Edge cloud resource scheduling method, device and system managed and controlled by edge autonomous center

Abstract

The invention provides an edge cloud resource scheduling method managed and controlled by an edge autonomous center. The method comprises the following steps: receiving a service request from a terminal; obtaining a state space of the edge cluster, the state space being used for representing a resource state of the edge cluster; inputting the state space of the edge cluster into a service request assignment model to obtain a state transition probability for assigning a service request action, wherein the service request assignment model comprises a deep reinforcement learning network; and determining a target cluster used for responding to the service request according to the state transition probability, the target cluster comprising the edge cluster or the cloud cluster, and the edge cluster comprising edge nodes. The invention further provides an edge cloud resource scheduling device controlled by the edge autonomous center and an edge cloud resource scheduling system controlled by the edge autonomous center.

Description

technical field [0001] The present disclosure relates to the technical field of resource scheduling, and more specifically, to an edge-cloud resource scheduling method, device, and system managed and controlled by an edge autonomous center. Background technique [0002] As an important supporting technology of the Internet, cloud computing technology has been applied in all aspects of human life and production, such as education, medical care, finance, government affairs, travel, etc. In layman's terms, all applications that need to run on the Internet are inseparable from the support of cloud computing. However, with the rapid rise of new technologies such as the Internet of Things, big data, and artificial intelligence, various types of cloud applications generate massive data and occupy network bandwidth, and at the same time have strict requirements for service response delays, such as smart factories, Disaster prediction, autonomous driving and other fields, these appl...

AI Technical Summary

Problems solved by technology

However, with the rapid rise of new technologies such as the Internet of Things, big data, and artificial intelligence, various types of cloud applications generate massive data and occupy network bandwidth, and at the same time have strict requirements for service response delays, such as smart factories, Disaster prediction, autonomous driving and other fields, these applications have caused enormous pressure on communication networks and cloud clusters

[0003] Edge computing is expected to become the next-generation computing model based on cloud computing, but the integration process of the two is still difficult

[0004] In the process of realizing the concept of the present disclosure, the inventors found that there are at least the following problems in related technologies: only relying on cloud clusters to calculate and process massive data increases the computing pressure of cloud clusters, and cannot meet the requirements of cloud clusters for response delays. Strict requirements

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