To assure performance on the fly, planning is arguably one of the most
important steps for self-adaptive systems (SASs), especially when they are
highly configurable with a daunting number of adaptation options. However,
there has been little understanding of the planning landscape or ways by which
it can be analyzed. This inevitably creates barriers to the design of better
and tailored planners for SASs. In this paper, we showcase how the planning
landscapes of SASs can be quantified and reasoned, particularly with respect to
the different environments. By studying four diverse real-world SASs and 14
environments, we found that (1) the SAS planning landscapes often provide
strong guidance to the planner, but their ruggedness and multi-modality can be
the major obstacle; (2) the extents of guidance and number of global/local
optima are sensitive to the changing environment, but not the ruggedness of the
surface; (3) the local optima are often closer to the global optimum than other
random points; and (4) there are considerable (and useful) overlaps on the
global/local optima between landscapes under different environments. We then
discuss the potential implications to the future work of planner designs for
SASs.

本文旨在研究自适应系统中的规划领域及其分析方法，进而为未来自适应系统的规划器设计提供指导。通过对四个真实的自适应系统和 14 种不同环境下的规划结果进行研究，发现规划地形通常对规划器提供强烈的指导，但其崎岖不平和多样性可能是主要障碍。同时，本文讨论了由此对未来自适应系统规划器设计的潜在影响