Virtual network function deployment optimization algorithm based on deep reinforcement learning

Abstract

The invention relates to a virtual network function deployment optimization algorithm based on deep reinforcement learning, and belongs to the technical field of mobile communication. According to themethod, VNF sharing is considered under the constraint of a physical layer CPU, bandwidth resources and SFC end-to-end time delay, and the total cost of a service provider and the SFC end-to-end timedelay are jointly optimized by deploying VNF and allocating CPU resources; secondly, since the state space and the action space of the scheme are continuous value sets, a VNF intelligent deployment algorithm based on deep reinforcement learning is provided, and thus an approximately optimal VNF deployment and resource allocation strategy is obtained. And on each discrete time slot, the VNF is deployed to a proper destination server according to the arrival rate of the SFC, the remaining CPU resources of the universal server and the remaining bandwidth resources of the physical link, and the VNF is allocated to the CPU resources of the destination server. According to the VNF deployment optimization algorithm provided by the invention, the compromise between the total cost of the service provider and the end-to-end delay of the SFC can be realized, and the resource utilization of the physical network is improved.

Description

Technical field [0001] The invention belongs to the field of mobile communication technology, and relates to a virtual network function deployment optimization algorithm based on deep reinforcement learning. Background technique [0002] In recent years, as an important paradigm shift in network service provisioning, network function virtualization (NFV) technology has received widespread attention from the industry and academia. Under the NFV architecture, a series of virtual network function VNFs are composed of SFCs in a specific order. Users provide services, and the same type of VNF can be deployed or re-instantiated on different general servers without the need to repurchase hardware. By separating network functions from traditional dedicated hardware, NFV can significantly reduce service providers’ operating costs and capital expenditures, and NFV can also help deploy new virtual network services flexibly and quickly. In addition, software-defined networking (SDN) togethe...

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Problems solved by technology

[0003] At present, the relevant existing technologies have the following shortcomings: in the literature on VNF deployment, most of the work only stays on solving the resource scheduling problem of a single deployment cycle, but in actual network scenarios, if the network resources are not dynamically allocated to handle changes business requests, which may cause a backlog of data, increase the probability of queue overflow, and increase the end-to-end delay. Therefore, the network should dynamically adjust resource allocation according to the current queue status to provide stable services, and the existing The VNF deployment mechanism does not jointly consider the total cost of the service provider and the minimization of the end-to-end delay of the SFC. Most of the literature research is based on the known state of the environment, and does not take into account the dynamic changes of the environment over time.

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