如，工作负载和资源可用性）。许多这些方法通过规划或模型检查工具在线综合适应计划来推断最佳适应方式。这种方法能够探索丰富的解决方案空间，但最佳解决方案和其他保证（例如，约束满足）在计算上是昂贵的，导致规划时间过长，在此期间更改可能会使计划无效。在线规划的替代方案包括在运行时从捕获修复和优化任务的预定义适应策略库中选择最适合当前系统和环境条件的适应。这种方法不会产生运行时开销，但需要工程师付出额外的努力，他们必须指定策略并且缺乏系统评估质量的支持。在本文中，我们介绍了 MOSAICO，这是一种离线合成适应策略库的方法，它新颖地使用状态空间的离散抽象，以可扩展的方式灵活地适应功能外的行为。该方法支持进行权衡：（i）在多个功能外关注点之间，以及（ii）在计算时间和适应质量（不同的抽象分辨率）之间进行权衡。我们的结果表明，与手动指定的曲目相比，系统质量有了显着提高。更有趣的是，抽象分辨率的适度增加可以显着提高质量，