Resource allocation method and device based on deep reinforcement learning

Abstract

The embodiment of the invention provides a resource allocation method and device based on deep reinforcement learning. The method comprises the steps of determining services of multiple to-be-allocated resources contained in an application request of a user and allocation priorities of the services; determining state parameters of the current edge micro-cloud system, wherein the state parameters comprise a resource balance degree evaluation parameter, a response delay evaluation parameter and a resource surplus of each computing node in each micro-cloud; inputting the state parameters into a pre-trained resource balance optimization model to obtain a first target computing node of the first service, wherein the resource balance optimization model is completed based on deep reinforcement learning training, and deploying the first service to a first target computing node; and updating the state parameter, and returning to the parameter input step until the service of each to-be-allocatedresource contained in the application program request completes resource allocation. Compared with a traditional resource allocation method, the method has the advantages that the communication delayrequirement can be met, and the high resource utilization balance degree can be achieved.

Description

technical field [0001] The present invention relates to the field of wireless communication technology, in particular to a resource allocation method and device based on deep reinforcement learning. Background technique [0002] In recent years, with the continuous development of informatization and networking, information systems have played an increasingly important role in military, disaster relief and other fields. In this highly dynamic environment, mission plans and equipment composition may change frequently, and network connectivity may fluctuate. The service resources based on stand-alone devices are very limited and cannot cope with complex computing tasks. Cloud computing technology is an effective means to deal with this scenario. In cloud computing technology, resource configuration can be customized according to task requirements, so as to provide convenient and flexible management services for large-scale applications. However, traditional cloud platforms ar...

AI Technical Summary

Problems solved by technology

[0004] However, when the existing edge micro-cloud technology allocates resources for services, resource fragmentation often occurs, that is, resource allocation is unbalanced, resulting in waste of resources in a certain dimension

In addition, resource allocation not only needs to consider the resource requirements of each service, but also needs to consider the communication requirements between services, which further increases the complexity of resource allocation, and the existing related technologies do not consider the communication requirements between services

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