Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management. To handle the automation of deployment, maintenance, autoscaling, and networking of containerized applications, container orchestration is proposed as an essential research problem. However, the highly dynamic and diverse feature of cloud workloads and environments considerably raises the complexity of orchestration mechanisms. Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics. Such insights could further improve the quality of resource provisioning decisions in response to the changing workloads under complex environments. In this paper, we present a comprehensive literature review of existing machine learning-based container orchestration approaches. Detailed taxonomies are proposed to classify the current researches by their common features. Moreover, the evolution of machine learning-based container orchestration technologies from the year 2016 to 2021 has been designed based on objectives and metrics. A comparative analysis of the reviewed techniques is conducted according to the proposed taxonomies, with emphasis on their key characteristics. Finally, various open research challenges and potential future directions are highlighted.

中文翻译：

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基于机器学习的容器编排：分类和未来方向

容器化是一种轻量级的应用虚拟化技术，提供高环境一致性、操作系统分发可移植性和资源隔离。现有的主流云服务提供商在其分布式系统基础设施中普遍采用容器技术来实现自动化应用程序管理。为了处理容器化应用程序的部署、维护、自动扩展和网络的自动化，容器编排被提出作为一个重要的研究问题。然而，云工作负载和环境的高度动态和多样化的特性大大提高了编排机制的复杂性。因此，容器编排系统采用机器学习算法进行多维性能指标的行为建模和预测。这些见解可以进一步提高资源配置决策的质量，以响应复杂环境下不断变化的工作负载。在本文中，我们对现有的基于机器学习的容器编排方法进行了全面的文献综述。提出了详细的分类法，通过它们的共同特征对当前的研究进行分类。此外，基于机器学习的容器编排技术从 2016 年到 2021 年的演变是基于目标和指标设计的。根据提议的分类法对审查的技术进行了比较分析，重点是它们的关键特征。最后，强调了各种开放的研究挑战和潜在的未来方向。