An Extended Study of the Performance of Flexible Controllers Composed of Micro-controllers

Generic controllers for self-adaptive systems can be configured parametrically according to system needs, even though their reuse is restricted because of the wide range of services that may be provided by each stage of a feedback control loop, like MAPE-K. Rainbow is a typical example of such a generic, monolithic controller. This article revisits and extends prior work that advocates structurally flexible controllers, as ensembles of micro-controllers each providing specific services. We experimented with our approach with three different architectural configurations for the controller—monolithic, decentralised and decentralised with a meta-controller. Our results indicate that despite the decentralised configuration with a meta-controller demanding more computational resources, it performed comparatively well compared to the other configurations, including when measuring the target system’s response time. Moreover, we found that variations of the control loop timing at the different layers of the controller impact the stability of the target system. We have also evolved the controller by adding a new micro-controller, which caused no impact on the other micro-controllers, and mostly kept the target system’s performance. We conclude that a multi-layered controller design, based on micro-controllers, provides the basis for defining structurally flexible controllers at operational-time and may promote reuse at development-time.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Autonomous and Adaptive Systems
Published
2025-01-27
DOI
https://doi.org/10.1145/3715145
Primary Topic
Advanced Software Engineering Methodologies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An Extended Study of the Performance of Flexible Controllers Composed of Micro-controllers

Bento R. Siqueira, Fabiano Cutigi Ferrari, Rogério de Lemos
ACM Transactions on Autonomous and Adaptive Systems
Advanced Software Engineering Methodologies
article

An Extended Study of the Performance of Flexible Controllers Composed of Micro-controllers

Bento R. Siqueira, Fabiano Cutigi Ferrari, Rogério de Lemos
article en

Abstract

Generic controllers for self-adaptive systems can be configured parametrically according to system needs, even though their reuse is restricted because of the wide range of services that may be provided by each stage of a feedback control loop, like MAPE-K. Rainbow is a typical example of such a generic, monolithic controller. This article revisits and extends prior work that advocates structurally flexible controllers, as ensembles of micro-controllers each providing specific services. We experimented with our approach with three different architectural configurations for the controller—monolithic, decentralised and decentralised with a meta-controller. Our results indicate that despite the decentralised configuration with a meta-controller demanding more computational resources, it performed comparatively well compared to the other configurations, including when measuring the target system’s response time. Moreover, we found that variations of the control loop timing at the different layers of the controller impact the stability of the target system. We have also evolved the controller by adding a new micro-controller, which caused no impact on the other micro-controllers, and mostly kept the target system’s performance. We conclude that a multi-layered controller design, based on micro-controllers, provides the basis for defining structurally flexible controllers at operational-time and may promote reuse at development-time.

ACM Transactions on Autonomous and Adaptive SystemsVol. 20(2)
Universidade Federal de Lavras (BR), Universidade Federal de São Carlos (BR), University of Kent (GB), Universidade Federal do Pará (BR)
Engineering and Physical Sciences Research Council
Openalex Percentile: Top 99%
Advanced Software Engineering Methodologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.