Active Monitoring Mechanism for Control-Based Self-Adaptive Systems

Abstract

Control-based self-adaptive systems (control-SAS) are susceptible to deviations from their pre-identified nominal models. If this model deviation exceeds a threshold, the optimal performance and theoretical guarantees of the control-SAS can be compromised. Existing approaches detect these deviations by locating the mismatch between the control signal of the managing system and the response output of the managed system. However, vague observations may mask a potential mismatch where the explicit system behavior does not reflect the implicit variation of the nominal model. In this paper, we propose the Active Monitoring Mechanism (AMM for short) as a solution to this issue. The basic intuition of AMM is to stimulate the control-SAS with an active control signal when vague observations might mask model deviations. To determine the appropriate time for triggering the active signals, AMM proposes a stochastic framework to quantify the relationship between the implicit variation of a control-SAS and its explicit observation. Based on this framework, AMM’s monitor and remediator enhance model deviation detection by generating active control signals of well-designed timing and intensity. Results from empirical evaluations on three representative systems demonstrate AMM’s effectiveness (33.0% shorter detection delay, 18.3% lower FN rate, 16.7% lower FP rate) and usefulness (19.3% lower abnormal rates and 88.2% higher utility).