Hybrid Planning for Decision Making in Self-Adaptive Systems

Ashutosh Pandey, Gabriel A. Moreno, J. Cámara, D. Garlan
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引用次数: 39

Abstract

Run-time generation of adaptation plans is a powerful mechanism that helps a self-adaptive system to meet its goals in a dynamically changing environment. In the past, researchers have demonstrated successful use of various automated planning techniques to generate adaptation plans at run time. However, for a planning technique, there is often a trade-off between timeliness and optimality of the solution. For some self-adaptive systems, ideally, one would like to have a planning approach that is both quick and finds an optimal adaptation plan. To find the right balance between these conflicting requirements, this paper introduces a hybrid planning approach that combines more than one planner to obtain the benefits of each. In this paper, to instantiate a hybrid planner we combine deterministic planning with Markov Decision Process (MDP) planning to obtain the best of both worlds: deterministic planning provides plans quickly when timeliness is critical, while allowing MDP planning to generate optimal plans when the system has sufficient time to do so. We validate the hybrid planning approach using a realistic workload pattern in a simulated cloud-based self-adaptive system.
自适应系统决策的混合规划
适应计划的运行时生成是一种强大的机制,可以帮助自适应系统在动态变化的环境中实现其目标。在过去,研究人员已经成功地使用了各种自动化规划技术来在运行时生成适应计划。然而,对于计划技术来说,通常在解决方案的及时性和最优性之间存在权衡。理想情况下,对于一些自适应系统,人们希望有一种既快速又能找到最佳适应计划的规划方法。为了在这些相互冲突的需求之间找到适当的平衡,本文引入了一种混合规划方法,该方法结合了多个规划器以获得每个规划器的好处。在本文中,为了实例化一个混合规划器,我们将确定性规划与马尔可夫决策过程(MDP)规划结合起来,以获得两者的最佳效果:确定性规划在及时性至关重要时快速提供计划,同时允许MDP规划在系统有足够时间时生成最佳计划。我们在模拟的基于云的自适应系统中使用现实的工作负载模式验证了混合规划方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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