Machine learning (ML) has become the de-facto approach for various scientific domains such as computer vision and natural language processing. Despite recent breakthroughs, machine learning has only made its way into the fundamental challenges in computer systems and networking recently. This article attempts to shed light on recent literature that appeals for machine learning-based solutions to traditional problems in computer systems and networking. To this end, we first introduce a taxonomy based on a set of major research problem domains. Then, we present a comprehensive review per domain, where we compare the traditional approaches against the machine learning-based ones. Finally, we discuss the general limitations of machine learning for computer systems and networking, including lack of training data, training overhead, real-time performance, and explainability, and reveal future research directions targeting these limitations.

机器学习 (ML) 已成为计算机视觉和自然语言处理等各个科学领域的实际方法。尽管最近取得了突破，但机器学习直到最近才开始解决计算机系统和网络的基本挑战。本文试图阐明最近的文献，这些文献呼吁基于机器学习的解决方案来解决计算机系统和网络中的传统问题。为此，我们首先介绍基于一组主要研究问题领域的分类法。然后，我们对每个领域进行全面审查，将传统方法与基于机器学习的方法进行比较。最后，我们讨论了计算机系统和网络机器学习的一般局限性，包括缺乏训练数据、训练开销、