Edge artificial intelligence computing task scheduling method based on peer-to-peer network

Abstract

The invention discloses an edge artificial intelligence computing task scheduling method based on a peer-to-peer network, which comprises the following steps: broadcasting a request packet to other Internet of Things nodes of the peer-to-peer network according to an artificial neural network model, receiving reply packets which are sent by the other Internet of Things nodes and respond to the request packet, establishing a routing table between the Internet of Things nodes according to the reply packet, establishing associated information of the Internet of Things nodes and the calculation task of the artificial neural network model according to the routing table, and broadcasting a calculation task allocation packet according to the routing table, thereby realizing distributed calculationof the calculation task of the artificial intelligence calculation model at the edge end. The computing power of the Internet of Things node is fully utilized, the processing speed of the artificialintelligence computing task is improved, and various defects of completing the computing task of the artificial intelligence computing model at the cloud end are avoided.

Description

technical field [0001] The invention relates to the technical field of edge computing, in particular to a peer-to-peer network-based edge artificial intelligence computing task scheduling method. Background technique [0002] With the development of IoT technology, there are more and more IoT nodes, and these IoT nodes are usually powered by batteries, so their computing power is often limited, and due to the limitation of battery life, these IoT nodes are not It is not suitable to independently undertake more complex artificial intelligence computing tasks based on machine learning models, such as the computing tasks of artificial neural network-based artificial intelligence environmental monitoring models. Therefore, in order to avoid processing these computing tasks locally at the IoT nodes, the existing technicians often send the data collected by the IoT nodes to the cloud for processing, which will bring a lot of communication delay, and it is necessary to equip the Io...

AI Technical Summary

Problems solved by technology

[0002] With the development of IoT technology, there are more and more IoT nodes, and these IoT nodes are usually powered by batteries, so their computing power is often limited, and due to the limitation of battery life, these IoT nodes are not It is not suitable to independently undertake more complex artificial intelligence computing tasks based on machine learning models, such as the computing tasks of artificial intelligence environmental monitoring models based on artificial neural networks

Therefore, in order to avoid processing these computing tasks locally at the IoT nodes, existing technicians often send the data collected by the IoT nodes to the cloud for processing. Gateways or wireless communication modules, thus increasing hardware costs, while the computing power of IoT nodes is not fully utilized

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