论文标题
自适应系统的终身动态优化：事实还是虚构？
Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?
论文作者
论文摘要
面对不断变化的环境时，高度可配置的软件系统需要动态搜索有希望的适应计划，以保持最佳性能，例如，较高的吞吐量或较小的延迟 - 自适应系统（SASS）的典型计划问题。但是，鉴于具有多个本地Optima的坚固且复杂的搜索景观，这种SAS计划尤其是在动态环境中具有挑战性的。在本文中，我们提出了Lidos，这是SAS计划的终生动态优化框架。使Lidos唯一的原因是要处理“动态”，我们将SAS计划作为多模式优化问题，旨在保留有用的信息，以更好地处理在动态环境变化下的本地Optima问题。这与现有规划者的不同之处在于，在计划过程中，“动态”并未明确处理。因此，Lidos中的搜索和计划在SAS的寿命中不断运行，仅在离线或搜索空间被环境覆盖时才终止。 三个现实世界中的实验结果表明，作为SAS计划搜索的一部分，明确处理动态的概念是有效的，因为Lidos的表现优于其固定量的总体，最高可提高10倍。与最先进的计划者相比，它在总体上可以取得更好的成绩，并在生成有希望的适应计划时以1.4倍至10倍的速度取得了更好的成绩。
When faced with changing environment, highly configurable software systems need to dynamically search for promising adaptation plan that keeps the best possible performance, e.g., higher throughput or smaller latency -- a typical planning problem for self-adaptive systems (SASs). However, given the rugged and complex search landscape with multiple local optima, such a SAS planning is challenging especially in dynamic environments. In this paper, we propose LiDOS, a lifelong dynamic optimization framework for SAS planning. What makes LiDOS unique is that to handle the "dynamic", we formulate the SAS planning as a multi-modal optimization problem, aiming to preserve the useful information for better dealing with the local optima issue under dynamic environment changes. This differs from existing planners in that the "dynamic" is not explicitly handled during the search process in planning. As such, the search and planning in LiDOS run continuously over the lifetime of SAS, terminating only when it is taken offline or the search space has been covered under an environment. Experimental results on three real-world SASs show that the concept of explicitly handling dynamic as part of the search in the SAS planning is effective, as LiDOS outperforms its stationary counterpart overall with up to 10x improvement. It also achieves better results in general over state-of-the-art planners and with 1.4x to 10x speedup on generating promising adaptation plans.
