CN110730131B - SDN satellite network multi-QoS constraint routing method based on improved ant colony - Google Patents

SDN satellite network multi-QoS constraint routing method based on improved ant colony Download PDF

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CN110730131B
CN110730131B CN201911003387.5A CN201911003387A CN110730131B CN 110730131 B CN110730131 B CN 110730131B CN 201911003387 A CN201911003387 A CN 201911003387A CN 110730131 B CN110730131 B CN 110730131B
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node
path
pheromone
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CN110730131A (en
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廖丹
李航
李慧
张明
李玉娟
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CHENGDU RESEARCH INSTITUTE OF UESTC
University of Electronic Science and Technology of China
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/302—Route determination based on requested QoS
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/18521—Systems of inter linked satellites, i.e. inter satellite service
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H04L45/123—Evaluation of link metrics
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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Abstract

The invention discloses an SDN satellite network multi-QoS constraint routing method based on improved ant colony, which reconstructs the traditional satellite network by using the SDN idea to realize the light weight, convenient and reliable operation of a satellite network system; the routing path has global optimality in the selection, so that the occurrence of link congestion can be avoided, and the flow is distributed on the link with better service quality; the heuristic function of the ant colony algorithm is reconstructed by using the link QoS parameter information, the ant colony pheromone updating mechanism is improved, multi-QoS constraint routing is carried out by fusing different path QoS parameter information such as evaluation delay, bandwidth and packet loss rate through multiple attributes, and the problem of reducing the satellite operation pressure is solved, so that the satellite operation pressure is realized with high efficiency and light weight while the reliable communication service quality is ensured; and how to realize multi-QoS constraint routing of service differentiation according to the specific service requirements of users.

Description

SDN satellite network multi-QoS constraint routing method based on improved ant colony
Technical Field
The invention relates to the field of satellite networking, in particular to an SDN satellite network multi-QoS constraint routing method based on an improved ant colony.
Background
In recent years, rapid development of internet technology brings great convenience to life of people, the demand of people on network communication technology is exponentially increased, and meanwhile, with the continuous increase of communication range required by people, a traditional ground communication network is difficult to cover places such as remote mountainous areas, deserts and oceans where base stations are difficult to establish. Therefore, the advantages of the satellite network communication system are gradually highlighted, and the satellite network communication system has the characteristics of large communication capacity, large coverage area, long transmission distance, no influence of ground natural disasters, flexible networking and the like, so that the satellite network communication system has been developed to become an indispensable important component in the whole global communication system.
In the traditional satellite network technology, calculation, storage and data forwarding of routing tables are all undertaken by the satellite, and with the rapid increase of the demand of people for the communication service types and the traffic of the satellite network, the satellite bears more and more operating pressure. Therefore, how to reduce the operating pressure of the satellite ensures reliable communication service quality and realizes high-efficiency and light-weight operation; and how to realize service-differentiated multi-QoS constraint routing according to specific service requirements of users, the two problems become two urgent engineering problems in satellite network communication system research.
Disclosure of Invention
Aiming at the defects in the prior art, the SDN satellite network multi-QoS constraint routing method based on the improved ant colony solves the problem of reducing the satellite operation pressure, so that the satellite operation pressure is realized efficiently and in a light weight way while the reliable communication service quality is ensured; and how to realize multi-QoS constraint routing of service differentiation according to the specific service requirements of users.
In order to achieve the above object, the technical solution adopted by the present invention is an SDN satellite network multi-QoS constraint routing method based on improved ant colony, comprising the following steps:
s1, networking and reconstructing a multilayer satellite network through an SDN technology, wherein a logic architecture based on the SDN satellite network can be divided into a user layer, a control layer, a data forwarding layer, a southbound interface and a northbound interface, and communication links among satellites are divided into IS L interplanetary links, IO L interplanetary links and UD L interplanetary links;
s2, carrying out topology optimization on the data forwarding layer through the enhanced grouping virtual topology strategy to obtain a MEO/L EO double-layer optimized network topology of the data forwarding layer;
s3, acquiring global network state information of a data forwarding layer MEO/L EO in the current time slot through a control layer and a southward interface, and abstracting a data forwarding layer MEO/L EO double-layer optimized network topology into a weighted graph model by adopting a graph theory;
s4, improving the ant colony algorithm according to engineering characteristics controlled by the control layer of the SDN satellite network on the global network state information to obtain an improved ant colony algorithm;
s5, performing multi-QoS constraint routing calculation on the weighted graph model through an improved ant colony algorithm to obtain an optimal path conforming to QoS constraint.
Further: in step S1, the user layer implements flexible programmable configuration operations on SDN satellite network security, routing, and resource allocation services through a northbound API interface provided by the control layer;
the control layer comprises a ground super control center and relay equipment;
the ground super control center detects state information of links and nodes between satellites in the data forwarding layer through the southbound interface so as to finish capturing global network topology state information of the data forwarding layer, performs centralized operation control according to a globally optimal view angle, and finishes tasks of strategy formulation, routing calculation, flow table generation, topology management and resource allocation;
the relay equipment comprises a subordinate ground station group and a GEO satellite group;
the GEO satellite group comprises a left GEO satellite GEO _ L, a middle GEO satellite GEO _ M and a right GEO satellite GEO _ L, is used for realizing the complete global coverage and rapidly issuing a flow table of the ground super control center to the data forwarding layer in a broadcasting mode;
the MEO satellite is responsible for receiving flow table information broadcasted by a GEO satellite group and sending the flow table information to an intra-group L EO satellite with a corresponding routing request, and a source satellite can only be a L EO satellite, and then the source satellite completes data packet strategy forwarding between the MEO/L EO double-layer satellite network according to the flow table information;
the southbound interface is used for transmitting information between the control layer and the data forwarding layer;
the north interface is used for transferring information between a user layer and a control layer;
the IS L interplanetary link IS a same-layer interplanetary link and comprises an in-orbit interplanetary link and an inter-orbit interplanetary link, wherein the in-orbit interplanetary link IS used for carrying out data interaction on satellites with the same height and in the same orbit;
the IO L interplanetary link is an interplanetary link between satellites in different height layers and is used for data interaction of the satellites in different height layers;
the UD L interplanetary link is a user data link used for data interaction between a ground communication terminal user or a gateway station and a satellite.
Further, the step S2 of enhancing the grouping virtual topology policy specifically includes performing grouping management on L EO satellites, enabling each L EO satellite to select the MEO satellite with the longest signal coverage time as its administrator, and reconstructing the MEO/L EO topology network.
Further, the step of abstracting the topology of the data forwarding layer MEO/L EO two-layer optimized network into a weighted graph model by using graph theory in the step S3 includes:
a1, abstracting MEO satellite and L EO satellite into nodes in a weighted graph by adopting graph theory;
a2, abstracting IS L interplanetary links, IO L interplanetary links and UD L interplanetary links into edges e in a weighted graph by adopting graph theory;
a3, abstracting a QoS parameter set into weights corresponding to each edge e by adopting a graph theory, wherein the QoS parameter set comprises time delay, residual bandwidth and packet loss rate;
a4, carrying out constraint limitation on the QoS parameter set, and establishing an optimal path decision function f based on the constraint limitation.
Further: the optimal path decision function f in step a4 is:
Figure BDA0002241996360000041
wherein, (s, d) is a routing path between the source satellite node s and the destination satellite node d; delay (s, D) is path delay, and the value is less than or equal to the upper delay limit Dσ(ii) a bandwith (s, d) is the path residual bandwidth whose value is greater than or equal to the bandwidth lower limit BσLoss (s, d) is the path packet loss rate, which is less than or equal to the upper limit L of the packet loss rateσ，w1As a delay weight, w2Is a residual bandwidth weight, w3Is the packet loss rate weight.
Further: heuristic function θ of the ant colony algorithm improved in step S4uvComprises the following steps:
Figure BDA0002241996360000042
wherein, delay*(u, v) is the link (u, v) time delay after the actual link parameter normalization, bandwith*(u, v) is the bandwidth, loss, of the link (u, v) after the actual link parameter normalization*And (u, v) is the packet loss rate of the link (u, v) after the actual link parameters are normalized.
Further: the state transition rule expression of the ant colony algorithm improved in step S4 is:
Figure BDA0002241996360000043
Figure BDA0002241996360000044
α is the influence factor of pheromone concentration value on ant path optimization, β is heuristic function thetauvInfluencing factor for ant path optimization, tauuv(t) is the concentration value of the pheromone on the link (u, v) at time t, u and v are any two satellite nodes, allowedkRepresenting that the ant k selects the next node set at the node u, and p is the value range of [0, 1 ]]Random number between, P0∈[0，1]Is a constant parameter, having P0The proportional ants are selected to have [ tau ] at node uuv(t)]α[θuv]β the node with the maximum value is taken as the next hop node v and correspondingly has 1-P0The ratio of ants at node u will be represented by the probability formula
Figure BDA0002241996360000051
Performing biased search of a next hop node v, y indicates allowedkAny node in the set.
Further: step S5 includes the following steps:
s51, initializing parameters including initial pheromone concentration tau (0), source node satellite S, destination node satellite d, pheromone factor α, heuristic function factor β, pheromone residual coefficient rho and number M of search antsnumAnd the set maximum number of iterations NCmax；
S52, placing ants in a source satellite node S, and adding the node into a taboo table;
s53, according to the state transfer rule function, completing the jump of ants to the next hop node, and writing the selected node into a taboo table to prevent the node from being repeatedly walked;
s54, judging whether the node where the ant is located is the target satellite node, if so, indicating that the ant successfully seeks the path, and jumping to the step S56; if not, go to step S55;
s55, ants need to judge whether the set allowed of the node is empty, if yes, it indicates that no next hop optional node jumps, and if the ant fails to find a path, the step S56 is skipped; if not, jumping to step S53;
s56, updating pheromone, namely after each iteration of the ant colony is finished, performing fusion evaluation on the multi-QoS parameter information of each feasible path by adopting a TOPSIS algorithm to finish the calculation of the pheromone increment value of each path, and establishing an m × n-order attribute matrix H (x) of each feasible pathmn) And successively obtaining a weighting matrix Q ═ Q (Q)mn) Ideal optimal solution q+Ideal worst solution q-Similar compactness value of single ant
Figure BDA0002241996360000052
And increase of pheromone
Figure BDA0002241996360000053
Total increase value delta tau of pheromone newly released by all ants passing through link (u, v)uv(t, t +1), and further by the formula τuv(t +1) completing pheromone updating of all paths, and increasing the iteration number NC by 1;
s57, judging whether the iteration number NC reaches the maximum set iteration number NCmaxIf yes, go to step S58, otherwise, go to step S52;
s58, judging whether all ants travel along the same path, if so, the path at the moment is the optimal solution path of the current problem; otherwise, jumping to step S59;
and S59, carrying out path choice by adopting a minf function to obtain the optimal path which meets the QoS constraint.
Further: each feasible path attribute matrix
Figure BDA0002241996360000061
Wherein i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n; the weighting matrix
Figure BDA0002241996360000062
Each of which is
Figure BDA0002241996360000063
The ideal optimal solution
Figure BDA0002241996360000064
Wherein, KbAs a value of the criterion of profit, KcIs a cost criteria value; the ideal worst solution
Figure BDA0002241996360000065
Figure BDA0002241996360000066
The similar compactability value
Figure BDA0002241996360000067
Wherein the distance parameter
Figure BDA0002241996360000068
Distance parameter
Figure BDA0002241996360000069
The single ant newly releases the pheromone increment value on the link (u, v)
Figure BDA00022419963600000610
Wherein R is an pheromone intensity parameter; the link (u, v) pheromone update formula τuv(t+1)＝ρτuv(t)+Δτuv(t, t +1), where ρ is the pheromone residual coefficient, τuv(t) is the original pheromone value on the link (u, v),
Figure BDA00022419963600000611
Figure BDA0002241996360000071
total delta value of pheromone newly released for all ants passing through link (u, v).
The invention has the beneficial effects that:
1. the traditional satellite network is reconstructed by utilizing the SDN idea, so that the lightweight, convenient and reliable operation of a satellite network system is realized, and the ground super control center has the advantage of global network topology optimization, so that the global optimality is realized in the selection of routing paths, the occurrence of link congestion can be avoided to the greatest extent, and the flow is distributed on links with better service quality. Meanwhile, compared with the traditional fixed mode that the routing table of the satellite network is calculated by adopting virtual topology off-line calculation and actual satellite routing forwarding to inquire the static routing table at any moment, the routing table based on the SDN satellite network framework provided by the invention is calculated according to the running parameters of the real-time satellite network state, and has real-time performance and real dynamic performance.
2. The heuristic function of the ant colony algorithm is reconstructed by utilizing the link QoS parameter information, so that ants are easy to deviate from a link with excellent QoS when selecting a next hop node.
3. An ant colony pheromone updating mechanism is improved, different QoS parameter information such as time delay, bandwidth and packet loss rate of each path is fused and evaluated through a TOPSIS multi-attribute algorithm, a global pheromone updating mode is adopted, the magnitude of an pheromone increment value is represented by the magnitude of a similar tightness value, the greater the path QoS is, the greater the similar tightness value is, the greater the pheromone increment value is, so that when ants select the paths, different weights of parameters such as time delay, bandwidth and packet loss rate can be given according to actual specific QoS parameter requirements and different service requirements, the optimal solution path with multi-QoS constraint which meets user requirements better is selected, and a routing calculation method which only considers the shortest distance or single QoS requirements in the traditional routing algorithm is changed.
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Fig. 1 is a schematic diagram of an SDN satellite network multi-QoS constraint routing method based on an improved ant colony.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, in an embodiment of the present invention, an SDN satellite network multi-QoS constraint routing method based on improved ant colony includes the following steps:
s1, networking and reconstructing a multilayer satellite network through an SDN technology, wherein a logic architecture based on the SDN satellite network can be divided into a user layer, a control layer, a data forwarding layer, a southbound interface and a northbound interface, and communication links among satellites are divided into IS L interplanetary links, IO L interplanetary links and UD L interplanetary links;
in step S1, the user layer implements flexible programmable configuration operations on SDN satellite network security, routing, and resource allocation services through a northbound API interface provided by the control layer;
the control layer comprises a ground super control center and relay equipment;
the ground super control center detects state information of links and nodes between satellites in the data forwarding layer through the southbound interface so as to finish capturing global network topology state information of the data forwarding layer, performs centralized operation control according to a globally optimal view angle, and finishes tasks of strategy formulation, routing calculation, flow table generation, topology management and resource allocation;
the relay equipment comprises a subordinate ground station group and a GEO satellite group;
the GEO satellite group comprises a left GEO satellite GEO _ L, a middle GEO satellite GEO _ M and a right GEO satellite GEO _ L, is used for realizing the complete global coverage and rapidly issuing a flow table of the ground super control center to the data forwarding layer in a broadcasting mode;
the MEO satellite is responsible for receiving flow table information broadcasted by a GEO satellite group and sending the flow table information to an intra-group L EO satellite with a corresponding routing request, and a source satellite can only be a L EO satellite, and then the source satellite completes data packet strategy forwarding between the MEO/L EO double-layer satellite network according to the flow table information;
the southbound interface is used for transmitting information between the control layer and the data forwarding layer;
the north interface is used for transferring information between a user layer and a control layer;
the IS L interplanetary link IS a same-layer interplanetary link and comprises an in-orbit interplanetary link and an inter-orbit interplanetary link, wherein the in-orbit interplanetary link IS used for carrying out data interaction on satellites with the same height and in the same orbit;
the IO L interplanetary link is an interplanetary link between satellites in different height layers and is used for data interaction of the satellites in different height layers;
the UD L interplanetary link is a user data link used for data interaction between a ground communication terminal user or a gateway station and a satellite.
S2, carrying out topology optimization on the data forwarding layer through the enhanced grouping virtual topology strategy to obtain a MEO/L EO double-layer optimized network topology of the data forwarding layer;
the enhanced grouping virtual topology strategy in the step S2 is specifically to perform grouping division management on L EO satellites, so that each L EO satellite selects the MEO satellite with the longest signal coverage time as its administrator, and reconstruct the MEO/L EO topology network.
S3, acquiring global network state information of a data forwarding layer MEO/L EO in the current time slot through a control layer and a southward interface, and abstracting a data forwarding layer MEO/L EO double-layer optimized network topology into a weighted graph model by adopting a graph theory;
in step S3, abstracting the data forwarding layer MEO/L EO double-layer optimized network topology into a weighted graph model by using graph theory includes:
a1, abstracting MEO satellite and L EO satellite into nodes in a weighted graph by adopting graph theory;
a2, abstracting IS L interplanetary links, IO L interplanetary links and UD L interplanetary links into edges e in a weighted graph by adopting graph theory;
a3, abstracting a QoS parameter set into weights corresponding to each edge e by adopting a graph theory, wherein the QoS parameter set comprises time delay, residual bandwidth and packet loss rate;
a4, carrying out constraint limitation on the QoS parameter set, and establishing an optimal path decision function f based on the constraint limitation.
The optimal path decision function f in step a4 is:
Figure BDA0002241996360000101
wherein, (s, d) is a routing path between the source satellite node s and the destination satellite node d; delay (s, D) is path delay, and the value is less than or equal to the upper delay limit Dσ(ii) a bandwith (s, d) is the path residual bandwidth whose value is greater than or equal to the bandwidth lower limit BσLoss (s, d) is the path packet loss rate, which is less than or equal to the upper limit L of the packet loss rateσ，w1As a delay weight, w2Is a residual bandwidth weight, w3Is the packet loss rate weight.
S4, improving the ant colony algorithm according to engineering characteristics controlled by the control layer of the SDN satellite network on the global network state information to obtain an improved ant colony algorithm;
heuristic function θ of the ant colony algorithm improved in step S4uvComprises the following steps:
Figure BDA0002241996360000102
wherein, delay*(u, v) is the link (u, v) time delay after the actual link parameter normalization, bandwith*(u, v) is the bandwidth, loss, of the link (u, v) after the actual link parameter normalization*And (u, v) is the packet loss rate of the link (u, v) after the actual link parameters are normalized.
The state transition rule expression of the ant colony algorithm improved in step S4 is:
Figure BDA0002241996360000103
Figure 1
α is the influence factor of pheromone concentration value on ant path optimization, β is heuristic function thetauvInfluencing factor for ant path optimization, tauuv(t) is the concentration value of the pheromone on the link (u, v) at time t, u and v are any two satellite nodes, allowedkRepresenting that the ant k selects the next node set at the node u, and p is the value range of [0, 1 ]]Random number between, P0∈[0，1]Is a constant parameter, having P0The proportional ants are selected to have [ tau ] at node uuv(t)]α[θuv]βThe node with the maximum value is taken as a next hop node v and correspondingly has 1-P0The ratio of ants at node u will be represented by the probability formula
Figure BDA0002241996360000112
Performing biased search of a next hop node v, y indicates allowedkAny node in the set.
S5, performing multi-QoS constraint routing calculation on the weighted graph model through an improved ant colony algorithm to obtain an optimal path conforming to QoS constraint.
Step S5 includes the following steps:
s51, initializing parameters including initial pheromone concentration tau (0), source node satellite S, destination node satellite d, pheromone factor α, heuristic function factor β, pheromone residual coefficient rho and number M of search antsnumAnd the set maximum number of iterations NCmax；
S52, placing ants in a source satellite node S, and adding the node into a taboo table;
s53, according to the state transfer rule function, completing the jump of ants to the next hop node, and writing the selected node into a taboo table to prevent the node from being repeatedly walked;
s54, judging whether the node where the ant is located is the target satellite node, if so, indicating that the ant successfully seeks the path, and jumping to the step S56; if not, go to step S55;
s55, ants need to judge whether the set allowed of the node is empty, if yes, it indicates that no next hop optional node jumps, and if the ant fails to find a path, the step S56 is skipped; if not, jumping to step S53;
s56, updating pheromone, namely after each iteration of the ant colony is finished, performing fusion evaluation on the multi-QoS parameter information of each feasible path by adopting a TOPSIS algorithm to finish the calculation of the pheromone increment value of each path, and establishing an m × n-order attribute matrix H (x) of each feasible pathmn) And successively obtaining a weighting matrix Q ═ Q (Q)mn) Ideal optimal solution q+Ideal worst solution q-Similar compactness value of single ant
Figure BDA0002241996360000121
And increase of pheromone
Figure BDA0002241996360000122
Total increase value delta tau of pheromone newly released by all ants passing through link (u, v)uv(t, t +1), and further by the formula τuv(t +1) completing pheromone updating of all paths, and increasing the iteration number NC by 1;
s57, judging whether the iteration number NC reaches the maximum set iteration number NCmaxIf yes, go to step S58, otherwise, go to step S52;
s58, judging whether all ants travel along the same path, if so, the path at the moment is the optimal solution path of the current problem; otherwise, jumping to step S59;
and S59, carrying out path choice by adopting a minf function to obtain the optimal path which meets the QoS constraint.
Each feasible path attribute matrix
Figure BDA0002241996360000123
Wherein i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n; the weighting matrix
Figure BDA0002241996360000124
Each of which is
Figure BDA0002241996360000125
The ideal optimal solution
Figure BDA0002241996360000126
Wherein, KbAs a value of the criterion of profit, KcIs a cost criteria value; the ideal worst solution
Figure BDA0002241996360000127
Figure BDA0002241996360000128
The similar compactability value
Figure BDA0002241996360000129
Wherein the distance parameter
Figure BDA0002241996360000131
Distance parameter
Figure BDA0002241996360000132
The single ant newly releases the pheromone increment value on the link (u, v)
Figure BDA0002241996360000133
Wherein R is an pheromone intensity parameter; the link (u, v) pheromone update formula τuv(t+1)＝ρτuv(t)+Δτuv(t, t +1), where ρ is the pheromone residual coefficient, τuv(t) is the original pheromone value on the link (u, v),
Figure BDA0002241996360000134
Figure BDA0002241996360000135
total delta value of pheromone newly released for all ants passing through link (u, v).
According to the invention, the traditional satellite network is reconstructed by utilizing the SDN idea, so that the lightweight, convenient and reliable operation of a satellite network system is realized, and the ground super control center has the advantage of global network topology optimization, so that the global optimality is realized in the selection of routing paths, the occurrence of link congestion can be avoided to the greatest extent, and the flow is distributed on links with better service quality. Meanwhile, compared with the traditional fixed mode that the routing table of the satellite network is calculated by adopting virtual topology off-line calculation and actual satellite routing forwarding to inquire the static routing table at any moment, the routing table based on the SDN satellite network framework provided by the invention is calculated according to the running parameters of the real-time satellite network state, and has real-time performance and real dynamic performance. And the heuristic function of the ant colony algorithm is reconstructed by utilizing the link QoS parameter information, so that ants are easy to deviate from a link with excellent QoS when selecting a next hop node. The invention also improves the ant colony pheromone updating mechanism, integrates and evaluates different QoS parameter information such as time delay, bandwidth, packet loss rate and the like of each path through a TOPSIS multi-attribute algorithm, adopts a global pheromone updating mode, and represents the magnitude of the pheromone increment value by the magnitude of the similar compactness value, the greater the route QoS is, the greater the similar compactness value is, the greater the pheromone increment value is, so that ants can give different weights to the parameters such as time delay, bandwidth, packet loss rate and the like according to actual specific QoS parameter requirements and different service requirements when selecting the path, so as to select a multi-QoS constraint optimal solution path which meets the user requirements better, and change the routing calculation method which only considers the shortest distance or single QoS requirements in the traditional routing algorithm.

Claims (5)

1. An SDN satellite network multi-QoS constraint routing method based on improved ant colony is characterized by comprising the following steps:
s1, networking and reconstructing a multilayer satellite network through an SDN technology, wherein a logic architecture based on the SDN satellite network can be divided into a user layer, a control layer, a data forwarding layer, a southbound interface and a northbound interface, and communication links among satellites are divided into IS L interplanetary links, IO L interplanetary links and UD L interplanetary links;
s2, carrying out topology optimization on the data forwarding layer through the enhanced grouping virtual topology strategy to obtain a MEO/L EO double-layer optimized network topology of the data forwarding layer;
the enhanced grouping virtual topology strategy in the step S2 is specifically to perform grouping division management on L EO satellites, so that each L EO satellite selects the MEO satellite with the longest signal coverage time as an administrator thereof, and a MEO/L EO topology network is reconstructed;
s3, acquiring global network state information of a data forwarding layer MEO/L EO in the current time slot through a control layer and a southward interface, and abstracting a data forwarding layer MEO/L EO double-layer optimized network topology into a weighted graph model by adopting a graph theory;
s4, improving the ant colony algorithm according to engineering characteristics controlled by the control layer of the SDN satellite network on the global network state information to obtain an improved ant colony algorithm;
the heuristic function θ of the ant colony algorithm improved in the step S4uvComprises the following steps:
Figure FDA0002524730380000011
wherein, delay*(u, v) is the link (u, v) time delay after the actual link parameter normalization, bandwith*(u, v) is the bandwidth, loss, of the link (u, v) after the actual link parameter normalization*(u, v) is the packet loss rate of the link (u, v) after the actual link parameter normalization;
the state transition rule expression of the ant colony algorithm in step S4 is as follows:
Figure FDA0002524730380000021
Figure FDA0002524730380000022
α is the influence factor of pheromone concentration value on ant path optimization, β is heuristic function thetauvInfluencing factor for ant path optimization, tauuv(t) pheromone concentration value on the link (u, v) at time tU and v are any two satellite nodes, allowedkRepresenting that the ant k selects the next node set at the node u, and p is the value range of [0, 1 ]]Random number between, P0∈[0，1]Is a constant parameter, having P0The proportional ants are selected to have [ tau ] at node uuv(t)]α[θuv]βThe node with the maximum value is taken as a next hop node v and correspondingly has 1-P0The ratio of ants at node u will be represented by the probability formula
Figure FDA0002524730380000023
Performing biased search of a next hop node v, y indicates allowedkAny node in the set;
s5, performing multi-QoS constraint routing calculation on the weighted graph model through an improved ant colony algorithm to obtain an optimal path conforming to QoS constraint;
the step S5 includes the steps of:
s51, initializing parameters including initial pheromone concentration tau (0), source node satellite S, destination node satellite d, pheromone factor α, heuristic function factor β, pheromone residual coefficient rho and number M of search antsnumAnd the set maximum number of iterations NCmax；
S52, placing ants in a source satellite node S, and adding the node into a taboo table;
s53, according to the state transfer rule function, completing the jump of ants to the next hop node, and writing the selected node into a taboo table to prevent the node from being repeatedly walked;
s54, judging whether the node where the ant is located is the target satellite node, if so, indicating that the ant successfully seeks the path, and jumping to the step S56; if not, go to step S55;
s55, ants need to judge whether the set allowed of the node is empty, if yes, it indicates that no next hop optional node jumps, and if the ant fails to find a path, the step S56 is skipped; if not, jumping to step S53;
s56, updating pheromone: after the ant colony iteration is finished each time, adopting TOPSIS algorithm to carry out operation on each feasible pathThe multi-QoS parameter information is fused and evaluated to complete the calculation of pheromone increment value of each path, and an m × n-order attribute matrix H (x) of each feasible path is establishedmn) And successively obtaining a weighting matrix Q ═ Q (Q)mn) Ideal optimal solution q+Ideal worst solution q-Similar compactness value of single ant
Figure FDA0002524730380000031
And increase of pheromone
Figure FDA0002524730380000032
Total increase value delta tau of pheromone newly released by all ants passing through link (u, v)uv(t, t +1), and further by the formula τuv(t +1) completing pheromone updating of all paths, and increasing the iteration number NC by 1;
s57, judging whether the iteration number NC reaches the maximum set iteration number NCmaxIf yes, go to step S58, otherwise, go to step S52;
s58, judging whether all ants travel along the same path, if so, the path at the moment is the optimal solution path of the current problem; otherwise, jumping to step S59;
and S59, carrying out path choice by adopting a minf function to obtain the optimal path which meets the QoS constraint.
2. The improved ant colony-based multi-QoS-constrained routing method for the SDN satellite network is characterized in that in the step S1, the user layer realizes flexible programmable configuration operation of SDN satellite network security, routing and resource allocation services through a northbound API interface provided by the control layer;
the control layer comprises a ground super control center and relay equipment;
the ground super control center detects state information of links and nodes between satellites in the data forwarding layer through the southbound interface so as to finish capturing global network topology state information of the data forwarding layer, performs centralized operation control according to a globally optimal view angle, and finishes tasks of strategy formulation, routing calculation, flow table generation, topology management and resource allocation;
the relay equipment comprises a subordinate ground station group and a GEO satellite group;
the GEO satellite group comprises a left GEO satellite GEO _ L, a middle GEO satellite GEO _ M and a right GEO satellite GEO _ L, is used for realizing the complete global coverage and rapidly issuing a flow table of the ground super control center to the data forwarding layer in a broadcasting mode;
the data forwarding layer is an MEO/L EO double-layer satellite network and comprises an administrator MEO satellite and a L EO satellite, wherein the administrator MEO satellite is used for collecting L EO and network state information of the administrator MEO satellite and sending network parameter information to a subordinate ground station under a coverage range, and meanwhile, the administrator MEO satellite is responsible for receiving flow table information broadcasted by a GEO satellite group and sending the flow table information to a L EO satellite in a group with a corresponding routing request, a source target satellite can only be a L EO satellite, and then the source satellite completes data packet strategy forwarding between the MEO/L EO double-layer satellite network according to the flow table information;
the southbound interface is used for transmitting information between the control layer and the data forwarding layer;
the north interface is used for transferring information between a user layer and a control layer;
the IS L interplanetary link IS a same-layer interplanetary link and comprises an in-orbit interplanetary link and an inter-orbit interplanetary link, wherein the in-orbit interplanetary link IS used for carrying out data interaction on satellites with the same height and in the same orbit;
the IO L interplanetary link is an interplanetary link between satellites in different height layers and is used for data interaction of the satellites in different height layers;
the UD L interplanetary link is a user data link used for data interaction between a ground communication terminal user or a gateway station and a satellite.
3. The improved ant colony based multi-QoS-constrained routing method for the SDN satellite network, according to claim 2, wherein the step of abstracting the topology of the data forwarding layer MEO/L EO two-layer optimized network into a weighted graph model by using graph theory in the step S3 includes:
a1, abstracting an administrator MEO satellite and a L EO satellite into nodes in a weighted graph by adopting graph theory;
a2, abstracting IS L interplanetary links, IO L interplanetary links and UD L interplanetary links into edges e in a weighted graph by adopting graph theory;
a3, abstracting a QoS parameter set into weights corresponding to each edge e by adopting a graph theory, wherein the QoS parameter set comprises time delay, residual bandwidth and packet loss rate;
a4, carrying out constraint limitation on the QoS parameter set, and establishing an optimal path decision function f based on the constraint limitation.
4. The improved ant colony based SDN satellite network multi-QoS constraint routing method according to claim 3, wherein the optimal path decision function f in the step A4 is:
Figure FDA0002524730380000051
wherein, (s, d) is a routing path between the source satellite node s and the destination satellite node d; delay (s, D) is path delay, and the value is less than or equal to the upper delay limit Dσ(ii) a bandwith (s, d) is the path residual bandwidth whose value is greater than or equal to the bandwidth lower limit BσLoss (s, d) is the path packet loss rate, which is less than or equal to the upper limit L of the packet loss rateσ，w1As a delay weight, w2Is a residual bandwidth weight, w3Is the packet loss rate weight.
5. The improved ant colony based SDN satellite network multi-QoS constraint routing method of claim 1, wherein each feasible path attribute matrix
Figure FDA0002524730380000052
Wherein i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n; what is needed isThe weighting matrix
Figure FDA0002524730380000053
Each of which is
Figure FDA0002524730380000054
The ideal optimal solution
Figure FDA0002524730380000055
Wherein, KbAs a value of the criterion of profit, KcIs a cost criteria value; the ideal worst solution
Figure FDA0002524730380000056
Figure FDA0002524730380000061
The similar compactability value
Figure FDA0002524730380000062
Wherein the distance parameter
Figure FDA0002524730380000063
Distance parameter
Figure FDA0002524730380000064
The single ant newly releases the pheromone increment value on the link (u, v)
Figure FDA0002524730380000065
Wherein R is an pheromone intensity parameter; the link (u, v) pheromone update formula τuv(t+1)＝ρτuv(t)+Δτuv(t, t +1), where ρ is the pheromone residual coefficient, τuv(t) is the original pheromone value on the link (u, v),
Figure FDA0002524730380000066
Figure FDA0002524730380000067
total delta value of pheromone newly released for all ants passing through link (u, v).
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