TY - GEN
T1 - Planning as Optimization
T2 - 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019
AU - Fredericks, Erik M.
AU - Gerostathopoulos, Ilias
AU - Krupitzer, Christian
AU - Vogel, Thomas
PY - 2019/6/1
Y1 - 2019/6/1
N2 - 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.
AB - 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.
KW - Bayesian optimization
KW - evolutionary search
KW - optimization
KW - planning
KW - traffic routing model problem
UR - http://www.scopus.com/inward/record.url?scp=85070539145&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070539145&partnerID=8YFLogxK
U2 - 10.1109/SASO.2019.00010
DO - 10.1109/SASO.2019.00010
M3 - Conference contribution
AN - SCOPUS:85070539145
T3 - International Conference on Self-Adaptive and Self-Organizing Systems, SASO
SP - 1
EP - 10
BT - Proceedings - 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019
PB - IEEE Computer Society
Y2 - 16 June 2019 through 20 June 2019
ER -