A hybrid approach combining control theory and AI for engineering self-adaptive systems
Paper i proceeding, 2020

Control theoretical techniques have been successfully adopted as methods for self-adaptive systems design to provide formal guarantees about the effectiveness and robustness of adaptation mechanisms. However, the computational effort to obtain guarantees poses severe constraints when it comes to dynamic adaptation. In order to solve these limitations, in this paper, we propose a hybrid approach combining software engineering, control theory, and AI to design for software self-adaptation. Our solution proposes a hierarchical and dynamic system manager with performance tuning. Due to the gap between high-level requirements specification and the internal knob behavior of the managed system, a hierarchically composed components architecture seek the separation of concerns towards a dynamic solution. Therefore, a two-layered adaptive manager was designed to satisfy the software requirements with parameters optimization through regression analysis and evolutionary meta-heuristic. The optimization relies on the collection and processing of performance, effectiveness, and robustness metrics w.r.t control theoretical metrics at the offline and online stages. We evaluate our work with a prototype of the Body Sensor Network (BSN) in the healthcare domain, which is largely used as a demonstrator by the community. The BSN was implemented under the Robot Operating System (ROS) architecture, and concerns about the system dependability are taken as adaptation goals. Our results reinforce the necessity of performing well on such a safety-critical domain and contribute with substantial evidence on how hybrid approaches that combine control and AI-based techniques for engineering self-adaptive systems can provide effective adaptation.

hybrid

optimization

control theory

self-adaptive software

Författare

Ricardo Diniz Caldas

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Physical Systems

Arthur Rodrigues

Universidade de Brasilia

Eric Bernd Gil

Universidade de Brasilia

Genaina Nunes Rodrigues

Universidade de Brasilia

Thomas Vogel

Humboldt-Universität zu Berlin

Patrizio Pelliccione

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Testing, Requirements, Innovation and Psychology

Proceedings - 2020 IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2020

9-19

15th IEEE/ACM International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2020
Virtual, Online, South Korea,

Ämneskategorier

Inbäddad systemteknik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1145/3387939.3391595

Mer information

Senast uppdaterat

2020-11-06