End-to-end network resource allocation method based on Bayesian learning

A technology of network resource allocation and Bayesian learning, applied in the field of end-to-end network resource allocation based on Bayesian learning, can solve the problem of ignoring the actual needs of service providers, and achieve the effect of avoiding network congestion and resource shortage.

Pending Publication Date: 2022-01-21
NANJING UNIV OF POSTS & TELECOMM +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, most of the resource scheduling studies continue the idea of ​​traditional network resource scheduling algorithms, such as rea

Method used

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  • End-to-end network resource allocation method based on Bayesian learning
  • End-to-end network resource allocation method based on Bayesian learning
  • End-to-end network resource allocation method based on Bayesian learning

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Embodiment Construction

[0040] The technical solution of the invention is described in detail below in conjunction with the accompanying drawings: the present embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following the described embodiment.

[0041] Specific steps are as follows:

[0042] Embodiment 1 provides an end-to-end network resource allocation based on Bayesian learning.

[0043] Such as figure 1 As shown, the end-to-end network resource allocation scheme based on Bayesian learning provided by the first embodiment includes: the benefit function of the service provider and the operator; the decision threshold of the service provider; the benefit of the operator when reaching equilibrium; the service merchant arrival rate.

[0044] The first step is to set that each service provider has a dif...

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Abstract

The invention discloses an end-to-end network resource allocation method based on Bayesian learning, and belongs to the technical field of calculation, reckoning or counting. According to the method, a solution based on a game framework is adopted, a network resource pricing strategy reflecting service provider demand information is provided, and more reasonable network resource pricing and distribution are realized through analysis of network resource states and service provider behaviors. The method comprises the following steps: firstly, establishing game models of a plurality of service providers and operators, and selecting a unit network slice price of a first stage by the operators according to limited demand information; and updating the demand information in the second stage to determine the price. A simulation result shows the existence of a Nash equilibrium point of the system model, and a response type network resource slice allocation scheme is provided, so that resource shortage and network congestion caused when a service provider intensively requests resources are avoided, and reasonable allocation of network resources is realized.

Description

technical field [0001] The invention relates to communication technology, specifically discloses an end-to-end network resource allocation method based on Bayesian learning, and belongs to the technical field of calculation, calculation or counting. Background technique [0002] In recent years, it is difficult for existing networks to support application scenarios in 5G networks. It is unrealistic to deploy multiple different communication networks for different application scenarios, and the huge costs incurred are beyond the reach of operators. Therefore, the Next Generation Mobile Network Alliance has proposed the concept of network slicing, that is, on a physical infrastructure, different network slices are built on demand by using software-defined networks and technologies such as network function virtualization. Various resource allocation schemes aim to achieve resource isolation between slices, so that the failure of one slice in a dynamic network environment will n...

Claims

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Application Information

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IPC IPC(8): H04W72/04G06F17/18G06Q10/04G06Q30/02
CPCG06F17/18G06Q10/04G06Q30/0206H04W72/53
Inventor 李大鹏柳晓寒朱天林蒋锐王小明徐友云
Owner NANJING UNIV OF POSTS & TELECOMM
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