CN115765837A - SDN controller deployment method facing low-orbit satellite Internet of things - Google Patents
SDN controller deployment method facing low-orbit satellite Internet of things Download PDFInfo
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Abstract
The invention belongs to the technical field of satellite communication, and discloses a low-earth orbit satellite Internet of things-oriented SDN controller deployment method, which comprises the following steps of 1, visibility analysis: analyzing the number of low orbit satellites covering a given grid by using the divided grid coordinates and the expression of the low orbit satellite coverage area, and selecting an access mode in step 2: the satellite side estimates the access success rate of the satellite and counts the number and load of the terminal which is accessed successfully finally; the terminal side of the Internet of things calculates the satisfaction degree of accessing all satellites covering the grid, network activity factors and network state variables, analyzes the access probability of different satellites covering the grid, and selects the satellite to be accessed; step 3, optimizing a controller deployment strategy: and establishing a controller deployment optimization problem to determine the minimum required number of controllers and the corresponding deployment positions of the controllers. According to the invention, the access load and the end-to-end transmission delay of the low-orbit satellite Internet of things can be met by optimizing the control quantity and the deployment position of the SDN.
Description
Technical Field
The invention relates to the technical field of satellite communication, in particular to a low-earth-orbit satellite Internet of things-oriented SDN controller deployment method.
Background
The application of the internet of things is gradually deepening into various fields of human activities, but due to the limitation of factors such as space, environment and the like, the ground internet of things cannot provide reliable service for certain fields of data acquisition such as cross-region and severe environment, and the phenomenon that the service capability is not matched with the requirement occurs, the reason is that for the internet of things depending on wireless access, a communication network must be built by enough base stations, but in some remote areas which cannot be reached by human beings, the base stations are arranged and the communication network is very difficult to build: (1) Large-area ocean, desert and other areas cannot establish base stations; (2) The cost of building base stations in remote areas with sparse population is high; (3) Natural disasters can cause serious damage to ground networks. Therefore, how to establish a communication network in a remote area or an area where people are difficult to reach is a difficult problem which must be overcome to realize the interconnection of everything. Among many candidates, the low earth orbit satellite communication system has the advantage of realizing seamless coverage on the earth in a constellation mode due to the characteristics of low transmission loss and time delay, and becomes one of reliable choices for assisting a ground network to realize interconnection of everything.
However, as the number of satellites increases, a series of problems such as many nodes in a satellite network, rapid change of a topology structure, and difficulty in network management become more serious, and a new network management method is required. At this time, a Software Defined Network (SDN) is generated, and the SDN forms a new Network architecture. The introduction of the SDN further improves the expandability and the flexibility of the heaven-earth integrated information network. In order to meet the requirements of low-delay and high-reliability service of emergency tasks, a plurality of SDN controllers need to be deployed to realize distributed control of a satellite network, and therefore, the number and the positions of the controllers are key problems to be considered in designing a controller placement scheme and improving the flexibility of the satellite network.
However, the solution of placing the controller on the whole LEO layer based on the satellite constellation can fully utilize the low latency characteristics of the LEO layer, but needs to select a large number of satellite nodes as the controller to meet the association coverage of the controller to the switch. As the number of nodes increases and the dynamics increase, the control nodes need to synchronize to maintain a global network view. At this time, the network delay is increased due to a large amount of signaling interaction among the nodes, and the low-delay requirement of the emergency task dynamic networking still cannot be well met.
Disclosure of Invention
In order to solve the problems, the invention provides a low-earth orbit satellite internet of things-oriented SDN controller deployment method, which is characterized in that a satellite traffic based on load balancing is obtained through a service model under a satellite internet of things scene; the optimal number and placement scheme of the controllers are determined by establishing an optimization target of minimizing the number of the controllers and solving by using a genetic algorithm; the method not only effectively searches a proper solution for the multiple controllers in a short time, but also ensures that the network has better performance in the aspects of time delay and load balancing.
In order to achieve the purpose, the invention is realized by the following technical scheme:
the invention discloses a low earth orbit satellite Internet of things-oriented SDN controller deployment method, which comprises three parts of visibility analysis, access mode selection and controller deployment strategy optimization, and specifically comprises the following steps:
step 1: and (3) visibility analysis: uniformly dividing the earth surface into a plurality of grids according to longitude and latitude based on the given longitude and latitude division precision, analyzing the condition that a low-orbit satellite covers the grid aiming at any grid, further acquiring all satellite number sets covering the grid by utilizing the analyzed condition, and counting the number of the satellites in the sets to acquire the number of the low-orbit satellites covering the grid;
step 2: selecting an access mode: the satellite side estimates the access success rate of the satellite based on the estimated load, counts the number of the terminal which is finally and successfully accessed and the load, and the networking terminal side calculates the satisfaction degree of accessing all satellites covering the grid, the network activity factor and the network state variable by combining the active terminal number of the grid where the networking terminal side is located, the grid center position and the elevation angle of the visible satellite, the available front derivative of the visible satellite and the estimated access success rate, further analyzes the access probability of different satellites covering the grid where the networking terminal side is located, and selects the satellite to be accessed based on the analysis result;
and 3, step 3: optimizing a deployment strategy of the controller: when time delay and access load are given, a controller deployment optimization problem is established so as to determine the minimum required number of controllers and the deployment positions corresponding to the controllers.
The invention is further improved in that: in step 1, given a grid g, the condition that a low-earth satellite n completely covers the grid is as follows:
wherein r is b Which represents the radius of the earth and is,
indicates the satellite elevation angle, r h Indicates the satellite altitude, r z ＝r b +r h Maximum distance of satellite to coverage area
(x l ,y l ,z l ) T Denotes the vertex coordinates of the mesh g, and l denotes the vertices of the mesh g.
The invention is further improved in that: the step 2 specifically comprises the following steps:
step 2-1: given grid g and terminal deployment density λ of the grid g Calculating the number of new services generated in each time slot, and assuming that the terminal of the internet of things is from T =0 to T S When a certain time node in the ith time slot is triggered, the expression of the number of terminals triggered by the grid g in the ith time slot is as follows:
wherein: s g Denotes the area of the grid g, λ g Indicates the end of grid gEnd density, N g G is more than or equal to 1 and less than or equal to N g P (t) represents the probability distribution function of the Beta distribution,
alpha and Beta are Beta distribution parameters, alpha is more than 0, beta is more than 0 i ＝it s Indicates the time corresponding to the ith time slot, t s Indicating the length of the slot, T S Representing the total time, and entering the step 2-2;
step 2-2: the satellite n estimates the load, i.e. the number of terminals accessed, from its own preamble usage
And calculates the success rate of the access,
the expression of (c) is:
wherein: I.C. A i-1,n Number of idle preamble sequences, R, for the i-1 th time slot satellite n i-1,n Number of preamble sequences available for satellite n, M i-1,n For the number of loads successfully accessed to the satellite n in the i-1 th time slot, lambertiw (-) represents lambertian w function, and the access success rate of the satellite n in the i-1 th time slot is:
wherein: m i,n The number of terminals successfully accessed by satellite n for the ith time slot,
estimating the total number of accessed terminals for the ith time slot satellite n, and entering the step 2-3;
step 2-3: calculating the satellite elevation according to the central coordinate of the grid g where the terminal is located and the real-time satellite point
Number of combined satellite coverage grids q i,n Access success rate P i,n And the available front derivative R i,n Calculating satisfaction of satellite n
Thereby obtaining the network state variable of the satellite n
Thereby obtaining the access probability of each visible satellite
Entering the step 2-4;
step 2-4: for grid g, the terminals of the internet of things with the activated ith time slot respectively generate random numbers R which are subjected to uniform distribution h U (0, 1), and the cumulative probability matrix
The elements in the matrix are compared in sequence, the first element number which is larger than the random number in the matrix is taken out as the satellite number of respective random access, N g Representing the number of satellites covering the grid g, and entering the step 2-5;
step 2-5: the terminal generates a random number mu of 0-1, and a satellite n broadcast access grade limiting parameter lambda n If mu is less than or equal to lambda, the terminal is required to be accessed, otherwise, the terminal is retreated for retransmission; wherein λ is n The expression of (c) is:
wherein: r in The number of preamble sequences available to satellite n,
estimating the number of accessed terminals for the satellite, and entering the step 2-6;
step 2-6: after the number of the terminals is controlled, starting to send random access requests to corresponding satellites, and if collision occurs in the process, retreating and retransmitting; and if no collision is generated, counting the number of the successfully accessed terminals and the number of idle preamble resources.
The invention is further improved in that: in step 3, the average total delay expression of the end-to-end transmission of the network is:
wherein: v = { V = 1 ,v 2 ,...,v M Represents all independent switch nodes, M is the total number of switches, C = { C = } 1 ,c 2 ,...,c K Represents all controller nodes, K is the number of controllers, x cv Allocating a matrix X = [ X ] for a switch cv ] K×M The element in (1) only has two values of 0 and 1, which represents the control relationship between the controller and the switch, d represents the shortest distance between any two nodes in the satellite, and d represents the shortest distance between any two nodes in the satellite w (,) the shortest distance between any two nodes in the satellite network, w ∈ {1,2}.
The static load balancing expression of the controller is as follows:
wherein K is the number of controllers and the load value of the controllers
C is more than or equal to 1 and less than or equal to K, and a switch V v Load value of
V is more than or equal to 1 and less than or equal to M, and the average load of the controller
The optimized model expression based on minimizing the number of controllers is:
s.t.
T total ≤T
B rate ≤B
wherein K is the number of controllers, x cv Allocating a matrix X = [ X ] for a switch cv ] K×M The elements in (1) have only two values of 0 and 1, and V = { V = 1 ,v 2 ,...,v M Represents all independent switch nodes, C = { C = } 1 ,c 2 ,...,c K Represents all controller nodes, B rate Is the static load balancing rate, T, of the controller total The average total time delay of end-to-end transmission of the network, T represents the maximum average total time delay of end-to-end allowed by the network, B represents the maximum unbalanced load rate of a controller allowed by the network, N represents the number of visible satellites, x cv Indicating the control relationship between the switch and the controller.
The invention is further improved in that: the step 3 specifically comprises the following steps:
step 3-1: obtaining satellite node distance d w Number of visible satellites N and satellite number O N Traffic q of each satellite n N is more than or equal to 1 and less than or equal to N, and L chromosomes are randomly generated to form a chromosome population chrom, wherein each chromosome represents a controller deployment method;
step 3-2: calculating the fitness of the chromosome population, wherein the expression is as follows:
Fit＝ω 1 T total +ω 2 B rate
wherein: omega 1 、ω 2 Is a weight coefficient; b rate Is the static load balancing rate, T, of the controller total Average total delay for end-to-end transmission of the network;
step 3-3: carrying out different operations of selection, crossing and variation on chromosomes in the population for multiple times to obtain a new chromosome population newchrom;
step 3-4: calculating the fitness of the new chromosome population, and carrying out different operations of selection, crossing and variation on the chromosomes in the population for many times to obtain a new chromosome population newchrom;
step 3-5: and (5) circularly iterating the step 3-3 and the step 3-4, and when the iteration times exceed the algorithm termination condition, terminating the loop and outputting the number of the controllers and the deployment scheme.
The beneficial effects of the invention are: according to the characteristics of dynamic change of a satellite network, the satellite service volume based on load balance is obtained by combining a service model under the low-orbit satellite Internet of things;
the invention establishes an optimization model with the minimized number of controllers, and solves the problem by utilizing a genetic algorithm, so that the optimal controller deployment strategy can be found in a short time, and the network can be ensured to have better performance in the aspects of time delay and load balance.
According to the invention, the access load and the end-to-end transmission delay of the low earth orbit satellite Internet of things can be met by optimizing the control quantity and the deployment position of the SDN.
Drawings
FIG. 1 is a block diagram of the process of the present invention.
Fig. 2 is a comparative graph of the relationship between two different algorithms and two different load balancing rates in the method of the present invention.
Fig. 3 is a comparison graph of the relationship between two different algorithms and two different network delays in the method of the present invention.
Detailed Description
In the following description, for purposes of explanation, numerous implementation details are set forth in order to provide a thorough understanding of the embodiments of the invention. It should be understood, however, that these implementation details should not be taken to limit the invention. That is, in some embodiments of the invention, such implementation details are not necessary.
As shown in fig. 1, the invention is a low earth orbit satellite internet of things-oriented SDN controller deployment method, which includes three parts of visibility analysis, access mode selection and controller deployment policy optimization, and specifically includes the following steps:
step 1: and (3) visibility analysis:
uniformly dividing the earth surface into a plurality of grids according to the longitude and the latitude based on the given longitude and latitude division precision, analyzing the condition that a low-orbit satellite covers the grid aiming at any grid, further obtaining a satellite number set covering the grid by utilizing the analyzed condition, and counting the number of the satellites in the set to obtain the number of the low-orbit satellites covering the grid;
given a grid g, the condition that a low-orbit satellite n completely covers the grid is:
wherein the radius r of the earth b Elevation angle of satellite
Satellite altitude r h ，r z ＝r b +r h Maximum distance of satellite to coverage area
(x l ,y l ,z l ) T Denotes the vertex coordinates of the mesh g, and l denotes the vertices of the mesh g.
Step 2: selecting an access mode:
the satellite side estimates the possible access success rate of the satellite based on the estimated load, and counts the number and the load of the terminal which is accessed successfully finally; and the terminal side of the Internet of things calculates the satisfaction degree of accessing and covering all the satellites of the grid, the network activity factor and the network state variable based on the number of active terminals of the grid, the grid center position and the elevation angle of the visible satellite and by combining the available front derivative of the visible satellite and the estimated access success rate, further analyzes the access probability of different satellites covering the grid, and selects the satellite to be accessed based on the analysis result.
(2.1) grid division is carried out on a certain region on the earth surface, the division precision is respectively delta lat and delta lon, the delta lat represents that the latitude precision is 2 degrees, the delta lon represents that the longitude precision is 2.5 degrees, the grid division is carried out in the region, the latitude is divided at intervals of 2 degrees, the longitude is divided at intervals of 2.5 degrees, business analysis is carried out according to the terminal distribution density under the scene of satellite internet of things, and the terminal deployment density lambda in a given range is determined g Entering the step (2.2);
(2.2) given grid g and terminal deployment density λ of the grid g Calculating the number of new services generated in each time slot, adopting Beta (alpha, beta) distribution, and assuming that the terminal of the internet of things is from T =0 to T s When a certain time node in the ith time slot is triggered, the expression of the number of terminals triggered by the grid g in the ith time slot is as follows:
wherein: s g Denotes the area of the grid g, λ g Representing the terminal density, N, of the grid g g G is more than or equal to 1 and less than or equal to N g P (t) represents the probability distribution function of the Beta distribution,
α and β are distribution parameters of Beta, and in the embodiment of the present invention, α =3, β =4,
t i ＝it s indicates the time corresponding to the ith time slot, t s Indicating the slot length, T S Representing the total time, entering the step (2.3);
(2.3) estimating the number of accessed terminals by the satellite n according to the preamble usage condition of the satellite n
And calculating the access success rate, wherein the satellite side load estimation expression at the moment is as follows:
wherein: i is i-1,n Number of idle preamble sequences, R, for the i-1 th time slot satellite n i-1,n Number of preamble sequences available for satellite n, M i-1,n Lambertiw (-) represents a lambertian w-function for the number of loads that successfully access satellite n in the i-1 th slot.
In the ith time slot, the possible access success rate of the satellite n is as follows:
wherein: m i,n The number of terminals successfully accessed by satellite n for the ith time slot,
estimating the total number of accessed terminals for the ith time slot satellite n, and entering the step (2.4);
(2.4) calculating the satellite elevation according to the central coordinate of the grid g where the terminal is located and the real-time satellite down-pointing point of the satellite
Number of combined satellite coverage grids q i,n Access success rate P i,n And the available front derivative R i,n Calculating satisfaction of satellite n
Thereby obtaining the network state variable of the satellite n
Thereby obtaining the access probability of each visible satellite
Entering the step (2.5);
(2.5) for grid g, the terminals of the Internet of things with activated ith time slot respectively generate random numbers R which are subjected to uniform distribution h U (0, 1), and the cumulative probability matrix
The elements in the matrix are compared in sequence, the first element number which is larger than the random number in the matrix is taken out as the satellite number of respective random access, N g Representing the number of satellites covering the grid g, and entering the step (2.6);
(2.6) the terminal generates a random number mu of 0 to 1, and a satellite n broadcast access grade limiting parameter lambda n If mu is less than or equal to lambda, the terminal is required to be accessed, otherwise, the terminal is retreated for retransmission; wherein λ is n The expression of (a) is:
wherein: r i,n The number of preamble sequences available to satellite n,
estimating the number of accessed terminals for the satellite, and entering the step (2.7);
(2.7) after the number of the terminals is controlled, starting to send random access requests to corresponding satellites, wherein collision may be generated in the process, and if the collision is generated, retreating and retransmitting; and if no collision exists, counting the number of the successfully accessed terminals and the number of idle preamble resources.
And step 3: optimizing a deployment strategy of the controller:
and when the time delay and the access load are given, establishing a controller deployment optimization problem to determine the minimum required number of controllers and the deployment positions corresponding to the controllers.
The average total delay expression for end-to-end transmission of the network is:
wherein: v = { V) 1 ,v 2 ,...,v M Represents all independent switch nodes, M is the total number of switches, C = { C = } 1 ,c 2 ,...,c K Represents all controller nodes, K is the total number of controllers to be deployed, x cv Allocating a matrix X = [ X ] for a switch cv ] K×M The element in (1) has only two values of 0 and 1, and represents the control relationship between the controller and the switch.
Dividing the cycle into T a Time slice, each time node changed is t 1 ,t 2 ,...,t Ta },sat i ,sat j Is a satellite node, any two nodes sat in the satellite network i And sat j Has a shortest distance of
Where w is equal to {1,2}, sat i ∈{C,V}，
Is shown at the t z Time slot node sat i And sat j The shortest distance therebetween.
The static load balancing expression of the controller is as follows:
wherein K is the number of controllers and the load value of the controllers
C is more than or equal to 1 and less than or equal to K, and a switch V v Load value of
1≤v≤M, controller average load
The optimized model expression based on minimizing the number of controllers is:
s.t.
T total ≤T
B rate ≤B
the algorithm comprises the following specific steps:
(3.1) obtaining the satellite node distance d w Number of visible satellites N and satellite number O N Traffic q of each satellite n N is more than or equal to 1 and less than or equal to N, and L chromosomes are randomly generated to form a chromosome population chrom, wherein each chromosome represents a controller deployment method;
(3.2) calculating the fitness of the chromosome population, wherein the expression is as follows:
Fit＝ω 1 T total +ω 2 B rate
wherein: omega 1 、ω 2 As the weight coefficient, in the embodiment of the present invention, ω 1 ＝0.9，ω 2 =0.1 true, T total Average total delay end-to-end for the current chromosome, B rate The static load balancing rate of the controller in the current chromosome;
(3.3) carrying out different operations of selection, crossing and mutation on the chromosomes in the population for multiple times to obtain a new chromosome population newchrom;
(3.4) calculating the fitness of the new chromosome population, and carrying out different selection, crossing and mutation operations on the chromosomes in the population for multiple times to obtain a new chromosome population newchrom;
and (3.5) circularly iterating the steps (3.3) - (3.4), and when the iteration number exceeds the algorithm termination condition, terminating the loop, and outputting the controller number and the deployment scheme.
According to the characteristics of dynamic change of a satellite network, the satellite service volume based on load balance is obtained by combining a service model under the low-orbit satellite Internet of things; an optimization model with the minimized number of controllers is established, a genetic algorithm is used for solving, a proper deployment scheme can be found in a short time, and the performance of the whole network in the aspects of time delay and load balance is effectively improved.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention, and such modifications and adaptations are intended to be within the scope of the invention.
Claims (6)
1. A SDN controller deployment method facing a low earth orbit satellite Internet of things is characterized by comprising the following steps: the SDN controller deployment method comprises three parts of visibility analysis, access mode selection and controller deployment strategy optimization, and specifically comprises the following steps:
step 1: and (3) visibility analysis: uniformly dividing the earth surface into a plurality of grids according to the longitude and the latitude based on the given longitude and latitude division precision, analyzing the condition that a low-orbit satellite covers the grid aiming at any grid, further obtaining a satellite number set covering the grid by utilizing the analyzed condition, and counting the number of the satellites in the set to obtain the number of the low-orbit satellites covering the grid;
step 2: selecting an access mode: the satellite side estimates the access success rate of the satellite based on the estimated load, counts the number of the terminal which is finally and successfully accessed and the load, and the networking terminal side calculates the satisfaction degree of accessing all satellites covering the grid, the network activity factor and the network state variable by combining the active terminal number of the grid where the networking terminal side is located, the grid center position and the elevation angle of the visible satellite, the available front derivative of the visible satellite and the estimated access success rate, further analyzes the access probability of different satellites covering the grid where the networking terminal side is located, and selects the satellite to be accessed based on the analysis result;
and step 3: optimizing a deployment strategy of the controller: and when the time delay and the access load are given, establishing a controller deployment optimization problem to determine the minimum required number of controllers and the deployment positions corresponding to the controllers.
2. The SDN controller deployment method for the Internet of things of low earth orbit satellites as claimed in claim 1, wherein the SDN controller deployment method comprises the following steps: in the step 1, given a grid g, the condition that a low-orbit satellite n completely covers the grid is as follows:
wherein r is b Which represents the radius of the earth and is,
indicates the satellite elevation angle, r h Indicates the satellite altitude, r z ＝r b +r h Maximum distance of satellite to coverage area
(x l ,y l ,z l ) T Denotes the vertex coordinates of the mesh g, and l denotes the vertices of the mesh g.
3. The SDN controller deployment method for the Internet of things of low earth orbit satellites as claimed in claim 1, wherein the SDN controller deployment method comprises the following steps: the step 2 specifically comprises the following steps:
step 2-1: given grid g and terminal deployment density λ of the grid g Calculating the number of new services generated in each time slot, and assuming that the terminal of the internet of things is from T =0 to T S When a certain time node in the ith time slot is triggered, the expression of the number of terminals triggered by the grid g in the ith time slot is as follows:
wherein: s g Denotes the area of the grid g, λ g Representing the terminal density, N, of the grid g g G is more than or equal to 1 and less than or equal to N g P (t) represents the probability distribution function of the Beta distribution,
alpha and Beta are Beta distribution parameters, alpha is more than 0, beta is more than 0 i ＝it s Indicates the time corresponding to the ith time slot, t s Indicating the slot length, T S Representing the total time, and entering the step 2-2;
step 2-2: the satellite n estimates the load, i.e. the number of terminals accessed, from its own preamble usage
And calculates the success rate of the access,
the expression of (a) is:
wherein: I.C. A i-1,n Number of idle preamble sequences, R, for the i-1 th time slot satellite n i-1,n Number of preamble sequences available for satellite n, M i-1,n For the number of loads successfully accessed to the satellite n in the i-1 th time slot, lambertiw (-) represents lambertian w function, and the access success rate of the satellite n in the i-1 th time slot is:
wherein: m is a group of i,n For the number of terminals successfully accessed by satellite n for the ith time slot,
estimating the total number of accessed terminals for the ith time slot satellite n, and entering the step 2-3;
step 2-3: calculating the satellite elevation according to the central coordinate of the grid g where the terminal is located and the real-time satellite point
Number of combined satellite coverage grids q i,n Access success rate P i,n And the available front derivative R i,n Calculating satisfaction of satellite n
Thereby obtaining the network state variable of the satellite n
Thereby obtaining the access probability of each visible satellite
Entering the step 2-4;
step 2-4: for grid g, the terminals of the internet of things with the activated ith time slot respectively generate random numbers R which are subjected to uniform distribution h U (0, 1), and the cumulative probability matrix
The elements in the matrix are compared in sequence, the first element number which is larger than the random number in the matrix is taken out as the satellite number of respective random access, N g Representing the number of satellites covering the grid g, and entering the step 2-5;
step 2-5: the terminal generates a random number mu of 0-1, and the satellite n broadcastsEntry level limiting parameter lambda n If mu is less than or equal to lambda, the terminal is required to be accessed, otherwise, the terminal is retreated to retransmit; wherein λ is n The expression of (c) is:
wherein: r i,n The number of preamble sequences available to satellite n,
estimating the number of accessed terminals for the satellite, and entering the step 2-6;
step 2-6: after the number of the terminals is controlled, starting to send random access requests to corresponding satellites, and if collision occurs in the process, retreating and retransmitting; and if no collision is generated, counting the number of the successfully accessed terminals and the number of the idle preamble resources.
4. The SDN controller deployment method for the low earth orbit satellite Internet of things as claimed in claim 1, wherein: in step 3, the average total delay expression of the end-to-end transmission of the network is as follows:
wherein: v = { V = 1 ,v 2 ,...,v M Represents all independent switch nodes, M is the total number of switches, C = { C = } 1 ,c 2 ,...,c K Represents all controller nodes, K is the number of controllers, x cv Allocating a matrix X = [ X ] for a switch cv ] K×M The element in (1) only has two values of 0 and 1, which represents the control relationship between the controller and the switch, d represents the shortest distance between any two nodes in the satellite, and d represents the shortest distance between any two nodes in the satellite w (,) the shortest distance between any two nodes in the satellite network, w ∈ {1,2}.
The static load balancing expression of the controller is as follows:
wherein K is the number of controllers and the load value of the controllers
C is more than or equal to 1 and less than or equal to K, and a switch V v Load value of
V is more than or equal to 1 and less than or equal to M, and the average load of the controller
5. The SDN controller deployment method for the Internet of things of low earth orbit satellites as claimed in claim 1 or 4, wherein: in step 3, the expression of the optimization model based on the minimized number of controllers is as follows:
T total ≤T
B rate ≤B
wherein K is the number of controllers, x cv Allocating a matrix X = [ X ] for a switch cv ] K×M The elements in (1) have only two values of 0 and 1, and V = { V = 1 ,v 2 ,...,v M Represents all independent switch nodes, C = { C = } 1 ,c 2 ,...,c K Represents all controller nodes, B rate Is the static load balancing rate, T, of the controller total The average total time delay of end-to-end transmission of the network, T represents the maximum average total time delay of end-to-end allowed by the network, B represents the maximum unbalance rate of the load of a controller allowed by the network, N represents the number of visible satellites, x cv Representing the control relationship between the switch and the controller.
6. The SDN controller deployment method for the low earth orbit satellite Internet of things as claimed in claim 5, wherein: the step 3 specifically comprises the following steps:
step 3-1: obtaining satellite node distance d w Number of visible satellites N and satellite number O N Traffic q of each satellite n N is more than or equal to 1 and less than or equal to N, and L chromosomes are randomly generated to form a chromosome population chrom, wherein each chromosome represents a controller deployment method;
step 3-2: calculating the fitness of the chromosome population, wherein the expression is as follows:
Fit＝ω 1 T total +ω 2 B rate
wherein: omega 1 、ω 2 Is a weight coefficient; b is rate Is the static load balancing rate, T, of the controller total Average total delay for end-to-end transmission of the network;
step 3-3: carrying out different operations of selection, crossing and variation on chromosomes in the population for multiple times to obtain a new chromosome population newchrom;
step 3-4: calculating the fitness of the new chromosome population, and carrying out different operations of selection, crossing and variation on the chromosomes in the population for many times to obtain a new chromosome population newchrom;
step 3-5: and (5) circularly iterating the step 3-3 and the step 3-4, and when the iteration times exceed the algorithm termination condition, terminating the loop and outputting the number of the controllers and the deployment scheme.
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| CN117320027A (en) * | 2023-11-30 | 2023-12-29 | 鹏城实验室 | Controller deployment method of satellite network |
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| CN117674960A (en) * | 2023-11-15 | 2024-03-08 | 航天恒星科技有限公司 | Low orbit satellite network controller deployment method based on reliability correction |
| CN117320027A (en) * | 2023-11-30 | 2023-12-29 | 鹏城实验室 | Controller deployment method of satellite network |
| CN117320027B (en) * | 2023-11-30 | 2024-02-13 | 鹏城实验室 | Controller deployment method of satellite network |
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