Routing optimization method and related equipment based on simulated annealing algorithm

Abstract

The present disclosure provides a routing optimization method and related equipment based on simulated annealing algorithm, which relates to the fields of network technology and security technology. For example, the method includes obtaining target routing information to be calculated in the global; inputting the target routing information into a pre-built simulated annealing algorithm model to determine the target routing strategy. The present disclosure can reduce the routing link delay.

Description

Background Art

[0002] Metric is the core of IP (Internet Protocol) network. As the business requirements for network carrying capacity continue to increase, the scientific nature of metric design has received more and more attention. In IP networks, especially large IP networks, metric changes affect the entire network. Therefore, metric settings need to be scientific and extensible to meet the business development needs of the next 5-10 years. From the perspective of business carrying requirements, metrics need to carry as much information as possible to maximize the value of automatic routing. Modern optimization algorithms emerged in the early 1980s and are mainly used to solve NP (Non-deterministic Polynomial) problems. Simulated annealing algorithm is one of them. Its core idea is to accept a solution that is worse than the current solution with a certain probability, so it is possible to jump out of the local optimal solution and achieve the global optimal solution or a better solution. In the design of large-scale IP networks, the scientificity and rationality of metric design has always been a difficult problem. On the one hand, the metric settings of large-scale networks need to have a certain degree of continuity, and do not want to change them at will to affect the flow of traffic. At the same time, as the business requirements for the bearer network increase, metric settings usually need to take into account the convenience of hierarchical management and optimal latency. Due to the large scale of nodes and bureaus in large-scale networks, it is necessary to take into account the efficiency of each node in order to obtain a global optimal solution.

[0003] In the prior art, the routing metric setting method is mainly manually set by network experts based on their expert experience. There are few tried solutions, and it is impossible to determine whether it is the optimal solution. There are major limitations and the delay of some IP links is too high.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The present disclosure provides a routing optimization method and related equipment based on a simulated annealing algorithm, which at least to a certain extent overcomes the problem of high routing link delay in the related art.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0007] According to one aspect of the present disclosure, a routing optimization method based on a simulated annealing algorithm is provided, comprising: obtaining target routing information to be calculated globally; inputting the target routing information into a pre-built simulated annealing algorithm model to determine a target routing strategy.

[0008] In some embodiments, the pre-built simulated annealing algorithm model includes: determining an objective function according to target routing information to be calculated globally, wherein the target routing information is determined according to distance information between routes and traffic information between routes; inputting a preset initial solution into the objective function to determine a first routing strategy value, wherein the initial solution is preset with an iterative number; a second solution determination step: determining a second solution according to a preset step difference; inputting the second solution into the objective function to determine a second routing strategy value, and subtracting one from the iterative number; judging whether the first routing strategy value is greater than the second routing strategy value; if so, accepting the second routing strategy value as the current solution; if not, accepting the second routing strategy value as the current solution according to probability, wherein the probability is determined according to the target routing information; judging whether the preset iterative number is less than zero; if so, judging whether a termination condition is met; when the termination condition is met, determining the current solution as the target routing strategy; when the termination condition is not met, setting T to gradually decrease, and going to the second solution determination step, wherein T is determined according to the sum of the whole network Metric.

[0009] In some embodiments, before inputting the preset initial solution into the objective function, the method further includes: randomly selecting a metric of a corresponding level according to a preset metric pool; and assigning the metric to an edge of a corresponding route according to a metric constraint to determine an initial solution.

[0010] In some embodiments, the preset metric pools include: between cores; between cores and tandems; and between tandems.

[0011] In some embodiments, the metric constraint condition includes: determining the metric according to distance information between routes; multiple levels are preset between cores; multiple levels are preset from core to tandem; and multiple levels are preset between tandems.

[0012] In some embodiments, the objective function is calculated by the following formula:

[0013]

[0014] Among them, x represents the metric set, D ij Represents the distance information from routing node i to routing node j, T ij It represents the traffic information from routing node i to routing node j, and G represents the total number of routing nodes in the world.

[0015] In some embodiments, the probability is calculated by the following formula:

[0016] Δf=f(x new )-f(x);

[0017]

[0018] exp(-Δf / (KT));

[0019] Wherein, Δf represents the difference between the second routing strategy value and the first routing strategy value, f(x new ) represents the second routing strategy value, f(x) represents the first routing strategy value, exp represents the natural exponent, K represents the constant, T represents the sum of the metrics of the entire network, and exp(-Δf / (KT)) represents the probability.

[0020] In some embodiments, the termination condition includes: a preset number of consecutive current solutions are not accepted.

[0021] In some embodiments, after determining whether the preset number of iterations is less than zero, the method further includes: if not, proceeding to a second solution determination step.

[0022] According to another aspect of the present disclosure, a routing optimization device based on a simulated annealing algorithm is also provided, including: a target routing information acquisition module, used to obtain the target routing information to be calculated globally; a target routing strategy determination module, used to input the target routing information into a pre-built simulated annealing algorithm model to determine the target routing strategy.

[0023] According to another aspect of the present disclosure, an electronic device is also provided, which includes: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the above-mentioned routing optimization methods based on the simulated annealing algorithm by executing the executable instructions.

[0024] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the routing optimization method based on the simulated annealing algorithm described above is implemented.

[0025] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned routing optimization methods based on the simulated annealing algorithm.

[0026] The routing optimization method and related devices based on the simulated annealing algorithm provided in the embodiments of the present disclosure obtain the target routing information to be calculated in the global context; the target routing information is input into a pre-built simulated annealing algorithm model to determine the target routing strategy. In the embodiments of the present disclosure, since the routing information is calculated using the simulated annealing algorithm, a routing link with lower latency can be obtained from the global context, thereby helping to reduce the latency of the routing link.

[0027] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0029] Figure 1 A flow chart of a routing optimization method based on a simulated annealing algorithm in an embodiment of the present disclosure is shown;

[0030] Figure 2 A flowchart showing the construction of a simulated annealing algorithm model in a routing optimization method based on a simulated annealing algorithm in an embodiment of the present disclosure is shown;

[0031] Figure 3 Another flow chart of a routing optimization method based on a simulated annealing algorithm in an embodiment of the present disclosure is shown;

[0032] Figure 4 A flow chart of another routing optimization method based on a simulated annealing algorithm in an embodiment of the present disclosure is shown;

[0033] Figure 5 A schematic diagram of a route optimization device based on a simulated annealing algorithm in an embodiment of the present disclosure is shown;

[0034] Figure 6 A structural block diagram of a computer device for route optimization based on a simulated annealing algorithm in an embodiment of the present disclosure is shown;

[0035] Figure 7 A schematic diagram showing a computer-readable storage medium in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0037] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0038] Figure 1 A flow chart of a routing optimization method based on a simulated annealing algorithm in an embodiment of the present disclosure is shown as follows: Figure 1 As shown, the routing optimization method based on simulated annealing algorithm provided in the embodiment of the present disclosure includes the following steps:

[0039] S102, obtaining the target routing information to be calculated globally.

[0040] It should be noted that the above-mentioned global may be the global of the network. The above-mentioned target routing information may be a plurality of routing information for traffic transmission in the global, for example, routing distance information and routing traffic information.

[0041] S104, inputting the target routing information into a pre-built simulated annealing algorithm model to determine the target routing strategy.

[0042] It should be noted that the simulated annealing algorithm model can be established based on the target routing information. For example, the simulated annealing algorithm model calculates the optimal path between multiple routes, that is, the path with the lowest routing link delay in the world, based on the input target routing information through the simulated annealing algorithm.

[0043] In specific implementation, the present disclosure utilizes simulated annealing to calculate the acquired global routing information, and can obtain a routing link with lower latency from the global perspective, thereby helping to reduce the latency of the routing link.

[0044] Figure 2 A flowchart showing a simulated annealing algorithm model construction of a routing optimization method based on a simulated annealing algorithm in an embodiment of the present disclosure is shown as follows: Figure 2 As shown, the construction of a simulated annealing algorithm model of a routing optimization method based on a simulated annealing algorithm provided in an embodiment of the present disclosure includes the following steps:

[0045] S202, determining an objective function according to target route information to be calculated globally, wherein the target route information is determined according to distance information between routes and flow information between routes;

[0046] S204, inputting a preset initial solution into the objective function to determine a first routing strategy value, wherein the initial solution is preset with an iterative algebra;

[0047] S206, second solution determination step: determining the second solution according to a preset step difference;

[0048] S208, inputting the second solution into the objective function, determining the second routing strategy value, and subtracting one from the iterative algebra;

[0049] S210, determining whether the first routing policy value is greater than the second routing policy value;

[0050] S2121, if yes, accept the second routing strategy value as the current solution;

[0051] S2122, if not, accepting the second routing policy value as the current solution according to probability, wherein the probability is determined according to the target routing information;

[0052] S214, determining whether the preset number of iterations is less than zero;

[0053] S2161, if yes, determine whether the termination condition is met;

[0054] S2181, when the termination condition is met, determining the current solution as the target routing strategy;

[0055] S2182, when the termination condition is not met, set T to gradually decrease and go to the second solution determination step, where T is determined based on the sum of the metrics of the entire network.

[0056] It should be noted that T represents temperature in the simulated annealing algorithm, and in the present disclosure, it is determined based on the sum of the metrics of the entire network to simulate temperature changes.

[0057] It should be noted that the principle of the simulated annealing algorithm is as follows: during the heating process of the solid, the internal particles become disordered and the internal energy increases. During the slow cooling process, the particles gradually return to an ordered state and finally reach a steady state at room temperature with the minimum internal energy. This algorithm obtains the optimal solution with the minimum internal energy under a certain probability by simulating the annealing process.

[0058] In the specific implementation, the objective function of the simulated annealing algorithm is constructed according to the distance information between routes and the flow information between routes to solve the problem of high latency of routing links in the global system.

[0059] In one embodiment of the present disclosure, Figure 3 As shown, in the above step S214, after determining whether the preset iteration number is less than zero, the following steps are also included:

[0060] S2162, if not, go to S206 second solution determination step.

[0061] In one embodiment of the present disclosure, Figure 4 As shown, the routing optimization method based on the simulated annealing algorithm provided in the embodiment of the present disclosure can determine the initial solution through the following steps, and can determine a suitable initial solution according to the current global routing information:

[0062] S402, randomly selecting a metric of a corresponding level according to a preset metric pool;

[0063] S404: According to the Metric constraint condition, the Metric is assigned to the edge of the corresponding route to determine an initial solution.

[0064] It should be noted that Metric is a measurement standard used by the routing algorithm to determine the best path to the destination.

[0065] In one embodiment of the present disclosure, the preset metric pools include: between cores; from core to tandem; and between tandems.

[0066] For example, between cores: the Metric value is 4000-9000, and the step difference is 500; between core and tandem: the Metric value is 12000-18000, and the step difference is 1000; between tandems: the Metric value is 20000-23000, and the step difference is 1000. The number under Metric in the routing table is the integer value of the required hops for the route. It is used to select the route that best matches the destination address in the forwarding packet from multiple routes in the routing table. The selected route has the least hops. The hops can reflect the number of hops, path speed, path reliability, path throughput, and management attributes.

[0067] In one embodiment of the present disclosure, the Metric constraint condition includes: determining the Metric according to the distance information between routes; multiple levels are preset between cores; multiple levels are preset from core to tandem; and multiple levels are preset between tandems.

[0068] For example, metrics are allocated based on transmission distance, that is, the local metrics of the same layer are in a positive growth relationship with the distance; the number of layers is limited: 5 layers between cores, 2 layers from core to aggregation, and 2 layers between aggregations.

[0069] In one embodiment of the present disclosure, the objective function is calculated by the following formula (1):

[0070]

[0071] Among them, x represents the metric set, D ij Represents the distance information from routing node i to routing node j, T ijIt represents the traffic information from routing node i to routing node j, and G represents the total number of routing nodes in the world.

[0072] In a specific example, the objective function formula (1) can be set as:

[0073]

[0074] There are 31 provinces (cities) in the world, 31 represents 31 provinces, and the end-to-end IP routing distance of the entire network can be obtained through x, D ij represents the IP routing distance from province i to province j, T ij To calculate the traffic from province i to province j, use the traffic proportion as the weight to sum the end-to-end distance of the entire network to obtain the weighted distance of the entire network traffic. By minimizing the weighted distance of the traffic, we can get the optimal solution of the metric.

[0075] In one embodiment of the present disclosure, the probability is calculated by the following formula (2), formula (3) and formula (4):

[0076] Δf=f(x new )-f(x); (2)

[0077]

[0078] exp(-Δf / (KT)); (4)

[0079] Wherein, Δf represents the difference between the second routing strategy value and the first routing strategy value, f(x new ) represents the second routing strategy value, f(x) represents the first routing strategy value, exp represents the natural exponent, K represents the constant, T represents the sum of the metrics of the entire network, and exp(-Δf / (KT)) represents the probability.

[0080] Through the above embodiments, according to the objective function as a constraint and using the optimization algorithm, an optimal solution for Metric setting under a certain probability can be obtained.

[0081] In one embodiment of the present disclosure, the termination condition includes: a preset number of consecutive current solutions are not accepted.

[0082] For example, if 100 consecutive xs are not accepted, the current solution is accepted as the optimal solution and the program ends.

[0083] In a specific example, the simulated annealing algorithm process is as follows:

[0084] The first step: set the objective function f(x) and the initial solution x;

[0085] Step 2: Calculate f(x), the number of iterations L for each value of x;

[0086] Step 3: Generate a new solution x new , calculate the objective function f(x new );

[0087] Step 4: Calculate the increment Δf=f(x new )-f(x), if Δf<0, then accept x new is a new solution, otherwise accept x with probability exp(-Δf / (KT)) new As a new solution;

[0088] Step 5: If the termination condition is met, output the current solution as the optimal solution and end the program; otherwise, gradually reduce T and repeat steps 2 to 4.

[0089] In a specific example, first, the metric pool solution space is set to include core-to-core, core-to-tandem, and tandem-to-tandem, where core-to-core: 4000-9000, step difference is 500; core-to-tandem: 12000-18000, step difference is 1000; tandem-to-tandem: 20000-23000, step difference is 1000; then, set the metric constraint conditions: allocate metrics based on transmission distance, guide traffic to take the shortest transmission route, and reduce network latency. The transmission distance is considered within the same level, that is, the local metric and distance at the same level are in a positive growth relationship. Considering simple operation and maintenance, the number of levels needs to be limited: 5 levels between cores, 2 levels from tandem to core, and 2 levels between tandems. After that, modeling and solving are performed: setting the objective function (equivalent to the above formula (1-1)); setting the initial solution: randomly selecting the metrics of the corresponding level from the metric pool, assigning the metrics to the corresponding edges (routing edges) according to the metric constraints, and changing the metric allocation scheme in a certain order. The new metric set is x new ; Calculate the objective function f(x new ); calculate Δf = f(x new )-f(x), if Δf is less than 0, then accept x new As a new solution, otherwise accept x_new as the current solution with probability exp(-Δf / (kT)). k is a constant, exp is a natural exponent, T needs to be converted from the metric set represented by x, and the distance of each edge is taken as the weight, and the metric of the entire network is summed up, that is, the above formula (3); Termination condition: If 100 consecutive x are not accepted, the current solution is accepted as the optimal solution and the program ends.

[0090] When this disclosure is actually applied, corresponding adjustments need to be made based on network scale, management requirements, etc.

[0091] The metric setting method disclosed in the present invention comprehensively considers the requirements of latency optimization and simple operation and maintenance management, coordinates the interests of each node, and uses the minimum global traffic weighted distance as the metric setting evaluation standard, thereby enhancing the logic and scientificity of metric design; the present invention optimizes and corrects the metric solution space, quickly obtains the optimal solution, reduces manual intervention, and improves the iteration efficiency of network construction.

[0092] Based on the same inventive concept, the present disclosure also provides a route optimization device based on a simulated annealing algorithm, as described in the following embodiments. Since the principle of solving the problem in the device embodiment is similar to that in the above method embodiment, the implementation of the device embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be repeated.

[0093] Figure 5 A schematic diagram of a route optimization device based on a simulated annealing algorithm in an embodiment of the present disclosure is shown. Figure 5 As shown, the device includes: a target routing information acquisition module 51, a target routing strategy determination module 52, and an initial solution determination module 53.

[0094] Wherein, the target routing information acquisition module 51 is used to acquire the target routing information to be calculated in the global;

[0095] The target routing strategy determination module 52 is used to input the target routing information into a pre-built simulated annealing algorithm model to determine the target routing strategy.

[0096] In one embodiment of the present disclosure, the target routing strategy determination module 52 is specifically used to: determine the target function according to the target routing information to be calculated in the global, wherein the target routing information is determined according to the distance information between the routes and the traffic information between the routes; input the preset initial solution into the target function to determine the first routing strategy value, wherein the initial solution is preset with an iterative algebra; the second solution determination step: determine the second solution according to the preset step difference; input the second solution into the target function to determine the second routing strategy value, and reduce the iterative algebra by one; determine whether the first routing strategy value is greater than the second routing strategy value; if so, accept the second routing strategy value as the current solution; if not, accept the second routing strategy value as the current solution according to probability, wherein the probability is determined according to the target routing information; determine whether the preset iterative algebra is less than zero; if so, determine whether the termination condition is met; when the termination condition is met, determine the current solution as the target routing strategy; when the termination condition is not met, set T to gradually decrease, and go to the second solution determination step, wherein T is determined according to the sum of the whole network Metric.

[0097] In one embodiment of the present disclosure, the routing optimization device based on the simulated annealing algorithm further includes an initial solution determination module 53: randomly selecting a metric of a corresponding level according to a preset metric pool; assigning the metric to a corresponding edge according to a metric constraint condition, and determining an initial solution.

[0098] Further, in one embodiment of the present disclosure, the metric pools preset in the initial solution determination module 53 include: between cores; from core to tandem; and between tandems.

[0099] Furthermore, in one embodiment of the present disclosure, the Metric constraint conditions in the initial solution determination module 53 include: determining the Metric according to the distance information between routes; multiple levels are preset between cores; multiple levels are preset from core to tandem; multiple levels are preset between tandems.

[0100] In one embodiment of the present disclosure, the objective function in the route optimization device based on the simulated annealing algorithm is calculated by the above formula (1).

[0101] In one embodiment of the present disclosure, the probability in the above-mentioned route optimization device based on the simulated annealing algorithm is calculated by the above-mentioned formula (2), the above-mentioned formula (3), and the above-mentioned formula (4).

[0102] In one embodiment of the present disclosure, the termination condition in the target routing strategy determination module 52 includes: a preset number of consecutive current solutions are not accepted.

[0103] In one embodiment of the present disclosure, after determining whether the preset iteration number is less than zero, the target routing strategy determination module 52 is further configured to: if not, proceed to the second solution determination step.

[0104] It should be noted that the target routing information acquisition module 51 and the target routing strategy determination module 52 correspond to S102 to S104 in the method embodiment, and the examples and application scenarios implemented by the modules and the corresponding steps are the same, but are not limited to the contents disclosed in the method embodiment. It should be noted that the modules as part of the device can be executed in a computer system such as a set of computer executable instructions.

[0105] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0106] Refer to the following Figure 6 The electronic device 600 according to this embodiment of the present disclosure is described. Figure 6 The electronic device 600 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0107] like Figure 6 As shown, the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 may include but are not limited to: at least one processing unit 610, at least one storage unit 620, and a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610).

[0108] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 executes the steps described in the above “Exemplary Method” section of this specification according to various exemplary embodiments of the present disclosure.

[0109] For example, the processing unit 610 may execute the following steps of the above method embodiment: obtaining the target routing information to be calculated in the global system; inputting the target routing information into a pre-built simulated annealing algorithm model to determine the target routing strategy.

[0110] For example, the processing unit 610 may perform the following steps of the above method embodiment: determining the objective function according to the target routing information to be calculated globally, wherein the target routing information is determined according to the distance information between the routes and the traffic information between the routes; inputting the preset initial solution into the objective function to determine the first routing strategy value, wherein the initial solution is preset with an iteration number; the second solution determination step: determining the second solution according to the preset step difference; inputting the second solution into the objective function to determine the second routing strategy value, and subtracting one from the iteration number; judging whether the first routing strategy value is greater than the second routing strategy value; if so, accepting the second routing strategy value as the current solution; if not, accepting the second routing strategy value as the current solution according to probability, wherein the probability is determined according to the target routing information; judging whether the preset iteration number is less than zero; if so, judging whether the termination condition is met; when the termination condition is met, determining the current solution as the target routing strategy; when the termination condition is not met, setting T to gradually decrease, and going to the second solution determination step, wherein T is determined according to the sum of the whole network Metric.

[0111] For example, the processing unit 610 may perform the following steps of the above method embodiment: randomly select a metric of a corresponding level according to a preset metric pool; assign the metric to a corresponding edge according to a metric constraint condition, and determine an initial solution.

[0112] For example, the processing unit 610 executes the preset metric pools in the above method embodiment including: between cores; from core to tandem; and between tandems.

[0113] For example, the processing unit 610 executes the Metric constraint conditions in the above method embodiment including: determining the Metric according to the distance information between routes; multiple levels are preset between cores; multiple levels are preset from core to tandem; multiple levels are preset between tandems.

[0114] For example, the processing unit 610 performs calculation of the objective function in the above method embodiment by using the above formula (1).

[0115] For example, the processing unit 610 executes the probability in the above method embodiment by calculating the above formula (2), the above formula (3), and the above formula (4).

[0116] For example, the termination condition in the execution of the above method embodiment by the processing unit 610 includes: a preset number of consecutive current solutions are not accepted.

[0117] For example, the processing unit 610 may execute the following steps of the above method embodiment: determine whether the preset number of iterations is less than zero; if not, proceed to the second solution determination step.

[0118] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0119] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0120] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0121] The electronic device 600 may also communicate with one or more external devices 640 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 660. As shown, the network adapter 660 communicates with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0122] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0123] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart may be implemented as a computer program product, which includes: a computer program, which implements the above-mentioned routing optimization method based on the simulated annealing algorithm when executed by a processor.

[0124] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is further provided, which may be a readable signal medium or a readable storage medium. Figure 7 A schematic diagram of a computer-readable storage medium in an embodiment of the present disclosure is shown. Figure 7 As shown, a program product capable of implementing the above method of the present disclosure is stored on the computer-readable storage medium 700. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present disclosure described in the above “Exemplary Method” section of this specification.

[0125] For example, when the program product in the embodiment of the present disclosure is executed by a processor, the following steps are implemented: obtaining the target routing information to be calculated in the global world; inputting the target routing information into a pre-built simulated annealing algorithm model to determine the target routing strategy.

[0126] For example, when the program product in the embodiment of the present disclosure is executed by a processor, the following steps are implemented: determining the target function according to the target routing information to be calculated globally, wherein the target routing information is determined according to the distance information between the routes and the traffic information between the routes; inputting the preset initial solution into the target function to determine the first routing strategy value, wherein the initial solution is preset with an iterative number; the second solution determination step: determining the second solution according to the preset step difference; inputting the second solution into the target function to determine the second routing strategy value, and subtracting one from the iterative number; judging whether the first routing strategy value is greater than the second routing strategy value; if so, accepting the second routing strategy value as the current solution; if not, accepting the second routing strategy value as the current solution according to probability, wherein the probability is determined according to the target routing information; judging whether the preset iterative number is less than zero; if so, judging whether the termination condition is met; when the termination condition is met, determining the current solution as the target routing strategy; when the termination condition is not met, setting T to gradually decrease, and going to the second solution determination step, wherein T is determined according to the sum of the whole network Metric.

[0127] For example, when the program product in the embodiment of the present disclosure is executed by a processor, the following steps are implemented: randomly selecting a metric of a corresponding level according to a preset metric pool; assigning the metric to a corresponding edge according to a metric constraint condition, and determining an initial solution.

[0128] For example, when the program product in the embodiment of the present disclosure is executed by a processor, the preset Metric pools include: between cores; from core to tandem; and between tandems.

[0129] For example, the Metric constraint conditions when the program product in the embodiment of the present disclosure is executed by the processor include: determining the Metric according to the distance information between routes; multiple levels are preset between cores; multiple levels are preset from core to tandem; and multiple levels are preset between tandems.

[0130] For example, the objective function when the program product in the embodiment of the present disclosure is executed by a processor is calculated using the above formula (1).

[0131] For example, the probability of the program product in the embodiments of the present disclosure being executed by a processor is calculated using the above formula (2), the above formula (3), and the above formula (4).

[0132] For example, a termination condition when the program product in the embodiment of the present disclosure is executed by a processor includes: a preset number of consecutive current solutions are not accepted.

[0133] For example, when the program product in the embodiment of the present disclosure is executed by a processor, the method implements the following steps: determining whether a preset iteration number is less than zero; if not, proceeding to the second solution determination step.

[0134] More specific examples of computer-readable storage media in the present disclosure may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0135] In the present disclosure, a computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, wherein a readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A readable signal medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0136] Alternatively, the program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0137] In a specific implementation, the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0138] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0139] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0140] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0141] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A routing optimization method based on simulated annealing algorithm, characterized in that: include: Get the target routing information to be calculated globally; Inputting the target routing information into a pre-built simulated annealing algorithm model to determine the target routing strategy; The pre-built simulated annealing algorithm model includes: determining the objective function according to the target routing information to be calculated globally, wherein the target routing information is determined according to the distance information between the routes and the flow information between the routes; inputting the preset initial solution into the objective function to determine the first routing strategy value, wherein the initial solution is preset with an iterative number; the second solution determination step: determining the second solution according to the preset step difference; inputting the second solution into the objective function to determine the second routing strategy value, and subtracting one from the iterative number; judging whether the first routing strategy value is greater than the second routing strategy value; if so, accepting the second routing strategy value as the current solution; if not, accepting the second routing strategy value as the current solution according to probability, wherein the probability is determined according to the target routing information; judging whether the preset iterative number is less than zero; if so, judging whether the termination condition is met; when the termination condition is met, determining the current solution as the target routing strategy; when the termination condition is not met, setting T to gradually decrease and going to the second solution determination step; The objective function is calculated by the following formula: Among them, x represents the metric set, D ij Represents the distance information from routing node i to routing node j, T ij Represents the traffic information from routing node i to routing node j, and G represents the total number of routing nodes in the world; The probability is calculated by the following formula: △f=f(x new )-f(x); exp(-△f / (KT)); Wherein, △f represents the difference between the second routing strategy value and the first routing strategy value, f(x new ) represents the second routing strategy value, f(x) represents the first routing strategy value, exp represents the natural exponent, K represents the constant, T represents the sum of the metrics of the entire network, and exp(-△f / (KT)) represents the probability.

2. The routing optimization method based on simulated annealing algorithm according to claim 1, characterized in that: Before inputting the preset initial solution into the objective function, the method further comprises: According to the preset metric pool, randomly select the metric of the corresponding level; According to the Metric constraint, the Metric is assigned to the edge of the corresponding route to determine the initial solution.

3. The routing optimization method based on simulated annealing algorithm according to claim 2, characterized in that: The preset metric pools include: Core Room; core to tandem; Meeting room.

4. The routing optimization method based on simulated annealing algorithm according to claim 2, characterized in that: The metric constraints include: Determine the metric based on the distance information between routes; There are multiple levels of core-to-core presets; There are multiple levels of core-to-tandem presets; There are multiple levels of pre-set tandem rooms.

5. The routing optimization method based on simulated annealing algorithm according to claim 1, characterized in that: The termination conditions include: The current solution has not been accepted for a preset number of consecutive times.

6. The routing optimization method based on simulated annealing algorithm according to claim 1, characterized in that: After determining whether the preset number of iterations is less than zero, the method further includes: If not, go to the second solution determination step.

7. A route optimization device based on simulated annealing algorithm, characterized in that: include: The target routing information acquisition module is used to obtain the target routing information to be calculated globally; A target routing strategy determination module is used to input the target routing information into a pre-built simulated annealing algorithm model to determine the target routing strategy; The pre-built simulated annealing algorithm model includes: determining the objective function according to the target routing information to be calculated globally, wherein the target routing information is determined according to the distance information between the routes and the flow information between the routes; inputting the preset initial solution into the objective function to determine the first routing strategy value, wherein the initial solution is preset with an iterative number; the second solution determination step: determining the second solution according to the preset step difference; inputting the second solution into the objective function to determine the second routing strategy value, and subtracting one from the iterative number; judging whether the first routing strategy value is greater than the second routing strategy value; if so, accepting the second routing strategy value as the current solution; if not, accepting the second routing strategy value as the current solution according to probability, wherein the probability is determined according to the target routing information; judging whether the preset iterative number is less than zero; if so, judging whether the termination condition is met; when the termination condition is met, determining the current solution as the target routing strategy; when the termination condition is not met, setting T to gradually decrease and going to the second solution determination step; The objective function is calculated by the following formula: Among them, x represents the metric set, D ij Represents the distance information from routing node i to routing node j, T ij Represents the traffic information from routing node i to routing node j, and G represents the total number of routing nodes in the world; The probability is calculated by the following formula: △f=f(x new )-f(x); exp(-△f / (KT)); Wherein, △f represents the difference between the second routing strategy value and the first routing strategy value, f(x new ) represents the second routing strategy value, f(x) represents the first routing strategy value, exp represents the natural exponent, K represents the constant, T represents the sum of the metrics of the entire network, and exp(-△f / (KT)) represents the probability.

8. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the routing optimization method based on the simulated annealing algorithm according to any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the routing optimization method based on the simulated annealing algorithm described in any one of claims 1 to 6 is implemented.

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