Construction of an Intelligent APP for Dance Training Mobile Information Management Platform Based on Edge Computing
Author(s) -
Yan Gao,
Dazhi Xu
Publication year - 2021
Publication title -
mobile information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.346
H-Index - 34
eISSN - 1875-905X
pISSN - 1574-017X
DOI - 10.1155/2021/3019009
Subject(s) - computer science , edge computing , cloud computing , server , mobile edge computing , edge device , distributed computing , computer network , operating system
In recent years, with the rapid development of modern technology and the continuous promotion of information technology, information technology has been widely used in modern performing arts. Information management has become the most practical and effective method and means in performing arts training management, but as the amount of various data grows exponentially, the requirements for computing processing power and speed for massive amounts of data and information are also increasing day by day. This article aims to study the use of edge computing to solve the problems of high latency and high cost when traditional cloud computing centers provide services. In response to these problems, this paper proposes a data acquisition and processing system architecture based on edge computing, which uses edge computing to mine the computing power of edge terminals in the network, performs partial or all calculations at the edge terminals, processes private data, and reduces cloud computing. The center’s computing, transmission bandwidth load, and energy consumption, combined with cloud computing, provide data acquisition, processing, and analysis solutions with low latency and high processing capabilities. This article details how to optimize edge server development to minimize access latency and consider network reliability when requesting access to edge servers. This paper uses the proposed edge server deployment algorithm and system load optimization, which can effectively reduce the network delay and system load of the edge server, and the experimental results show that the system performance is improved by 23.5% after effective optimization.
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