A survey on edge and fog nodes' placement methods, techniques, parameters, and constraints

IF 1.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
IET Networks Pub Date : 2023-06-05 DOI:10.1049/ntw2.12087
Samraa Adnan Al-Asadi, Safaa O. Al-Mamory
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引用次数: 0

Abstract

Within Edge and Fog computing, edge and fog nodes must be optimally located at the network edge to minimise the network's overall latency. This survey addresses all aspects of these nodes' placement problems. Literature on edge and fog nodes' placement is collected from reputable databases (IEEE Xplore digital library, Scopus, ScienceDirect, and Web of Science) using a search query. Manual search using keywords and the snowball method is also used to get as many related papers as possible. According to defined inclusion criteria, retrieved documents are filtered to 64 articles for eight years (2015–2022). Depending on the optimisation method used, literature is classified into six categories. The first relies on Integer programming, accounting for 20.3% (13/64). The second category depends on heuristic and metaheuristic methods, accounting for 20.3% (13/64). The third category depends on hybrid methods between the two aforementioned categories accounting for 18.7% (12/64). Forth category depends on clustering methods, accounting for 11% (7/64). The fifth category depends on reinforcement learning, accounting for 6.3% (4/64). And the final category depends on the hybrid methods between two or more methods mentioned above, accounting for 23.4% (15/64). Papers have been analysed to get information like the optimisation problem, the method used for solving it, considered parameters, objectives, constraints, implementation tools, and evaluation methods.

Abstract Image

边缘和雾节点的放置方法、技术、参数和约束的综述
在边缘和雾计算中,边缘和雾节点必须最佳地位于网络边缘,以最大限度地减少网络的总体延迟。本调查解决了这些节点安置问题的所有方面。关于边缘和雾节点位置的文献是通过搜索查询从知名数据库(IEEE Xplore数字图书馆、Scopus、ScienceDirect和Web of Science)中收集的。使用关键词和滚雪球法进行人工搜索,以获得尽可能多的相关论文。根据定义的纳入标准,检索到的文档被过滤为64篇文章,为期8年(2015-2022)。根据使用的优化方法,文献可分为六类。第一种依赖于整数规划,占20.3%(13/64)。第二类依赖于启发式和元启发式方法,占20.3%(13/64)。第三类依赖于上述两类的混合方法,占18.7%(12/64)。第四类依赖于聚类方法,占11%(7/64)。第五类依赖于强化学习,占6.3%(4/64)。最后一类依赖于上述两种或两种以上方法的混合方法,占23.4%(15/64)。论文已被分析,以获得信息,如优化问题,用于解决它的方法,考虑参数,目标,约束,实施工具和评估方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IET Networks
IET Networks COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
5.00
自引率
0.00%
发文量
41
审稿时长
33 weeks
期刊介绍: IET Networks covers the fundamental developments and advancing methodologies to achieve higher performance, optimized and dependable future networks. IET Networks is particularly interested in new ideas and superior solutions to the known and arising technological development bottlenecks at all levels of networking such as topologies, protocols, routing, relaying and resource-allocation for more efficient and more reliable provision of network services. Topics include, but are not limited to: Network Architecture, Design and Planning, Network Protocol, Software, Analysis, Simulation and Experiment, Network Technologies, Applications and Services, Network Security, Operation and Management.
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