面向可配置系统的蒸馏式终身自适应
Distilled Lifelong Self-Adaptation for Configurable Systems
AI总结：
本文提出 DLiSA 框架，通过终身规划与蒸馏知识播种实现可配置系统在时变负载下的高效自适应，实验显示性能最高提升 229%、资源加速最高 2.22 倍。
摘要：
现代可配置系统为构建未来智能软件系统提供了巨大机遇。其中的一个关键难点在于，如何对运行中系统的配置进行有效的自适应调整，使其性能（如运行时间和吞吐量）在时变工作负载下得到优化。遗憾的是，现有方法仍未解决这一问题，因为它们要么忽略了可用的历史知识，要么依赖对历史知识的静态利用，在规划自适应时没有对信息的有用性进行推理。本文针对这一具有挑战性的问题提出了 DLiSA，一个对可配置系统进行自适应的框架。DLiSA 具有两个特性：第一，它支持终身规划，因此规划过程在系统的整个生命周期中持续运行，允许动态利用积累的知识以实现快速自适应。第二，针对新出现工作负载的规划通过蒸馏知识播种得到增强，其中知识被动态纯化，使得仅在必要时播种有用的历史配置，从而减少误导性信息。大量实验表明，所提出的 DLiSA 显著优于最先进的方法，在生成有前景的自适应配置方面实现了最高 229% 的性能提升和最高 2.22 倍的资源加速。所有数据和源代码可在我们的仓库中找到：https://github.com/ideas-labo/dlisa。
英文摘要：
Modern configurable systems provide tremendous opportunities for engineering future intelligent software systems. A key difficulty thereof is how to effectively self-adapt the configuration of a running system such that its performance (e.g., runtime and throughput) can be optimized under time-varying workloads. This unfortunately remains unaddressed in existing approaches as they either overlook the available past knowledge or rely on static exploitation of past knowledge without reasoning the usefulness of information when planning for self-adaptation. In this paper, we tackle this challenging problem by proposing DLiSA, a framework that self-adapts configurable systems. DLiSA comes with two properties: firstly, it supports lifelong planning, and thereby the planning process runs continuously throughout the lifetime of the system, allowing dynamic exploitation of the accumulated knowledge for rapid adaptation. Secondly, the planning for a newly emerged workload is boosted via distilled knowledge seeding, in which the knowledge is dynamically purified such that only useful past configurations are seeded when necessary, mitigating misleading information. Extensive experiments suggest that the proposed DLiSA significantly outperforms state-of-the-art approaches, demonstrating a performance improvement of up to 229% and a resource acceleration of up to 2.22x on generating promising adaptation configurations. All data and sources can be found at our repository: https://github.com/ideas-labo/dlisa.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
