Buffering architectures and policies for their efficient management are one of the core ingredients of network architecture. However, despite strong incentives to experiment with and deploy new policies, opportunities for changing or automatically choosing anything beyond a few parameters in a predefined set of behaviors still remain very limited. We introduce a novel buffer management framework based on machine learning approaches which automatically adapts to traffic conditions changing over time and requires only limited knowledge from network operators about the dynamics and optimality of desired behaviors. We validate and compare various design options with a comprehensive evaluation study.

中文翻译：

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迈向声明式自适应缓冲区管理

缓冲架构和有效管理策略是网络架构的核心要素之一。然而，尽管有强烈的动机去尝试和部署新政策，但在一组预定义的行为中改变或自动选择超出几个参数的任何东西的机会仍然非常有限。我们引入了一种基于机器学习方法的新型缓冲区管理框架，该框架自动适应随时间变化的交通状况，并且只需要网络运营商对所需行为的动态性和最优性的有限了解。我们通过综合评估研究验证和比较各种设计选项。