Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations

Erik M. Fredericks, Ilias Gerostathopoulos, Christian Krupitzer, Thomas Vogel

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019
PublisherIEEE Computer Society
Pages1-10
Number of pages10
ISBN (Electronic)9781728127316
DOIs
Publication statusPublished - 1 Jun 2019
Externally publishedYes
Event13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019 - Umea, Sweden
Duration: 16 Jun 201920 Jun 2019

Publication series

NameInternational Conference on Self-Adaptive and Self-Organizing Systems, SASO
Volume2019-June
ISSN (Print)1949-3673
ISSN (Electronic)1949-3681

Conference

Conference13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019
CountrySweden
CityUmea
Period16/06/1920/06/19

Keywords

  • Bayesian optimization
  • evolutionary search
  • optimization
  • planning
  • traffic routing model problem

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  • Cite this

    Fredericks, E. M., Gerostathopoulos, I., Krupitzer, C., & Vogel, T. (2019). Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations. In Proceedings - 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019 (pp. 1-10). [8780559] (International Conference on Self-Adaptive and Self-Organizing Systems, SASO; Vol. 2019-June). IEEE Computer Society. https://doi.org/10.1109/SASO.2019.00010