Mobile Edge Computing (MEC) is considered an essential future service for the implementation of 5G networks and the Internet of Things, as it is the best method of delivering computation and communication resources to mobile devices. It is based on the connection of the users to servers located on the edge of the network, which is especially relevant for real-time applications that demand minimal latency. In order to guarantee a resource-efficient MEC (which, for example, could mean improved Quality of Service for users or lower costs for service providers), it is important to consider certain aspects of the service model, such as where to offload the tasks generated by the devices, how many resources to allocate to each user (specially in the wired or wireless device-server communication) and how to handle inter-server communication. However, in the MEC scenarios with many and varied users, servers and applications, these problems are characterized by parameters with exceedingly high levels of dimensionality, resulting in too much data to be processed and complicating the task of finding efficient configurations. This will be particularly troublesome when 5G networks and Internet of Things roll out, with their massive amounts of devices. To address this concern, the best solution is to utilize Machine Learning (ML) algorithms, which enable the computer to draw conclusions and make predictions based on existing data without human supervision, leading to quick near-optimal solutions even in problems with high dimensionality. Indeed, in scenarios with too much data and too many parameters, ML algorithms are often the only feasible alternative. In this paper, a comprehensive survey on the use of ML in MEC systems is provided, offering an insight into the current progress of this research area. Furthermore, helpful guidance is supplied by pointing out which MEC challenges can be solved by ML solutions, what are the current trending algorithms in frontier ML research and how they could be used in MEC. These pieces of information should prove fundamental in encouraging future research that combines ML and MEC.

中文翻译：

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机器学习在不断发展的边缘和云中遇到计算和通信控制：挑战和未来展望

移动边缘计算 (MEC) 被认为是实施 5G 网络和物联网的一项必不可少的未来服务，因为它是向移动设备提供计算和通信资源的最佳方法。它基于用户与位于网络边缘的服务器的连接，这与需要最小延迟的实时应用程序尤其相关。为了保证资源高效的 MEC（例如，这可能意味着提高用户的服务质量或降低服务提供商的成本），重要的是要考虑服务模型的某些方面，例如在哪里卸载任务由设备生成，分配给每个用户多少资源（特别是在有线或无线设备-服务器通信中）以及如何处理服务器间通信。然而，在拥有众多不同用户、服务器和应用程序的 MEC 场景中，这些问题的特点是参数具有极高的维度，导致需要处理的数据过多，并使寻找有效配置的任务变得复杂。当 5G 网络和物联网推出时，会出现大量设备，这将特别麻烦。为了解决这个问题，最好的解决方案是利用机器学习 (ML) 算法，该算法使计算机能够在没有人工监督的情况下根据现有数据得出结论并做出预测，从而即使在高维问题中也能快速找到接近最优的解决方案。事实上，在数据过多、参数过多的场景中，ML 算法往往是唯一可行的选择。在本文中，提供了对 MEC 系统中 ML 使用的全面调查，提供了对该研究领域当前进展的深入了解。此外，通过指出 ML 解决方案可以解决哪些 MEC 挑战、前沿 ML 研究中的当前趋势算法以及如何在 MEC 中使用它们，提供了有用的指导。这些信息应该被证明是鼓励未来结合 ML 和 MEC 的研究的基础。