CN112073842B - Photoelectric conversion device deployment planning method, system, network equipment and storage medium - Google Patents
Photoelectric conversion device deployment planning method, system, network equipment and storage medium Download PDFInfo
- Publication number
- CN112073842B CN112073842B CN201910498092.3A CN201910498092A CN112073842B CN 112073842 B CN112073842 B CN 112073842B CN 201910498092 A CN201910498092 A CN 201910498092A CN 112073842 B CN112073842 B CN 112073842B
- Authority
- CN
- China
- Prior art keywords
- photoelectric conversion
- conversion device
- service
- deployment
- optical
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q11/00—Selecting arrangements for multiplex systems
- H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
- H04Q11/0005—Switch and router aspects
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q11/00—Selecting arrangements for multiplex systems
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q11/00—Selecting arrangements for multiplex systems
- H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
- H04Q11/0062—Network aspects
- H04Q11/0067—Provisions for optical access or distribution networks, e.g. Gigabit Ethernet Passive Optical Network (GE-PON), ATM-based Passive Optical Network (A-PON), PON-Ring
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q11/00—Selecting arrangements for multiplex systems
- H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
- H04Q11/0062—Network aspects
- H04Q11/0071—Provisions for the electrical-optical layer interface
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q11/00—Selecting arrangements for multiplex systems
- H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
- H04Q11/0062—Network aspects
- H04Q2011/009—Topology aspects
Abstract
Embodiments of the present invention provide a deployment planning method, system, network device, and storage medium for a photoelectric conversion device, which can plan a deployment scheme of a photoelectric conversion device according to topology information, resource information, and service information in an optical network by using a greedy algorithm, a simulated annealing algorithm, and the like and referring to a genetic algorithm, while taking into account multiple requirements such as network transmission capacity, network service requirements, and network deployment cost, thereby reducing the difficulty of network deployment planning and the burden of network planning personnel, and solving the problem of difficulty in deployment planning of a photoelectric conversion device in related technologies.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a method, a system, a network device, and a storage medium for planning deployment of a photoelectric conversion apparatus.
Background
In a SLICE (Spectrum Sliced Optical Path Network, Elastic Optical Network), one Optical transmission Path needs to satisfy constraints of both Spectrum continuity and Spectrum consistency. With the dynamic change of the service in the network, the frequent establishment and removal of the optical transmission path, the spectrum fragments in the network will increase, thereby causing the increase of the network blocking rate and the reduction of the transmission capacity of the network. In order to solve the above problems, related researches propose to allow services to flexibly adjust the wavelength, the number of frequency slots, and the modulation mode of an optical channel during transmission by deploying a photoelectric conversion device in the optical transmission channel. Meanwhile, the service is allowed to be flexibly converged and split in the transmission process. However, the price of the photoelectric conversion device is very expensive, resulting in a sharp increase in the cost of network deployment. Therefore, when planning the deployment of the photoelectric conversion device, it is necessary to consider the network deployment cost, and meanwhile, it is necessary to ensure the service requirement in the network and the requirement of the network transmission capacity, which makes the deployment of the photoelectric conversion device very difficult, and therefore, it is urgently needed to provide a planning scheme for the deployment of the photoelectric conversion device.
Disclosure of Invention
The photoelectric conversion device deployment planning method, system, network equipment and storage medium provided by the embodiment of the invention mainly solve the technical problems that: the deployment planning scheme of the photoelectric conversion device can be automatically output, so that the problem of difficulty in deployment planning of the photoelectric conversion device in the related technology is solved.
To solve the foregoing technical problem, an embodiment of the present invention provides a method for planning deployment of a photoelectric conversion apparatus, including:
determining an initial solution of a deployment scheme of the photoelectric conversion device in the network according to a greedy algorithm by combining topology information, resource information and service information in the optical network;
executing an iteration process based on the initial solution to obtain a final solution, wherein the iteration process comprises the following steps:
the current feasible solution is subjected to variation to obtain a variation feasible solution, the current feasible solution in the first iteration process is an initial solution, and the current feasible solution in the non-first iteration process is a reserved feasible solution;
optimizing each variation feasible solution through a simulated annealing algorithm to obtain an optimized feasible solution;
screening each optimized feasible solution to obtain a reserved feasible solution;
judging whether the current conditions for exiting the iterative flow are met, if so, selecting one of the reserved feasible solutions as a final solution and exiting the iterative flow; if not, continuing to execute the iteration flow;
and outputting the deployment scheme of the photoelectric conversion device corresponding to the final solution.
The embodiment of the invention also provides network equipment, which comprises a processor, a memory and a communication bus;
the communication bus is used for realizing connection communication between the processor and the memory;
the processor is configured to execute one or more programs stored in the memory to implement the steps of the photoelectric conversion apparatus deployment planning method described above.
An embodiment of the present invention further provides a deployment planning system for a photoelectric conversion apparatus, where the deployment planning system for a photoelectric conversion apparatus includes a UI interaction device, an SDN controller, a traffic engineering database, and the network device according to claim 9; the SDN controller is in communication connection with the traffic engineering database, and the network equipment is in communication connection with the UI interaction equipment and the traffic engineering database respectively;
the SDN controller is used for collecting topology information, resource information and service information in the optical network through a southbound protocol;
the traffic engineering database is used for storing topology information, resource information and service information collected by the SDN controller;
the network equipment is used for planning the deployment scheme of the photoelectric conversion device of the optical network according to the deployment planning method of the photoelectric conversion device;
the UI interaction equipment is used for performing diagrammatized display on the deployment scheme of the photoelectric conversion device, and the diagrammatized display comprises at least one of graphic display and table display.
An embodiment of the present invention further provides a storage medium, where one or more programs are stored, and the one or more programs are executable by one or more processors to implement the steps of the deployment planning method for a photoelectric conversion apparatus.
The invention has the beneficial effects that:
according to the deployment planning method, the deployment planning system, the network equipment and the storage medium of the photoelectric conversion device, an initial solution of a deployment scheme of the photoelectric conversion device in a network is determined according to a greedy algorithm in combination with topology information, resource information and service information in the optical network, then the initial solution is used as a current feasible solution, a genetic algorithm is used for carrying out variation to obtain a variation feasible solution, then each variation feasible solution is optimized through a simulated annealing algorithm, and each optimized feasible solution is screened to obtain a reserved feasible solution. And judging whether the conditions for exiting the iteration process are met or not at present after the reserved feasible solutions are obtained, if not, continuing to perform variation by taking the reserved feasible solutions as the current feasible solutions, circulating the process until the conditions for exiting the iteration process are met, then selecting one of the reserved feasible solutions as a final solution, and outputting a deployment scheme of the photoelectric conversion device corresponding to the final solution. By the deployment planning scheme of the photoelectric conversion device provided by the embodiment of the invention, the network equipment can plan a deployment scheme of the photoelectric conversion device by using a greedy algorithm, a genetic algorithm, a simulated annealing algorithm and the like according to topology information, resource information and service information in an optical network under the condition of considering a plurality of requirements such as network transmission capacity, network service requirements, network deployment cost and the like, the difficulty of network deployment planning and the burden of network planning personnel are reduced, and the problem of difficulty in deployment planning of the photoelectric conversion device in the related technology is solved.
Additional features and corresponding advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a schematic transmission diagram of a service according to a first embodiment of the present invention;
fig. 2 is a schematic transmission diagram of another service shown in the first embodiment of the present invention;
fig. 3 is a schematic transmission diagram of another service shown in the first embodiment of the present invention;
fig. 4 is a flowchart of a deployment planning method for a photoelectric conversion apparatus according to a first embodiment of the present invention;
FIG. 5 is a flowchart illustrating a greedy-based algorithm for determining an initial solution according to a first embodiment of the present invention;
FIG. 6 is a flowchart illustrating a method for determining a feasible solution to a mutation based on a genetic algorithm according to an embodiment of the present invention;
fig. 7 is a schematic hardware structure diagram of a network device according to a second embodiment of the present invention;
fig. 8 is a schematic structural diagram of a deployment planning system of a photoelectric conversion apparatus according to a third embodiment of the present invention;
fig. 9 is a schematic structural diagram of another deployment planning system of a photoelectric conversion apparatus according to a third embodiment of the present invention;
fig. 10 is a flowchart of a deployment planning method for a photoelectric conversion apparatus according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The first embodiment is as follows:
with the development of 5G network technology, the amount of traffic and data traffic required to be carried in the network will increase explosively, and the bandwidth resources in the network will become strained. How to improve the utilization rate of bandwidth resources in a network has become an increasingly concerned topic. In a conventional WDM (Wavelength Division Multiplexing) network, since bandwidth can be allocated to a service only with a fixed Wavelength as a minimum granularity, it is difficult to adapt to service requests of different granularities, which results in a low spectrum utilization rate. Compared with a WDM network, the SLICE can select different modulation modes and frequency slot numbers according to service requirements and transmission distances, so that the frequency spectrum utilization rate in the optical network is improved.
Due to the dynamic change of services in the network, the establishment and the removal of an optical transmission path are frequent, so that the frequency spectrum fragments in the network are increased, and in order to solve the problems of network blockage and network transmission capacity reduction, the wavelength, the frequency slot number and the modulation mode of an optical channel can be flexibly adjusted by deploying a photoelectric conversion device in the optical transmission channel, and the services can be flexibly converged and split in the transmission process. Several typical scenarios requiring deployment of photoelectric conversion devices are described below:
scene one: the modulation mode or spectral width of the traffic transmission optical channel changes. As shown in fig. 1, a service a needs to be transmitted from a network element a to a network element C, assuming that optical fibers a-B include 4 optical channels and optical fibers B-C include 3 optical channels. When a service a passes through optical fibers A-B, the service a is transmitted in an optical channel 3 with the center frequency of f3, the spectrum width of 37.5Ghz and the modulation mode of 8-QAM (orthogonal amplitude modulation with the number of constellation points of 8); however, when passing through the optical fibers B-C, the service a is transmitted through the optical channel 3 having the center frequency of f3, the spectral width of 75Ghz, and the modulation scheme of 16-QAM (quadrature amplitude modulation with 16 constellation points). In this scenario, a photoelectric conversion device needs to be deployed on the network element B for the optical channels for transmitting the service a in the optical fibers a-B and B-C, so that the service a can be transmitted in two adjacent optical fibers through optical channels with different spectral widths and modulation modes. It should be understood that, in the example given in fig. 1, the change of the modulation mode of the optical channel is illustrated by the change of the filling color of the optical channel, and the change of the spectral width of the optical channel is illustrated by the change of the width of the optical channel, in fig. 1, both the modulation mode and the spectral width of the optical channel where the service a is located are changed, but in other examples, even if only the modulation mode or the spectral width is changed, the photoelectric conversion device needs to be provided.
Scene two: the center frequency of the traffic transmitting optical channel changes. As shown in fig. 2, when the service B passes through the optical fibers a-B and B-C, the center frequency of the optical channel where the service B is located is shifted from f3 to f2 (the change of the center frequency of the optical channel is illustrated by the change of the number in fig. 2). In this scenario, a network element B needs to deploy a photoelectric conversion device for optical channels for transmitting a service B in optical fibers a-B and optical fibers B-C, so as to ensure that the service B is transmitted in two adjacent optical fibers by selecting optical channels with different center frequencies.
Scene three: the service is converged or split in the transmission process. As shown in fig. 3, service a and service B pass through fibers a-B, B-D, and service C passes through fibers C-B, B-D. The service a is transmitted by selecting an optical channel 2 in the optical fiber A-B, the service B is transmitted by selecting an optical channel 3 in the optical fiber A-B, and the service C is transmitted by selecting an optical channel 3 in the optical fiber C-B, but the three services are converged into the optical channel 3 when passing through the optical fibers B-D. At this time, the network element B needs to deploy photoelectric conversion devices for the optical channels 2 and 3 on the optical fibers a-B, the optical channel 3 on the optical fibers C-B, and the optical channel 3 on the optical fibers B-D, respectively, to ensure convergence operation of the three services.
Scene four: a relay scenario. Optical signals are attenuated by noise, dispersion and other factors during transmission. Therefore, there is a need for amplifying and enhancing optical signals by a relay device during traffic transmission. At this time, the relay node needs to deploy a photoelectric conversion device for the optical channel where the service is located.
As can be seen from the above description, in an optical network, there are many scenarios where the demand of the optical-electrical deployment device is required, and there are differences in the costs of the optical-electrical conversion devices of the optical channels of different modulation schemes. There are differences in noise and attenuation generated when optical signals pass through optical channels of different spectral widths and modulation modes. Therefore, due to these factors, the deployment planning of the photoelectric conversion device is very complex and difficult to plan, and for this reason, the present embodiment provides a deployment planning method of the photoelectric conversion device, please refer to the deployment planning flowchart of the photoelectric conversion device shown in fig. 4:
s402: and determining an initial solution of a deployment scheme of the photoelectric conversion device in the network according to a greedy algorithm by combining topology information, resource information and service information in the optical network.
In this embodiment, when performing deployment planning of the photoelectric conversion device, the network device needs to consider that all service requests in the optical network can be satisfied, and needs to control the network deployment cost, need to consider the network transmission capacity, and also need to consider the network transmission quality, so when performing deployment planning of the photoelectric conversion device, the network device needs to perform the deployment planning according to topology information, resource information, and service information in the optical network.
In some examples of this embodiment, the optical network topology information, the resource information, and the service information according to which the network device performs deployment planning on the photoelectric conversion device may be acquired by an SDN controller: in some examples of the present embodiment, the network device may obtain various information for the deployment plan of the photoelectric conversion apparatus directly from the SDN controller. In some other examples of this embodiment, the SDN controller may further collect topology information, resource information, and traffic information of the optical network in advance, and store the collected information in a database, for example, a Traffic Engineering Database (TEDB). When the network equipment needs to perform deployment planning on the photoelectric conversion device, the topology information, the resource information and the service information of the optical network can be directly obtained from the traffic engineering database.
In this embodiment, the SDN controller may collect topology information, resource information, and traffic information of the optical network based on the southbound protocol. In the SDN controller, a southbound protocol module may be deployed, and the southbound protocol module is responsible for collecting information required for deployment planning of the photoelectric conversion device.
Greedy algorithm, also called greedy algorithm, means that when solving a problem, always the choice that seems best at present is made. That is, rather than being considered globally optimal, it makes a locally optimal solution in some sense. The greedy algorithm is not capable of obtaining an overall optimal solution for all problems, the key point is selection of a greedy strategy, and the selected greedy strategy must have no aftereffect, namely, the previous process of a certain state cannot influence the later state and is only related to the current state. In this embodiment, when the network device determines the greedy policy, the main purpose of reducing the network deployment cost may be to, for example, in some examples of this embodiment, the network device always occupies the least photoelectric conversion devices during transmission for each service in an initial solution (that is, an initial photoelectric conversion device deployment scheme) determined by the greedy algorithm, and thus, in the initial solution determined based on the greedy algorithm, the deployment cost of the photoelectric conversion devices is very low.
Please refer to the flowchart of fig. 5 for determining the initial solution of the deployment scheme of the photoelectric conversion device based on the greedy algorithm:
s502: preprocessing the service request in the optical network and converging the service requests of the same source and the same destination.
The service request can be obtained from the service information, the network device obtains each service request in the optical network according to the service information, determines which service requests belong to the service requests of the same source and the same destination, and then converges each service request belonging to the same source and the same destination. In some other examples of this embodiment, before the network device aggregates the service requests, the service requests may be further sorted.
S504: and calculating a path with consistent frequency spectrum by using a First-Fit algorithm, and calculating the shortest path under the condition of allowing service hopping and convergence aiming at the failed service request.
For the service request with consistent path frequency spectrum, because no wave hopping and relay exist, the minimum photoelectric conversion device can be guaranteed to be used.
S506: spectrum resources are allocated on the basis of the principle that the number of photoelectric conversion devices is minimum.
The calculation process of the traffic takes into account the transmission capacity. The greedy algorithm cannot obtain the optimal solution, mainly because only the minimum cost of the current service is ensured, but the minimum cost of the batch or global service cannot be ensured.
S404: and carrying out variation on the current feasible solution to obtain a variation feasible solution.
In this embodiment, when the network device determines the initial solution and then proceeds to S404, the obtained initial feasible solution may be used as a "current feasible solution," that is, the current feasible solution is the initial solution in the first iteration flow. After the iteration process is executed for the first time, the network device may obtain one or more reserved feasible solutions, and then, when the iteration process is executed again and the step of S404 is executed, the reserved feasible solution may be used as the current feasible solution, that is, if the network device is not currently executing the iteration process for the first time, the current feasible solution is the reserved feasible solution obtained by the last iteration process.
After determining the current feasible solution, the network device may mutate the current feasible solution by referring to a mutation process in a Genetic Algorithm (Genetic Algorithm), thereby obtaining a mutated feasible solution. The genetic algorithm is a calculation model of a biological evolution process simulating natural selection and genetic mechanism of Darwinian biological evolution theory, and is a method for searching an optimal solution by simulating the natural evolution process. Genetic algorithms start with a population (population) representing a possible potential solution set to the problem, and a population consists of a certain number of individuals (individual) encoded by genes (gene). Each individual is actually an entity with a characteristic of the chromosome (chromosome). Chromosomes, which are the main carriers of genetic material, are collections of genes whose internal expression (i.e., genotype) is a certain combination of genes that determines the external expression of an individual's shape, e.g., black hair, whose characteristics are determined by a certain combination of genes in the chromosome that control this characteristic. Therefore, mapping from phenotype to genotype, i.e., coding work, needs to be achieved in the beginning. Because the work of imitating gene coding is complex, simplification is often performed, such as binary coding, after the generation of the initial generation population, generation-by-generation evolution generates better and better approximate solutions according to the principle of survival and superior-inferior of fittest, in each generation, individuals are selected (selection) according to the fitness (fitness) of individuals in a problem domain, and the natural genetic operators (genetic operators) are used for carrying out combined crossing (cross) and mutation (mutation) to generate a population representing a new solution set. This process will cause the population of the next generation, like natural evolution, to be more environment-adaptive than the previous generation, and the optimal individuals in the population of the last generation can be decoded (decoding) as a near-optimal solution to the problem.
Since the current feasible solution is the initial feasible solution in the process of executing the iterative flow for the first time, and only one initial feasible solution exists, n current feasible solutions may exist in the non-first-time iterative flow, and n is greater than or equal to 2.
The process of determining the variant feasible solution is explained below with reference to the flowchart shown in fig. 6:
s602: and determining mutation operators from each service of the optical network based on the photoelectric conversion device deployment scheme corresponding to the current feasible solution.
In a certain current feasible solution, or in a deployment scheme of the photoelectric conversion device corresponding to a certain current feasible solution, it is determined through which optical channels a certain service is transmitted, and both the route allocation and the spectrum allocation of the service are determined, so in this case, it is also determined how to deploy the photoelectric conversion device for the service. However, if an optical channel is newly selected for the service, the routing configuration and the spectrum configuration of the service in the optical network and the photoelectric conversion device in the optical network naturally change, so that the network device can obtain other feasible solutions different from the current feasible solution, and the feasible solutions obtained by the variation are the variation feasible solutions.
Therefore, the mutation operator that can make the current feasible solution generate mutation is the service in the optical network. It should be understood that selecting different services as mutation operators from the optical network will result in different feasible solutions for mutation, and therefore, the process of selecting mutation operators may affect the quality of the final solution. Several ways of determining mutation operators are provided below for reference, however, it should be understood by those skilled in the art that the ways of determining mutation operators are not limited to the following:
the first method is as follows: determining the number of the photoelectric conversion devices required by the current optical path of each service based on the photoelectric conversion device deployment scheme corresponding to the current feasible solution; and according to the number of the photoelectric conversion devices required by each service in the optical path at present, probabilistically selecting the service with more photoelectric conversion devices as a mutation operator. Alternatively, if the number of photoelectric conversion devices required for a service is larger, the probability that the service is selected as a mutation operator is larger.
In the deployment scheme of the photoelectric conversion device corresponding to the currently feasible solution, the number of the photoelectric conversion devices required to be used in the transmission process of each service is already determined, so that the network equipment can determine the photoelectric conversion device required to be used in the transmission of each service in the optical network. It is assumed that a certain optical network includes a service a, a service b, and a service c, where the service a needs to use two photoelectric conversion devices for transmission, the service b needs to use 1 photoelectric conversion device, and the service c needs to use 4 photoelectric conversion devices, so that in this case, the possibility that the network device selects the service c as the mutation operator is greater.
The second method comprises the following steps: determining the load condition of each optical fiber in the optical network based on the photoelectric conversion device deployment scheme corresponding to the current feasible solution; and selecting the service on the optical fiber with large load as a mutation operator according to the load condition probability. Generally, if the load on an optical fiber is greater, the probability that traffic on that fiber will be selected as a mutation operator is greater.
In the deployment scheme of the photoelectric conversion device corresponding to the currently feasible solution, it is determined how much traffic each optical fiber needs to carry, so in this case, the network device may preferentially select the traffic on the optical fiber with the larger load as the mutation operator.
The third method comprises the following steps: determining the number of services transmitted on each photoelectric conversion device in the optical network based on the photoelectric conversion device deployment scheme corresponding to the current feasible solution; according to the number of services transmitted by each photoelectric conversion device, the probability selects the service transmitted by the photoelectric conversion device with the smaller number of transmission services as a mutation operator, and the probability that the service on the photoelectric conversion device is selected as the mutation operator is larger if the number of the services transmitted by one photoelectric conversion device is about small.
For example, assuming that in a deployment scheme of a photoelectric conversion device corresponding to a certain currently feasible solution, a total of 4 photoelectric conversion devices, which are w, x, y, and z respectively, need to be deployed, and 4, 13, 6, and 2 services are transmitted on the 4 photoelectric conversion devices respectively, based on the above selection principle, the probability that the network device selects the service on the photoelectric conversion device z as the mutation operator is relatively high.
S604: and rerouting and distributing the frequency spectrum to each service selected as the mutation operator to obtain a feasible mutation solution.
After the services serving as mutation operators are selected, the network device can perform spectrum allocation domain routing allocation on the services serving as the mutation operators again, so that a new deployment scheme of the photoelectric conversion device is obtained as a feasible solution of mutation.
It should be noted that when the network device selects a mutation operator, the network device may also select multiple services instead of only one service as the mutation operator, and in this case, there are relatively many feasible solutions of the mutation obtained through the mutation.
S406: and optimizing each variation feasible solution through a simulated annealing algorithm to obtain an optimized feasible solution.
After determining the feasible variation solutions, the network device may "evolve" each feasible variation solution, that is, optimize the deployment schemes of the photoelectric conversion devices corresponding to the feasible variation solutions, thereby obtaining a better deployment scheme of the photoelectric conversion devices, that is, an optimized solution. It should be understood that for each variant feasible solution, the network device optimizes it to obtain the corresponding optimized feasible solution. However, the difference between the evolutionary process and the mutation process is that the evolutionary process does not increase the number of feasible solutions on the basis of the mutated feasible solutions. Therefore, if the network device obtains m feasible solutions of variation with reference to the variation process in the genetic algorithm, m feasible solutions of optimization will be obtained after the evolution process.
In this embodiment, the network device mainly optimizes each variant feasible solution based on a simulated annealing algorithm (SA), which is a general probabilistic algorithm for finding the optimal solution of a proposition in a large search space. Two ways of evolving variant feasible solutions are provided below:
the first method comprises the following steps:
for the deployment schemes of the photoelectric conversion devices corresponding to the variable feasible solutions, the network device determines optical channels with repeated paths in the deployment schemes of the photoelectric conversion devices respectively, and then converges the optical channels with repeated paths into an optical channel adopting a higher modulation mode or an optical channel with wider spectrum width.
Optionally, the network device may select a plurality of optical channels having repeated paths, and determine whether the optical channels can be converged into an optical channel adopting a higher modulation scheme or other spectral widths, so as to reduce the number of end-to-end optical channels in the optical network, and further reduce the deployment cost of the optical-to-electrical conversion device in the network.
And the second method comprises the following steps:
and for the photoelectric conversion device deployment scheme corresponding to each variable feasible solution, frequency spectrum fragments in the optical network are sorted under the photoelectric conversion device deployment scheme, and the wave hopping scene of a service transmission channel is reduced. By arranging the frequency spectrum fragments in the network, the jump wave scene of the transmission channel of the service is avoided as much as possible, and the deployment cost of the photoelectric conversion device in the network can be further reduced.
It will be appreciated by those skilled in the art that although only two schemes for optimizing variant feasible solutions are shown, in practice, the scheme for evolutionary processing is not limited to these two schemes, and any scheme that yields a more optimized feasible solution based on variant feasible solutions is feasible.
S408: and screening each optimized feasible solution to obtain a reserved feasible solution.
Through the evolution process, a plurality of optimization feasible solutions generally exist in the population (i.e., the current set of feasible solutions), and for the optimization feasible solutions, the network device may evaluate and select, so as to screen out one or more optimization feasible solutions. In this embodiment, the proportion or the number of the network devices for screening out the optimization feasible solutions is not limited, and the network devices may even screen out the optimization feasible solutions with different numbers in different iteration flows. For the variant feasible solution retained after the screening, this embodiment will be referred to as "retained feasible solution".
In some examples of this embodiment, the network device may probabilistically select the feasible solution to be retained according to the cost of each variant feasible solution, and the lower the cost of the variant feasible solution, the higher the probability of selecting the variant feasible solution to be the feasible solution to be retained.
S410: and judging whether the condition of exiting the iterative flow is met currently.
If the judgment result is yes, the iteration process is exited, and S412 is executed; if not, it indicates that the iteration flow cannot be exited currently, so the iteration flow needs to be executed continuously, and the process proceeds to S404. As can be seen from the foregoing description, the current feasible solution when entering the iterative process is the remaining feasible solution that the network device has remained in the last iterative process.
For those cases in which the iterative process can be ended, it is briefly stated that the conditions for exiting the iterative process include, but are not limited to, at least one of the following:
1) the execution times of the iteration process reach preset times;
2) the convergence degree of the current reserved feasible solution meets the requirement;
3) the duration of the deployment planning of the photoelectric conversion device has reached a preset duration.
S412: and selecting one of the reserved feasible solutions as a final solution and outputting a photoelectric conversion device deployment scheme corresponding to the final solution.
If the decision is made that the iteration process can be exited, the final deployment scheme of the photoelectric conversion device is only one of the currently reserved feasible solutions, and therefore the network device can select one of the reserved feasible solutions as the final solution. The principle of selecting the final solution may be based on the principle of lowest cost, the principle of maximum transmission capacity, etc., or may be to select one of the reserved solutions that is more balanced in terms of cost, transmission capacity and network transmission quality as the final solution.
After the final solution is selected, the network device may output the deployment scheme of the photoelectric conversion device corresponding to the final solution, so that a network planner can know what the deployment scheme is. In order to enable a network planner to more intuitively know the deployment situation of the photoelectric conversion device in the final solution, the network device may perform a diagrammatized display on the deployment scheme of the photoelectric conversion device corresponding to the final solution, where the diagrammatized display may be a graphical display, a table display, or a display mode combining a graph and a table.
It can be understood that, in the present embodiment, each solution (including the initial solution, the variant feasible solution, the optimization feasible solution, the reservation feasible solution, and the final solution) not only represents how the optical-to-electrical conversion device in the optical network should be deployed, but also includes a routing allocation situation of each service in the optical network and a spectrum allocation situation of each service, and therefore, in the final output optical-to-electrical conversion device deployment scheme, not only the deployment scheme of the optical-to-electrical conversion device but also the routing allocation and spectrum allocation scheme of the service may be presented to a network planner.
The "optical network" in this embodiment may be not only a WDM network but also a SLICE network. The modulation modes of the traffic in the optical network include, but are not limited to, QPSK (Quadrature Phase Shift key), 8-QAM, and 16-QAM.
According to the deployment planning method for the photoelectric conversion device, provided by the embodiment, according to topology information, resource information and service information in an optical network, a greedy algorithm, a simulated annealing algorithm and the like are utilized, and a deployment scheme of the photoelectric conversion device can be provided while considering a plurality of requirements such as network transmission capacity, network service requirements, network deployment cost and the like, so that not only is the difficulty of network deployment planning reduced and the burden of network planning personnel reduced, but also an initial solution, a reserved feasible solution, a final solution and the like can be determined according to the most important factors of the network planning at present, and therefore the final deployment scheme of the photoelectric conversion device can meet the requirements. For example, when the network planning is very cost-intensive, an initial solution with low cost can be selected, an optimization feasible solution with low cost is retained, a final solution with low cost is selected, and the like, so that a deployment scheme of the photoelectric conversion device which meets the network service requirement and is low in cost is selected, and the network planning quality is improved.
Example two:
the present embodiment provides a storage medium, in which one or more computer programs that can be read, compiled and executed by one or more processors can be stored, and in the present embodiment, the computer-readable storage medium can store a deployment planning program for a photoelectric conversion device, and the deployment planning program for a photoelectric conversion device can be used by one or more processors to execute a flow for implementing any one of the deployment planning methods for a photoelectric conversion device described in the foregoing embodiments.
The present embodiment further provides a network device, as shown in fig. 7: the network device 70 includes a processor 71, a memory 72, and a communication bus 73 for connecting the processor 71 and the memory 72, wherein the memory 72 may be the aforementioned storage medium storing the deployment planning program of the photoelectric conversion apparatus. The processor 71 may read the photoelectric conversion device deployment planning program, compile and execute the procedure of implementing the photoelectric conversion device deployment planning described in the foregoing embodiments:
the processor 71 determines an initial solution of a deployment scheme of the photoelectric conversion device in the network according to a greedy algorithm by combining topology information, resource information and service information in the optical network, then takes the initial solution as a current feasible solution, then performs variation on the current feasible solution to obtain a variation feasible solution, then optimizes each variation feasible solution through a simulated annealing algorithm, and screens each optimized feasible solution to obtain a reserved feasible solution. After obtaining the retained feasible solutions, the processor 71 determines whether the conditions for exiting the iterative process are currently met, if not, the retained feasible solutions are used as the current feasible solutions to continue to perform the variation, the process is circulated until the conditions for exiting the iterative process are determined to be met, then the processor 71 selects one of the retained feasible solutions as a final solution, and outputs a deployment scheme of the photoelectric conversion device corresponding to the final solution.
In some examples of this embodiment, the optical network topology information, the resource information, and the service information according to which the processor 71 performs the deployment planning of the photoelectric conversion device may be acquired by an SDN controller: in some examples of the present embodiment, the processor 71 may obtain various information for the photoelectric conversion device deployment plan directly from the SDN controller. In some other examples of this embodiment, the SDN controller may further collect topology information, resource information, and service information of the optical network in advance, and store the collected information in a database, for example, in the TEDB. When the processor 71 needs to perform deployment planning on the photoelectric conversion device, the topology information, the resource information, and the service information of the optical network may be directly obtained from the traffic engineering database.
In some examples of this embodiment, the deployment scheme of the optical-to-electrical conversion device output by the processor 71 may not only reflect the deployment situation of the optical-to-electrical conversion device in the optical network, but also characterize the spectrum allocation domain route allocation of each service in the optical network.
When determining an initial solution of a deployment scheme of the photoelectric conversion device in the network according to a greedy algorithm in combination with topology information, resource information, and service information in the optical network, the processor 71 may first preprocess a service request in the optical network, and converge service requests of the same source and the same destination; and then according to the topology information and the optical network resource information, routing and spectrum allocation are carried out on the services corresponding to the converged service requests by utilizing a First-Fit algorithm to obtain an initial solution, and the initial solution can ensure that each service in the optical network occupies the least photoelectric conversion device when being deployed.
When the current feasible solution is mutated in the mutation process of the reference genetic algorithm to obtain a mutated feasible solution, the processor 71 determines a mutation operator from each service of the optical network based on the deployment scheme of the photoelectric conversion device corresponding to the current feasible solution, and then performs routing and spectrum allocation on each service selected as the mutation operator again to obtain the mutated feasible solution.
In this embodiment, processor 71 may select the mutation operator based on a variety of principles:
for example, in an example of the present embodiment, the processor 71 determines the number of the photoelectric conversion devices required by the optical path where each service is currently located based on the photoelectric conversion device deployment scheme corresponding to the currently feasible solution; and selecting the service with a large number of required photoelectric conversion devices as a mutation operator according to the number of the required photoelectric conversion devices of each service in the optical path at present. If the number of photoelectric conversion devices required for a service is larger, the probability that the service is selected as a mutation operator is larger.
In another example of this embodiment, the processor 71 determines the load condition of each optical fiber in the optical network based on the deployment scheme of the optical-to-electrical conversion device corresponding to the current feasible solution, and then selects the service on the optical fiber with a large load as a mutation operator according to the load condition probability. Generally, if the load on an optical fiber is greater, the probability that traffic on that fiber will be selected as a mutation operator is greater.
In another example of this embodiment, the processor 71 determines the number of services transmitted on each photoelectric conversion device in the optical network based on the deployment scheme of the photoelectric conversion device corresponding to the currently feasible solution, and then selects the service transmitted on the photoelectric conversion device with the smaller number of transmitted services as the mutation operator according to the number of services transmitted on each photoelectric conversion device, where the probability that the service on the photoelectric conversion device is selected as the mutation operator is greater when the number of services transmitted on one photoelectric conversion device is about smaller.
For the photoelectric conversion device deployment schemes corresponding to the variable solutions, when the processor 71 performs optimization processing, in some examples of this embodiment, the processor 71 may determine optical channels with repeated paths in the photoelectric conversion device deployment schemes respectively, and then converge the optical channels with repeated paths into an optical channel adopting a higher modulation method or an optical channel with a wider spectral width. In other examples of this embodiment, for the deployment scheme of the photoelectric conversion device corresponding to each variant feasible solution, the processor 71 is configured to sort out the spectrum fragments in the optical network, and reduce the hopping scene of the service transmission channel.
When the processor 71 determines whether the iterative process can be exited, it may determine whether the number of times of execution of the iterative process reaches a preset number of times. Whether the convergence degree of the current reserved feasible solution meets the requirement or not can also be judged. In some examples of the present embodiment, the processor 71 may further determine whether a duration of the current deployment plan of the photoelectric conversion apparatus has reached a preset duration. Of course, it will be appreciated by those skilled in the art that processor 71 may also combine two or three of the above determinations.
After selecting the final solution, the processor 71 may output a deployment scheme of the photoelectric conversion device corresponding to the final solution, so that a network planner can know what the deployment scheme is. In order to enable the network planner to more intuitively understand the deployment situation of the photoelectric conversion device in the final solution, the processor 71 may graphically display the deployment scheme of the photoelectric conversion device corresponding to the final solution, where the graphical display may be a graphical display, a table display, or a display mode combining a graph and a table.
The network device provided by this embodiment can output the deployment scheme of the photoelectric conversion device according to the network resource information, the topology information, and the service information in the network, and simultaneously output the routing and spectrum allocation scheme of the service, and the skills are both in consideration of the transmission requirements of the service in the network, and can also control the network deployment cost.
Example three:
the present embodiment provides a deployment planning system for a photoelectric conversion apparatus, where the deployment planning system for a photoelectric conversion apparatus includes the network device provided in the foregoing embodiment.
In some examples of the present embodiment, the photoelectric conversion apparatus deployment planning system 8 includes a network device 70 and a UI interaction device 80, as shown in fig. 8: the network device 70 determines a photoelectric conversion apparatus deployment scheme according to the photoelectric conversion apparatus deployment planning method, and then the UI interaction device 80 displays the photoelectric conversion apparatus deployment scheme to a network planning staff. In some examples, the UI interaction device 80 may present the photoelectric conversion device deployment scheme in a graphical form, and in other examples of the present embodiment, the UI interaction device 80 may present the photoelectric conversion device deployment scheme in a tabular form. Of course, in some examples, the UI interaction device 80 may also use both graphics and tables to present the deployment scenario for the opto-electronic conversion devices in the optical network as needed.
It should be understood that, in some examples, the photoelectric conversion device deployment planning system 8 may not be provided with a separate UI interaction device, because the network device itself has a UI interaction function, and can show the photoelectric conversion device deployment scheme to the network planner through a UI interaction interface.
In some examples of the present embodiment, as shown in fig. 9, the photoelectric conversion device deployment planning system 8 further includes an SDN controller 81 and a traffic engineering database 82. Please refer to a flowchart of a deployment planning method of a photoelectric conversion device shown in fig. 10:
s1002: and the SDN controller collects information.
The SDN controller 81 may be configured as a southbound protocol module of the entire photoelectric conversion apparatus deployment planning system 8, collect topology information, resource information, and service information in the optical network through the southbound protocol, and then the SDN controller 81 stores the collected information in the traffic engineering database 82.
S1004: the network planner triggers the planning procedure.
When the network planner determines that the deployment of the photoelectric conversion device in the optical network needs to be planned, it may issue an instruction to the network device 70 through the UI interaction device 80, and trigger a photoelectric conversion device deployment planning process.
S1006: and the network equipment plans the deployment scheme of the photoelectric conversion device.
After the network device 70 receives the instruction issued by the network planner, it may plan a deployment scheme of the photoelectric conversion device according to the deployment planning method of the photoelectric conversion device provided in the foregoing embodiment, and then send the deployment scheme of the photoelectric conversion device to the UI interaction device 80, so that the scheme is displayed through the UI interaction device 80.
In this embodiment, the Algorithm corresponding to the flow in fig. 4 is referred to as an "E-EA Algorithm," which is an Enhanced-Evolution Algorithm. Therefore, after receiving the instruction issued by the network planner, the network device 70 may obtain the deployment scheme of the photoelectric conversion apparatus by executing the E-EA algorithm.
S1008: the UI interaction equipment displays a deployment scheme of the photoelectric conversion device.
The UI interaction device 80 may present the photoelectric conversion device deployment scenario returned by the network device 70 to the network planner in a graphical form.
The photoelectric conversion device deployment planning system 8 provided by the embodiment has at least the following advantages:
1) the E-EA algorithm used by the photoelectric conversion device deployment planning system 8 considers not only the WDM scenario but also the elastic optical network scenario.
2) The E-EA algorithm used by the photoelectric conversion device deployment planning system 8 also considers the influence of various modulation modes on service transmission and device deployment, such as QPSK,8-QAM,16-QAM, and the like.
3) The E-EA algorithm used by the deployment planning system 8 for the photoelectric conversion device can support the operations of wave hopping, convergence and splitting of the service in the transmission process, further improve the flexibility of spectrum management, and simultaneously improve the utilization rate of spectrum resources.
4) The E-EA algorithm used by the photoelectric conversion apparatus deployment planning system 8 can reduce the number of ports deployed in the network and the cost.
5) The photoelectric conversion device deployment planning system 8 fully utilizes the function of the SDN controller, and effectively collects topology and service information in the network.
It will be apparent to those skilled in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software (which may be implemented in program code executable by a computing device), firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed over computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media), executed by a computing device, and in some cases may perform the steps shown or described in a different order than here. The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art. Thus, the present invention is not limited to any specific combination of hardware and software.
The foregoing is a more detailed description of embodiments of the present invention, and the present invention is not to be considered limited to such descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (12)
1. A photoelectric conversion apparatus deployment planning method comprising:
determining an initial solution of a deployment scheme of a photoelectric conversion device in an optical network according to a greedy algorithm by combining topology information, resource information and service information in the optical network;
executing an iterative process based on the initial solution to obtain a final solution, wherein the iterative process comprises:
performing variation on the current feasible solution to obtain a variation feasible solution, wherein the current feasible solution in the first iteration process is the initial solution, and the current feasible solution in the non-first iteration process is a reserved feasible solution;
optimizing each variable feasible solution through a simulated annealing algorithm to obtain an optimized feasible solution;
screening each optimized feasible solution to obtain a reserved feasible solution;
judging whether the current condition of exiting the iterative flow is met, if so, selecting one of the reserved feasible solutions as a final solution and exiting the iterative flow; if not, continuing to execute the iteration flow;
and outputting the deployment scheme of the photoelectric conversion device corresponding to the final solution.
2. The deployment planning method for an optical-electrical conversion device according to claim 1, wherein the outputted deployment plan for the optical-electrical conversion device is further capable of characterizing spectrum allocation domain route allocation for each service in the optical network.
3. The deployment planning method for photoelectric conversion devices according to claim 1, wherein the service information includes a service request, and the determining an initial solution of a deployment scheme for photoelectric conversion devices in an optical network according to a greedy algorithm in combination with topology information, resource information, and service information in the optical network includes:
preprocessing the service request in the optical network, and converging the service requests of the same source and the same destination;
and carrying out routing and spectrum allocation on the services corresponding to the converged service requests by utilizing a First-adaptation First-Fit algorithm according to the topology information and the optical network resource information to obtain an initial solution, wherein the initial solution can ensure that each service in the optical network occupies the least photoelectric conversion device when being deployed.
4. The deployment planning method for photoelectric conversion devices according to claim 1, wherein the mutating the current feasible solution to obtain a mutated feasible solution comprises:
determining mutation operators from each service of the optical network based on a photoelectric conversion device deployment scheme corresponding to a current feasible solution;
and rerouting and distributing the frequency spectrum to each service selected as the mutation operator to obtain a feasible mutation solution.
5. The deployment planning method for an optical-to-electrical conversion device according to claim 4, wherein the manner of determining a mutation operator from each service of the optical network based on the deployment scheme of the optical-to-electrical conversion device corresponding to the currently feasible solution includes at least one of:
the first method is as follows:
determining the number of the photoelectric conversion devices required by the optical path where each service is currently located based on the photoelectric conversion device deployment scheme corresponding to the currently feasible solution;
according to the number of the photoelectric conversion devices required by each service in an optical path at present, the service with the large number of the required photoelectric conversion devices is selected as a mutation operator in a probability mode, and the probability that the service is selected as the mutation operator is larger when the number of the photoelectric conversion devices required by one service is larger;
the second method comprises the following steps:
determining the load condition of each optical fiber in the optical network based on the photoelectric conversion device deployment scheme corresponding to the current feasible solution;
selecting the service on the optical fiber with large load as a mutation operator according to the load condition probability, wherein the probability that the service on the optical fiber is selected as the mutation operator is larger when the load on one optical fiber is larger;
the third method comprises the following steps:
determining the number of services transmitted on each photoelectric conversion device in the optical network based on a photoelectric conversion device deployment scheme corresponding to a current feasible solution;
according to the number of services transmitted by each photoelectric conversion device, the service transmitted by the photoelectric conversion device with the smaller number of transmission services is selected as a mutation operator with probability, and the probability that the service on the photoelectric conversion device is selected as the mutation operator is higher when the number of the services transmitted by one photoelectric conversion device is about small.
6. The deployment planning method for photoelectric conversion devices according to claim 1, wherein the optimization of each of the variant feasible solutions by the simulated annealing algorithm to obtain an optimized feasible solution includes any one of the following two ways:
the first method comprises the following steps:
determining optical channels with repeated paths in the photoelectric conversion device deployment scheme for the photoelectric conversion device deployment scheme corresponding to each variable feasible solution;
converging the repeated optical channels of each path into an optical channel adopting a higher modulation mode or an optical channel with wider spectrum width;
and the second method comprises the following steps:
and for the photoelectric conversion device deployment scheme corresponding to each variable feasible solution, sorting the frequency spectrum fragments in the optical network under the photoelectric conversion device deployment scheme, and reducing the wave hopping scene of a service transmission channel.
7. The deployment planning method for photoelectric conversion devices according to claim 1, wherein the determining whether the condition for exiting the iterative process is currently satisfied includes at least one of:
the number of times of executing the iteration process reaches the preset number of times;
the convergence degree of the current reserved feasible solution meets the requirement;
the duration of deployment planning of the photoelectric conversion device has reached a preset duration.
8. The photoelectric conversion device deployment planning method according to claim 1, wherein the outputting the photoelectric conversion device deployment scenario corresponding to the final solution includes:
and performing diagrammatized display on the deployment scheme of the photoelectric conversion device corresponding to the final solution, wherein the diagrammatized display comprises at least one of graphic display and table display.
9. The deployment planning method for photoelectric conversion devices according to any one of claims 1 to 8, wherein before determining an initial solution of a deployment scheme for photoelectric conversion devices in an optical network according to a greedy algorithm in combination with topology information, resource information, and service information in the optical network, the deployment planning method further includes:
and a control Software Defined Network (SDN) controller collects topology information, resource information and service information in the optical network through a southbound protocol.
10. A network device, comprising a processor, a memory, and a communication bus;
the communication bus is used for realizing connection communication between the processor and the memory;
the processor is configured to execute one or more programs stored in the memory to implement the steps of the photoelectric conversion apparatus deployment planning method according to any one of claims 1 to 9.
11. An optical-to-electrical conversion device deployment planning system, comprising a UI interaction device, an SDN controller, a traffic engineering database, and the network device of claim 10; the SDN controller is in communication connection with the traffic engineering database, and the network device is in communication connection with the UI interaction device and the traffic engineering database respectively;
the SDN controller is used for collecting topology information, resource information and service information in the optical network through a southbound protocol;
the traffic engineering database is used for storing the topology information, the resource information and the service information collected by the SDN controller;
the network equipment is used for planning a deployment scheme of the optical-to-electrical conversion device of the optical network according to the deployment planning method of the optical-to-electrical conversion device of any one of claims 1 to 9;
the UI interaction equipment is used for performing diagrammatized display on the deployment scheme of the photoelectric conversion device, and the diagrammatized display comprises at least one of graphic display and table display.
12. A storage medium characterized in that the storage medium stores one or more programs executable by one or more processors to implement the steps of the photoelectric conversion apparatus deployment planning method according to any one of claims 1 to 9.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910498092.3A CN112073842B (en) | 2019-06-10 | 2019-06-10 | Photoelectric conversion device deployment planning method, system, network equipment and storage medium |
| PCT/CN2020/085578 WO2020248712A1 (en) | 2019-06-10 | 2020-04-20 | Photovoltaic conversion device deployment planning method, system, network device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910498092.3A CN112073842B (en) | 2019-06-10 | 2019-06-10 | Photoelectric conversion device deployment planning method, system, network equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112073842A CN112073842A (en) | 2020-12-11 |
| CN112073842B true CN112073842B (en) | 2022-09-13 |
Family
ID=73658463
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910498092.3A Active CN112073842B (en) | 2019-06-10 | 2019-06-10 | Photoelectric conversion device deployment planning method, system, network equipment and storage medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN112073842B (en) |
| WO (1) | WO2020248712A1 (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103595495A (en) * | 2013-10-27 | 2014-02-19 | 西安电子科技大学 | Routing and spectrum resource allocation method for static service flow in elastic optical network |
| CN107896347A (en) * | 2017-12-04 | 2018-04-10 | 国网江苏省电力公司南京供电公司 | A kind of EPON planing method, equipment and EPON |
| CN108171374A (en) * | 2017-12-27 | 2018-06-15 | 中国电子科技集团公司第五十四研究所 | A kind of earth observation satellite mission planning method based on simulated annealing |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7907535B2 (en) * | 2007-11-26 | 2011-03-15 | Alcatel-Lucent Usa Inc. | Anomaly detection and diagnosis using passive monitoring |
| US9730009B1 (en) * | 2014-03-27 | 2017-08-08 | Amazon Technologies, Inc. | Sparse Wi-Fi access point database for mobile devices |
| US9602387B2 (en) * | 2014-12-29 | 2017-03-21 | Juniper Networks, Inc. | Network topology optimization |
| US20160255428A1 (en) * | 2015-02-26 | 2016-09-01 | Board Of Trustees Of The University Of Arkansas | Method and systems for logical topology optimization of free space optical networks |
| WO2016135023A1 (en) * | 2015-02-27 | 2016-09-01 | Koninklijke Philips N.V. | Simulation-based systems and methods to help healthcare consultants and hospital administrators determine an optimal human resource plan for a hospital |
| US10097621B2 (en) * | 2015-09-11 | 2018-10-09 | At&T Intellectual Property I, L.P. | Application deployment engine |
| US10187292B2 (en) * | 2016-04-15 | 2019-01-22 | Microsoft Technology Licensing, Llc | Data center topology having multiple classes of reliability |
| CN109039694B (en) * | 2018-06-04 | 2022-01-11 | 全球能源互联网研究院有限公司 | Global network resource allocation method and device for service |
-
2019
- 2019-06-10 CN CN201910498092.3A patent/CN112073842B/en active Active
-
2020
- 2020-04-20 WO PCT/CN2020/085578 patent/WO2020248712A1/en active Application Filing
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103595495A (en) * | 2013-10-27 | 2014-02-19 | 西安电子科技大学 | Routing and spectrum resource allocation method for static service flow in elastic optical network |
| CN107896347A (en) * | 2017-12-04 | 2018-04-10 | 国网江苏省电力公司南京供电公司 | A kind of EPON planing method, equipment and EPON |
| CN108171374A (en) * | 2017-12-27 | 2018-06-15 | 中国电子科技集团公司第五十四研究所 | A kind of earth observation satellite mission planning method based on simulated annealing |
Non-Patent Citations (2)
| Title |
|---|
| A greedy approach for floopanning mass modules;Jyh Perrng Fang, Yang Lang Chang, Jong Yu Jen, Tai-Long Wang;《2012 Internetional Conference on Information Security and Intelligient Cotrol》;20130207;全文 * |
| 面向弹性光网络中路由和频谱分配问题的算法研究;黄天天;《中国优秀硕士学位论文全文数据库》;20150930(第09期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112073842A (en) | 2020-12-11 |
| WO2020248712A1 (en) | 2020-12-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP1943784B1 (en) | Method for configuring an optical network | |
| Moharrami et al. | Resource allocation and multicast routing in elastic optical networks | |
| CN110661715B (en) | Service path optimization method, device, equipment and readable storage medium | |
| US9264344B2 (en) | Method and apparatus for providing a route recommendation | |
| CN105743794A (en) | Network topology optimization with feasible optical paths | |
| Jaumard et al. | Efficient spectrum utilization in large scale RWA problems | |
| Rubio-Largo et al. | Multiobjective metaheuristics for traffic grooming in optical networks | |
| Vincent et al. | Scalable capacity estimation for nonlinear elastic all-optical core networks | |
| Goścień | Two metaheuristics for routing and spectrum allocation in cloud-ready survivable elastic optical networks | |
| Velasco et al. | Provisioning, Recovery, and In-Operation Planning in Elastic Optical Networks | |
| CN110913285B (en) | Route distribution method and device | |
| Alashaikh et al. | Embedded network design to support availability differentiation | |
| Przewozniczek et al. | The evolutionary cost of Baldwin effect in the routing and spectrum allocation problem in elastic optical networks | |
| JP2013009264A (en) | Path accommodation control method | |
| Villamayor-Paredes et al. | Routing, modulation level, and spectrum assignment in elastic optical networks. A route-permutation based genetic algorithms | |
| CN112073842B (en) | Photoelectric conversion device deployment planning method, system, network equipment and storage medium | |
| Goścień et al. | Artificial bee colony for optimization of cloud-ready and survivable elastic optical networks | |
| Patel et al. | On Efficient Candidate Path Selection for Dynamic Routing in Elastic Optical Networks | |
| Vale et al. | Network-state-dependent routing and route-dependent spectrum assignment for PRMLSA problem in all-optical elastic networks | |
| CN115633083A (en) | Power communication network service arrangement method, device and storage medium | |
| CN102325070B (en) | Service grooming method and device | |
| CN108462535B (en) | Network element level optical layer cross capacity management method and device | |
| Amjad et al. | Towards regeneration in flexible optical network planning | |
| Raayatpanah et al. | Design of survivable wireless backhaul networks with reliability considerations | |
| Habibi et al. | Reducing blocking probability and QoT violation in dynamic elastic optical networks via load-aware margin selection |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |