Dynamic resource allocation to parallel queues is a cornerstone of network scheduling, yet classical solutions often fail when accounting for the overhead of switching delays to queues with superior link conditions. In particular, system performance is further degraded when switching delays are stochastic and inhomogeneous. In this domain, the myopic, Max-Weight policy struggles, as it is agnostic to switching delays. This paper introduces ACI, a non-myopic, frame-based scheduling framework that directly amortizes these switching delays. We first use a Lyapunov drift analysis to prove that backlog-driven ACI is throughput-optimal with respect to a scaled capacity region; then validate ACI's effectiveness on multi-UAV networks with an FSO backhaul. Finally, we demonstrate how adapting its core urgency metric provides the flexibility to navigate the throughput-latency trade-off.

翻译：动态资源分配至并行队列是网络调度的基石，然而当考虑向链路条件更优队列切换所产生的延迟开销时，经典解决方案往往失效。特别是当切换延迟具有随机性与非均匀性时，系统性能会进一步恶化。在此领域中，短视的Max-Weight策略因未考虑切换延迟而表现不佳。本文提出ACI——一种非短视的基于帧的调度框架，可直接分摊这些切换延迟。我们首先通过李雅普诺夫漂移分析证明，基于积压驱动的ACI在缩放容量区域内具有吞吐量最优性；随后在采用自由空间光回程的多无人机网络上验证了ACI的有效性。最后，我们展示了如何通过调整其核心紧迫度度量，为权衡吞吐量与延迟提供灵活性。