CN116208513A - Gateway Health Prediction Method and Device - Google Patents

Abstract

The present application provides a gateway health degree prediction method and device. The method includes: clustering the index data collected from the API application programming interface gateway according to the preset classification conditions, and determining the preset data type of each index data; each index data in the preset data type, Input the prediction model obtained according to the historical index data training corresponding to the preset data type to obtain the health score corresponding to the preset data type; determine the health of the API gateway according to each health score corresponding to each preset data type. The method for predicting the health degree of a gateway provided in the embodiment of the present application can predict the health degree of an API gateway, so as to improve the operation and maintenance efficiency of network services and reduce the operation and maintenance cost.

Description

Gateway Health Prediction Method and Device

technical field

The present application relates to the field of network technologies, and in particular to a gateway health degree prediction method and device.

Background technique

Enterprise network services generally have a client and a server, and there is an API (Application Programming Interface, application programming interface) gateway between the client and the server, which is used to uniformly receive calls from the client or external partners. Request, and according to the different logic of each interface, perform certain verification and logic processing, and then forward it to the server.

The API gateway can realize functions such as authentication, monitoring, load balancing, caching, request fragmentation and management, and static response processing. It is an important part of network communication, so the health of the API gateway is very important. However, in related technologies, only when there is a problem with the health of the API gateway, the operation and maintenance staff will passively collect data for analysis, and cannot make timely judgments on the health of the API gateway, resulting in the operation and maintenance efficiency of the entire network service At the same time, the operation and maintenance cost is also greatly increased.

Contents of the invention

The embodiments of the present application provide a gateway health degree prediction method and device, which can predict the health degree of an API gateway, so as to improve the operation and maintenance efficiency of network services and reduce operation and maintenance costs.

In the first aspect, the embodiment of the present application provides a method for predicting the health degree of a gateway, including:

Cluster the index data collected from the API application programming interface gateway according to the preset classification conditions, and determine the preset data type of each index data;

Input each index data in the preset data type into the prediction model obtained according to the training of each historical index data corresponding to the preset data type, and obtain the health score corresponding to the preset data type;

The health degree of the API gateway is determined according to each health degree score corresponding to each preset data type.

In one embodiment, the clustering of the indicator data collected from the API application programming interface gateway according to preset classification conditions, and the determination of the preset data type of each indicator data include:

Clustering each of the index data according to the number of visits by the API gateway within a preset period of time, determining the preset data type of each of the index data whose number of visits is greater than a preset value, and the number of visits is less than or equal to A preset data type of each of the index data of the preset value.

In one embodiment, each of the index data is clustered according to the number of accesses by the API gateway within the first preset period, and the preset data of each of the index data whose access times are greater than a preset value is determined type, and the preset data type of each indicator data whose access times are less than or equal to the preset value, including:

clustering each of the index data according to the system type corresponding to each of the index data, and obtaining the index data of each of the system types;

Clustering the index data in the system type according to the number of accesses by the API gateway within a preset period of time, and determining the expected value of each index data in the system type whose access times are greater than a preset value. Set the data type, and the preset data type of each of the index data whose access times are less than or equal to the preset value in the system type;

Wherein, the preset value is determined according to the system type.

In one embodiment, the clustering of the indicator data collected from the API application programming interface gateway according to preset classification conditions, and the determination of the preset data type of each indicator data include:

Clustering each of the index data according to the API response time corresponding to each of the index data, and determining the preset data type of each of the index data whose corresponding API response time is greater than the preset duration, and the API response time is less than or A preset data type of each indicator data equal to the preset duration.

In one embodiment, the index data is clustered according to the API response time corresponding to each of the index data, and the preset data of each of the index data whose corresponding API response time is greater than the preset duration is determined type, and the preset data type of each indicator data whose API response time is less than or equal to the preset duration, including:

clustering each of the index data according to the system type corresponding to each of the index data, and obtaining the index data of each of the system types;

Cluster the index data in the system type according to the API response time corresponding to the index data in the system type, and determine the system types whose corresponding API response time is longer than the preset duration. The preset data type of the indicator data, and the preset data type of each indicator data whose API response time is less than or equal to the preset duration in the system type;

Wherein, the preset duration is determined according to the system type.

In one embodiment, determining the health of the API gateway according to the health scores corresponding to each of the preset data types includes:

comparing the health score corresponding to the preset data type with the preset fusing threshold corresponding to the preset data type, and obtaining a comparison result corresponding to the preset data type;

When each comparison result corresponding to each preset data type is that the health score is greater than the preset fusing threshold, it is determined that the API gateway is normal.

In one embodiment, also includes:

When any of the comparison results is that the health score is less than or equal to the preset fusing threshold, the API gateway is closed.

In the second aspect, the embodiment of the present application provides a device for predicting the health of a gateway, including:

The data type determination module is used to cluster the index data collected from the API application programming interface gateway according to the preset classification conditions, and determine the preset data type of each index data;

The health score acquisition module is used to input each index data in the preset data type into the prediction model obtained according to the training of each historical index data corresponding to the preset data type, and obtain the data corresponding to the preset data type. health score;

A health degree prediction module, configured to determine the health degree of the API gateway according to each health degree score corresponding to each preset data type.

In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory storing a computer program. When the processor executes the program, the steps of the gateway health degree prediction method described in the first aspect are implemented.

In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the gateway health degree prediction method described in the first aspect are implemented.

The method and device for predicting the health of the gateway provided by the embodiment of the present application, after clustering the index data collected from the API gateway, input the index data of each preset data type into the prediction trained by the historical index data In the model, the health score corresponding to each preset data type is obtained, so as to realize the health prediction of the API gateway according to each health score corresponding to each preset data type, so that the API gateway can be completed through the index data of the API gateway The health portrait of the API gateway avoids passively collecting data for analysis when there is a problem with the health of the API gateway. Instead, it actively detects and predicts the health of the API gateway, thereby improving the operation and maintenance efficiency of network services and reducing operation and maintenance costs.

Description of drawings

In order to more clearly illustrate the technical solutions in this application or the prior art, the accompanying drawings that need to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings in the following description are the present For some embodiments of the application, those skilled in the art can also obtain other drawings based on these drawings without creative work.

FIG. 1 is a schematic flowchart of a gateway health degree prediction method provided by an embodiment of the present invention;

Fig. 2 is a schematic structural diagram of a health degree prediction device of a gateway provided by the present invention;

Fig. 3 is a schematic structural diagram of an electronic device provided by the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of this application Examples, not all examples. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application.

Embodiments of the present application will be described in detail below in conjunction with the accompanying drawings.

Referring to FIG. 1 , it is one of the flow diagrams of the method for predicting the health of the gateway provided by the embodiment of the present invention. The method is applied to electronic devices, where the electronic device can specifically be a server or a terminal device, and is used to perform the health degree of the API gateway. predict. As shown in Figure 1, a method for predicting the health of a gateway provided in this embodiment includes:

Step 101, clustering the index data collected from the API application programming interface gateway according to preset classification conditions, and determining the preset data type of each index data;

Step 102, input each index data in the preset data type into the prediction model trained according to the historical index data corresponding to the preset data type, and obtain the health score corresponding to the preset data type;

Step 103: Determine the health of the API gateway according to the health scores corresponding to the preset data types.

After clustering the indicator data collected from the API gateway, input the indicator data of each preset data type into the prediction model trained by the historical indicator data, and obtain the health score corresponding to each preset data type , to realize the health degree prediction of the API gateway based on the health degree scores corresponding to each preset data type, so that the health degree portrait of the API gateway can be completed through the various index data of the API gateway, avoiding the health problems of the API gateway. Passively collect data for analysis, but actively detect and predict the health of the API gateway, thereby improving the operation and maintenance efficiency of network services and reducing operation and maintenance costs.

In step 101, each index data of the API gateway is collected from the API gateway in advance through the collector. Among them, the collector is a script used to collect the data of each indicator, and the script can be installed in the gateway of the API in the form of a plug-in.

In one embodiment, each indicator data includes multi-dimensional data such as API logs of the API gateway and other operation and maintenance data, wherein the operation and maintenance data includes external related data such as system attributes, system types, and request times corresponding to API requests, And the data of the gateway itself such as the working hours of the API gateway.

In an embodiment, each indicator data can be a specific type of API request log and multi-dimensional data in AIP-related background components, such as only collecting API request logs corresponding to some high-priority systems and API-related background components multidimensional data. Among them, the system with high priority can be a pre-designated self-developed system, a pre-designated third-party system or other pre-designated evaluation platforms. Therefore, the amount of data to be processed is reduced, and the processing efficiency of subsequent processing of the collected index data is improved.

After the data of each indicator is collected, the data of each indicator can be cleaned and transformed to form structured data, which is then passed into the message queue for subsequent processing.

In one embodiment, the KMEANS algorithm can be used to cluster each index data. The KMEANS algorithm is a clustering algorithm based on division, and the distance is used as the standard for measuring the similarity between data objects, that is, the smaller the distance between data objects , the higher their similarity is, the more likely they are in the same cluster. The central idea is to determine the constant K in advance, and the constant K means the final number of clustering categories. First, the initial point is randomly selected as the centroid, and the sample points are classified into the most similar by calculating the similarity between each sample and the centroid. Then, recalculate the centroid of each class (that is, the class center), repeat this process until the centroid does not change, and finally determine the category to which each sample belongs and the centroid of each class.

Assume that the set of extracted index data is (x1,x2,...,xn), and each xi is a d-dimensional vector. The purpose of K-means clustering is to divide the original data into k categories under the condition of a given number of type groups k (k≤n), S={S1,S2,...,Sk}.

In an embodiment, K=2, that is, the preset data type includes a primary index data type and a secondary index data type. Different preset data types have different impacts on the health of the API gateway. Among them, the data type of the primary index represents a data type that has a greater impact on the health of the API gateway, and the data type of the secondary index represents a data type that has a lesser impact on the health of the API gateway. The indicator data in the primary indicator data type is the primary indicator data, and the indicator data in the secondary indicator data type is the secondary indicator data.

In an embodiment, the preset classification condition may be to classify according to the system type corresponding to each index data. Each system type is preset with a corresponding preset priority. If the system type is a self-developed system, the degree of importance is higher, so the default priority is higher; if the system type is a third-party system or other evaluation platform, the default priority is lower. Exemplarily, after each index data is collected, each index data is clustered according to the system type corresponding to each index data, and the system type corresponding to each index data is determined. If the index data is index data about a self-developed system, the system type corresponding to the index data is a self-developed system.

After determining the system type of each index data, divide the index data corresponding to the system type with a preset priority higher than the specified level into the same preset data type, specifically the above-mentioned main index data types, and obtain each main index data; and divide the index data corresponding to the system type whose preset priority is less than or equal to the specified level into the same preset data type, specifically the above-mentioned secondary index data type, to obtain each secondary index data.

The data of each index is classified according to the importance of the system type, and the data of the main index and the data of the secondary index are determined, so that the subsequent prediction of the health of the API gateway is more targeted.

In an embodiment, in addition to the system type as the classification condition, the number of API visits may also be used as the classification basis. Specifically, the described index data collected from the API application programming interface gateway is clustered according to preset classification conditions, and the preset data type of each index data is determined, including:

Clustering each of the index data according to the number of visits by the API gateway within a preset period of time, determining the preset data type of each of the index data whose number of visits is greater than a preset value, and the number of visits is less than or equal to A preset data type of each of the index data of the preset value.

In one embodiment, each indicator data is clustered according to the number of times each indicator data is accessed by the API within a preset period of time, and each indicator data whose API access times are greater than a preset value is divided into main indicator data, and the API access Each indicator data whose frequency is less than or equal to the preset value is divided into secondary indicator data. Wherein, the preset value can be set according to actual conditions.

In one embodiment, if a certain indicator data is accessed by the API more than a preset value, it can be determined as a dynamic access, that is, the indicator data is frequently accessed. Otherwise, it is judged as static access.

Due to the large number of API accesses in a short period of time, it means that API accesses are frequent and problems are prone to occur. Therefore, the data of each indicator is clustered according to the number of API visits, so that subsequent targeted supervision can be carried out for the visits of the API gateway, thereby improving the accuracy of the health prediction of the subsequent API gateway.

In order to further improve the accuracy of the follow-up health prediction for the API gateway, in one embodiment, each of the index data is clustered according to the number of times the API gateway visits within the first preset time period, and the visit is determined. The preset data type of each index data whose number of visits is greater than the preset value, and the preset data type of each of the index data whose access times are less than or equal to the preset value include:

clustering each of the index data according to the system type corresponding to each of the index data, and obtaining the index data of each of the system types;

Clustering the index data in the system type according to the number of accesses by the API gateway within a preset period of time, and determining the expected value of each index data in the system type whose access times are greater than a preset value. Set the data type, and the preset data type of each of the index data whose access times are less than or equal to the preset value in the system type;

Wherein, the preset value is determined according to the system type.

In an embodiment, different system types are bound with different preset values in advance. If the system type is a self-developed system, the default value of the corresponding number of API visits is preset value A; if the system type is a third-party system, the default value of the corresponding number of API visits is preset value B. After obtaining the index data, first cluster the index data according to each system type to obtain the index data of each system type, for example, the corresponding system type is the index data of a third-party system. Then cluster the indicator data corresponding to the third-party system according to the number of API gateway visits, obtain the indicator data whose API access times are greater than the preset value B as the main indicator data, and obtain the API access times less than or equal to Each index data of the preset value B is used as the secondary index data. The same applies to index data of self-developed systems or other evaluation platforms. In this way, the clustering of the system type and the clustering of the number of visits are combined to divide the data of each index, and obtain the data of each main index and each secondary index. Since the system type and the number of API accesses are considered, the classification results of each index data are more accurate, so that when the health degree of the API gateway is predicted based on the classified index data, the prediction results are more accurate.

Considering that the speed of API access determines the quality of network response and affects the normal operation and maintenance of the data system, that is, the speed of API access can effectively reflect the health of the API gateway. Therefore, in order to improve the accuracy of the health prediction of the API gateway, in In an embodiment, the API response time may also be used as a classification basis for each index data.

Specifically, each of the index data is clustered according to the API response time corresponding to each of the index data, and the preset data type of each of the index data whose corresponding API response time is greater than the preset duration is determined, and the API response A preset data type of each indicator data whose time is less than or equal to the preset duration.

In one embodiment, each index data is clustered according to the API response time corresponding to each index data, each index data whose corresponding API response time is shorter than the preset duration is divided into main index data, and the number of API visits greater than or Each indicator data equal to the preset duration is divided into secondary indicator data. Wherein, the preset duration can be set according to actual conditions. If the API response time is less than or equal to the preset time, it means that the API response is relatively fast, indicating that the network connection is normal and the system is running normally. At this time, the corresponding indicator data has little impact on the health of the API gateway, so it can be divided into secondary Need indicator data; if the API response time is longer than the preset time, it means that the API response is relatively slow, which may be caused by network congestion or gateway problems. At this time, the corresponding indicator data has a greater impact on the health of the API gateway, so Can be divided into main indicator data. Therefore, when predicting the health degree of the API gateway in the future, the health degree prediction of the API gateway can be performed according to the network response quality, and the accuracy of the health degree prediction of the subsequent API gateway can be improved.

In order to further improve the accuracy of subsequent health degree prediction for the API gateway, in one embodiment, the index data is clustered according to the API response time corresponding to each of the index data, and the corresponding API response is determined. The preset data type of each indicator data whose time is longer than the preset duration, and the preset data type of each indicator data whose API response time is less than or equal to the preset duration include:

clustering each of the index data according to the system type corresponding to each of the index data, and obtaining the index data of each of the system types;

Cluster the index data in the system type according to the API response time corresponding to the index data in the system type, and determine the system types whose corresponding API response time is longer than the preset duration. The preset data type of the indicator data, and the preset data type of each indicator data whose API response time is less than or equal to the preset duration in the system type;

Wherein, the preset duration is determined according to the system type.

In an embodiment, different system types are bound for different preset durations in advance. If the system type is a self-developed system, the default duration of the corresponding API response time is preset duration 1; if the system type is a third-party system, the default duration of the corresponding API response time is preset duration 2. After obtaining the index data, first cluster the index data according to each system type to obtain the index data of each system type, for example, the corresponding system type is the index data of a third-party system. Then cluster the index data whose corresponding system type is a third-party system according to the corresponding API response time, obtain the index data whose corresponding API response time is greater than the preset duration 2 as the main index data, and obtain the corresponding API response Each indicator data whose time is less than or equal to the preset duration 2 is used as secondary indicator data. The same applies to index data of self-developed systems or other evaluation platforms. In this way, the clustering of the system type and the clustering of the API response time are combined to divide the data of each index, and the data of each main index and each secondary index are obtained. Since the factors of system type and API response time are considered, the classification result of each index data is more accurate, so that when the health degree of the API gateway is predicted based on the classified index data, the prediction result is more accurate.

In an embodiment, different system types may also be bound in advance with different preset values and different preset durations. If the system type is a self-developed system, the default value of the corresponding number of API visits is preset value A, and the default duration of the corresponding API response time is preset duration 1; the system type is a third-party system, and the corresponding The preset value of the number of API visits is preset value B, and the preset duration of the corresponding API response time is preset duration 2. After obtaining the index data, first cluster the index data according to each system type to obtain the index data of each system type, for example, the corresponding system type is the index data of a third-party system. Then cluster the index data of the corresponding system type as the third-party system according to the corresponding number of API visits and the corresponding API response time, and obtain the number of API visits greater than the preset value B, and the corresponding API response time is greater than the preset value Let the index data of duration 2 be the main index data, and the other index data be the secondary index data. The same applies to index data of self-developed systems or other evaluation platforms. In this way, the clustering of the system type, the number of API gateway visits and the API response time is combined to divide the data of each index, and obtain the data of each main index and each secondary index. Due to the consideration of the system type, the number of API gateway visits and the API response time, the classification results of each index data are more accurate, so that when the health of the API gateway is predicted based on the classified index data, the prediction The result is more accurate.

In step 102, taking the preset data type as the main index data type and the secondary index data type as an example, each main index data in the main index data type is input into the training result obtained from the historical main index data of the main index data type In the first prediction model, the first health score can be obtained. And input the secondary index data in the secondary index data type into the second prediction model obtained by training the historical secondary index data of the secondary index data type, so as to obtain the second health degree score.

In an embodiment, after obtaining the first health score and the second health score, each main indicator data can also be used as a training sample of the first prediction model, and the first prediction model can be optimized for training, and each time The index data is used as the training sample of the second prediction model, and the second prediction model is optimized for training, so that the accuracy of the prediction model is higher and the predicted score is more accurate.

In an embodiment, after obtaining the health scores corresponding to each preset data type, each health score can be sent to a display interface of a designated terminal, and each health score can be visually displayed. Exemplarily, when the preset data types are the main indicator data type and the secondary indicator data type, the health score corresponding to the main indicator data type can be displayed through the display interface, and the data type used to display the secondary indicator data can be generated on the display interface The indicator control of the corresponding health score. When the user clicks the indicator control through the specified terminal, the health score corresponding to the secondary indicator data type is displayed on the display interface; if the user does not click the indicator control, the health score corresponding to the secondary indicator data type is hidden.

In step 103, after obtaining the health scores corresponding to each preset data type, each health score can be weighted according to the preset weight corresponding to each preset data type, so as to determine the health of the API gateway.

Considering that when determining the health of the API gateway through weighting, the impact of some indicator data will be diluted, resulting in the final prediction of the health of the API gateway may not be accurate enough. In order to improve the accuracy of the predicted health of the API gateway, in one embodiment, the health of the API gateway is determined according to the health scores corresponding to each of the preset data types, including:

comparing the health score corresponding to the preset data type with the preset fusing threshold corresponding to the preset data type, and obtaining a comparison result corresponding to the preset data type;

When each comparison result corresponding to each preset data type is that the health score is greater than the preset fusing threshold, it is determined that the API gateway is normal.

Exemplarily, the preset data type includes a primary index data type and a secondary index data type, the primary index data type is preset with a corresponding first preset fusing threshold, and the secondary index data type is preset with a corresponding second preset Fuse threshold. After getting the health score of the main indicator data type and the health score of the secondary indicator data type, compare the health score of the main indicator data type with the first preset fusing threshold, and compare the health score of the secondary indicator data type The health score of is compared with the second preset fusing threshold. If the health score of the primary indicator data type is greater than the first preset fuse threshold, and the health score of the secondary indicator data type is greater than the second preset fuse threshold, it is determined that the API gateway is normal.

In order to be more convenient to manage and control, in an embodiment, it also includes:

When any of the comparison results is that the health score is less than or equal to the preset fusing threshold, the API gateway is closed.

Exemplarily, when the health score of the primary indicator data type is less than or equal to the first preset circuit breaker threshold, or the health score of the secondary indicator data type is less than or equal to the second preset circuit breaker threshold, it is determined that the API gateway The health degree is abnormal. At this time, the fuse mechanism is triggered, and the API gateway fuse implements the fuse and closes the API gateway.

By proactively triggering a circuit breaker when the health score is less than or equal to the preset circuit breaker threshold, the investment in API gateway back-end service resources can be reduced, and at the same time, the processing efficiency of API gateway problems can be improved.

In one embodiment, when the API gateway is closed due to the triggering of the fuse, the information of the fuse API gateway can also be obtained to generate an alarm, and uploaded to the background, and the information of the fuse API gateway is displayed on the display interface of the designated terminal, as shown in this The identifier of the API gateway, thereby further improving processing efficiency.

The gateway health degree prediction device provided by the present invention is described below, and the gateway health degree prediction device described below and the gateway health degree prediction method described above can be referred to in correspondence.

In one embodiment, as shown in FIG. 2 , a device for predicting the health degree of a gateway is provided, including:

The data type determination module 210 is used to cluster the index data collected from the API application programming interface gateway according to the preset classification conditions, and determine the preset data type of each index data;

The health score acquisition module 220 is configured to input each index data in the preset data type into a prediction model trained according to the historical index data corresponding to the preset data type, and obtain the preset data type Corresponding health score;

The health degree prediction module 230 is configured to determine the health degree of the API gateway according to each health degree score corresponding to each preset data type.

After clustering the indicator data collected from the API gateway, input the indicator data of each preset data type into the prediction model trained by the historical indicator data, and obtain the health score corresponding to each preset data type , to realize the health degree prediction of the API gateway based on the health degree scores corresponding to each preset data type, so that the health degree portrait of the API gateway can be completed through the various index data of the API gateway, avoiding the health problems of the API gateway. Passively collect data for analysis, but actively detect and predict the health of the API gateway, thereby improving the operation and maintenance efficiency of network services and reducing operation and maintenance costs.

In one embodiment, the data type determining module 210 is specifically used for:

Clustering each of the index data according to the number of visits by the API gateway within a preset period of time, determining the preset data type of each of the index data whose number of visits is greater than a preset value, and the number of visits is less than or equal to A preset data type of each of the index data of the preset value.

In one embodiment, the data type determining module 210 is specifically used for:

clustering each of the index data according to the system type corresponding to each of the index data, and obtaining the index data of each of the system types;

Clustering the index data in the system type according to the number of accesses by the API gateway within a preset period of time, and determining the expected value of each index data in the system type whose access times are greater than a preset value. Set the data type, and the preset data type of each of the index data whose access times are less than or equal to the preset value in the system type;

Wherein, the preset value is determined according to the system type.

In one embodiment, the data type determining module 210 is specifically used for:

Clustering each of the index data according to the API response time corresponding to each of the index data, and determining the preset data type of each of the index data whose corresponding API response time is greater than the preset duration, and the API response time is less than or A preset data type of each indicator data equal to the preset duration.

In one embodiment, the data type determining module 210 is specifically used for:

clustering each of the index data according to the system type corresponding to each of the index data, and obtaining the index data of each of the system types;

Cluster the index data in the system type according to the API response time corresponding to the index data in the system type, and determine the system types whose corresponding API response time is longer than the preset duration. The preset data type of the indicator data, and the preset data type of each indicator data whose API response time is less than or equal to the preset duration in the system type;

Wherein, the preset duration is determined according to the system type.

In one embodiment, the health degree prediction module 230 is specifically used for:

comparing the health score corresponding to the preset data type with the preset fusing threshold corresponding to the preset data type, and obtaining a comparison result corresponding to the preset data type;

When each comparison result corresponding to each preset data type is that the health score is greater than the preset fusing threshold, it is determined that the API gateway is normal.

In one embodiment, the health degree prediction module 230 is also used for:

When any of the comparison results is that the health score is less than or equal to the preset fusing threshold, the API gateway is closed.

FIG. 3 illustrates a schematic diagram of the physical structure of an electronic device. As shown in FIG. 3 , the electronic device may include: a processor (processor) 810, a communication interface (Communication Interface) 820, a memory (memory) 830 and a communication bus 840, Wherein, the processor 810 , the communication interface 820 , and the memory 830 communicate with each other through the communication bus 840 . The processor 810 may call the computer program in the memory 830 to execute the steps of the method for predicting the health of the gateway, including, for example:

Cluster the index data collected from the API application programming interface gateway according to the preset classification conditions, and determine the preset data type of each index data;

Input each index data in the preset data type into the prediction model obtained according to the training of each historical index data corresponding to the preset data type, and obtain the health score corresponding to the preset data type;

The health of the API gateway is determined according to the health scores corresponding to the preset data types.

In addition, the above logic instructions in the memory 830 may be implemented in the form of software functional units and when sold or used as an independent product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes. .

On the other hand, the embodiment of the present application also provides a computer program product, the computer program product includes a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, and the computer program is executed by a processor , the computer can execute the steps of the method for predicting the health of the gateway provided by the above embodiments, for example, including:

Cluster the index data collected from the API application programming interface gateway according to the preset classification conditions, and determine the preset data type of each index data;

Input each index data in the preset data type into the prediction model obtained according to the training of each historical index data corresponding to the preset data type, and obtain the health score corresponding to the preset data type;

The health degree of the API gateway is determined according to each health degree score corresponding to each preset data type.

On the other hand, the embodiment of the present application also provides a processor-readable storage medium, the processor-readable storage medium stores a computer program, and the computer program is used to make the processor execute the methods provided by the above-mentioned embodiments Steps include, for example:

Cluster the index data collected from the API application programming interface gateway according to the preset classification conditions, and determine the preset data type of each index data;

Input each index data in the preset data type into the prediction model obtained according to the training of each historical index data corresponding to the preset data type, and obtain the health score corresponding to the preset data type;

The health of the API gateway is determined according to the health scores corresponding to the preset data types.

The processor-readable storage medium can be any available medium or data storage device that can be accessed by a processor, including but not limited to magnetic storage (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO), etc.), optical storage (e.g., CD, DVD, BD, HVD, etc.), and semiconductor memory (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state disk (SSD)), etc.

The device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in One place, or it can be distributed to multiple network elements. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. It can be understood and implemented by those skilled in the art without any creative efforts.

Through the above description of the implementations, those skilled in the art can clearly understand that each implementation can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic discs, optical discs, etc., including several instructions to make a computer device (which may be a personal computer, server, or network device, etc.) execute the methods described in various embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent replacements are made to some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims (10)

1. A method for predicting the health of a gateway, comprising:

clustering all index data acquired from an API application programming interface gateway according to preset classification conditions, and determining preset data types of all index data;

inputting each index data in the preset data type into a prediction model obtained by training according to each historical index data corresponding to the preset data type, and obtaining a health score corresponding to the preset data type;

and determining the health degree of the API gateway according to the health degree scores corresponding to the preset data types.

2. The method for predicting the health of a gateway according to claim 1, wherein clustering the index data collected from the API gateway according to a predetermined classification condition, determining a predetermined data type of the index data, includes:

Clustering the index data according to the access times of the API gateway in a preset period, and determining the preset data type of the index data with the access times larger than a preset value and the preset data type of the index data with the access times smaller than or equal to the preset value.

3. The method according to claim 2, wherein clustering each of the index data according to the number of accesses by the API gateway in a first preset period of time, determining a preset data type of each of the index data having the number of accesses greater than a preset value, and a preset data type of each of the index data having the number of accesses less than or equal to the preset value, comprises:

clustering the index data according to the system types corresponding to the index data to obtain the index data of the system types;

clustering each index data in the system type according to the access times of the API gateway in a preset period, and determining the preset data type of each index data with the access times larger than a preset value in the system type and the preset data type of each index data with the access times smaller than or equal to the preset value in the system type;

Wherein the preset value is determined according to the system type.

4. The method for predicting the health of a gateway according to claim 1, wherein clustering the index data collected from the API gateway according to a predetermined classification condition, determining a predetermined data type of the index data, includes:

clustering the index data according to the API response time corresponding to the index data, and determining the preset data type of the index data with the corresponding API response time being longer than the preset time length and the preset data type of the index data with the API response time being shorter than or equal to the preset time length.

5. The method for predicting the health of a gateway according to claim 4, wherein said clustering each of the index data according to the API response time corresponding to each of the index data, determining the preset data type of each of the index data having the corresponding API response time greater than a preset duration, and the preset data type of each of the index data having the API response time less than or equal to the preset duration, comprises:

clustering the index data according to the system types corresponding to the index data to obtain the index data of the system types;

Clustering each index data in the system type according to the API response time corresponding to each index data in the system type, determining the preset data type of each index data with the corresponding API response time being longer than the preset duration in the system type, and determining the preset data type of each index data with the API response time being shorter than or equal to the preset duration in the system type;

the preset duration is determined according to the system type.

6. The method for predicting the health of a gateway according to any one of claims 1 to 5, wherein determining the health of the API gateway according to the health score corresponding to each of the preset data types includes:

comparing the health score corresponding to the preset data type with a preset fusing threshold corresponding to the preset data type to obtain a comparison result corresponding to the preset data type;

and when the health score is larger than the preset fusing threshold value according to the comparison results corresponding to the preset data types, judging that the API gateway is normal.

7. The method of claim 6, further comprising:

And closing the API gateway when any comparison result in the comparison results is that the health score is smaller than or equal to the preset fusing threshold value.

8. A health prediction apparatus of a gateway, comprising:

the data type determining module is used for clustering all index data acquired from the API application programming interface gateway according to preset classification conditions and determining preset data types of all the index data;

the health degree score acquisition module is used for inputting each index data in the preset data type into a prediction model obtained by training according to each historical index data corresponding to the preset data type to acquire a health degree score corresponding to the preset data type;

and the health degree prediction module is used for determining the health degree of the API gateway according to each health degree score corresponding to each preset data type.

9. An electronic device comprising a processor and a memory storing a computer program, characterized in that the processor implements the steps of the method for predicting the health of a gateway according to any one of claims 1 to 7 when executing the computer program.

10. A computer program product comprising a computer program, characterized in that the computer program when executed by a processor implements the steps of the method for predicting the health of a gateway according to any one of claims 1 to 7.

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