  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.
