We revisit the online dynamic acknowledgment problem. In the problem, a sequence of requests arrive over time to be acknowledged, and all outstanding requests can be satisfied simultaneously by one acknowledgement. The goal of the problem is to minimize the total request delay plus acknowledgement cost. This elegant model studies the trade-off between acknowledgement cost and waiting experienced by requests. The problem has been well studied and the tight competitive ratios have been determined. For this well-studied problem, we focus on how to effectively use machine-learned predictions to have better performance. We develop algorithms that perform arbitrarily close to the optimum with accurate predictions while concurrently having the guarantees arbitrarily close to what the best online algorithms can offer without access to predictions, thereby achieving simultaneous optimum consistency and robustness. This new result is enabled by our novel prediction error measure. No error measure was defined for the problem prior to our work, and natural measures failed due to the challenge that requests with different arrival times have different effects on the objective. We hope our ideas can be used for other online problems with temporal aspects that have been resisting proper error measures.

翻译：我们重新审视在线动态确认问题。在该问题中，请求序列随时间到达并需被确认，所有未处理的请求可通过一次确认同时得到满足。问题的目标是使总请求延迟与确认成本之和最小化。这个优雅的模型研究了确认成本与请求等待时间之间的权衡。该问题已被充分研究，并确定了紧致的竞争比。针对这一研究充分的问题，我们聚焦于如何有效利用机器学习预测以提升性能。我们开发的算法在具备准确预测时性能可任意接近最优解，同时在不依赖预测的情况下提供与最优在线算法任意接近的保证，从而同时实现最优的一致性和鲁棒性。这一新成果得益于我们提出的新型预测误差度量。在该问题中，此前未定义任何误差度量，而传统度量因请求到达时间不同对目标函数的影响差异而失效。我们希望这一思路能为其他受限于缺乏适当误差度量的时序性在线问题提供借鉴。