# Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations

## TL;DR

This paper presents a runtime planning approach that uses optimization techniques to dynamically discover optimal configurations for complex systems in varying environmental situations, demonstrated on a traffic routing system.

## Contribution

It introduces a novel planning-as-optimization framework that operates with simple models and applies it to real-world system adaptation, contrasting different optimization strategies.

## Key findings

- Bayesian optimization outperforms evolutionary methods in finding optimal configurations.
- Clustering effectively identifies distinct environmental situations for adaptation.
- The approach enables dynamic, situation-specific configuration discovery at runtime.

## Abstract

The large number of possible configurations of modern software-based systems, combined with the large number of possible environmental situations of such systems, prohibits enumerating all adaptation options at design time and necessitates planning at run time to dynamically identify an appropriate configuration for a situation. While numerous planning techniques exist, they typically assume a detailed state-based model of the system and that the situations that warrant adaptations are known. Both of these assumptions can be violated in complex, real-world systems. As a result, adaptation planning must rely on simple models that capture what can be changed (input parameters) and observed in the system and environment (output and context parameters). We therefore propose planning as optimization: the use of optimization strategies to discover optimal system configurations at runtime for each distinct situation that is also dynamically identified at runtime. We apply our approach to CrowdNav, an open-source traffic routing system with the characteristics of a real-world system. We identify situations via clustering and conduct an empirical study that compares Bayesian optimization and two types of evolutionary optimization (NSGA-II and novelty search) in CrowdNav.