Data processing delay optimization method and system based on edge computing

Abstract

The invention discloses a data processing delay optimization method and system based on edge computing. The method comprises the following steps: building a network architecture model; determining thecomputation delay of an edge computing layer by using a Lagrangian multiplier method; determining the communication delay of the edge computing layer by adopting a Kruskal method; determining the computation delay of a cloud computing layer; determining the communication delay of the cloud computing layer by adopting a balanced transmission method; determining a data processing delay optimizationmodel according to the computation delay and the communication delay of the edge computing layer, and the computation delay and the communication delay of the cloud computing layer; and determining an optimal data processing delay value according to the data processing delay optimization model. The data processing delay optimization method based on the edge computing provided by the invention firstly determines the data processing delay of the edge computing layer, then determines the data processing delay of the cloud computing layer, and finally, determines the optimal data processing delayvalue according to the data processing delay of the edge computing layer and the data processing delay of the cloud computing layer, therefore the system data processing delay is reduced, and the data processing efficiency is improved.

Description

technical field [0001] The present invention relates to the technical field of data processing delay, in particular to a data processing delay optimization method and system based on edge computing. Background technique [0002] With the rapid development of science and technology, astronomy, finance, medical care, the Internet and other industries will generate massive amounts of data. Today, the collection, collation, analysis and application of big data require corresponding technical implementation and support. If not handled properly, it will generate high data processing delays and affect service efficiency. [0003] At present, the traditional data processing of the Internet of Things is concentrated on the cloud computing platform, and the data is processed in batches through Hadoop to reduce the delay of data processing, so as to solve the problems of insufficient storage capacity and slow operation speed of mobile terminal equipment. However, this method has the f...

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Problems solved by technology

However, this method has the following disadvantages: (1) The mobile terminal device is far away from the cloud data center, and the ultra-long-distance data transmission will generate high delay and energy consumption, affect the service quality of the system, and reduce the system service efficiency

(2) With the development of the Internet of Things, the amount of data transmission is growing explosively. The huge number of devices and massive real-time data transmission will bring huge pressure to the network bandwidth and cloud data center, increasing the burden on the cloud data center

(3) The data transmission time is too long, which increases the risk of data being attacked

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