Internet of Things edge computing task unloading method and system

Abstract

The embodiment of the invention provides an Internet of Things edge computing task unloading method and system. The method comprises the steps of obtaining a scene model of an Internet of Things edgecomputing network; designing a task unloading strategy based on a deep reinforcement learning PPO algorithm, and defining an environment state vector and a behavior vector; and setting a preset task request specification according to the environment state variable and the behavior vector, and completing calculation task unloading requests of a plurality of network elements in the scene model basedon the preset task request specification. According to the embodiment of the invention, the edge computing technology and the deep reinforcement learning technology are introduced under the scene ofthe Internet of Things, step-by-step learning is achieved by using the PPO algorithm in deep reinforcement learning and a neural network model is perfected, and a better edge computing task unloadingstrategy is applied, so that the network delay can be flexibly reduced under the condition of ensuring low complexity.

Description

technical field [0001] The invention relates to the technical field of network resource allocation, in particular to a method and system for offloading edge computing tasks of the Internet of Things. Background technique [0002] As an important computing application mode of the network, cloud computing rapidly promotes the development of the Internet. The high-speed and reliable air interface enables the remote cloud data center to provide computing services for application devices. However, the long data transmission link between the cloud data center and the edge device leads to the long response delay of mobile Internet applications, which cannot meet the needs of most delay-sensitive Internet applications today, resulting in edge computing technology . Edge computing technology has the characteristics of low latency, low bandwidth cost, high security, and high flexibility. Various scenarios such as networked wireless sensors, augmented reality technology, etc. By dep...

AI Technical Summary

Problems solved by technology

Although the present invention can reduce the energy consumption of IoT terminals and the complexity of task offloading in mobile edge computing, thereby improving the efficiency of task offloading, the number of terminals in the edge network has a greater impact on it. When the network is relatively busy, the allocation strategy May not be able to cope with a sudden increase in computing tasks in time

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