CN110730131A - 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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CN110730131A
CN110730131A CN201911003387.5A CN201911003387A CN110730131A CN 110730131 A CN110730131 A CN 110730131A CN 201911003387 A CN201911003387 A CN 201911003387A CN 110730131 A CN110730131 A CN 110730131A
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satellite
link
node
path
layer
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CN110730131B (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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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
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • 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
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • 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
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H04L45/125—Shortest path evaluation based on throughput or bandwidth

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 ISL interplanetary links, IOL interplanetary links and UDL interplanetary links;
s2, carrying out topology optimization on the data forwarding layer by enhancing the grouping virtual topology strategy to obtain a MEO/LEO double-layer optimized network topology of the data forwarding layer;
s3, acquiring global network state information of a data forwarding layer MEO/LEO in the current time slot through a control layer and a southward interface, and abstracting the MEO/LEO double-layer optimized network topology of the data forwarding layer 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 subordinate ground station group is used for collecting network topology state information of a summarized data forwarding layer and transmitting the information to the ground super control center for processing; 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 quickly issuing a flow table of the ground super control center to a data forwarding layer in a broadcasting mode;
the data forwarding layer is an MEO/LEO double-layer satellite network and comprises an MEO satellite and an LEO satellite; the group leader MEO satellite is used for collecting network state information of the LEOs in the group and the MEO group leader satellite, and transmitting the network parameter information to the subordinate ground stations under the coverage range; meanwhile, the MEO satellite is responsible for receiving flow table information broadcasted by the GEO satellite group and sending the flow table information to an in-group LEO satellite with a corresponding routing request, and a source target satellite can only be set as the LEO satellite, and then the source satellite completes data packet strategy forwarding between the MEO/LEO 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 ISL interplanetary link is a same-layer interplanetary link and comprises an intra-orbit interplanetary link and an inter-orbit interplanetary link; the intra-orbit inter-satellite link is used for carrying out data interaction on satellites with the same height and in the same orbit; the inter-orbit satellite link is used for carrying out data interaction on satellites with the same height and different orbits;
the IOL 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;
and the UDL interplanetary link is a user data link and is used for carrying out data interaction between a ground communication terminal user or a gateway station and a satellite.
Further: the enhanced packet virtual topology policy in step S2 specifically includes: grouping and dividing the LEO satellites for management, enabling each LEO satellite to select the MEO satellite with the longest signal coverage time as an administrator, and reconstructing the MEO/LEO topological network.
Further: in step S3, abstracting the data forwarding layer MEO/LEO double-layer optimized network topology into a weighted graph model by using graph theory includes:
a1, abstracting MEO satellite and LEO satellite into nodes in a weighted graph by adopting graph theory;
a2, abstracting ISL interplanetary link, IOL interplanetary link and UDL interplanetary link into edge 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 Bσ(ii) a loss (s, d) is the path packet loss rate, and the value 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
wherein alpha is an influence factor of the pheromone concentration value on ant path optimization, and beta is a 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]The node with the maximum value of beta 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 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 each parameter, including: initial pheromone concentration tau (0), source node satellite s, destination node satellite d, pheromone factor alpha, heuristic function factor beta, 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, fusion evaluation is carried out on the multi-QoS parameter information of each feasible path by adopting a TOPSIS algorithm so as to complete the calculation of the pheromone incremental value of each path; establishing an m multiplied by 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 belongs toSex matrix
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 solutionWherein, 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 parameterDistance parameter
Figure BDA0002241996360000069
The single ant newly releases the pheromone increment value on the link (u, v)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.
Drawings
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 ISL interplanetary links, IOL interplanetary links and UDL 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 subordinate ground station group is used for collecting network topology state information of a summarized data forwarding layer and transmitting the information to the ground super control center for processing; 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 quickly issuing a flow table of the ground super control center to a data forwarding layer in a broadcasting mode;
the data forwarding layer is an MEO/LEO double-layer satellite network and comprises an MEO satellite and an LEO satellite; the group leader MEO satellite is used for collecting network state information of the LEOs in the group and the MEO group leader satellite, and transmitting the network parameter information to the subordinate ground stations under the coverage range; meanwhile, the MEO satellite is responsible for receiving flow table information broadcasted by the GEO satellite group and sending the flow table information to an in-group LEO satellite with a corresponding routing request, and a source target satellite can only be set as the LEO satellite, and then the source satellite completes data packet strategy forwarding between the MEO/LEO 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 ISL interplanetary link is a same-layer interplanetary link and comprises an intra-orbit interplanetary link and an inter-orbit interplanetary link; the intra-orbit inter-satellite link is used for carrying out data interaction on satellites with the same height and in the same orbit; the inter-orbit satellite link is used for carrying out data interaction on satellites with the same height and different orbits;
the IOL 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;
and the UDL interplanetary link is a user data link and is used for carrying out 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 by enhancing the grouping virtual topology strategy to obtain a MEO/LEO double-layer optimized network topology of the data forwarding layer;
the enhanced packet virtual topology policy in step S2 specifically includes: grouping and dividing the LEO satellites for management, enabling each LEO satellite to select the MEO satellite with the longest signal coverage time as an administrator, and reconstructing the MEO/LEO topological network.
S3, acquiring global network state information of a data forwarding layer MEO/LEO in the current time slot through a control layer and a southward interface, and abstracting the MEO/LEO double-layer optimized network topology of the data forwarding layer into a weighted graph model by adopting a graph theory;
in step S3, abstracting the data forwarding layer MEO/LEO double-layer optimized network topology into a weighted graph model by using graph theory includes:
a1, abstracting MEO satellite and LEO satellite into nodes in a weighted graph by adopting graph theory;
a2, abstracting ISL interplanetary link, IOL interplanetary link and UDL interplanetary link into edge 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 Bσ(ii) a loss (s, d) is the path packet loss rate, and the value 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) after the actual link parameter normalizationV) time delay, 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
wherein alpha is an influence factor of the pheromone concentration value on ant path optimization, and beta is a 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 each parameter, including: initial pheromone concentration tau (0), source node satellite s, destination node satellite d, pheromone factor alpha, heuristic function factor beta, 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, fusion evaluation is carried out on the multi-QoS parameter information of each feasible path by adopting a TOPSIS algorithm so as to complete the calculation of the pheromone incremental value of each path; establishing an m multiplied by 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 matrixEach of which isThe ideal optimal solution
Figure BDA0002241996360000126
Wherein, KbAs a value of the criterion of profit, KcIs a cost criteria value; the ideal worst solution
Figure BDA0002241996360000128
The similar compactability value
Figure BDA0002241996360000129
Wherein the distance parameterDistance parameter
Figure BDA0002241996360000132
The single ant newly releases the pheromone increment value on the link (u, v)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 signal on the link (u, v)The value of the pheromone is set,
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 (9)

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 ISL interplanetary links, IOL interplanetary links and UDL interplanetary links;
s2, carrying out topology optimization on the data forwarding layer by enhancing the grouping virtual topology strategy to obtain a MEO/LEO double-layer optimized network topology of the data forwarding layer;
s3, acquiring global network state information of a data forwarding layer MEO/LEO in the current time slot through a control layer and a southward interface, and abstracting the MEO/LEO double-layer optimized network topology of the data forwarding layer 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.
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 subordinate ground station group is used for collecting network topology state information of a summarized data forwarding layer and transmitting the information to the ground super control center for processing; 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 quickly issuing a flow table of the ground super control center to a data forwarding layer in a broadcasting mode;
the data forwarding layer is an MEO/LEO double-layer satellite network and comprises an MEO satellite and an LEO satellite; the group leader MEO satellite is used for collecting network state information of the LEOs in the group and the MEO group leader satellite, and transmitting the network parameter information to the subordinate ground stations under the coverage range; meanwhile, the MEO satellite is responsible for receiving flow table information broadcasted by the GEO satellite group and sending the flow table information to an in-group LEO satellite with a corresponding routing request, and a source target satellite can only be set as the LEO satellite, and then the source satellite completes data packet strategy forwarding between the MEO/LEO 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 ISL interplanetary link is a same-layer interplanetary link and comprises an intra-orbit interplanetary link and an inter-orbit interplanetary link; the intra-orbit inter-satellite link is used for carrying out data interaction on satellites with the same height and in the same orbit; the inter-orbit satellite link is used for carrying out data interaction on satellites with the same height and different orbits;
the IOL 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;
and the UDL interplanetary link is a user data link and is used for carrying out data interaction between a ground communication terminal user or a gateway station and a satellite.
3. The improved ant colony-based multi-QoS constraint routing method for the SDN satellite network according to claim 2, wherein the step S2 of enhancing the packet virtual topology policy specifically includes: grouping and dividing the LEO satellites for management, enabling each LEO satellite to select the MEO satellite with the longest signal coverage time as an administrator, and reconstructing the MEO/LEO topological network.
4. The improved ant colony based multi-QoS-constrained routing method for the SDN satellite network, according to claim 3, wherein the step of abstracting the MEO/LEO two-layer optimized network topology of the data forwarding layer into a weighted graph model by using graph theory in the step S3 includes:
a1, abstracting MEO satellite and LEO satellite into nodes in a weighted graph by adopting graph theory;
a2, abstracting ISL interplanetary link, IOL interplanetary link and UDL interplanetary link into edge 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.
5. The improved ant colony based SDN satellite network multi-QoS constraint routing method according to claim 4, wherein the optimal path decision function f in the step A4 is:
Figure FDA0002241996350000031
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 Bσ(ii) a loss (s, d) is the path packet loss rate, and the value 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.
6. The improved ant colony based SDN satellite network multi-QoS constraint routing method according to claim 1, wherein in the step S4Heuristic function theta for improved ant colony algorithmuvComprises the following steps:
Figure FDA0002241996350000032
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.
7. The improved ant colony based multi-QoS constraint routing method for the SDN satellite network, according to claim 6, wherein the state transition rule expression of the improved ant colony algorithm in the step S4 is as follows:
Figure FDA0002241996350000042
wherein alpha is an influence factor of the pheromone concentration value on ant path optimization, and beta is a 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 FDA0002241996350000043
Performing biased search of a next hop node v, y indicates allowedkAny node in the set.
8. The improved ant colony based multi-QoS constrained routing method for SDN satellite networks according to claim 7, wherein the step S5 comprises the steps of:
s51, initializing each parameter, including: initial pheromone concentration tau (0), source node satellite s, destination node satellite d, pheromone factor alpha, heuristic function factor beta, 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, fusion evaluation is carried out on the multi-QoS parameter information of each feasible path by adopting a TOPSIS algorithm so as to complete the calculation of the pheromone incremental value of each path; establishing an m multiplied by 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 FDA0002241996350000051
And increase of pheromone
Figure FDA0002241996350000052
Via a link (u, v)Total increase value delta tau of pheromone newly released by antsuv(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.
9. The improved ant colony based SDN satellite network multi-QoS constraint routing method of claim 8, wherein each feasible path attribute matrix
Figure FDA0002241996350000053
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 FDA0002241996350000054
Each of which is
Figure FDA0002241996350000055
The ideal optimal solution
Figure FDA0002241996350000061
Wherein, KbAs a value of the criterion of profit, KcIs a cost criteria value; the ideal worst solution
Figure FDA0002241996350000062
Figure FDA0002241996350000063
The similar compactability value
Figure FDA0002241996350000064
Wherein the distance parameter
Figure FDA0002241996350000065
Distance parameter
Figure FDA0002241996350000066
The single ant newly releases the pheromone increment value on the link (u, v)
Figure FDA0002241996350000067
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 FDA0002241996350000068
Figure FDA0002241996350000069
total delta value of pheromone newly released for all ants passing through link (u, v).
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