Optimization method and device of virtual network mapping algorithm

A virtual network mapping and optimization method technology, which is applied in network interconnection, data exchange network, computing, etc., can solve the problems of parameter selection affecting the quality of the solution, slow convergence speed, poor local search optimization ability of genetic algorithm, etc.

Active Publication Date: 2021-01-26
江苏移动信息系统集成有限公司 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The common algorithm used to solve the virtual network mapping problem in the prior art is the genetic algorithm. The local search and optimization ability of the genetic algorithm is poor, and improper parameter selection will seriously affect the quality of the solution.
Although the harmony search algorithm makes up for the shortcomings of the genetic algorithm, it has a slow convergence speed and a strong dependence on the initial memory bank.

Method used

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  • Optimization method and device of virtual network mapping algorithm
  • Optimization method and device of virtual network mapping algorithm
  • Optimization method and device of virtual network mapping algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0084] Represent the physical network as an undirected graph ,in and Represents the set of physical nodes and the set of physical links respectively. The CPU capacity of the i-th physical node is recorded as , the location attribute of the i-th physical node is denoted as . The bandwidth of the jth physical link is recorded as . Represent the jth virtual network as an undirected graph ,in and respectively represent the virtual node set and the virtual link set of the jth virtual network. The CPU capacity of the i-th virtual node of the j-th virtual network is recorded as , the location attribute of the i-th virtual node of the j-th virtual network is denoted as . The bandwidth of the i-th virtual link of the j-th virtual network is recorded as .

[0085] Complete the physical network with the jth virtual network The revenue obtained after the mapping of is defined as the cumulative sum of all virtual node CPU capacity and virtual link bandwidth of t...

Embodiment 2

[0104] On the basis of Embodiment 1, the initial population described in step 1 is a virtual node set:

[0105]

[0106] in, , represents the first set of solutions of the initial population; k represents the number of virtual nodes that need to be mapped in the j-th virtual network, M represents the population size, Represents the Mth set of solutions of the initial population.

[0107] The process of encoding the solution of the problem is further defined as: , where k=| |, use a string of length k to represent the solution to the problem, and the value of the i-th element in the string comes from the set , Represents a virtual node All physical nodes mapped.

Embodiment 3

[0109] On the basis of Embodiment 1, the process of calculating the fitness of each individual is as follows:

[0110] Calculate the mapping cost after completing the virtual link mapping:

[0111]

[0112] In the formula, Indicates the mapping cost of the i-th chromosome, represents a physical node, Represents a collection of physical nodes, Indicates the weight to control the balanced utilization of physical nodes, is a constant that tends to 0 set to avoid the divisor of the objective function being 0, is mapped to a physical node the only node of The value is 0, Indicates the weight to control the balanced utilization of physical links, Indicates the set of virtual links of the jth virtual network, Indicates the first virtual link flow through the physical link ( , ) traffic, Indicates the remaining CPU capacity of any physical node after accepting the virtual node mapping in the jth virtual network, Indicates the remaining bandwidth of a...

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Abstract

The invention relates to an optimization method and device for a virtual network mapping algorithm, and the method comprises the steps: carrying out the coding of a solution of a problem, and generating an initial population; calculating the fitness of each individual; updating the population, and performing selection operation, crossover operation and mutation operation; selecting a predeterminednumber of individuals from the generated new population as solution vectors of an initial harmony memory bank; randomly selecting three rules based on HMRS and PAR to obtain a new harmony solution vector; updating the memory bank; and repeating the process of obtaining a new harmony solution vector and updating the memory bank until the maximum number of iterations is reached or a stop criterionis met, and outputting an optimal solution. According to the method, the income of a physical network provider and the acceptance rate of the virtual network request can be improved under the condition of reducing the virtual network mapping cost, the global optimization capability and the local search optimization capability are higher, and the convergence rate is higher than that of a traditional genetic algorithm.

Description

technical field [0001] The invention relates to a method and device for optimizing a virtual network mapping algorithm, and relates to the field of H04W wireless communication networks. Background technique [0002] Network virtualization technology constructs user business requests into mutually isolated, diverse and heterogeneous virtual networks (Virtual Networks) through abstraction and reconstruction mechanisms, so that new network services and network protocols can run independently in virtual networks . A virtual network consists of a virtual node (Virtual Node, VN) and a virtual link (Virtual Link, VL), corresponding to the underlying physical nodes and physical paths. Generally, a single virtual network mapping problem can be decomposed into two sub-problems, namely virtual node mapping sub-problem and virtual link mapping sub-problem. [0003] The common algorithm used to solve the virtual network mapping problem in the prior art is the genetic algorithm. The gen...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24H04L12/46G06N3/12G06N3/00
CPCG06N3/006G06N3/126H04L12/4641H04L41/0823H04L41/0893H04L41/142H04L41/145
Inventor 郭永安韩镇宇郑长岭金花姚洁徐姝婷
Owner 江苏移动信息系统集成有限公司
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