We present a learning-augmented online algorithm for the preemptive FIFO buffer management problem, where packets arrive online to a finite-capacity buffer, must be transmitted in FIFO order, and the algorithm may preemptively discard buffered packets to accommodate future arrivals. Our algorithm simultaneously achieves 1-consistency, η-smoothness, and asymptotic \sqrt{3}-robustness, where ηdenotes the prediction error. Specifically, it attains an optimal competitive ratio of 1 under perfect predictions, degrades smoothly as the prediction error increases, and maintains an asymptotic competitive ratio of \sqrt{3} under arbitrarily inaccurate predictions, matching the best-known worst-case guarantee for the classical online problem, established by Englert and Westermann in 2009 [Algorithmica 53(4): 523-548]. A key technical contribution of our work is the introduction of an \emph{output-based prediction error metric}. Because capacity constraints dictate that only a strictly bounded subset of arriving packets is ultimately transmitted, our metric assesses prediction quality over the resulting optimal schedules rather than the raw input sequences, avoiding artificial error penalties. To guarantee robustness, our algorithm dynamically monitors predictions and executes a \emph{buffer-clearing strategy} upon transitioning to a worst-case fallback mechanism. We prove that the competitive loss incurred by this clearing operation is bounded by an additive capacity constant that vanishes asymptotically. Finally, we show that our algorithm provides a generalized framework for learning-augmented buffer management: substituting the fallback module with any β-competitive online algorithm immediately yields asymptotic β-robustness.

翻译：我们针对抢占式FIFO缓冲区管理问题提出一种基于学习的在线算法。该问题中，数据包在线到达有限容量缓冲区，需按FIFO顺序传输，算法可主动丢弃缓冲数据包以容纳未来到达的数据包。所提算法同时实现了1-一致性、η-平滑性和渐近√3-鲁棒性，其中η表示预测误差。具体而言，在完美预测下算法达到最优竞争比1，竞争比随预测误差增加而平滑退化，且在任意不准确预测下保持渐近竞争比√3，这匹配了Englert与Westermann于2009年建立的经典在线问题最优最坏情形保证[Algorithmica 53(4): 523-548]。本文的关键技术贡献是引入了一种基于输出的预测误差度量。由于容量约束决定了最终仅严格有界子集的数据包被传输，我们的度量在生成的最优调度上评估预测质量而非原始输入序列，从而避免人为误差惩罚。为保障鲁棒性，算法动态监测预测并在切换至最坏情形后备机制时执行缓冲区清空策略。我们证明该清空操作导致的竞争损失被渐近消失的加性容量常数所界。最后，我们证明本算法为基于学习的缓冲区管理提供了通用框架：将后备模块替换为任意β-竞争在线算法即可立即得到渐近β-鲁棒性。