CN109067580B - Self-adaptive software-defined wireless network multi-controller deployment method - Google Patents

Self-adaptive software-defined wireless network multi-controller deployment method Download PDF

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CN109067580B
CN109067580B CN201810876056.1A CN201810876056A CN109067580B CN 109067580 B CN109067580 B CN 109067580B CN 201810876056 A CN201810876056 A CN 201810876056A CN 109067580 B CN109067580 B CN 109067580B
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李飞翔
徐晓斌
李晓伟
姚海鹏
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Beijing University of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

In order to solve the problem that the existing software-defined wireless network cannot reasonably deploy multiple controllers, the invention provides a self-adaptive software-defined wireless network multiple controller deployment method. The invention creatively provides a corresponding method model according to the wireless transmission model in the multi-controller deployment scheme, effectively reduces the influence of energy consumption in a wireless network, and solves the problem of reasonably deploying the multi-controller.

Description

Self-adaptive software-defined wireless network multi-controller deployment method
Technical Field
The invention relates to the field of software-defined wireless networks, in particular to the field of self-adaptive software-defined wireless network multi-controller deployment.
Background
With the rapid expansion of the scale of wireless networks, the original wireless network technology cannot meet the requirements of wireless terminals. Therefore, the software defined wireless network proposes to solve the wireless network problem by using the software defined network technology, decouple the data layer and the control layer of the wireless network, and improve the control capability of the wireless network by adopting a centralized control mode. Compared with the original wireless network architecture, the software defined wireless network improves the flexibility of network control and enhances the support capability of a new wireless network technology.
The software defined wireless network is the same as the software defined network, the interaction between the control layer and the data layer is realized through a southward interface, and the current main communication protocols include OpenFlow, superflow, Cross Flow and the like. At present, the prototype design of the wireless node of the software-defined wireless network is successful, such as a Xilinx-Artix software microwave platform, and the device can be embedded into the wireless node and a controller to realize the wireless communication of the wireless node and the controller.
The network state of the software-defined wireless network is mainly responsible for the controller, and as the control elements in the software-defined wireless network grow, the controller of one software-defined wireless network has difficulty in controlling all the elements due to the limitation of the capability of the controller. Meanwhile, once a single controller fails, the whole network is broken down. The mainstream mode is to distribute a plurality of controllers in a software-defined wireless network, so that the control capability of a large-scale network is improved. However, in large-scale network deployment, the number and location of the controllers determine the cost of the network, and therefore how to effectively and reasonably deploy the controllers is a difficulty in software-defined wireless network. At present, the deployment problem of multiple controllers is quite mature and researched in a software defined network, the average delay and the minimum delay of nodes are mainly considered by a model, and solutions mainly comprise a clustering method, a simulated annealing algorithm, a particle swarm algorithm and the like. The current multi-controller deployment problem is just started in the field of software defined wireless networks, and the model mainly refers to the model of the software defined network, but the current multi-controller deployment problem in the software defined wireless network has the defects that the difference between the wireless network and a wired network is not considered, and the energy consumption of wireless equipment in the wireless network is one of the important factors for controller deployment. In summary, how to effectively and reasonably deploy the controller in the software defined wireless network is a deficiency of the prior art.
Disclosure of Invention
The invention provides a self-adaptive software-defined wireless network multi-controller deployment method, aiming at solving the problem that the existing software-defined wireless network cannot reasonably deploy multi-controllers. The method comprises the steps that the influence of energy consumption in a wireless network is creatively considered in a multi-controller deployment scheme, and a corresponding method model is provided according to a wireless transmission model; the clustering method of selecting the data field firstly divides the software defined wireless network into a plurality of sub-fields, and then deploys a controller in each sub-field, thereby effectively reducing the complexity.
The specific scheme of the network analysis, the mathematical model establishment and the method design of the invention is as follows:
network analysis
In software defined wireless networks, multiple controllers need to be deployed in order to meet network performance requirements. For a certain number of controllers, if the controllers are not deployed reasonably, the energy of the controllers is wasted. In the previous controller deployment proposals, the delay of the whole network is generally taken as a reference for the controller deployment. If the minimum transmission power is considered in the deployment position of the controller, the energy consumption of the controller in the wireless network is effectively saved, and the overall performance of the network is favorably improved.
(II) mathematical model establishment
The invention abstracts the problem of multi-controller deployment in a software defined wireless network into a graph theory problem, which is an NP difficult problem. For this multi-controller deployment optimization problem, assuming a total of N node locations in the wireless network, the goal is to deploy an appropriate number of controllers from among the N node locations, each with minimal transmission power within its domain. The received power of the receiver at a distance d from the transmitter is obtained according to the Flries radio transmission equation
Figure BDA0001752760140000021
Wherein the transmitter has a transmission antenna power of PtGain of transmission antenna of transmitter is GtGain of receiving antenna of receiver is GrReception power of receiver is PrThe operating wavelength of the transmitter is lambda, and the distance between the two antennas of the transmitter and the receiver is d. Assuming that the node positions in the present invention can be deployed with both controllers and control elements, both having a transmitting module and a receiving module, and the transmitting antenna gain and the receiving antenna gain are the same, it can be derived from the above formula that the target problem of the present invention is that the average minimum transmission power of a plurality of controllers is
Figure BDA0001752760140000022
Where M denotes the number of controllers, Pt(k) The transmission power of the kth controller is expressed, and the function formula expresses that the average transmission power of the M controllers is minimum.
To achieve this goal, the following constraints need to be satisfied. Let network element be S ═ S1,s2,…,sN-MC ═ C for controller1,c2,…,cM}. First, each network element sjWith and only belonging to one controller ckManagement of where sj∈S,j＝{1,2,…,N-M}，ckE C, k ═ {1,2, …, M }. Second, each controller ckNumber of elements controlled
Figure BDA0001752760140000023
(L ═ 1,2, …, L, L < N-M) must not exceed the controller ckMaximum value Max (G (c)) of maximum management elementsk)). Third, each controller ckHas a certain control range D (c)k) Element s it controlsjDistance d (c) ofk,sj) The control range of the controller cannot be exceeded.
(III) method design
In order to solve the above problems, we innovatively propose a self-adaptive software-defined wireless network multi-controller deployment method, which is divided into two sub-methods, and the sub-method 1 divides a network node into a plurality of sub-fields by using a data field method, and places one controller in each sub-field, so as to determine the number of controllers in a network. The flow chart is shown in fig. 1, and comprises the following steps:
1. input all nodes x in the whole network area OiI ═ 1,2, …, N, parameters k, σ, m of the input data field methodiWhere k is a distance index, k ≧ 1, σ is an influence factor, 0 < σ < 1, and m denotes a node xiM > 0, the weight of all nodes is the same by default.
2. Pseudo-nuclear force field formula based on data field method
Figure BDA0001752760140000031
And
Figure BDA0001752760140000032
calculating the potential value of any node x ', x' epsilon x
Figure BDA0001752760140000033
And a field strength F (x'), where xi-x' represents a distance vector between two nodes, | xi-x' | | represents the distance between two nodes, m represents the weight of a node, the range is (0, infinity), the default weight of each node is the same, k is the distance index, the range is [1, infinity "), σ is the influence factor for adjusting the interaction range, the range is (0, 1).
3. Root of herbaceous plantDrawing equipotential lines according to the potential value and field intensity of each node, and classifying the nodes in the same equipotential line into a sub-domain Oj；
4. Judging whether an outlier s exists in the networkjI.e. whether there is a sub-field OjIs 1, if any, according to the outlier sjMinimum distance from all neighborhoods, and forming the outlier and the nearest neighbor domain into a new domain Oj'。
And the sub-method 2 calculates the objective function value of each node by utilizing an exhaustion method aiming at each wireless network sub-domain, and finds out the optimal position deployment controller. The flow chart is shown in fig. 1, and comprises the following steps:
1. input each sub-field OjAll nodes x in `i；
5. Calculate each sub-field Oj' all nodes x iniFunction value Y ofj(xi) Wherein
Figure BDA0001752760140000034
Wherein d ismaxRepresenting a node xiMaximum distance, P, of two antennas from other nodes of the current sub-domainrIndicating node received power, default node received power being the same, GtRepresenting the transmission antenna gain, G, of the noderThe receiving antenna gain of the node is represented, the transmission antenna gain and the receiving antenna gain of all the nodes are the same, lambda represents the working wavelength of the node, and the working wavelength of each node is the same;
2. determining each sub-field Oj' all nodes in YjMinimum value of (A), YjThe node position corresponding to the minimum value is the sub-domain OjOptimal location in which to deploy the subdomain Oj' of the computer system.
By definition, P in the formular、Gt、GrAnd lambda is a determined value, the formula is further simplified to Yj(xi)＝Admax 2，
Figure BDA0001752760140000041
Hypothesis sonDomain j has 3 nodes in common, dikDenotes the distance between nodes i, k, d1，2＞d2,3＞d1,3And d is1，2＝d2，1，d2,3＝d3,2，d1,3＝d3,1Then according to formula Yj(xi) Then Y isj(x1)＝Ad1，2 2，Yj(x2)＝Ad1，2 2，Yj(x3)＝Ad2，3 2And obtaining Y in the current subdomain according to the size relation of the distances among the 3 nodesj(x1)＝Yj(x2)>Yj(x3) I.e. the function value Y corresponding to node 3 in the subfieldj(x3) And if the minimum value is obtained, the node 3 is the optimal position in the current sub-domain.
The optimal position in each subdomain is the deployment position of the controller, and Y corresponding to subdomain nodesj(xi) Is expressed by a Fries wireless transmission formula
Figure BDA0001752760140000042
Converted to, i.e. Yj(xi)＝Pt，PtRepresenting the transmission power of the controller, in other words the transmission power of the controller in each sub-domain is minimal, so the average minimum transmission power of all controllers in the whole network
Figure BDA0001752760140000043
I.e. minimum, thereby achieving the effect of saving the transmission energy consumption of the controller.
Advantageous effects
The invention divides the network nodes into a plurality of subdomains according to the method of the data field, determines the quantity of the deployed controllers according to the number of the subdomains, traverses all the node positions in each subdomain and takes the optimal node position as the position of the controller.
Drawings
FIG. 1 is a method flow diagram;
FIG. 2a is a graph of a node distribution;
FIG. 2b is a graph of the clustering effect of the data field method;
fig. 2c is the final multi-controller deployment diagram.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
1. Inputting parameters of the objective function, Gt＝15dB,Gr＝2dB,λ＝0.33m,PrSee table 1 for experimental parameters for the method 1W, 2 k, 0.15 σ, and 1 m. Figures 2a-c are one example of a software defined wireless network multi-controller deployment. This example is a china unicom backbone network node location, including harbin, vinpock, zongzi, beijing, tianjin, huishaite, lasui, taiyuan, zheng zhou, china, syzygium, nanjing, shanghai, zhou, fuzhou, xiamen, shenzhen, guangzhou, haikou, changshan, wuhan, dian, cheng, chongqing, guiyang, kunming, nanning, zhongwei, lanzhou, corning, uniquel, rasa, which is a distribution map of the network node location as shown in fig. 2 (a).
2. The potential value at each position is calculated by the data field method, and an equipotential chart is drawn as shown in fig. 2 (b).
3. Fig. 2(c) is a diagram in which a new subfield is formed by grouping the cluster point and the adjacent subfields, which is indicated by a rectangular box, and there are 5 subfields in total. Taking the sub-domain 1 as an example, the node Uruguaoqi obtained by the method in the step 2 is an outlier, and the sub-domain closest to the node is composed of three nodes of Xining, Lanzhou and Zhongwei, so that the Uruguaoqi and the sub-domain form a new domain.
4. Inputting the node position of each sub-domain, taking the sub-domain 1 as an example, the sub-domain comprises four node positions of Zhongwei, Lanzhou, Xining and Wulunqizi, calculating and utilizing the sub-method 2 to find out the optimal position Xining in the sub-domain, and deploying the controller at the node. FIG. 2(c) where the small squares in each subdomain are labeled are the locations where the controllers are deployed.
To verify the effectiveness of the present invention, a set of comparative experiments were performed. The experiment compares the method with a greedy algorithm, a genetic algorithm and a particle swarm algorithm, and as can be seen from table 2, the average minimum transmission power of the multi-controller deployment scheme of the method is 1909W, which is obviously smaller than that of the other three methods, thereby effectively reducing the minimum transmission power of the controller, saving the energy consumption of the controller in a wireless network and prolonging the service life of the network.
Table 1 experimental parameters of the method
Figure BDA0001752760140000051
TABLE 2 comparison of the method of the invention with existing methods
Figure BDA0001752760140000052

Claims (1)

1. A self-adaptive software-defined wireless network multi-controller deployment method is characterized by comprising the following steps:
1) input all nodes x in the whole network area OiI ═ 1,2, …, N, the parameters k, σ, m of the input data field method, where k is the distance index, k ≧ 1, σ is the impact factor, 0 < σ < 1, m denotes the node weight, m > 0;
2) pseudo-nuclear force field formula based on data field method
Figure FDA0001752760130000011
And
Figure FDA0001752760130000012
calculating potential value of any node x' in the whole network area O
Figure FDA0001752760130000013
And a field strength F (x '), wherein | | | x' -xiI represents the distance between two nodes, xi-x' represents a distance vector between two nodes;
3) according to potential value and field of each nodeStrongly, drawing an equipotential line, and dividing nodes on the same equipotential line into a sub-domain Oj；
4) Judging whether an outlier s exists in the networkjI.e. whether there is a sub-field OjIs 1, if any, according to the outlier sjMinimum distance from all neighborhoods, and forming the outlier and the nearest neighbor domain into a new domain Oj'；
5) Input each sub-field OjAll nodes x in `i；
6) Calculate each sub-field Oj' all nodes x iniFunction value Y ofj(xi) Wherein
Figure FDA0001752760130000014
Wherein d ismaxRepresenting a node xiMaximum distance, P, of two antennas from other nodes of the current sub-domainrIndicating node received power, default node received power being the same, GtRepresenting the transmission antenna gain, G, of the noderThe receiving antenna gain of the node is represented, the transmission antenna gain and the receiving antenna gain of all the nodes are the same, lambda represents the working wavelength of the node, and the working wavelength of each node is the same;
7) determining each sub-field Oj' all nodes in Yj(xi) Minimum value of (A), Yj(xi) The node position corresponding to the minimum value is the sub-domain OjOptimal location in which to deploy the subdomain Oj' of the computer system.
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