Abstract
With the popularization of mobile terminals and the rapid development of mobile communication technology, many PC‐based services have placed high demands on data processing and storage functions. Cloud laptops that transfer data processing tasks to the cloud cannot meet the needs of users due to low latency and high‐quality services. In view of this, some researchers have proposed the concept of mobile edge computing. Mobile edge computing (MEC) is based on the 5G evolution architecture. By deploying multiple service servers on the base station side near the edge of the user’s mobile core network, it provides nearby computing and processing services for user business. This article is aimed at studying the use of caching and MEC processing functions to design an effective caching and distribution mechanism across the network edge and apply it to civil aviation express marketing. This paper proposes to focus on mobile edge computing technology, combining it with data warehouse technology, clustering algorithm, and other methods to build an experimental model of MEC‐based caching mechanism applied to civil aviation express marketing. The experimental results in this paper show that when the cache space and the number of service contents are constant, the LECC mechanism among the five cache mechanisms is more effective than LENC, LRU, and RR in cache hit rate, average content transmission delay, and transmission overhead. For example, with the same cache space, ATC under the LECC mechanism is about 4%~9%, 8%~13%, and 18%~22% lower than that of LENC, LRU, and RR, respectively.
Highlights
In recent years, the ability of humans to use computer technology to generate and collect information has been greatly improved
(2) This paper proposes a collaborative caching mechanism based on machine learning under the distributed Mobile edge computing (MEC) service system
The mechanism uses the local caches of several MEC servers to form a cooperative cache domain as the overall structure and uses the migration learning method to predict the popularity of the content
Summary
The ability of humans to use computer technology to generate and collect information has been greatly improved. Large-scale database systems have been widely used in the management of research companies, and their development momentum is very strong. This raises a new question for managers, how can we effectively manage and apply a large amount of data? After gradually realizing the role of historical data resources in improving the competitiveness of enterprises and increasing economic benefits, they tried to obtain their own sales data and sales data of competing companies to assist in the formulation of sales decision information and timely adjustment of sales strategies and focus on sales and improve the level of revenue. How to obtain useful data faster to help companies analyze the actual needs of customers? In order to make accurate and timely business decisions, relevant information must be fully obtained and used to assist the decision-making process
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