Context-Aware Proactive Self-Adaptation: A Two-layer Model Predictive Control Approach

In self-adaptive software systems, the role of context is paramount, especially for proactive self-adaptation. Current research, however, does not fully explore context’s impact, for example on priorities of the requirements. To address this gap, we introduce a novel contextual goal model to capture these factors and their influence on the system. Using this, we propose a two-layer control mechanism with a context-aware model predictive control to achieve proactive adaptation for the software system and adaptation for the controller itself. By contextual prediction and a more accurate system model, our approach utilizes model predictive control to facilitate timely and efficient system adaptations, improving both performance and adaptability. Meanwhile, we perform requirement adaptation to update the contextual goal model, which in turn updates the objective function and constraints of the controller. Our experimental evaluations across two scenarios demonstrate the significant benefits of our approach in enhancing system performance.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Autonomous and Adaptive Systems
Published
2024-12-20
DOI
https://doi.org/10.1145/3708998
Citations
1
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
0.33
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Context-Aware Proactive Self-Adaptation: A Two-layer Model Predictive Control Approach

Zhengyin Chen, Eunsuk Kang, Nianyu Li, Wenpin Jiao et al.
1 citations
ACM Transactions on Autonomous and Adaptive Systems
Advanced Control Systems Optimization
0.33
article

Context-Aware Proactive Self-Adaptation: A Two-layer Model Predictive Control Approach

Zhengyin Chen, Eunsuk Kang, Nianyu Li, Wenpin Jiao, Jialong Li
article en
1 citations

Abstract

In self-adaptive software systems, the role of context is paramount, especially for proactive self-adaptation. Current research, however, does not fully explore context’s impact, for example on priorities of the requirements. To address this gap, we introduce a novel contextual goal model to capture these factors and their influence on the system. Using this, we propose a two-layer control mechanism with a context-aware model predictive control to achieve proactive adaptation for the software system and adaptation for the controller itself. By contextual prediction and a more accurate system model, our approach utilizes model predictive control to facilitate timely and efficient system adaptations, improving both performance and adaptability. Meanwhile, we perform requirement adaptation to update the contextual goal model, which in turn updates the objective function and constraints of the controller. Our experimental evaluations across two scenarios demonstrate the significant benefits of our approach in enhancing system performance.

ACM Transactions on Autonomous and Adaptive Systems
Waseda University (JP), Peking University (CN), Carnegie Mellon University (US)
Openalex Percentile: Top 38%
Advanced Control Systems Optimization
0.33
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.

Context-Aware Proactive Self-Adaptation: A Two-layer Model Predictive Control Approach — Zhengyin Chen, Eunsuk Kang, et al. · ACM Transactions on Autonomous and Adaptive Systems (2024) | TGRS Research Map | TGRS