Network operation relies on heuristics to solve many tasks rapidly and efficiently across the protocol stack. These heuristics are the result of thorough human-driven design rooted in expert knowledge of the target system and problem. Recently, approaches powered by Artificial Intelligence have shown promising results in devising solutions that outperform long-established heuristics in classical problems. We explore the possibility of applying such Automated Heuristic Design (AHD) frameworks to network environments by (i) discussing the general integration of AHD with network operation and the associated challenges, as well as (ii) proposing a practical implementation of AHD for a specific networking task, i.e., 5G decoding. Initial results show how modern AHD tools can devise heuristics for Low-Density Parity Check decoding on par with state-of-the-art solutions implemented in production systems.

翻译：网络运维依赖于启发式方法在协议栈各层级中快速高效地解决众多任务。这些启发式方法是基于对目标系统与问题的专家知识，经过深度人工设计的成果。近年来，以人工智能为驱动的方法在经典问题中展现出超越长期沿用的启发式方法的潜力。本文通过以下两方面探索在网络环境中应用自动启发式设计（AHD）框架的可能性：（i）讨论AHD与网络运维的通用集成方案及相关挑战，以及（ii）针对特定网络任务（即5G解码）提出AHD的实用化实现方案。初步结果表明，现代AHD工具能够为低密度奇偶校验码解码设计出性能与生产系统中部署的先进解决方案相媲美的启发式方法。