AI 中文总结
研究如何在网络边缘利用车辆计算资源，提出SMART不确定性感知准入机制利用BNN预测车辆容量，纳入不确定性到准入程序。该机制在仅计算准入模型下表现良好，与多个基线相比实现有利权衡，敏感性分析凸显定制机制的必要性。
AI 中文摘要
联网车辆越来越多地嵌入人工智能加速器，在网络边缘提供大量但不稳定的补充计算资源。与预配置的MEC主机不同，车辆资源高度动态：车辆可能离开小区、被本地占用或提供异构计算能力。因此，利用车辆资源需要在不知道任务执行期间可用计算能力的情况下做出准入决策。我们提出了SMART，一种不确定性感知准入机制，使ETSI MEC编排器能够在预测不确定性下机会性地利用车辆计算。SMART使用BNN预测未来车辆容量，其不确定性估计在名义95%水平的评估预测器中校准最佳，并将其校准后的预测不确定性纳入通过SAA和CVaR近似重新制定的机会约束准入程序。在仅计算准入模型下，SMART接纳95.6%的任务，同时保持约0.81%的中位数容量违规率。与七个反应式、仅均值和不确定性感知基线相比，它通过在建模假设下接近计算能力预言机的性能，实现了有利的准入-违规权衡。最后，敏感性分析表明基站计算可用性的可变性是准入性能的关键决定因素，突出了针对机会性基站计算池化定制校准准入和资源分配机制的必要性。
英文摘要
Connected vehicles increasingly embed AI accelerators, offering a substantial yet volatile source of supplemental compute near the network edge. Unlike provisioned MEC hosts, vehicular resources are highly dynamic: vehicles may leave the cell, become locally occupied, or offer heterogeneous compute capacities. Therefore, exploiting vehicular resources requires making admission decisions without knowing the compute capacity that will be available during task execution. We present SMART, an uncertainty-aware admission mechanism that enables an \acs{ETSI} \ac{MEC} orchestrator \textit{to opportunistically exploit vehicular compute under predictive uncertainty.} SMART predicts future vehicular capacity using a \ac{BNN} -- whose uncertainty estimates are the best calibrated among the evaluated forecasters at the nominal 95\% level, and incorporates its calibrated predictive uncertainty into a chance-constrained admission program reformulated through \ac{SAA} and \ac{CVaR} approximations. Under a compute-only admission model, SMART admits 95.6\% of tasks while maintaining a median capacity-violation rate of about 0.81\%. It achieves a favorable admission-violation tradeoff compared with seven reactive, mean-only, and uncertainty-aware baselines, by approaching the performance of a compute-capacity oracle under the modeled assumptions. Finally, the sensitivity analyses show that variability in base-station compute availability is a key determinant of admission performance, highlighting the need for a calibrated admission and resource allocation mechanism tailored to opportunistic base-station compute pooling.
Comments17 pages
