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Deployment method of virtualized network service function chain based on deep reinforcement learning

A virtualized network and service function chain technology, applied in the field of edge computing, can solve problems such as complex and changeable network environments

Active Publication Date: 2021-06-01
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the problem of complex and changeable network environment under edge computing, and to provide a deployment method of virtualized network service function chain. The goal is to improve the deployment efficiency and reduce the deployment cost as much as possible.

Method used

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  • Deployment method of virtualized network service function chain based on deep reinforcement learning
  • Deployment method of virtualized network service function chain based on deep reinforcement learning
  • Deployment method of virtualized network service function chain based on deep reinforcement learning

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Embodiment

[0074] In order to verify the beneficial effects of the present invention, the present embodiment is simulated and verified, and the experimental environment is a 7*3 undirected graph to represent the entire edge network. The whole network is divided into 3 columns, each column represents a class of nodes, and there are 7 nodes in each class. Each node serves as the physical infrastructure, and in the experiment, each node is regarded as an edge server. These three types of nodes can respectively provide a, b, and c three network services. After the simulation experiment, the results of Reward, Cost and Revenue and profit are obtained as follows: Figure 4 to Figure 6 shown.

[0075] Figure 4 It is shown that according to the DDPG algorithm of the present invention, in the 7*3 network topology, with the increase of the number of training sets, the average reward is basically stable after 400 times of training, and the value of Reward gradually converges. A notable finding...

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Abstract

The invention relates to a virtualized network service function chain deployment method based on deep reinforcement learning, which is used to solve the problem of virtualized network service function deployment under the background of edge computing, and belongs to the technical field of edge computing. This method, by solving the two problems of virtual function placement and traffic routing respectively, realizes the deployment of the service function chain at the minimum cost, and can take advantage of deep reinforcement learning to adapt to the time-varying traffic control requirements. This method uses the neural network as the basis for the accumulated reward Q value. In addition, deep reinforcement learning introduces the concept of an experience pool when inputting samples to a neural network. The present invention considers not only the total cost but also the end-to-end delay, especially the intermediate processing delay, and is suitable for applications in dynamic and complex scenarios requiring high communication cost and delay of the server.

Description

technical field [0001] The invention relates to a deep reinforcement learning and network function virtualization technology, in particular to a virtualized network service function chain deployment method based on a deep reinforcement learning algorithm, which is used to solve the problem of virtualized network service function deployment under the background of edge computing, It belongs to the field of edge computing technology. Background technique [0002] With the advent of the Internet era, various mobile smart terminals have exploded in popularity. All kinds of things in life are connected to the Internet, resulting in an explosive growth in the amount of network data. According to the prediction of the Internet Data Center (IDC), the total amount of global data will be greater than 40ZB in 2020. The traditional Internet-based cloud computing provides users with network services by using the huge resource system on the Internet, and uploads data to the cloud comput...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L12/24G06N3/04G06N3/08
CPCH04L41/0889H04L41/0826H04L41/0893G06N3/08G06N3/045
Inventor 杨松贺楠杨祚李凡
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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