Currently, Internet applications running on mobile devices generate a massive amount of data that can be transmitted to a Cloud for processing. However, one fundamental limitation of a Cloud is the connectivity with end devices. Fog computing overcomes this limitation and supports the requirements of time-sensitive applications by distributing computation, communication, and storage services along the Cloud to Things (C2T) continuum, empowering potential new applications, such as smart cities, augmented reality (AR), and virtual reality (VR). However, the adoption of Fog-based computational resources and their integration with the Cloud introduces new challenges in resource management, which requires the implementation of new strategies to guarantee compliance with the quality of service (QoS) requirements of applications.

In this context, one major question is how to map the QoS requirements of applications on Fog and Cloud resources. One possible approach is to discriminate the applications arriving at the Fog into Classes of Service (CoS). This paper thus introduces a set of CoS for Fog applications which includes, the QoS requirements that best characterize these Fog applications. Moreover, this paper proposes the implementation of a typical machine learning classification methodology to discriminate Fog computing applications as a function of their QoS requirements. Furthermore, the application of this methodology is illustrated in the assessment of classifiers in terms of efficiency, accuracy, and robustness to noise. The adoption of a methodology for machine learning-based classification constitutes a first step towards the definition of QoS provisioning mechanisms in Fog computing. Moreover, classifying Fog computing applications can facilitate the decision-making process for Fog scheduler.

当前，运行在移动设备上的Internet应用程序会生成大量数据，这些数据可以传输到Cloud进行处理。但是，云的一个基本限制是与终端设备的连接。雾计算克服了这一限制，并通过沿云到物（C2T）连续体分布计算，通信和存储服务，为潜在的新应用（例如智能城市，增强现实（AR））和增强功能提供了支持，从而满足了对时间敏感的应用程序的需求。虚拟现实（VR）。但是，采用基于Fog的计算资源并将其与Cloud集成带来了资源管理方面的新挑战，这要求实施新策略以确保符合应用程序的服务质量（QoS）要求。

在这种情况下，一个主要问题是如何在雾和云资源上映射应用程序的QoS要求。一种可能的方法是将到达雾的应用区分为服务等级（CoS）。因此，本文介绍了一套针对Fog应用程序的CoS，其中包括最能表征这些Fog应用程序的QoS要求。此外，本文提出了一种典型的机器学习分类方法的实现，以根据其QoS要求来区分Fog计算应用程序。此外，该方法的应用在效率，准确性和对噪声的鲁棒性方面的分类器评估中得到了说明。基于机器学习的分类方法的采用是雾计算中QoS定义机制定义的第一步。此外，对Fog计算应用程序进行分类可以简化Fog计划程序的决策过程。