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?
摘要：
当面对不断变化的环境时，高度可配置的软件系统需要动态地搜索有希望的适应计划，以保持最佳可能的性能，例如，更高的吞吐量或更小的延迟--这是自适应系统（SAS）的典型规划问题。然而，考虑到具有多个局部最优的崎岖和复杂的搜索环境，这样的SAS规划是具有挑战性的，特别是在动态环境中。在本文中，我们提出了LiDOS，SAS规划的终身动态优化框架。LiDOS的独特之处在于，为了处理“动态”，我们将SAS规划制定为多模态优化问题，旨在保留有用的信息，以便更好地处理动态环境变化下的局部最优问题。这与现有规划器的不同之处在于，在规划中的搜索过程中没有明确地处理“动态”。因此，LiDOS中的搜索和规划在SAS的整个生命周期内持续运行，仅在脱机或搜索空间已被环境覆盖时终止。在三个真实世界SAS上的实验结果表明，在SAS规划中显式处理动态作为搜索的一部分的概念是有效的，因为LiDOS整体上优于其静态对应物，最多可提高10倍。它还在总体上比最先进的规划器取得了更好的结果，并且在生成有希望的适应计划方面具有1.4倍至10倍的加速。
英文摘要：
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.
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。
