Industrial edge network system architecture and resource scheduling method

Abstract

The invention discloses an industrial edge network system architecture, which comprises an application layer, a control layer and a field layer, and the application layer is divided into a plurality of industrial virtual fragmentation networks; the control layer mainly comprises a software defined network (SDN) controller; and the field layer comprises an edge layer and a field subnet. The invention also discloses an industrial edge network system resource scheduling method, which comprises the following steps of: dividing a virtual sub-network into different NOMA clusters, describing a relationship between an AoI over-limit probability and a queue overflow probability, converting an AoI constraint into a queue overflow probability constraint, establishing a theoretical model of system power consumption minimization, and performing joint allocation on the bandwidth resources, the power resources and the computing resources; and secondly, converting and decomposing a random optimizationproblem of time averaging into two sub-problems solved by a single time slot, and respectively solving the two sub-problems. According to the method, the industrial virtual fragmentation network andthe virtual sub-network are established, so that better resource allocation is facilitated, and full space-time monitoring and cooperative control of key parameters in the industrial process are realized.

Description

technical field [0001] The present invention relates to the field of network information technology, in particular to an industrial edge network system architecture and resource scheduling method. Background technique [0002] With the vigorous development of the Industrial Internet of Things, the number of industrial field terminal devices has shown explosive growth, and time-sensitive-computing-intensive applications (Time sensitive computing intensive applications, TSCIA) and services are constantly emerging, such as product temperature monitoring images, product defect detection videos Wait. Such tasks not only require abundant computing resources, but also generate high energy consumption. However, the computing power and battery life of local devices are usually limited, which makes it difficult to support the processing of such tasks. Driven by software-defined networking (SDN) and network function virtualization, cloud computing has been proposed. It allows local d...

AI Technical Summary

Problems solved by technology

[0007] 1. In the existing system architecture, the cloud server is far away from the factory equipment, and offloading will cause a large delay and waste of resources. The cloud computing solution is difficult to meet the requirements of time-sensitive-computing-intensive application (TSCIA) tasks, and is no longer suitable for mass terminals. industrial site

[0008] 2. Existing system designs based on optimized AoI averages (e.g., latency, rate, and queue length) are insufficient to capture TSCIA mission requirements, as the averages tend to ignore extreme events that negatively impact overall performance (e.g., high-latency events ), leading to the deterioration of system perception and control performance

[0009] 3. Existing technologies pay more attentio

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