Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective

被引:187
作者
Rodrigues, Tiago Koketsu [1 ]
Suto, Katsuya [2 ]
Nishiyama, Hiroki [3 ]
Liu, Jiajia [4 ]
Kato, Nei [1 ]
机构
[1] Tohoku Univ, Grad Sch Informat Sci, Sendai, Miyagi 9808579, Japan
[2] Univ Electrocommun, Grad Sch Informat & Engn, Tokyo 1820021, Japan
[3] Tohoku Univ, Grad Sch Engn, Sendai, Miyagi 9808579, Japan
[4] Xidian Univ, Sch Cyber Engn, Xian 710071, Peoples R China
关键词
Cloud computing; Servers; Edge computing; Mobile handsets; 5G mobile communication; Task analysis; Internet of Things; Mobile edge computing; cloudlet; scalability; communication; computation resource management; ML; artificial intelligence; SMART CITIES; INTERNET; THINGS; NETWORKS; FOG; ALGORITHMS; ALLOCATION; MIDDLEWARE; MANAGEMENT; MIGRATION;
D O I
10.1109/COMST.2019.2943405
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:38 / 67
页数:30
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