Risk-Aware Cost-Constrained Scheduling for Resource- Constrained Dynamic Heterogeneous Redundancy Systems

Resource-constrained dynamic heterogeneous redundancy (DHR) systems use executor diversity and runtime reconfiguration to reduce stable attack surfaces. However, effective scheduling cannot rely only on heterogeneity or movement frequency, because repeated exposure, shared vulnerability sources, service disturbance, and switching overhead jointly shape executor-subset selection. This paper proposes RACS, a risk-aware cost-constrained scheduling method for resource-constrained DHR systems. RACS evaluates candidate subsets by jointly considering heterogeneity, historical confidence, readiness, common-vulnerability risk, exposure memory, and switching cost. We evaluate RACS using a controlled simulation protocol covering multiple scheduling principles and attacker behaviors, including common-vulnerability pressure, burst-adaptive exploitation, and adaptive target selection based on observed scheduling patterns. The results show that RACS does not optimize a single metric in isolation, but maintains a consistent security–cost trade-off. It reduces common-vulnerability risk and switching cost in common-vulnerability settings, reduces burst-triggering high-risk states under adaptive pressure, and maintains competitive risk–cost behavior when attackers adapt to historical scheduling behavior. Robustness and scalability analyses clarify the effects of vulnerability-family estimation errors, parameter choices, and executor-pool size. These findings provide controlled simulation evidence for joint risk–cost modeling in DHR executor-subset scheduling, while testbed validation remains future work.