Conditional Age-at-Risk for Task Assignment across Heterogeneous Servers

In many real-time mission-critical systems, Age of Information (AoI) is a key metric for data freshness. However, conventional approaches that minimize merely the average AoI often fail to mitigate the risk of rare but catastrophic high-AoI events, which can be fatal in applications like autonomous driving. We address this gap by introducing a risk-aware scheduling framework that minimizes a weighted combination of the mean AoI and the Conditional Age-at-Risk (CAaR), explicitly managing the AoI’s tail distribution. We formulate the problem as a fractional Markov Decision Process (MDP) and develop RASA (Risk-Aware Scheduling for AoI) algorithm with provable convergence guarantees, which can be further adaptable to prior-free settings. Our theoretical analysis reveals the conditions of the threshold policy, dynamically shifting from "slow but safe" servers to "fast but risky" servers as the current AoI increases. Based on the policy’s threshold structure, we design the Threshold Policy Optimization (TPO) algorithm, a computationally efficient alternative to value iteration. Extensive simulations validate our theoretical results, showing that RASA consistently outperforms optimal risk-oblivious baselines by achieving up to a 32% reduction in risk-aware AoI. We further show the optimality of the threshold-type policy in typical scenarios and demonstrate the proposed schemes’ effectiveness in managing AoI’s tail risk.