A SRv6-TE path optimization method and device based on entropy weight method

Abstract

The present invention discloses an SRv6-TE path optimization method and device based on the entropy weight method, wherein the method includes: real-time data collection of path calculation factors of the entire network, and storing them in a real-time database and a historical database; performing SRv6-TE path calculation through the path calculation factors in the historical database to obtain the path corresponding to each path calculation factor, and then performing entropy calculation to obtain the entropy weight corresponding to each path calculation factor; performing SRv6-TE path calculation through the path calculation factors and path calculation requirements in the real-time database to obtain the real-time optimal path; when a network failure or oscillation occurs, SRv6-TE path optimization is implemented according to the above-obtained path with the smallest entropy weight and the best real-time path. The method and device combine the entropy weight method with the SRv6-TE path calculation to achieve the combination of path calculation factors, thereby optimizing the SRv6-TE path calculation.

Description

Technical Field

[0001] The present invention relates to the technical field of SRv6-TE path calculation, and in particular to an SRv6-TE path calculation optimization method and device based on an entropy weight method. Background Art

[0002] When telecommunication operators construct and operate telecommunication networks, they need to calculate network tunnels and build network equipment control systems. Upper-layer applications calculate the optimal tunnel through services. Currently, the optimal network tunnel can only be calculated and issued through the real-time value of the routing factor.

[0003] SRv6-TE path calculation: The network established by the operator has multiple paths from point A to point Z. The real-time values ​​of multiple path calculation factors are calculated. For example, point A to point Z is calculated according to the requirements of minimum latency and minimum number of hops, and a multi-hop path from point A to point Z is obtained, thereby implementing the required tunnel delivery. This is similar to map navigation, and the optimal path is selected and executed as required.

[0004] Traditional SRv6-TE path calculation generally has the following defects:

[0005] 1. SRv6-TE path calculation only supports the calculation of real-time values ​​of path calculation factors, but does not support the calculation of historical values ​​of path calculation factors. For path calculation factors with special normal distribution and tidal effect, advance prediction cannot be achieved.

[0006] 2. The historical values ​​used in SRv6-TE path calculation are currently calculated based on the real-time values ​​of the path calculation factors. It is impossible to know the real-time impact of dynamic changes on the network.

[0007] As the complexity of services increases, there is an obvious trend of using historical values ​​of path calculation factors for network tunnel calculations. Therefore, the network device control system needs to analyze the historical values ​​of path calculation factors and calculate the historical values ​​of path calculation factors through the entropy weight method to optimize SRv6-TE path calculation.

[0008] The entropy weight method is an objective weighting method. Its basic idea is to determine the objective weight according to the variability of the indicator. The principle is that the smaller the variability of the indicator, the less information it reflects, and the corresponding weight should be lower. The entropy weight method is applied to SRv6-TE path calculation. The path calculation factor is calculated. When the entropy weight is smaller, the amount of information reflected is smaller, indicating that the path is more stable. After adding the entropy weights of multiple changing indicators, the most stable path can be obtained, so that the path can be selected when the path is switched. Summary of the invention

[0009] In order to overcome the above-mentioned defects of the traditional SRv6-TE path calculation, the present invention provides an SRv6-TE path calculation optimization method and device based on the entropy weight method, which combines the entropy weight method with the SRv6-TE path calculation to achieve the combination of path calculation factors, thereby optimizing the SRv6-TE path calculation.

[0010] To achieve the above object, the present invention adopts the following technical solutions:

[0011] In one embodiment of the present invention, an SRv6-TE path calculation optimization method based on an entropy weight method is proposed. The method includes:

[0012] Collect real-time data of the routing factors of the entire network and store them in the real-time database and historical database;

[0013] SRv6-TE path calculation is performed using the path calculation factors in the historical database to obtain the path corresponding to each path calculation factor. Entropy is then calculated to obtain the entropy weight corresponding to each path calculation factor.

[0014] SRv6-TE path calculation is performed based on the path calculation factors and path calculation requirements in the real-time database to obtain the real-time optimal path. When a network failure or oscillation occurs, SRv6-TE path calculation optimization is implemented based on the real-time optimal path with the smallest entropy weight obtained above.

[0015] Furthermore, SRv6-TE path calculation is performed using the path calculation factors in the historical database to obtain the path corresponding to each path calculation factor. Entropy calculation is then performed to obtain the entropy weight corresponding to each path calculation factor, including:

[0016] Perform SRv6-TE path calculation using the path calculation factors in the historical database to obtain the path corresponding to each path calculation factor.

[0017] Standardize the data of the path calculation factors in the historical database;

[0018] The paths corresponding to the multiple path calculation factors obtained above and the multiple path calculation factors constitute a forward matrix;

[0019] Determine whether there are negative numbers in the above forward matrix. If so, standardize the matrix once;

[0020] Calculate the probability matrix based on the previous step and obtain each element in the probability matrix;

[0021] Calculate the entropy value based on the previous step;

[0022] Define the information utility value corresponding to entropy, normalize the information utility value, and obtain the entropy weight corresponding to each path factor.

[0023] Furthermore, the entropy calculation formula is:

[0024]

[0025] Among them, n is the number of paths corresponding to the path calculation factor, p ij is each element in the probability matrix P, j is the jth path factor sample, i is the path corresponding to the i-th path factor, and m is the number of path factors.

[0026] In one embodiment of the present invention, an SRv6-TE path optimization device based on an entropy weight method is also proposed, and the device includes:

[0027] The routing factor perception module is used to collect real-time data on the routing factors of the entire network and store them in the real-time database and historical database;

[0028] The path factor entropy calculation module is used to perform SRv6-TE path calculation based on the path factors in the historical database, obtain the path corresponding to each path factor, and then calculate the entropy to obtain the entropy weight corresponding to each path factor.

[0029] The path calculation optimization module is used to perform SRv6-TE path calculation based on the path calculation factors and path calculation requirements in the real-time database to obtain the real-time optimal path. When a network failure or oscillation occurs, SRv6-TE path calculation optimization is implemented based on the real-time optimal path with the minimum entropy weight obtained above.

[0030] Furthermore, the path factor entropy calculation module is specifically used for:

[0031] Perform SRv6-TE path calculation using the path calculation factors in the historical database to obtain the path corresponding to each path calculation factor.

[0032] Standardize the data of the path calculation factors in the historical database;

[0033] The paths corresponding to the multiple path calculation factors obtained above and the multiple path calculation factors constitute a forward matrix;

[0034] Determine whether there are negative numbers in the above forward matrix. If so, standardize the matrix once;

[0035] Calculate the probability matrix based on the previous step and obtain each element in the probability matrix;

[0036] Calculate the entropy value based on the previous step;

[0037] Define the information utility value corresponding to entropy, normalize the information utility value, and obtain the entropy weight corresponding to each path factor.

[0038] Furthermore, the entropy calculation formula is:

[0039]

[0040] Among them, n is the number of paths corresponding to the path calculation factor, p ij is each element in the probability matrix P, j is the jth path factor sample, i is the path corresponding to the i-th path factor, and m is the number of path factors.

[0041] In one embodiment of the present invention, a computer device is also proposed, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the aforementioned SRv6-TE path optimization method based on the entropy weight method is implemented.

[0042] In one embodiment of the present invention, a computer-readable storage medium is further proposed, wherein the computer-readable storage medium stores a computer program for executing an SRv6-TE path optimization method based on an entropy weight method.

[0043] Beneficial effects:

[0044] 1. The present invention is an original algorithm for calculating SRv6-TE path calculation by real-time value of path calculation factor, adds analysis of historical value of path calculation factor, and weights it by entropy weight method, so as to realize that historical value of path calculation factor is combined with SRv6-TE path calculation by entropy weight method, realizes combination of real-time value and historical value of path calculation factor, and uses historical value of path calculation factor in SRv6-TE path calculation by entropy weight method.

[0045] 2. The present invention adds the historical value of the path calculation factor to the faulty or oscillating network through the entropy weight method, so that the path calculation optimization can be performed for the faulty or oscillating network. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of the SRv6-TE path optimization method based on the entropy weight method of the present invention;

[0047] Figure 2 It is the SRv6-TE path optimization diagram based on the entropy weight method of the present invention;

[0048] Figure 3 It is a structural schematic diagram of the SRv6-TE path optimization device based on the entropy weight method of the present invention;

[0049] Figure 4 It is a schematic diagram of the computer device structure of the present invention. DETAILED DESCRIPTION

[0050] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0051] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, device, apparatus, method or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0052] According to an implementation of the present invention, an SRv6-TE path calculation optimization method and device based on the entropy weight method are proposed. The entropy weight method is combined with the SRv6-TE path calculation to achieve the combination of path calculation factors, thereby optimizing the SRv6-TE path calculation.

[0053] The principle and spirit of the present invention are explained in detail below with reference to several representative embodiments of the present invention.

[0054] Figure 1 FIG. 1 is a flow chart of the SRv6-TE path optimization method based on the entropy weight method of the present invention. Figure 1 As shown, the method includes:

[0055] S1. Collect real-time data on the path calculation factors of the entire network (network delay, network packet loss, network jitter, network traffic, cost value and number of hops, etc.) and store them in the real-time database and historical database; the real-time database provides the capability for current dynamic switching, and the historical database provides basic data for the entropy calculation of the path calculation factors;

[0056] S2. Perform SRv6-TE path calculation using the path calculation factors in the historical database to obtain the path corresponding to each path calculation factor (existing technology), and then calculate the entropy. The smaller the entropy value, the more stable the path. The entropy weight corresponding to each path calculation factor is obtained.

[0057] S3. Perform SRv6-TE path calculation based on the path calculation factors and path calculation requirements in the real-time database to obtain the real-time optimal path (existing technology); when a network failure or oscillation occurs, SRv6-TE path calculation optimization is implemented based on the above-obtained path with the smallest entropy weight and the real-time optimal path.

[0058] The calculation method of the path factor entropy is as follows:

[0059] The data of the calculation factor is standardized. Given m calculation factors X 1 ,X2 ,…,X m , then the data of the path corresponding to the i-th path calculation factor of the j-th path calculation factor sample is standardized as shown in Table 1:

[0060] Table 1

[0061]

[0062] The path corresponding to n calculation factors and the forward matrix composed of m calculation factors (already forward-oriented) are as follows:

[0063]

[0064] Determine whether there are negative numbers in the matrix X. If so, another standardization method is needed to standardize the matrix X. The standardization formula is as follows:

[0065]

[0066] It is the value obtained from the above matrix X, and the standard value is obtained by subtracting the maximum value from the minimum value.

[0067] Based on the previous step, the probability matrix P is calculated. Each element in P is as follows:

[0068]

[0069] Divide it by the summed value to get the probability matrix P ij .

[0070] Through the probability matrix P ij The entropy value is calculated, and the entropy calculation formula is:

[0071]

[0072] Among them, n is the number of paths corresponding to the path calculation factor, p ij is each element in the probability matrix P, j is the jth path factor sample, i is the path corresponding to the i-th path factor, and m is the number of path factors.

[0073] e j The larger it is, the greater the entropy of the j-th path factor sample is, and the smaller the corresponding amount is.

[0074] The entropy weight method can be used to determine the entropy of the path calculation factor. When the entropy is small, it means that the path is more stable, and its path can be given priority when re-optimizing the path calculation.

[0075] After the calculation of the path factor entropy is completed, according to the current real-time optimal path factor (through the path factor and path requirement in the real-time database), the optimal path is 1->2->3->4->5. Figure 2 As shown in the figure; when link x has a problem (failure or oscillation), the path 1->2->3->4->5 needs to be recalculated; the entropy weight is calculated by the network-wide path calculation factors in the historical database, and it is found that the minimum entropy weight ranking is (from small to large) a, b, c, d, e, f; while meeting the quality requirements, the stability weighted path is selected as 1->6->7->8->11->12->5, as shown in the figure. Figure 2 shown.

[0076] It should be noted that, although the operations of the method of the present invention are described in a specific order in the above embodiments and the accompanying drawings, this does not require or imply that the operations must be performed in the specific order, or that all the operations 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.

[0077] At present, the corresponding business applications of this solution have been running on the controller system of China Mobile Guangdong Company. The running results are stable and reliable, and the algorithm is optimized. For example, the optimal delay algorithm is used while weighting the delay variation value to find the indicator with lower degree of variation, and then weighted according to the indicator to perform weighted evaluation of quality, so as to realize the selection and creation of the optimal path.

[0078] Based on the same inventive concept, the present invention also proposes an SRv6-TE path optimization device based on the entropy weight method. The implementation of the device can refer to the implementation of the above method, and the repeated parts will not be repeated. The term "module" used below can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0079] Figure 3 Schematic diagram of the structure of the SRv6-TE path optimization device based on the entropy weight method of the present invention. Figure 3 As shown, the device is mainly divided into three components: a path factor perception module 101, a path factor entropy calculation module 102 and a path optimization module 103.

[0080] The routing factor sensing module 101 is used to collect real-time data of routing factors of the entire network and store them in a real-time database and a historical database;

[0081] The path calculation factor entropy calculation module 102 is used to perform SRv6-TE path calculation using the path calculation factors in the historical database to obtain the path corresponding to each path calculation factor, and then perform entropy calculation to obtain the entropy weight corresponding to each path calculation factor;

[0082] The path calculation optimization module 103 is used to perform SRv6-TE path calculation based on the path calculation factors and path calculation requirements in the real-time database to obtain the real-time optimal path; when a network failure or oscillation occurs, SRv6-TE path calculation optimization is implemented based on the above-obtained real-time optimal path with the smallest entropy weight.

[0083] It should be noted that although several modules of the SRv6-TE path optimization device based on the entropy weight method are mentioned in the above detailed description, this division is only exemplary and not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules described above can be concretized in one module. Conversely, the features and functions of one module described above can be further divided into multiple modules for concretization.

[0084] Based on the above invention concept, Figure 4 As shown, the present invention also proposes a computer device 200, including a memory 210, a processor 220, and a computer program 230 stored in the memory 210 and executable on the processor 220. When the processor 220 executes the computer program 230, the aforementioned SRv6-TE path optimization method based on the entropy weight method is implemented.

[0085] Based on the aforementioned inventive concept, the present invention also proposes a computer-readable storage medium, which stores a computer program for executing the aforementioned SRv6-TE path optimization method based on the entropy weight method.

[0086] The present invention proposes an SRv6-TE path calculation optimization method and device based on the entropy weight method. The traditional SRv6-TE path calculation is calculated through the current real-time data of the path calculation factor. When the collected data exceeds the range or meets certain conditions to trigger the re-calculation and traffic allocation, the traditional path calculation method is only satisfied with a relatively stable state. Since the path calculation factor changes in real time, through the collection of historical values ​​and the calculation of entropy values ​​using the entropy weight method, the path with the smaller the entropy and the more stable it is obtained as the path with priority for path calculation, thereby realizing the optimization of SRv6-TE path calculation.

[0087] Although the spirit and principle of the present invention have been described with reference to several specific embodiments, it should be understood that the present invention is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and such division is only for the convenience of expression. The present invention is intended to cover various modifications and equivalent arrangements contained in the spirit and scope of the attached claims.

[0088] Regarding the limitation of the protection scope of the present invention, those skilled in the art should understand that, based on the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative work are still within the protection scope of the present invention.

Claims

1. A SRv6-TE path optimization method based on entropy weight method, characterized in that: The method includes: Collect real-time data of the routing factors of the entire network and store them in the real-time database and historical database; SRv6-TE path calculation is performed based on the path calculation factors in the historical database to obtain the path corresponding to each path calculation factor. Then, entropy calculation is performed to obtain the entropy weight corresponding to each path calculation factor, including: Perform SRv6-TE path calculation using the path calculation factors in the historical database to obtain the path corresponding to each path calculation factor. Standardize the data of path calculation factors in the historical database; The paths corresponding to the multiple path calculation factors obtained above and the multiple path calculation factors constitute a forward matrix; Determine whether there are negative numbers in the above forward matrix. If so, standardize the matrix once; Calculate the probability matrix based on the previous step and obtain each element in the probability matrix; Calculate the entropy value based on the previous step; Define the information utility value corresponding to entropy, normalize the information utility value, and obtain the entropy weight corresponding to each path calculation factor; SRv6-TE path calculation is performed based on the path calculation factors and path calculation requirements in the real-time database to obtain the real-time optimal path. When a network failure or oscillation occurs, SRv6-TE path calculation optimization is implemented based on the real-time optimal path with the smallest entropy weight obtained above.

2. The SRv6-TE path optimization method based on entropy weight method according to claim 1 is characterized in that: The calculation formula of the entropy is: Among them, n is the number of paths corresponding to the path calculation factor, p ij is each element in the probability matrix P, j is the jth path factor sample, i is the path corresponding to the i-th path factor, and m is the number of path factors.

3. An SRv6-TE path optimization device based on entropy weight method, characterized in that: The device includes: The routing factor perception module is used to collect real-time data on the routing factors of the entire network and store them in the real-time database and historical database; The path calculation factor entropy calculation module is used to perform SRv6-TE path calculation based on the path calculation factors in the historical database, obtain the path corresponding to each path calculation factor, and then calculate the entropy to obtain the entropy weight corresponding to each path calculation factor, including: Perform SRv6-TE path calculation using the path calculation factors in the historical database to obtain the path corresponding to each path calculation factor. Standardize the data of the path calculation factors in the historical database; The paths corresponding to the multiple path calculation factors obtained above and the multiple path calculation factors constitute a forward matrix; Determine whether there are negative numbers in the above forward matrix. If so, standardize the matrix once; Calculate the probability matrix based on the previous step and obtain each element in the probability matrix; Calculate the entropy value based on the previous step; Define the information utility value corresponding to entropy, normalize the information utility value, and obtain the entropy weight corresponding to each path calculation factor; The path calculation optimization module is used to perform SRv6-TE path calculation based on the path calculation factors and path calculation requirements in the real-time database to obtain the real-time optimal path. When a network failure or oscillation occurs, SRv6-TE path calculation optimization is implemented based on the real-time optimal path with the minimum entropy weight obtained above.

4. The SRv6-TE path optimization device based on entropy weight method according to claim 3 is characterized in that: The calculation formula of the entropy is: Among them, n is the number of paths corresponding to the path calculation factor, p ij is each element in the probability matrix P, j is the jth path factor sample, i is the path corresponding to the i-th path factor, and m is the number of path factors.

5. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 2 is implemented.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for executing the method according to any one of claims 1 to 2.

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