The growing technology of the fifth generation (5G) of mobile telecommunications has led to the special attention of cloud service providers (CSPs) to mobile cloud computing (MCC). Due to the limitations in processing power, storage space and energy capacity of mobile devices, cloud resources can be moved to the edge of the network to improve the quality of service (QoS). Server placement is a crucial emerging problem in both typical and edge types of MCC, different proposed methods of which are reviewed and evaluated in this paper. Proper placement of servers leads to more efficient utilization of these servers, reduces their response time and optimizes their energy consumption. A variety of techniques and approaches, including machine learning-based techniques, evolutionary models, optimization algorithms, heuristics and meta-heuristics have been employed by different server placement methods of the literature to find the optimal deployment map of servers. This paper provides a comprehensive analysis of these server placement methods in edge computing, fog computing and cloudlet, investigates their various aspects, dimensions and objectives, and evaluates their strengths and weaknesses. Furthermore, open challenges for server placement in MCC are provided, and future research directions are also explained and discussed.

中文翻译：

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移动云计算中的服务器布局：边缘计算、雾计算和cloudlet的综合调查

第五代（5G）移动通信技术的不断发展引起了云服务提供商（CSP）对移动云计算（MCC）的特别关注。由于移动设备处理能力、存储空间和能量容量的限制，云资源可以移动到网络边缘以提高服务质量（QoS）。服务器布局是典型和边缘类型 MCC 中一个重要的新兴问题，本文对不同提出的方法进行了回顾和评估。正确放置服务器可以更有效地利用这些服务器，减少其响应时间并优化其能源消耗。文献中的不同服务器放置方法已经采用了多种技术和方法，包括基于机器学习的技术、进化模型、优化算法、启发式和元启发式，来找到服务器的最佳部署图。本文对边缘计算、雾计算和云中的这些服务器放置方法进行了全面分析，研究了它们的各个方面、维度和目标，并评估了它们的优缺点。此外，还提供了 MCC 中服务器放置的开放挑战，并解释和讨论了未来的研究方向。