With the rapid developments in emerging mobile technologies, utilizing resource-hungry mobile applications such as media processing, online Gaming, Augmented Reality (AR), and Virtual Reality (VR) play an essential role in both businesses and entertainments. To soften the burden of such complexities incurred by fast developments of such serving technologies, distributed Mobile Edge Computing (MEC) has been developed, aimed at bringing the computation environments near the end-users, usually in one hop, to reach predefined requirements. In the literature, offloading approaches are developed to connect the computation environments to mobile devices by transferring resource-hungry tasks to the near servers. Because of some rising problems such as inherent software and hardware heterogeneity, restrictions, dynamism, and stochastic behavior of the ecosystem, the computation offloading issues consider as the essential challenging problems in the MEC environment. However, to the best of the author's knowledge, in spite of its significance, in machine learning-based (ML-based) computation offloading mechanisms, there is not any systematic, comprehensive, and detailed survey in the MEC environment. In this paper, we provide a review on the ML-based computation offloading mechanisms in the MEC environment in the form of a classical taxonomy to identify the contemporary mechanisms on this crucial topic and to offer open issues as well. The proposed taxonomy is classified into three main fields: Reinforcement learning-based mechanisms, supervised learning-based mechanisms, and unsupervised learning-based mechanisms. Next, these classes are compared with each other based on the essential features such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and weaknesses are discussed, as well. Finally, open issues and uncovered or inadequately covered future research challenges are argued, and the survey is concluded.

随着新兴移动技术的飞速发展，利用诸如媒体处理，在线游戏，增强现实（AR）和虚拟现实（VR）之类的资源匮乏的移动应用程序在企业和娱乐中都扮演着至关重要的角色。为了减轻由于这种服务技术的快速发展而引起的这种复杂性的负担，已经开发了分布式移动边缘计算（MEC），旨在使计算环境通常以一跳的方式接近最终用户，从而达到预定义的要求。在文献中，开发了卸载方法以通过将耗费资源的任务传输到附近的服务器来将计算环境连接到移动设备。由于一些日益严重的问题，例如固有的软件和硬件异质性，限制，动态性，以及生态系统的随机行为，计算分流问题被认为是MEC环境中必不可少的挑战性问题。但是，据作者所知，尽管具有重要意义，但在基于机器学习（基于ML）的计算卸载机制中，MEC环境中没有任何系统，全面和详细的调查。在本文中，我们以经典分类法的形式对MEC环境中基于ML的计算分载机制进行了综述，以识别有关此关键主题的当代机制，并提出一些未解决的问题。拟议的分类法分为三个主要领域：强化学习为基础的机制，监督学习为基础的机制和无监督学习为基础的机制。下一个，根据性能指标，案例研究，使用的技术和评估工具等基本功能，将这些类进行相互比较，并讨论它们的优缺点。最后，讨论了未解决的问题以及未发现或未充分涵盖的未来研究挑战，并得出了结论。