Hybrid Planning with Receding Horizon: A Case for Meta-self-awareness

TL;DR

This paper introduces HYPEZON, a hybrid planner for self-adaptive systems that uses receding horizon control and meta-self-awareness to balance planning quality and timeliness.

Contribution

It presents HYPEZON, a novel hybrid planning approach leveraging receding horizon control and meta-self-awareness for improved self-adaptive system decision-making.

Findings

HYPEZON effectively balances quality and timeliness in planning.

Utilizes runtime information via receding horizon control.

Addresses hybrid planning as a case for meta-self-awareness.

Abstract

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.