  The trade-off between the quality and timeliness of adaptation is a
multi-faceted challenge in engineering self-adaptive systems. Obtaining
adaptation plans that fulfill system objectives with high utility and in a
timely manner is the holy grail, however, as recent research revealed, it is
not trivial. Hybrid planning is concerned with resolving the time and quality
trade-off via dynamically combining multiple planners that individually aim to
perform either timely or with high quality. The choice of the most fitting
planner is steered based on assessments of runtime information. A hybrid
planner for a self-adaptive system requires (i) a decision-making mechanism
that utilizes (ii) system-level as well as (iii) feedback control-level
information at runtime.
  In this paper, we present HYPEZON, a hybrid planner for self-adaptive
systems. Inspired by model predictive control, HYPEZON leverages receding
horizon control to utilize runtime information during its decision-making.
Moreover, we propose to engineer HYPEZON for self-adaptive systems via two
alternative designs that conform to meta-self-aware architectures.
Meta-self-awareness allows for obtaining knowledge and reasoning about own
awareness via adding a higher-level reasoning entity. HYPEZON aims to address
the problem of hybrid planning by considering it as a case for
meta-self-awareness.
