Reliable proactive adaptation via prediction fusion and extended stochastic model predictive control

Abstract

Proactive self-adaptation has emerged as a vital approach in recent years, aiming to preemptively address potential goal violations or performance degradation, thus improving the system’s reliability. However, this approach encounters specific challenges in prediction and decision-making, including issues such as erroneous predictions and adaptation latency. Addressing these issues, our study presents an innovative framework that leverages evidence theory to improve prediction accuracy and employs stochastic model predictive control (SMPC) for devising reliable adaptation strategies. We further refine the decision-making process by incorporating a latency-aware system model and a novel utility model inspired by the technical debt metaphor into the SMPC. Our framework’s effectiveness is validated through experiments conducted on a cyber–physical system exemplar DARTSim, demonstrating notable improvements in prediction accuracy and system reliability within dynamic environments.