Network slice optimization processing method and system

Abstract

The invention provides a network slice optimization processing method and system, and the method comprises the steps: constructing a heterogeneous Markov decision process model, and constructing a hierarchical deep reinforcement learning architecture; constructing a joint optimization decision model based on a heterogeneous Markov decision process model and a hierarchical deep reinforcement learning architecture; wherein the joint optimization decision model comprises an upper-layer sub-model and a lower-layer sub-model, the upper-layer sub-model is used for executing an inter-slice resource allocation process, and the lower-layer sub-model is used for executing an intra-slice task scheduling process; and inputting network resource information and business request data obtained in real time into the joint optimization decision model to obtain a network slice optimal strategy solution output by the joint optimization decision model. The resources allocation and task scheduling of the network slice can be jointly optimized, so that the potential business capability of the system can be mined, the available resources of the system are fully mobilized to reduce the network delay of the user, and the user experience is improved.

Description

technical field [0001] The present invention relates to the technical field of network optimization, in particular to a network slice optimization processing method and system. Background technique [0002] There are a large number of unpredictable new services in the Internet of Vehicles scenario. According to the Service Level Agreement (SLA, Service Level Agreement), customized network slices provide services for users, which has limitations in network automation and service flexibility, and it is difficult to meet new business needs in a timely manner. Therefore, It is necessary to dynamically and efficiently allocate resources for network slicing in the Internet of Vehicles, and provide services for various emerging services. [0003] Currently, there are extensive researches on inter-slice resource allocation and intra-slice task scheduling respectively. However, the available resources of each network slice will affect its internal task scheduling, and the task sched...

AI Technical Summary

Problems solved by technology

However, the available resources of each network slice will affect its internal task scheduling, and the task scheduling process within the slice will in turn affect the overall resource utilization of the slice. Slice resource allocation and task scheduling are closely related and coupled with each other.

Existing research optimizes the resource allocation and task scheduling of network slicing as two separate problems, which makes it difficult to tap the potential service capabilities of the system and fully mobilize the available resources of the system to reduce user network delays.

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