A Hybrid Approach Combining Control Theory and AI for Engineering Self-Adaptive Systems

by   Ricardo Diniz Caldas, et al.

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.


page 1

page 2

page 3

page 4


Body Sensor Network: A Self-Adaptive System Exemplar in the Healthcare Domain

Recent worldwide events shed light on the need of human-centered systems...

Towards Mapping Control Theory and Software Engineering Properties using Specification Patterns

A traditional approach to realize self-adaptation in software engineerin...

A Learning Approach to Enhance Assurances for Real-Time Self-Adaptive Systems

The assurance of real-time properties is prone to context variability. P...

Kuksa*: Self-Adaptive Microservices in Automotive Systems

In pervasive dynamic environments, vehicles connect to other objects to ...

Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems

Many software systems today face uncertain operating conditions, such as...

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

The trade-off between the quality and timeliness of adaptation is a mult...

Neutrality: A Necessity for Self-Adaptation

Self-adaptation is used in all main paradigms of evolutionary computatio...

Please sign up or login with your details

Forgot password? Click here to reset