CN110996194B - Optical network routing and wavelength allocation method, system and storage medium based on graph theory - Google Patents
Optical network routing and wavelength allocation method, system and storage medium based on graph theory Download PDFInfo
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- CN110996194B CN110996194B CN201911173523.5A CN201911173523A CN110996194B CN 110996194 B CN110996194 B CN 110996194B CN 201911173523 A CN201911173523 A CN 201911173523A CN 110996194 B CN110996194 B CN 110996194B
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- H04Q11/00—Selecting arrangements for multiplex systems
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Abstract
The invention discloses a method, a system and a storage medium for optical network routing and wavelength allocation based on graph theory, wherein the method comprises the following steps: establishing a cluster map model according to an optical network; carrying out sparsification treatment on the cluster map model; performing initial division and coloring on the cluster map subjected to the thinning treatment to obtain an initial solution; improving the initial solution to obtain an optimal solution; and outputting the optimal solution, generating a network construction scheme according to the optimal solution and completing construction of the optical network. The scheme of the invention utilizes the graph dividing and coloring technology to model the problem of optical network routing and wavelength distribution to form a one-to-one correspondence, and then designs the routing and wavelength distribution scheme according to the graph dividing and coloring algorithm.
Description
Technical Field
The invention relates to the technical field of optical network transmission, in particular to a method, a system and a storage medium for optical network routing and wavelength allocation based on graph theory.
Background
The wavelength division multiplexing optical network effectively relieves the high bandwidth requirement of network transmission in the big data era, but because the number of wavelengths which can be multiplexed by the same optical fiber is limited, how to select a route and carry out wavelength distribution has great significance on the effective utilization of optical network resources and the improvement of network integrity. The purpose of solving the problem is to hope to give a specific allocation scheme that minimizes the number of wavelengths used, which is the so-called routing and wavelength allocation problem. This problem can be abstracted as an integer linear programming problem: the optimization aims to minimize the number of wavelengths used in transmission, and the constraint conditions generally include wavelength continuity, different wavelengths in the same optical fiber, optical channel continuity and the like.
The routing and wavelength assignment problem has proven to be NP-complete, so it is difficult to give a polynomial time algorithm for this problem. For this reason, research on this problem is currently focused on the design of approximation algorithms. The solution to the routing and wavelength assignment problem can be summarized as: an integral solution and a separate solution. The overall solution is performed by considering routing and wavelength allocation as an integral, and the method has poor performance due to the consideration of integrity, and is generally only suitable for a network with a small scale.
The separation solution is to solve the routing problem and the wavelength allocation problem respectively. For the former, the common method is a heuristic algorithm, and both algorithms can be realized by the idea of the shortest path; for the wavelength assignment problem, the wavelength is assigned to the optical channel by limiting the continuity of the wavelength and using different wavelengths for different signals in the same optical fiber after the routing algorithm is determined. At present, there are many algorithms for wavelength allocation, such as graph coloring method, tabu search, simulated annealing, ant colony algorithm, genetic algorithm, etc. Compared with the overall solution, the separate solution can be applied to a larger network, but the accuracy of the algorithm is not high due to the adoption of the heuristic strategy.
Summarizing the above algorithm, it can be found that the solution to the problem proposed at present is either only suitable for small networks (tens of vertices), and is not efficient for larger networks, or the accuracy of the algorithm is not sufficient, and the purpose of resource optimization is not achieved. In summary, how to design an accurate and efficient routing and wavelength allocation algorithm is an urgent problem to be solved.
Disclosure of Invention
To solve the above technical problems, the present invention aims to: the method, the system and the storage medium for optical network routing and wavelength allocation based on graph theory are high in precision and efficiency and suitable for large networks.
The first technical scheme adopted by the invention is as follows: a method for distributing optical network routing and wavelength based on graph theory includes the following steps: establishing a cluster map model according to an optical network; carrying out sparsification treatment on the cluster map model; performing initial division and coloring on the cluster map subjected to the thinning treatment to obtain an initial solution; improving the initial solution to obtain an optimal solution; and outputting the optimal solution, generating a network construction scheme according to the optimal solution and completing construction of the optical network.
Further, the step of performing initial partitioning and coloring on the thinned cluster map to obtain an initial solution specifically includes: setting the number of vertexes for dividing the independent sets; acquiring the standard degree of the vertex; obtaining a division independent set according to the standard degree; marking the vertices of said partitioned independent sets with an unassigned color; deleting the partition containing the colored vertex; and obtaining an initial solution according to the divided original cluster map where the colored vertex is deleted.
Further, the step of refining the initial solution to obtain an optimal solution includes: re-coloring the vertices in the initial solution; performing graph reduction on the recolored cluster graph; the despreading is performed based on the results of the graph reduction.
Further, the step of recoloring the vertices in the initial solution comprises: coloring the vertex of the initial solution by a greedy method to obtain a coloring sequence of the vertex; generating a new coloring sequence according to the coloring sequence; and allocating a feasible minimum color to the vertex of the initial solution according to the new coloring sequence, and finishing the re-coloring operation.
Further, the step of reducing the recoloring cluster map further includes: acquiring the division coloring and the number of used colors of the recolored cluster map; obtaining vertexes with the division degree smaller than the number of the used colors in the recolored cluster map; and deleting the vertex by a stacking method to obtain a small cluster graph.
Further, the step of performing despreading based on the result of graph reduction specifically includes: obtaining the division coloring of the small cluster map; selecting a vertex and assigning a partitioning coloring smaller than the small cluster to the vertex; and deleting the colored vertex in a stacking mode to obtain a division coloring of the original cluster graph.
The second technical scheme adopted by the invention is as follows: a graph-theory based optical network routing and wavelength allocation system, comprising:
the modeling unit is used for establishing a cluster map model according to the optical network;
a thinning processing unit; the cluster map model is used for carrying out sparsification treatment on the cluster map model;
the initial partitioning and coloring unit is used for performing initial partitioning and coloring on the sparse cluster map to obtain an initial solution;
the model optimization unit is used for improving the initial solution to obtain an optimal solution;
and the output unit is used for outputting the optimal solution and generating a network establishment scheme according to the optimal solution.
The third technical scheme adopted by the invention is as follows: a graph-theory based optical network routing and wavelength allocation system, comprising: at least one processor; at least one memory for storing at least one program; when the at least one program is executed by at least one processor, the at least one processor is enabled to implement the graph-theory-based optical network routing and wavelength allocation conversion method.
The fourth technical scheme adopted by the invention is as follows: a storage medium having stored therein executable instructions, which when executed by a processor, are configured to perform a graph theory based optical network routing and wavelength allocation method.
The invention has the beneficial effects that: the scheme of the invention utilizes the graph dividing and coloring technology to model the problem of optical network routing and wavelength allocation to form a one-to-one correspondence, and then designs the routing and wavelength allocation scheme according to the graph dividing and coloring algorithm.
Drawings
Fig. 1 is a diagram illustrating steps of a method for optical network routing and wavelength allocation based on graph theory according to an embodiment of the present invention;
FIG. 2 is a detailed flowchart for obtaining cluster map partition coloring according to an embodiment of the present invention;
FIG. 3 is a corresponding cluster diagram of an embodiment of the present invention;
FIG. 4 is a graph of clusters after thinning according to an embodiment of the present invention;
FIG. 5 is a diagram of a first process of obtaining a partitioned independent set with v3 as an initial point according to an embodiment of the present invention;
FIG. 6 is a diagram of a second process for obtaining a partitioned independent set with v3 as an initial point according to an embodiment of the present invention;
FIG. 7 is a diagram of a first process of obtaining a partitioned independent set with v5 as an initial point according to an embodiment of the present invention;
FIG. 8 is a diagram of a second process for obtaining a partitioned independent set with v5 as an initial point according to an embodiment of the present invention;
FIG. 9 is a cluster map of an embodiment of the present invention after deleting a partition in which a vertex is located in an independent set { v3, v9, v6} of the partition;
FIG. 10 is a first process diagram of cluster map coloring by deleting the partition where v12 is located and the cluster map according to an embodiment of the present invention;
FIG. 11 is a diagram of a second process of cluster map coloring by deleting the partition where v12 is located and the cluster map according to an embodiment of the present invention;
FIG. 12 is a partition coloring of a corresponding cluster map according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
As shown in fig. 1, step 1, modeling the problem, and converting the problem into a cluster map to be divided and colored: as for a given optical networkNodes in the network N represent network nodes, and each edge in the network N represents that two network nodes are connected by optical fibers. Let us assume that the available set of wavelengths is Γ ═ λ1,λ2,…,λk}. It is assumed that each network node can receive optical signals at a certain wavelength and transmit them out with optical signals at the same wavelength. Let R {(s) { (S)1,d1),(s2,d2),…,(sl,dl) Denotes a source-destination node set, where siRepresenting a source node, diRepresenting the destination node. For any source node-destination node pair(s)i,di) By P(s)i,di) Representing a slave node siTo diAll possible communication paths. The problem is to select P(s) for each source-target nodei,di) One way of (p)(s)i,di) And assigns a wavelength thereto. In the course of path selection and wavelength assignment, it is required that any two paths p(s) for selection are selectedi,di) And p(s)j,dj) If they have shared edges, then the wavelengths allocated to them must be different, while requiring a minimum total number of wavelengths. For this problem, a new graph G (V, E, P) is defined, where each vertex in V corresponds to P(s)i,di) Any two vertices are adjacent if and only if their corresponding links share a certain length of fiber, E is an edge set, and P ═ V ∈ {1,2, …, l }, where E is the edge set1,V2,…,VlIs the division of the set of vertices V (V)iCorresponds to P(s) for each vertex in (1)i,di) I ═ 1,2, …, l. The routing and wavelength allocation of the network N can thus be converted into coloring of the graph G (V, E, P): require to be respectively driven from ViTake exactly one vertex and assign it a color, minimizing the number of colors used while ensuring that adjacent vertices are of different colors. The graph G (V, E, P) is referred to as a cluster graph, and the above coloring thereof is referred to as division coloring, and the minimum number of colors is referred to as a division coloring number. Thus, the problem of routing and wavelength allocation of an optical network is equivalent to the problem of partitioning and coloring of the corresponding cluster map.
Step 2, thinning a cluster map: deleting all edges of which the end points are in the same partition in the G (V, E, P) to obtain a new cluster map with the reduced number of edges, and recording the new cluster map as G;
step 3, carrying out initial division coloring on G: searching a maximum division independent set of a current cluster map, distributing a minimum color which is not distributed currently for the maximum division independent set, and deleting all the divisions containing the vertexes in the found maximum division independent set from the current cluster map until all the vertexes are deleted;
step 4, carrying out the well on the initial solution to obtain an optimal solution;
and 5, comparing and selecting the best solution of the obtained original cluster map as output.
Further as a preferred embodiment, the present invention proposes the concept of dividing independent sets and a standard degree, specifically, for cluster map G (V, E, P) (where V is a vertex set, E is an edge set, and P ═ V ═ P1,V2,…,Vk}), the division independent set of G means that a subset S of V satisfies that any pair of vertices in S are not adjacent to each other in G and S contains at most each Vi(i ═ 1,2, …, k); for any vertex V in G (V, E, P), sd (G, V) represents the standard degree of V in G, and is defined as follows
Wherein n (V, V)i) V and V in GiThe number of adjacent vertices in the graph. Then, an algorithm for searching the divided independent sets is utilized to search a divided independent set S of the current cluster map G and mark a new color for the vertex in S; then deleting the division where the vertex in the S is located in the G, and still recording the obtained graph as G; this process is repeated until all vertices in G are deleted, thus resulting in a partition coloring f of the original cluster map G.
Further as a preferred embodiment, the scheme of the present invention further includes performing re-coloring on the vertex selected in step 3, by using a heuristic local search strategy based on permutation as a first improvement on the initial solution; in addition, in order to obtain a more optimal solution, for a larger graph, a graph reduction mechanism is designed, and then the partitioning coloring of an original graph is obtained by expanding the partitioning coloring of a small graph; the method adopted by the graph reduction and the graph de-expansion is a descending strategy based on division degrees and a greedy sequence priority expansion method, and the method can ensure that the number of the expanded divided colors is not larger than that of the current optimal solution, so that the improvement purpose is achieved.
Further, as a preferred embodiment, the specific content of the algorithm for finding and dividing the independent sets provided by the scheme of the present invention is as follows:
inputting: cluster map G (V, E, P), P ═ V1,V2,…,Vk}
And (3) outputting: g a partition independent set S*
1. Randomly selecting r vertexes from the V as a candidate set, and recording the candidate set as X;
2. initializing a current solution S and an optimal solution S*Is empty;
3. when X is not empty, the following operations are performed
4. Randomly selecting a vertex v from X, deleting v from X and adding v into S;
5. deleting the division of the vertex v and the adjacent points of v at G, and marking the obtained graph as H;
6. when V (H) is not empty, the following point selection operation is performed
7. Selecting a vertex y with the minimum standard degree in the H and adding the vertex y into the X;
8. deleting the division where y is located and the adjacent points of y in H, and still marking the obtained graph as H;
9. if the vertex number contained in S is larger than the current optimal solution S*The number of the top points is large, then order S*＝S；
10. Return to current S*
As a further preferred embodiment, the specific content of the point coloring local search algorithm based on the permutation provided by the scheme of the present invention is as follows:
inputting: graph G (V, E), V ═ V1,v2,…,vn}
And (3) outputting: minimum number of colorations k of G and corresponding colorations f
1. Coloring the vertices of G with greedy: each time, a maximum vertex of the current graph is selected and assigned with the smallest feasible color (ensuring that adjacent points are colored with different colors), and then deleted from the current graph. This results in an initial vertex coloring f for graph G, the coloring order of the vertices being denoted as P ({1,2, …, n }, each element corresponding to a vertex subscript), and the number of colors used being denoted as k
2. Setting an algorithm stop criterion;
3. when the stop criterion is not satisfied:
4. generating a new substitution P' on the basis of the original substitution: randomly selecting one from the 2 nd to the nth position and placing the selected position in front of the first position;
5. coloring each vertex in turn according to P', and assigning a feasible minimum color to the current vertex each time (ensuring adjacent points are not colored)
Same color) to obtain a new vertex coloring f 'of graph G, the color number used being denoted k'
6. If k' < k
7. Let f ═ f ', k ═ k';
8. return k, f
As a further preferred embodiment, the method for reducing and expanding graphs provided by the scheme of the present invention specifically includes the following steps:
firstly, for the graph reduction method, a rule based on the number of divided colors is proposed: let the cluster map G (V, E, P) currently found (where P ═ { V ═ V)1,V2,…,Vk) }) the number of colors used for coloring f is k, then repeatedly searching the vertex with division degree (number adjacent to the division) smaller than k in the graph, adding the vertex into an initially set stack S, and deleting the division with the point from the current cluster graph until no vertex exists;
solving the division coloring g of the current small cluster map by utilizing an algorithm for searching the division independent set and a point coloring local search algorithm based on the replacement, and expanding the division coloring g into one division coloring of the original cluster map: a vertex is selected from the end in turn from the stack S, marked with a feasible minimum number of colors (under g), and removed from the stack until the stack S is empty. Thus, a division coloring of the original cluster map is obtained, and the number of colors used for the division coloring obtained at this time is ensured to be not more than k, so that the original division coloring f can be improved.
In order to make the scheme and the algorithm flow of the present invention more clear, the following detailed description is made in conjunction with a specific example and the accompanying drawings. It should be understood that the specific examples described herein are intended to be illustrative only and are not intended to be limiting, and are to be given as follows:
first, the routing and wavelength allocation problem of an instance is translated into a graph partitioning coloring problem corresponding to a cluster graph. This step is not critical and is well understood and therefore will not be described in detail here. Only the transformed cluster map (fig. 3) and the specific flow of coloring the cluster map are considered; secondly, acquiring an initial solution of the cluster map; finally, an improvement to the initial solution.
Fig. 2 is a specific flowchart for obtaining cluster map partition coloring implemented by the present invention, and the detailed steps of partition coloring of fig. 3 are given below based on fig. 2.
Let the diagram shown in fig. 3 be G (V, E, P), where V ═ Vi|i＝1,2,…,12},P＝{V1＝{v1,v2},V2＝{v3,v4},V3＝{v5,v6},V4＝{v7,v8},V5＝{v9,v10},V6＝{v11,v12}}。
Step 1: firstly, the graph G is thinned, and the edge v is deleted1v2,v3v4,v5v6,v7v8,v9v10,v11v12The resulting graph is again labeled G, as shown in fig. 4.
Step 2: solving an initial division coloring of the thinned cluster map G;
in step 2, a partition independent set of G is first solved: randomly selecting r vertices as candidate sets according to an algorithm for finding a partitioned independent set, where the value of r depends on the scale of the graph, and for efficiency of the algorithm, r does not exceed the general number of partitionsHere, let r be 2 and let candidate set X be v3,v5}. (1) Selection of v3Obtaining a division independent set as an initial point: v is to be3The neighboring points and the partitions where the neighboring points are located are deleted from G, and the obtained cluster map is still marked as G, as shown in FIG. 5; the standard degree, sd (G, v), is calculated and obtained as follows1)＝4,sd(G,v6)＝3,sd(G,v8)＝3,sd(G,v9)＝2,sd(G,v11)＝2,sd(G,v12) 2; note that v9,v11,v12Is the smallest and the same, when one is selected at random, where v is selected9(ii) a V is to be9The neighboring points and the partitions where the neighboring points are located are deleted from G, and the obtained cluster map is still marked as G, as shown in FIG. 6; the same method calculates the degree of normalization of the vertices in the remaining cluster maps G, selecting the smallest one, here v6Thus, a partition independent set { v } is obtained3,v9,v6}. (2) Selection of v5Obtaining a division independent set as an initial point: v is to be5The neighboring points and the partitions where the neighboring points are located are deleted from G, and the obtained cluster map is still marked as G, as shown in FIG. 7; the degree of standardization, sd (G, v), is calculated as follows1)＝3,sd(G,v8)＝2,sd(G,v10)＝3,sd(G,v11) 2; selection of v8(ii) a V is to be8The neighboring points and the partitions where the neighboring points are located are deleted from G, and the obtained cluster graph is still marked as G, as shown in FIG. 8; finally select v11Thus, a partition independent set { v } is obtained5,v8,v11}。
Since the partition independent sets of the two selections contain the same number of vertices, one is randomly selected, here by { v }3,v9,v6And is v3,v9,v6Marking color 1 v3,v9,v6The division is deleted from the original cluster map to obtain a cluster map G1As shown in fig. 9; for G1According to the method, a partition independent set { v } is obtained7,v1(here, only one vertex v is selected from the candidate set)7) Is v is7,v1Marking color 2 at G1On the basis of (c), delete v7,v1Is divided intoThen, a cluster map containing only one partition is obtained, so that one vertex is arbitrarily selected, where v is selected12And marking the color 3 for the original cluster map, thus obtaining an initial partition coloring f: f (v) of the original cluster map3)＝f(v9)＝f(v6)＝1；f(v7)＝f(v1)＝2；f(v12)＝3。
And step 3: improving the initial solution by using a point coloring local search algorithm based on replacement;
in step 3, note v1,v6,v12A triangle is formed so that 3 colors are already optimal for the vertices selected by the initial solution, so this step does not improve the initial solution.
And 4, step 4: refining the initial solution with a reduction strategy;
in step 4, since the initial solution uses 3 colors, the original cluster map G is first searched for vertices with division degrees smaller than 3, only v12The condition is satisfied. Firstly, v is12The partitions are removed from G, resulting in a small cluster map H, as shown in fig. 10. Since H no longer contains points with a degree of division less than 3, it cannot be reduced. According to the method in step 2, a divisional coloration f of H is obtainedH:fH(v3)＝fH(v9)＝fH(v6) 1 (black); f. ofH(v2)＝fH(v8) 2 (white), as shown in fig. 11.
And 5: will f isHOne partition coloring f (v) extended into original cluster map G12Marking a feasible minimum color, here 2 (white): f (v)3)＝f(v9)＝f(v6) 1 (black); f (v)2)＝f(v8)＝f(v12) 2 (white), as shown in fig. 12.
In the embodiment of the invention, a good initial solution is obtained by designing a partitioning and coloring algorithm based on a partitioning independent set and a standard degree; the initial solution is then refined using a permutation-based graph coloring local search method and a division-based graph reduction method. The method can not only ensure the quality of the initial solution, but also can well improve the initial solution for many times through recoloring and graph reduction, thereby obtaining better partition coloring of the original cluster graph. The algorithm is based on a heuristic strategy, so that the method has good efficiency and is suitable for a larger network.
In addition, an embodiment of the present invention further provides a graph theory-based optical network routing and wavelength allocation system, including:
the modeling unit is used for establishing a cluster map model according to the optical network;
a thinning processing unit; the cluster map model is used for carrying out sparsification treatment on the cluster map model;
the initial partitioning and coloring unit is used for performing initial partitioning and coloring on the sparse cluster map to obtain an initial solution;
the model optimization unit is used for improving the initial solution to obtain an optimal solution;
and the output unit is used for outputting the optimal solution and generating a network establishment scheme according to the optimal solution.
The invention also provides a graph theory-based optical network routing and wavelength distribution system, which comprises:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by at least one processor, the at least one processor is enabled to implement the graph-theory-based optical network routing and wavelength allocation conversion method.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.
In addition, a storage medium is provided in an embodiment of the present invention, where processor-executable instructions are stored, and when executed by a processor, the processor-executable instructions are configured to perform the graph-theory-based optical network routing and wavelength allocation conversion method.
Compared with the prior art, the optical network routing and wavelength allocation method, system and storage medium based on graph theory have the following advantages:
1) the invention is suitable for the technical field of wavelength division multiplexing optical network transmission, and can adopt as few wavelength resources as possible while selecting a route, thereby improving the optimal utilization of resources and reducing the construction cost of the network;
2) the scheme has high efficiency for larger networks, not only can ensure the quality of the solution, but also can ensure the solving efficiency, thereby effectively saving the utilization of wavelength resources and providing a reasonable network building scheme;
3) the scheme utilizes the graph division coloring technology to model the problem of optical network routing and wavelength distribution to form a one-to-one correspondence, and then designs a heuristic algorithm for graph division coloring to design a routing and wavelength distribution scheme, which comprises the following steps: a concept of dividing independent sets is provided, and a high-quality initial solution is obtained based on the concept of dividing independent sets, so that the operation efficiency of the algorithm is improved; obtaining key parameters influencing vertex selection, and carrying out consistent specification on the key parameters to form a unified index called as a standard degree; the design drawing reduction method reduces a large drawing into a plurality of small drawings for dividing and treating the large drawing so as to obtain higher efficiency; and designing a heuristic strategy of local search to improve the operation efficiency of the algorithm.
The step numbers in the above method embodiments are set for convenience of illustration only, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A method for distributing optical network routing and wavelength based on graph theory is characterized by comprising the following steps:
establishing a cluster map model according to an optical network;
carrying out sparsification treatment on the cluster map model;
performing initial division and coloring on the cluster map subjected to the thinning treatment to obtain an initial solution;
improving the initial solution to obtain an optimal solution;
outputting the optimal solution, generating a network construction scheme according to the optimal solution and completing construction of an optical network;
the step of performing initial partitioning and coloring on the sparse cluster map to obtain an initial solution specifically includes:
setting the number of vertexes for dividing the independent sets;
acquiring the standard degree of the vertex;
obtaining a division independent set according to the standard degree;
marking the vertices of said partitioned independent sets with an unassigned color;
deleting the partition containing the colored vertex;
obtaining an initial solution according to the divided original cluster map where the colored vertex is deleted;
the step of improving the initial solution to obtain an optimal solution comprises the following steps:
re-coloring the vertices in the initial solution;
performing graph reduction on the recolored cluster graph;
the despreading is performed based on the results of the graph reduction.
2. The method according to claim 1, wherein the step of obtaining and dividing the independent sets according to the criteria comprises:
generating a candidate set from a set of vertices of the cluster map;
deleting the adjacent points of the top points in the candidate set and the partitions where the adjacent points are located;
selecting a vertex with the minimum standard degree from the remaining vertexes after deletion operation, and deleting the adjacent points and the partitions of the vertex with the minimum standard degree;
and combining the successively deleted vertexes to obtain a division independent set.
3. The method according to claim 1, wherein the step of recoloring the vertices in the initial solution comprises:
coloring the vertex of the initial solution by a greedy method to obtain a coloring sequence of the vertex;
generating a new coloring sequence according to the coloring sequence;
and allocating a feasible minimum color to the vertex of the initial solution according to the new coloring sequence, and finishing the re-coloring operation.
4. The method according to claim 1, wherein the step of performing graph reduction on the recoloring cluster map further comprises:
acquiring the division coloring and the number of used colors of the recolored cluster map;
obtaining vertexes with the division degree smaller than the number of the used colors in the recolored cluster map;
and deleting the vertex by a stacking method to obtain a small cluster graph.
5. The method according to claim 4, wherein the step of performing the despreading based on the graph reduction result specifically comprises:
obtaining the division coloring of the small cluster map;
selecting a vertex and assigning a partitioning coloring smaller than the small cluster to the vertex;
and deleting the colored vertex in a stacking mode to obtain a division coloring of the original cluster graph.
6. A graph-theory based optical network routing and wavelength allocation system, comprising:
the modeling unit is used for establishing a cluster map model according to the optical network;
a thinning processing unit; the cluster map model is used for carrying out sparsification treatment on the cluster map model;
the initial partitioning and coloring unit is used for performing initial partitioning and coloring on the sparse cluster map to obtain an initial solution;
the model optimization unit is used for improving the initial solution to obtain an optimal solution;
the output unit is used for outputting the optimal solution and generating a network establishment scheme according to the optimal solution;
the initial partitioning and coloring of the cluster map after the sparsification processing to obtain an initial solution comprises the following steps:
setting the number of vertexes for dividing the independent sets;
acquiring the standard degree of the vertex;
obtaining a division independent set according to the standard degree;
marking the vertices of said partitioned independent sets with an unassigned color;
deleting the partition containing the colored vertex;
obtaining an initial solution according to the divided original cluster map where the colored vertex is deleted;
the improving the initial solution to obtain an optimal solution comprises:
re-coloring the vertices in the initial solution;
performing graph reduction on the recolored cluster graph;
the despreading is performed based on the results of the graph reduction.
7. A graph-theory based optical network routing and wavelength allocation system, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by at least one processor, cause the at least one processor to implement a graph-theory based optical network routing and wavelength allocation conversion method according to any one of claims 1-5.
8. A storage medium having stored therein executable instructions, wherein the processor executable instructions, when executed by a processor, are configured to perform a graph-theory based optical network routing and wavelength allocation method according to any one of claims 1-5.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7716271B1 (en) * | 2001-06-05 | 2010-05-11 | Massachusetts Institute Of Technology | Routing and wavelength assignment in optical networks |
| CN101808254A (en) * | 2010-02-12 | 2010-08-18 | 重庆邮电大学 | Static routing and wavelength allocation method based on layered graph |
| CN105357132A (en) * | 2015-10-30 | 2016-02-24 | 中国人民武装警察部队工程大学 | Multi-domain ASON damage perception multicast routing method based on hypergraph model |
| US10298356B1 (en) * | 2018-02-02 | 2019-05-21 | Ciena Corporation | Optimal partial reconfiguration of spectrum in optical networks |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9197350B2 (en) * | 2012-10-08 | 2015-11-24 | Fujitsu Limited | Systems and methods for routing and wavelength assignment for network virtualization |
-
2019
- 2019-11-26 CN CN201911173523.5A patent/CN110996194B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7716271B1 (en) * | 2001-06-05 | 2010-05-11 | Massachusetts Institute Of Technology | Routing and wavelength assignment in optical networks |
| CN101808254A (en) * | 2010-02-12 | 2010-08-18 | 重庆邮电大学 | Static routing and wavelength allocation method based on layered graph |
| CN105357132A (en) * | 2015-10-30 | 2016-02-24 | 中国人民武装警察部队工程大学 | Multi-domain ASON damage perception multicast routing method based on hypergraph model |
| US10298356B1 (en) * | 2018-02-02 | 2019-05-21 | Ciena Corporation | Optimal partial reconfiguration of spectrum in optical networks |
Non-Patent Citations (3)
| Title |
|---|
| Static Routing and Wavelength Assignment Inspired by Particle Swarm Optimization;A. Hassan;<2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications>;20080411;全文 * |
| 图的全着色研究综述;朱恩强;《广州大学学报(自然科学版)》;20190815(第04期);全文 * |
| 波长选路光网络的一种新的波长分配算法;魏雪松等;《北京邮电大学学报》;19990620(第02期);全文 * |
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