The invention provides an edge computing task allocation method based on a deep neural network, which comprises the following steps of: obtaining parameter quantity data, and respectively computing parameter quantities of a network layer to be computed in the neural network; obtaining calculated amount data, and obtaining the calculated amount data according to the parameter amount of the networklayer to be calculated; allocating calculation tasks, and obtaining the calculation tasks of the terminal equipment according to the calculation amount data; obtaining a computing task of the edge server according to the computing task of the terminal equipment; and judging whether the remaining computing tasks need to be executed in the cloud server. Besides, the invention further provides an edge computing task allocation device based on the deep neural network and a storage medium, the real-time residual computing resource condition of each layer of equipment can be fully considered, the parameter quantity and the computing quantity of each layer are calculated on the basis, a corresponding deployment scheme is obtained, and the computing capacity of each layer of equipment is fully utilized.

Edge computing task allocation method and device based on deep neural network

The invention belongs to the field of 5G Internet of Vehicles and cloud computing, and particularly relates to an edge computing method for improving 5G Internet of Vehicles communication service quality. The edge calculation method comprises the following steps: 1, connecting a 5G Internet of Vehicles terminal with an edge cloud for the first time; and 2, ensuring the dynamic service quality of 5G Internet of Vehicles communication. The 5G-based edge computing method provided by the invention can ensure that the Internet of Vehicles terminal is always connected to the nearest edge cloud service, and ensures the low-delay access of the Internet of Vehicles terminal to the services. Compared with two baseline methods of always migration and on-demand migration, the method has the advantagesthat the complexity is low, the migration cost (less migration times) is lower than that of the former one, and the end-to-end time delay (mobile sensing migration) is lower than that of the latter one, so that the technical scheme provided by the invention becomes a reliable method for ensuring the communication service quality of the 5G Internet of Vehicles.

Edge calculation method for improving 5G Internet of Vehicles communication service quality

The invention discloses a deep learning task allocation algorithm based on edge calculation in VANET, wherein the distribution algorithm comprises an off-line distribution algorithm and an on-line distribution algorithm, the off-line distribution algorithm mainly aims at the condition of a deep learning task sequence which arrives within a period of time known in advance, and the on-line distribution algorithm mainly aims at the condition that the deep learning task sequence which needs to be processed is not known in advance; the algorithm comprehensively considers and determines the number of layers of the neural network distributed to the edge server by the cloud server according to the calculation expenditure and the bandwidth demand generated by the deep learning task, so that the calculation resources of the edge server are more effectively utilized, and the processing efficiency of the deep learning task is improved. The deep learning task distribution algorithm provided by theinvention maximizes the number of the deep learning tasks running in the edge server, thereby not only reducing the network flow transmitted from the Internet of Vehicles terminal equipment to the cloud server, but also reducing the transmission delay caused by information transmission.

Deep learning task allocation algorithm based on edge calculation in VANET

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Deep learning task allocation algorithm based on edge calculation in VANET