Implementation method of load balancing health examination

Abstract

The invention relates to the technical field of digital information transmission, in particular to a load balancing health check implementation method, which comprises the following steps of: acquiring current monitoring data of a server; adjusting a load balancing check parameter of the server based on the current monitoring data to obtain a first adjusted check parameter; based on current monitoring data and historical monitoring data of the server, adjusting the first adjusted check parameter to obtain a second adjusted check parameter; and carrying out load balancing health check on the server based on the second called check parameter. According to the method, the efficiency and accuracy of health check can be improved, unnecessary resource consumption can be reduced, and therefore the response speed of carrying out load balancing health check on the server is remarkably increased.

Description

Technical Field

[0001] The present invention relates to the technical field of data information transmission, and in particular to a method for implementing load balancing health check in the technical field of data information transmission. Background Art

[0002] In the related art, load balancers usually use a fixed-frequency health check method, such as performing a status check on all backend servers at regular intervals. When there are a large number of servers, this fixed-frequency check method will result in a large amount of resource consumption and delay. In a high-concurrency environment, the fixed-frequency health check method can easily cause the load balancer to become a bottleneck of the system, and it is impossible to dynamically adjust the frequency and strategy of the health check according to the actual load of the server, resulting in a slow response speed. Summary of the invention

[0003] In order to solve the technical problem of low response speed of health check on server load in the prior art, the object of the present invention is to provide a method for implementing load balancing health check, and the technical solution adopted is as follows: In a first aspect, an embodiment of the present invention provides a method for implementing a load balancing health check, the method being applied to a server, the method comprising: Get the current monitoring data of the server; Based on the current monitoring data, adjust the load balancing check parameter of the server to obtain a first adjusted check parameter; Based on the current monitoring data and the historical monitoring data of the server, adjusting the first adjusted inspection parameter to obtain a second adjusted inspection parameter; Based on the second adjusted check parameter, a load balancing health check is performed on the server.

[0004] In combination with the first aspect, in some possible implementations, adjusting the first adjusted inspection parameter based on the current monitoring data and the historical monitoring data of the server to obtain the second adjusted inspection parameter includes: Determine the load of the server based on the current monitoring data and historical monitoring data of the server; The first adjusted inspection parameter is adjusted based on the load condition to obtain the second adjusted inspection parameter.

[0005] In combination with the first aspect, in some possible implementations, determining the load status of the server based on the current monitoring data and historical monitoring data of the server includes: Performing data preprocessing on the current monitoring data and the historical monitoring data to obtain processed monitoring data; Extracting features from the processed monitoring data to obtain monitoring features; A trained load assessment model is used to perform load prediction based on the monitoring features to obtain the load status of the server; wherein the trained load assessment model is obtained by training the neural network model to be trained using the historical monitoring data.

[0006] In combination with the first aspect above, in some possible implementations, the method further includes: Determining a service type of the server; Based on the service type, a load balancing check parameter of the server is determined.

[0007] In combination with the first aspect, in some possible implementations, determining the load balancing check parameter of the server based on the service type includes: Based on the service type, determining a frequency for checking the load balancing of the server; The frequency is determined as the load balancing check parameter.

[0008] In combination with the first aspect, in some possible implementations, determining the frequency of checking the load balancing of the server based on the service type includes: Based on the service type, determining a load balancing check priority of the server; Determine the allocation weight of the service type corresponding to the server based on the load balancing check priority of the server; Based on the assigned weights, a frequency of checking the load balancing of the server is determined.

[0009] In combination with the first aspect above, in some possible implementations, adjusting the load balancing check parameter of the server based on the current monitoring data to obtain the first adjusted check parameter includes: Determining resource utilization of the server based on the current monitoring data; Based on the resource utilization of the server, the load balancing check parameter of the server is adjusted to obtain the first adjusted check parameter.

[0010] In combination with the first aspect above, in some possible implementations, adjusting the load balancing check parameter of the server based on the current monitoring data to obtain the first adjusted check parameter includes: Based on the current monitoring data, determining a time window corresponding to the server; Based on the time window, the load balancing check parameter of the server is adjusted to obtain the first adjusted check parameter.

[0011] In combination with the first aspect above, in some possible implementations, obtaining current monitoring data of the server includes: Collecting real-time performance indicators of the server, wherein the real-time performance indicators at least include: CPU utilization, memory usage, and network traffic; The real-time performance indicator is determined as the current monitoring data of the server.

[0012] In a second aspect, a system for implementing a load balancing health check is provided, the system comprising: The acquisition module is used to obtain the current monitoring data of the server; A first adjustment module, configured to adjust a load balancing check parameter of the server based on the current monitoring data to obtain a first adjusted check parameter; a second adjustment module, configured to adjust the first adjusted inspection parameter based on current monitoring data and historical monitoring data of the server to obtain a second adjusted inspection parameter; A checking module is used to perform a load balancing health check on the server based on the second adjusted checking parameter.

[0013] In a third aspect, a computer program product is provided, which includes: a computer program code, which, when executed on a computer, enables the computer to execute an implementation method of a load balancing health check in any one of the possible implementations described above.

[0014] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program code. When the computer program code runs on a computer, the computer executes the method for implementing the load balancing health check in any one of the possible implementation methods described above.

[0015] The present invention has the following beneficial effects: after obtaining the current monitoring data of the server, the load balancing check parameters of the server are analyzed by analyzing the current monitoring data to obtain the first adjusted check parameters, so that the first adjusted check parameters can better match the monitoring parameters of the server; then, the first adjusted check parameters are adjusted again through the current monitoring data and historical monitoring data of the server to obtain the second adjusted check parameters, so that the first adjusted check parameters are dynamically adjusted to make the second adjusted check parameters more compatible with the load situation of the server. Finally, the server is subjected to a load balancing health check through the second adjusted check parameters, which can not only improve the efficiency and accuracy of the health check, but also reduce unnecessary resource consumption, thereby significantly improving the response speed of the load balancing health check on the server. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram of an implementation flow of a method for implementing a load balancing health check provided by an embodiment of the present invention; Figure 2 It is another implementation flow diagram of a method for implementing a load balancing health check provided by an embodiment of the present invention; Figure 3 It is another implementation flow diagram of a method for implementing a load balancing health check provided by an embodiment of the present invention; Figure 4 It is a schematic diagram of an implementation architecture of a method for implementing a load balancing health check provided by an embodiment of the present invention; Figure 5 It is a schematic diagram of the composition structure of a load balancing health check implementation system provided by an embodiment of the present invention; Figure 6 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the implementation method of a load balancing health check proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form as described.

[0019] Among them, in the description of the embodiments of the present invention, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a way to describe the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" refers to two or more than two.

[0020] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0021] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0022] The present invention provides a specific solution for implementing a load balancing health check method. Figure 1 , which shows a schematic diagram of an implementation flow of a method for implementing a load balancing health check provided by an embodiment of the present invention. The method can be implemented by the following steps: 101, obtain the current monitoring data of the server.

[0023] Here, the current monitoring data of the server is obtained in real time, for example, the current monitoring data of the server is obtained in real time through a real-time monitoring module. The current monitoring data includes: multiple performance indicators of the backend server, such as central processing unit (CPU) utilization, memory usage, network traffic, etc.

[0024] In some possible implementations, the above step 101 can be performed by Figure 2 The steps shown achieve: 201, collecting real-time performance indicators of the server.

[0025] The real-time performance indicators include at least: CPU utilization, memory usage, and network traffic. For example, the real-time performance indicators of the server are collected in real time through multiple sensors.

[0026] 202. Determine the real-time performance indicator as current monitoring data of the server.

[0027] In this way, by collecting the server's CPU utilization, memory usage, network traffic and other performance indicators in real time, the server's current monitoring data can be accurately obtained, so as to accurately adjust the server's load balancing check parameters in the future.

[0028] 102. Adjust the load balancing check parameter of the server based on the current monitoring data to obtain a first adjusted check parameter.

[0029] Here, by analyzing the current monitoring data, the load balancing check parameters for the health check of the server are dynamically adjusted, thereby obtaining the first adjusted check parameters. The load balancing health check parameters include: the frequency of the load balancing check on the server.

[0030] In some possible implementations, the service type of the server may be determined first, and then the load balancing check parameters of the server may be set according to the service type of the server. For example, the service type of the server may be determined first; then, based on the service type, the frequency of checking the load balancing of the server may be determined; and the frequency may be determined as the load balancing check parameter.

[0031] Here, the service types of the server include: basic services, standard services, high-performance services, high-availability services, and security-sensitive services. Among them: Basic services include: This basic service is usually for the general public, providing basic load balancing functions and simple health check strategies. For example, basic access request processing for Web servers.

[0032] Standard services include: adding more functions and optimization measures to the basic services to meet more complex business needs, such as transaction processing for e-commerce websites.

[0033] Specialized services include: customized services for specific industries or application scenarios, which are highly specialized and personalized. For example, real-time trading systems in the financial industry.

[0034] High-performance services include: requiring extremely high processing speed and response time, and are suitable for scenarios with strict performance requirements, such as high-frequency trading platforms.

[0035] High availability services include: emphasizing the continuity and reliability of the system to ensure that the service can operate normally under any circumstances. For example, cloud storage services.

[0036] Security-sensitive services include those involving sensitive data or key information, which require special attention to data security and privacy protection. For example, online payment systems.

[0037] The frequencies and strategies determined for each service type are as follows: For basic services: Use a simple Hypertext Transfer Protocol (HTTP) status code check, such as checking whether the return status is 200.

[0038] For standard services: In addition to HTTP status code checking, you can also add response time checking to ensure that the service response speed meets the requirements.

[0039] For specialized services: Depending on the characteristics of the service, more in-depth checks may be required, such as database connection status, specific (Application Programming Interface, API) responses, etc.

[0040] For high-performance services: focus on the response time and throughput of the service and set higher performance thresholds.

[0041] For high availability services: In addition to regular health checks, you also need to monitor the availability and failover capabilities of the service.

[0042] For security-sensitive services: In addition to regular health checks, regular security vulnerability scans and risk assessments are also required.

[0043] In this way, the differentiated processing module can formulate appropriate health check standards and strategies according to different service types, thereby ensuring that each service can receive proper attention and management, improving the reliability and efficiency of the entire system.

[0044] In some possible implementations, the time window corresponding to the server or the resource utilization of the server is analyzed by current monitoring data so as to timely adjust the load balancing check parameters of the server, that is, the above step 102 can be implemented in the following ways: Method 1: First, based on the current monitoring data, determine the resource utilization of the server; then, based on the resource utilization of the server, adjust the load balancing check parameters of the server to obtain the first adjusted check parameters.

[0045] Here, since the current monitoring data includes the server utilization and memory usage, etc., the server resource utilization can be quickly calculated through the utilization and memory usage; wherein, the resource utilization includes: CPU, memory, disk I / O, etc. And through the server utilization, dynamically adjust the server's load balancing check parameters to obtain the first adjusted check parameters. Adjust the frequency and strategy of health checks according to the server's resource utilization. When the resource utilization is high, increase the inspection frequency to promptly discover potential problems; when the resource utilization is low, reduce the inspection frequency to reduce the impact on system performance. In this way, by analyzing the server's resource utilization, the server's inspection frequency and measurement can be adjusted in time, so that the server's load balancing can be checked more accurately.

[0046] Method 2: determining the time window corresponding to the server based on the current monitoring data; and adjusting the load balancing check parameters of the server based on the time window to obtain the first adjusted check parameters.

[0047] Here, the time window corresponding to the server is the time period during which the server performs a health check. By analyzing whether the time window is within the preset time window, the load balancing check parameters of the server are dynamically adjusted. In some possible implementations, if the time window corresponding to the server is within the preset time window (for example, the preset time window is a peak period), the frequency of health checks is increased to ensure the stability of the system during critical periods; if the time window corresponding to the server is during non-peak periods, the check frequency can be reduced to save resources.

[0048] Method 3 can be achieved by following the steps below: The first step is to determine the load balancing check priority of the server based on the service type.

[0049] Here, the service type corresponding to the server is obtained through the current monitoring data, so as to divide the load balancing check priority of the server according to different service types. The load balancing check priority can be set by user, or it can be set according to the importance of servers of different service types in the system.

[0050] In the second step, based on the load balancing check priority of the server, the allocation weight of the service type corresponding to the server is determined.

[0051] Here, a larger allocation weight is set for a higher load balancing check priority, and a smaller allocation weight is set for a smaller load balancing check priority.

[0052] The third step is to determine the frequency and strategy of checking the load balancing of the server based on the allocated weights.

[0053] Here, if the allocation weight is large, a higher frequency is set; if the allocation weight is small, a smaller frequency is set; in this way, different weights are assigned to different types of services, and services with high priority undergo more frequent health checks, while services with low priority have less frequent checks. This method can allocate resources based on business importance. In this way, the allocation weight is set by the service type of the server, so that the load balancing check parameters of the server can be adjusted in an orderly and accurate manner according to the load balancing check priority.

[0054] In other embodiments, the load balancing check parameters of the server may also be adjusted in the following ways, including: 1. Adjust the server's load balancing check parameters based on threshold adjustment. For example, set different load thresholds. When the server load exceeds a certain threshold, increase the frequency of health checks; when the load is below a certain threshold, reduce the frequency of health checks. This method is simple and intuitive, but you may need to adjust the threshold according to actual conditions.

[0055] 2. Adaptively adjust the server's load balancing check parameters. For example, based on historical data and current load conditions, use machine learning algorithms to predict future load change trends, and adjust the frequency and strategy of health checks accordingly. This approach is more intelligent and can better adapt to load changes.

[0056] 3. Adjust the server's load balancing check parameters through weight allocation. For example, assign different weights to different types of services, so that services with high priority have more frequent health checks, while services with low priority have less frequent checks. This approach can allocate resources based on business importance.

[0057] 4. Adjust the server's load balancing check parameters through feedback control. For example, dynamically adjust the check frequency based on the results of the health check. If multiple consecutive health checks fail, it may mean that there is a problem with the server. At this time, the check frequency should be increased to find the problem faster; conversely, if multiple consecutive health check results are normal, the check frequency can be appropriately reduced to save resources. In this way, by combining these adjustment methods, a more flexible and efficient load balancing health check strategy can be implemented. The specific implementation method can be determined based on actual business needs and technical conditions.

[0058] 103. Adjust the first adjusted inspection parameter based on the current monitoring data and the historical monitoring data of the server to obtain a second adjusted inspection parameter.

[0059] Here, the historical monitoring data of the server includes: the CPU utilization, memory usage, network traffic, etc. of the server before the current moment. The server load is predicted by inputting the current monitoring data and historical monitoring data of the server into the trained neural network model to be trained. Afterwards, the first adjusted inspection parameter is increased or decreased according to the server load, thereby obtaining the second adjusted inspection parameter. For example, the inspection frequency is increased when the server load is high, and the inspection frequency is reduced when the server load is low.

[0060] In some possible implementations, this can be accomplished by Figure 3 Steps 301 to 303 shown predict the load of the server: 301 , performing data preprocessing on the current monitoring data and the historical monitoring data to obtain processed monitoring data.

[0061] Here, data preprocessing can be achieved by performing noise reduction or standardization on current monitoring data and historical monitoring data, thereby obtaining processed monitoring data.

[0062] 302, extracting features from the processed monitoring data to obtain monitoring features.

[0063] Here, features that are helpful for prediction are extracted from the processed monitoring data, that is, monitoring features are obtained; monitoring features include: time series features (such as hours, days of the week), statistical features (such as mean, standard deviation), and other factors that may affect the load (such as the occurrence of specific events).

[0064] 303. Use the trained load assessment model to perform load prediction based on the monitoring features to obtain the load status of the server.

[0065] The trained load assessment model is obtained by training the neural network model to be trained with historical monitoring data. The load condition of the server can be obtained by inputting the monitoring features into the trained load assessment model. In this way, the trained load assessment model is obtained by training the neural network model to be trained, so that the load condition of the server can be more accurately predicted through the trained load assessment model, and then the load balancing check parameters of the server can be accurately adjusted according to the load condition.

[0066] In some possible implementations, the neural network model to be trained may be trained through the following process to obtain a trained load assessment model: 1. Data collection: Collect the server's operating data, including CPU usage, memory usage, disk I / O, network traffic and other indicators. This data can be obtained from the server's log files, monitoring systems or performance monitoring tools.

[0067] 2. Data preprocessing: Clean and process the collected data, remove outliers and noise, fill in missing values, and perform necessary data conversions. For example, convert non-numeric data to numerical data, or normalize the data.

[0068] 3. Feature engineering: Extract features from the raw data that are helpful for prediction. This may include time series features (such as hours, days of the week), statistical features (such as mean, standard deviation), and other factors that may affect the load (such as the occurrence of specific events).

[0069] 4. Model selection: Select an appropriate neural network model to be trained according to the nature of the problem. For time series prediction, commonly used neural network models to be trained include autoregressive integrated moving average model, seasonal autoregressive integrated moving average model, long short-term memory network, etc.

[0070] 5. Train the model: Use historical monitoring data to train the selected model. During the training process, you may need to adjust the model parameters to optimize the prediction performance.

[0071] 6. Evaluate the model: Test the accuracy and generalization ability of the model through cross-validation or other evaluation methods. Evaluation indicators include mean square error, mean absolute error, and coefficient of determination.

[0072] 7. Predict future load: Use the trained model to predict future server load. This involves inputting current state information into the model and getting a predicted load value.

[0073] 8. Interpretation and application of results: Develop corresponding strategies or measures based on the prediction results. For example, if it is predicted that the server load will increase significantly in a certain period of time in the future, more resources can be prepared in advance to cope with the potential pressure.

[0074] 9. Continuous update and maintenance: As time goes by and new data accumulates, the model should be retrained regularly to maintain its accuracy and effectiveness. At the same time, it is also necessary to pay attention to business changes and technological developments and adjust the prediction models and methods in a timely manner.

[0075] Through the above steps, historical monitoring data and current monitoring data can be used to predict the future load of the server, thereby providing a basis for resource planning and operation and maintenance decisions.

[0076] 104. Perform a load balancing health check on the server based on the second adjusted check parameter.

[0077] Here, a load balancing health check is performed on the server according to the frequency in the second adjusted check parameter. In this way, the first adjusted check parameter is obtained by analyzing the load balancing check parameter of the server according to the current monitoring data, so that the first adjusted check parameter can be made to better match the monitoring parameter of the server; then, the first adjusted check parameter is adjusted again according to the current monitoring data and historical monitoring data of the server to obtain the second adjusted check parameter, so that the second adjusted check parameter can be made to better match the load situation of the server by dynamically adjusting the first adjusted check parameter. Finally, the load balancing health check is performed on the server according to the second adjusted check parameter, which can not only improve the efficiency and accuracy of the health check, but also reduce unnecessary resource consumption, thereby significantly improving the response speed of the load balancing health check on the server.

[0078] In some embodiments, the Figure 4 The modules shown implement the server load balancing health check, including: The real-time monitoring module 401 is used to collect the performance indicators of each backend server (such as CPU utilization, memory usage, network traffic, etc.) in real time and send the collected data to the dynamic adjustment module.

[0079] The dynamic adjustment module 402 is used to dynamically adjust the frequency and strategy of health checks according to real-time monitoring data, for example, increasing the check frequency when the server load is high and reducing the check frequency when the load is low.

[0080] The intelligent decision engine module 403 is used to analyze historical data using machine learning algorithms to predict future server load conditions, adjust health check strategies in advance to ensure system stability and efficiency, and feed back the optimized health check strategies to the dynamic adjustment module for implementation.

[0081] Differentiation processing module 404 is used to formulate corresponding health check standards and strategies according to the characteristics of different types of services. It ensures the stable operation of various services and meets diverse business needs. In this way, intelligent load balancing health check is realized through the above modules, which can monitor the server status in real time and dynamically adjust the health check strategy, thereby improving the response speed and stability of the system.

[0082] The embodiment of the present invention provides a system for implementing a load balancing health check, such as Figure 5 As shown, the system 501 includes: Acquisition module 501, used to acquire current monitoring data of the server; A first adjustment module 502, configured to adjust the load balancing check parameter of the server based on the current monitoring data to obtain a first adjusted check parameter; A second adjustment module 503, configured to adjust the first adjusted inspection parameter based on the current monitoring data and the historical monitoring data of the server to obtain a second adjusted inspection parameter; The checking module 504 is used to perform a load balancing health check on the server based on the second adjusted checking parameter.

[0083] In some possible implementations, the second adjustment module 503 is further used to determine the load condition of the server based on the current monitoring data and historical monitoring data of the server; and adjust the first adjusted inspection parameter based on the load condition to obtain the second adjusted inspection parameter.

[0084] In some possible implementations, the second adjustment module 503 is further used to perform data preprocessing on the current monitoring data and the historical monitoring data to obtain processed monitoring data; perform feature extraction on the processed monitoring data to obtain monitoring features; use a trained load assessment model to perform load prediction based on the monitoring features to obtain the load status of the server; wherein the trained load assessment model is obtained by training the neural network model to be trained using the historical monitoring data.

[0085] In some possible implementations, the first adjustment module 502 is further configured to determine a service type of the server; and based on the service type, determine a load balancing check parameter of the server.

[0086] In some possible implementations, the first adjustment module 502 is further configured to determine, based on the service type, a frequency for checking the load balancing of the server; and determine the frequency as the load balancing check parameter.

[0087] In some possible implementations, the first adjustment module 502 is also used to determine the load balancing check priority of the server based on the service type; determine the allocation weight of the service type corresponding to the server based on the load balancing check priority of the server; and determine the frequency of checking the load balancing of the server based on the allocation weight.

[0088] In some possible implementations, the first adjustment module 502 is further used to determine the resource utilization of the server based on the current monitoring data; and to adjust the load balancing check parameters of the server based on the resource utilization of the server to obtain the first adjusted check parameters.

[0089] In some possible implementations, the first adjustment module 502 is further used to determine the time window corresponding to the server based on the current monitoring data; based on the time window, adjust the load balancing check parameters of the server to obtain the first adjusted check parameters.

[0090] In some possible implementations, the acquisition module 501 is also used to collect real-time performance indicators of the server, wherein the real-time performance indicators include at least: central processing unit CPU utilization, memory usage, and network traffic; and the real-time performance indicators are determined as current monitoring data of the server.

[0091] Optionally, the transmission medium for data transmission between the above modules can be a wired link (for example, but not limited to, coaxial cable, optical fiber and digital subscriber line (DSL), etc.) or a wireless link (for example, but not limited to, wireless Fidelity (WIFI), Bluetooth and mobile device network, etc.). It should be noted that: the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the method embodiments provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0092] Figure 6 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 6 As shown, the computer device 600 includes: a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and running on the processor 602, wherein when the processor 602 executes the computer program 603, the computer device can execute any one of the load balancing health check implementation methods described above.

[0093] In addition, an embodiment of the present invention also protects a system, which may include a memory and a processor, wherein an executable program code is stored in the memory, and the processor is used to call and execute the executable program code to perform a method for implementing a load balancing health check provided by an embodiment of the present invention. This embodiment can divide the system into functional modules according to the above method example. For example, it can correspond to each functional module, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic, which is only a logical function division. There may be other division methods in actual implementation. It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, which will not be repeated here.

[0094] It should be understood that the system provided in this embodiment is used to execute the above-mentioned implementation method of a load balancing health check, so the same effect as the above-mentioned implementation method can be achieved. In the case of an integrated unit, the system may include a processing module and a storage module. Among them, when the system is applied to a device, the processing module can be used to control and manage the actions of the device. The storage module can be used to support the device to execute mutual program codes, etc. Among them, the processing module can be a processor or a controller, which can implement or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure of the present invention. The processor can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module can be a memory.

[0095] In addition, the system provided by the embodiment of the present invention may be a chip, a component or a module, and the chip may include a connected processor and a memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute a method for implementing a load balancing health check provided by the above embodiment. This embodiment also provides a computer-readable storage medium, in which a computer program code is stored, and when the computer program code is run on a computer, the computer executes the above-mentioned related method steps to implement a method for implementing a load balancing health check provided by the above embodiment.

[0096] This embodiment also provides a computer program product. When the computer program product is run on a computer, the computer executes the above-mentioned related steps to implement a method for implementing a load balancing health check provided in the above embodiment. Among them, the system, computer-readable storage medium, computer program product or chip provided in this embodiment are all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be repeated here. Through the description of the above implementation mode, the technicians in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional modules as needed, that is, the internal structure of the system is divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiment described above is only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, system or unit, which may be electrical, mechanical or other forms.

[0097] It should be noted that the sequence of the above-mentioned embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or may be advantageous. The various embodiments in this specification are described in a progressive manner, and the same and similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments. The above content is only a specific implementation method of the present invention, but the protection scope of the present invention is not limited to this. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered within the protection scope of the present invention.

Claims

1. A method for implementing a load balancing health check, characterized in that: The method comprises: Get the current monitoring data of the server; Adjusting the load balancing check parameter of the server based on the current monitoring data to obtain a first adjusted check parameter; Adjusting the first adjusted inspection parameter based on current monitoring data and historical monitoring data of the server to obtain a second adjusted inspection parameter; Based on the second adjusted check parameter, a load balancing health check is performed on the server.

2. A method for implementing a load balancing health check according to claim 1, characterized in that: The adjusting the first adjusted inspection parameter based on the current monitoring data and the historical monitoring data of the server to obtain the second adjusted inspection parameter includes: Determining the load of the server based on current monitoring data and historical monitoring data of the server; The first adjusted inspection parameter is adjusted based on the load condition to obtain the second adjusted inspection parameter.

3. A method for implementing a load balancing health check according to claim 2, characterized in that: The determining the load condition of the server based on the current monitoring data and historical monitoring data of the server includes: Performing data preprocessing on the current monitoring data and the historical monitoring data to obtain processed monitoring data; Performing feature extraction on the processed monitoring data to obtain monitoring features; A trained load assessment model is used to perform load prediction based on the monitoring features to obtain the load status of the server; wherein the trained load assessment model is obtained by training the neural network model to be trained using the historical monitoring data.

4. A method for implementing a load balancing health check according to claim 1, characterized in that: The method further comprises: Determining a service type of the server; Based on the service type, a load balancing check parameter of the server is determined.

5. A method for implementing a load balancing health check according to claim 4, characterized in that: The determining, based on the service type, a load balancing check parameter of the server includes: Determining a frequency for checking load balancing of the server based on the service type; The frequency is determined as the load balancing check parameter.

6. A method for implementing a load balancing health check according to claim 5, characterized in that: The determining, based on the service type, a frequency of checking the load balancing of the server includes: Determining a load balancing check priority for the server based on the service type; Determining the allocation weight of the service type corresponding to the server based on the load balancing check priority of the server; Based on the assigned weights, a frequency of checking the load balancing of the server is determined.

7. A method for implementing a load balancing health check according to claim 1, characterized in that: The step of adjusting the load balancing check parameter of the server based on the current monitoring data to obtain a first adjusted check parameter includes: Determining resource utilization of the server based on the current monitoring data; Based on the resource utilization of the server, the load balancing check parameter of the server is adjusted to obtain the first adjusted check parameter.

8. A method for implementing a load balancing health check according to claim 1, characterized in that: The step of adjusting the load balancing check parameter of the server based on the current monitoring data to obtain a first adjusted check parameter includes: Determining a time window corresponding to the server based on the current monitoring data; Based on the time window, the load balancing check parameter of the server is adjusted to obtain the first adjusted check parameter.

9. A method for implementing a load balancing health check according to claim 1, characterized in that: The obtaining of the current monitoring data of the server includes: Collecting real-time performance indicators of the server, wherein the real-time performance indicators include at least: central processing unit (CPU) utilization, memory usage, and network traffic; The real-time performance indicator is determined as the current monitoring data of the server.

10. A load balancing health check implementation system, characterized in that: The system comprises: Acquisition module, used to obtain the current monitoring data of the server; A first adjustment module, configured to adjust a load balancing check parameter of the server based on the current monitoring data to obtain a first adjusted check parameter; a second adjustment module, configured to adjust the first adjusted inspection parameter based on current monitoring data and historical monitoring data of the server to obtain a second adjusted inspection parameter; A checking module is used to perform a load balancing health check on the server based on the second adjusted checking parameter.

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