Computing task allocation method and device in cloud edge computing environment

Abstract

The invention provides a computing task allocation method in a cloud edge computing environment. The method comprises the following steps: acquiring a computing task and available resource equipment;constructing a decision variable, sequentially distributing the calculation tasks to each available resource device to be processed, and assigning values to the decision variable to obtain a decisionvariable matrix of each calculation task; constructing a task processing efficiency variable, traversing a decision variable matrix of each calculation task, and assigning execution time, energy consumption and cost to the task processing efficiency variable by using different models when available resource equipment is allocated to the calculation tasks for processing to obtain a target functioncombination matrix of each calculation task; deleting the task processing efficiency variables meeting the preset conditions to update all the target function combination matrixes; and outputting theavailable resource equipment with the minimum execution cost and execution energy consumption in all the updated target function combination matrixes. By implementing the method, the information transmission delay can be reduced, the task processing efficiency can be improved, and the calculation tasks can be reasonably distributed to realize quick processing and persistent calculation.

Description

technical field [0001] The present invention relates to the technical field of cloud computing, in particular to a computing task allocation method and device in a cloud edge computing environment. Background technique [0002] The distribution network is equipped with a large number of handheld terminals that collect and analyze data, and cloud computing technology and edge computing technology are used to provide computing and storage services for handheld terminals. Among them, cloud computing technology provides computing and storage services for handheld terminals and public cloud resources in a scalable manner, but there are two obvious limitations: 1) The transmission distance from handheld terminals to public cloud resources is too long, resulting in the failure of mobile applications. The delay is too long; 2) The computing and storage at the edge of the network are insufficient to realize ubiquitous mobile computing. Among them, edge computing technology transfers...

AI Technical Summary

Problems solved by technology

Among them, cloud computing technology provides computing and storage services for handheld terminals and public cloud resources in a scalable manner, but there are two obvious limitations: 1) The transmission distance from handheld terminals to public cloud resources is too long, resulting in the failure of mobile applications. Too long latency; 2) Insufficient computing and storage at the edge of the network to enable ubiquitous mobile computing

Among them, edge computing technology transfers some computing tasks to the edge to meet the growing computing needs of handheld terminals, and ensures that handheld terminals can track information such as behavior, location, and environment in real time, reducing sensitive information between handheld terminals and public cloud resources. At the same time, it is more energy-efficient, but it is limited in terms of computing power, scalability, and high-energy sensitivity, and cannot perform long-term computing

It can be seen that there are deficiencies in the allocation of computing tasks in the above two computing environments, and it is necessary to improve

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