Mobile edge calculation method, framework and medium

Abstract

The invention discloses a mobile edge calculation method, a framework and a medium. The method comprises the following steps: the macro base station delivers to-be-processed task information, the first network communication state, the second network communication state, the first computing resource information and the second computing resource information to a task engine for integration according to received information, constructing global task information, a network communication state and computing resource information; sending the geographical location information to an intelligent traffic engine, and predicting the residence time of the mobile intelligent device in the current communication range of the macro base station in combination with the intelligent traffic information; starting a scheduling engine, and making a scheduling strategy of the current time slice by using the received information; and informing the intelligent equipment, the micro base station and the edge cloud server of the scheduling strategy through a network. Compared with the prior art, the embodiment of the invention expands the application range, improves the task distribution accuracy, and can more comprehensively consider the system energy consumption.

Description

technical field [0001] The present invention relates to the technical field of mobile communication, in particular to a mobile edge computing method, framework and medium. Background technique [0002] Like the development of other modern technologies, the development of mobile communication technology also presents a trend of accelerating. Among them, IoT devices such as smart cars and mobile phones can access the Internet through cellular networks and low-power wide-area networks, and can use the equipped sensors to perceive the state of the surrounding environment. For example, in the fields of Internet of Vehicles and augmented reality, these applications require real-time processing of collected video information and feedback of results to users. A large number of intensive computing tasks will inevitably speed up the energy consumption of IoT devices and shorten their service life. The combination of IoT devices and the cloud is the main mode of IoT applications. Ho...

AI Technical Summary

Problems solved by technology

[0004] However, the number of devices and base stations in the above technical solution is small, which is not enough to meet the needs of real mobile smart device application scenarios, and cannot accurately predict the stay time of devices and the time of participating in calculations, which affects the accuracy of task distribution

In addition, the above technical solution only focuses on the optimal energy consumption of mobile smart devices during task execution, and does not involve the energy consumption of base stations, the association between mobile smart devices and base stations, etc.

Therefore, the existing technology has the problems of limited application, poor accuracy of task distribution, and incomplete consideration of system energy consumption.

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