Flow processing fault prediction method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a traffic processing fault prediction method and device, a storage medium and electronic equipment, which are used for computing equipment deployed with a traffic processing agent, and the method comprises the following steps: acquiring operation log data of the traffic processing agent; based on the operation log data and a preset prediction fault database, prediction fault log data in the operation log data and corresponding prediction fault details are determined, and the prediction fault database comprises a plurality of preset parameter ranges and the corresponding prediction fault details; the preset parameter range refers to a parameter range which is set by taking the target fault log data as a reference and is used for carrying out pre-warning, and the predicted fault details comprise a fault reason and a solution corresponding to the target fault log data; and displaying the predicted fault log data and the predicted fault details. A user can conveniently know possible fault information and corresponding solutions in advance, so that measures can be adopted to process the fault before the fault occurs.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data processing technology, and in particular to a flow processing fault prediction method, device, storage medium and electronic device. Background Art

[0002] As the complexity of software applications increases, traditional software applications are gradually replaced by microservice architecture. Microservice architecture refers to splitting a large and complex software application into a series of small, independent microservices, each of which can be deployed and expanded independently. However, the use of microservice architecture has the problem of complex communication between microservices, so service mesh technology came into being. Service mesh manages the communication between microservices by inserting a proxy layer between microservices, thereby reducing the burden of microservices themselves in handling communication.

[0003] At present, in a computer cluster, the business traffic processing of the service grid deployed in the computing device is usually monitored. When the business traffic processing fails, the log data of the business traffic processing can be collected, counted and displayed through the Prometheus software, so that users can identify and solve the fault problem. However, this method can only be used to troubleshoot and solve the fault after it has occurred, and it cannot avoid the occurrence of the fault, so it cannot avoid the loss of users caused by the failure of business traffic processing. Summary of the invention

[0004] In view of this, embodiments of the present application provide a flow processing fault prediction method, device, storage medium and electronic device to at least partially solve the above problems.

[0005] According to a first aspect of an embodiment of the present application, a traffic processing fault prediction method is provided, which is applied to a computing device, wherein a traffic processing agent is deployed in the computing device, and the traffic processing agent is used to process the business traffic received and sent by the computing device. The method of the embodiment of the present application includes: obtaining operation log data of the traffic processing agent; based on the operation log data and a preset predicted fault database, determining the predicted fault log data in the operation log data, and the predicted fault details corresponding to the predicted fault data, the predicted fault database including multiple preset parameter ranges and predicted fault details corresponding to each preset parameter range, the preset parameter range refers to a parameter range set with reference to the target fault log data and used for advance warning, the predicted fault details include the fault cause and solution corresponding to the target fault log data; and displaying the predicted fault log data and the predicted fault details.

[0006] According to a second aspect of an embodiment of the present application, a traffic processing fault prediction device is provided, which is applied to a computing device. A traffic processing agent is deployed in the computing device, and the traffic processing agent is used to process the business traffic received and sent by the computing device. The device of the embodiment of the present application includes an acquisition module, a fault prediction module and a display module, wherein the acquisition module is used to acquire operation log data of the traffic processing agent; the fault prediction module is used to determine the predicted fault log data in the operation log data, and the predicted fault details corresponding to the predicted fault data based on the operation log data and a preset predicted fault database, the predicted fault database includes multiple preset parameter ranges and predicted fault details corresponding to each preset parameter range, the preset parameter range refers to a parameter range set with reference to the target fault log data and used for advance warning, and the predicted fault details include the fault cause and solution corresponding to the target fault log data; the display module is used to display the predicted fault log data and the predicted fault details.

[0007] According to a third aspect of an embodiment of the present application, a computer storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method described in the first aspect is implemented.

[0008] According to the fourth aspect of an embodiment of the present application, there is provided an electronic device, including a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; the memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform an operation corresponding to the method described in the first aspect.

[0009] The embodiment of the present application provides a method, device, storage medium and electronic device for predicting traffic processing faults, wherein the method for predicting traffic processing faults is applied to a computing device, a traffic processing agent is deployed in the computing device, and the traffic processing agent is used to process the service traffic sent and received by the computing device. The method of the embodiment of the present application includes: obtaining the operation log data of the traffic processing agent; based on the operation log data and a preset predicted fault database, determining the predicted fault log data in the operation log data, and the predicted fault details corresponding to the predicted fault data, the predicted fault database includes multiple preset parameter ranges and predicted fault details corresponding to each preset parameter range, the preset parameter range refers to the parameter range set with reference to the target fault log data for pre-warning, and the predicted fault details include the cause of the fault and the solution corresponding to the target fault log data; and displaying the predicted fault details. The present application analyzes the operation log data through the preset parameter range in the predicted fault database, determines the predicted fault log data that meets the preset parameter range, and obtains the predicted fault details, and then displays the predicted fault log data and the predicted fault details, so that the user can understand the possible fault information and the corresponding solution in advance, so that the user can take measures to deal with it according to the predicted fault details before the fault occurs, which can effectively avoid the occurrence of the fault and the loss caused by the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0011] Figure 1A to Figure 1B A schematic diagram of an application scenario of a flow processing fault prediction method according to an exemplary embodiment of the present application;

[0012] Figure 2 A flowchart of a method for predicting a flow processing fault according to an exemplary embodiment of the present application;

[0013] Figure 3 is a flow chart of steps of a method for predicting flow processing failures according to another exemplary embodiment of the present application;

[0014] Figure 4 is a structural block diagram of a flow processing fault prediction device according to an exemplary embodiment of the present application;

[0015] Figure 5 It is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the embodiments of the present application should fall within the scope of protection of the embodiments of the present application.

[0017] Before describing the flow processing fault prediction method according to the embodiment of the present application, the application scenario of the flow processing fault prediction method is briefly described to facilitate understanding.

[0018] Figure 1A to Figure 1B Schematic diagram of an application scenario of a flow processing fault prediction method according to an exemplary embodiment of the present application. The flow processing fault prediction method of this embodiment can be used in a computing device, referring to Figure 1A The computing device can be a computing node in a computer cluster, and the computer cluster can be a K8s (Kubernetes) cluster. The K8s cluster is a container orchestration management cluster consisting of a management node and multiple computing nodes. Computing nodes, also known as workload nodes, can run containerized business applications, i.e., services; management nodes are responsible for managing and controlling containerized business applications running in computing nodes, including: deploying containerized business applications to computing nodes. Both computing nodes and management nodes can be servers.

[0019] Reference Figure 1B , the computing device can also be a dedicated data processor DPU connected to the computing nodes in the computer cluster. DPU (Data Processing Unit) is a data-centric, I / O-intensive, software-defined technology route to support infrastructure resource layer virtualization, and a new generation of computing chips that can improve computing system efficiency, reduce the total cost of ownership of the overall system, improve data processing performance, and reduce the performance loss of other computing chips. The dedicated data processor can be plugged into the computing node through the PCIE slot to connect the dedicated data processor to the computing node. PCIE refers to PCI-Express, which is a bus and interface standard. The dedicated data processor DPU and the computing node can transmit data through PCIE to process the business traffic sent and received by the computing node through the dedicated data processor DPU.

[0020] As the complexity of business applications increases, traditional business applications are gradually replaced by microservice architecture. Microservice architecture refers to splitting a large and complex business application into a series of small, independent microservices, each of which can be deployed and expanded independently. However, the use of microservice architecture has the problem of complex communication between microservices, so service mesh technology came into being. Service mesh manages the communication between microservices by inserting a proxy layer between microservices, thereby reducing the burden of microservices themselves in handling communication.

[0021] A service mesh is a platform for managing communication between microservices in a microservice architecture. It usually consists of two components: a control plane and a data plane. The control plane is responsible for tasks such as service discovery, load balancing, and traffic management, while the data plane is responsible for actual request forwarding and processing.

[0022] In a computer cluster, service meshes are usually deployed in both management nodes and computing nodes. Among them, the data plane can usually be deployed as Envoy, which is a traffic processing proxy. Envoy is an open source, high-performance proxy that is usually used as the data plane of the service mesh and is responsible for actual request forwarding and processing. The architecture of Envoy can be divided into the data plane, management plane, and control plane. The control plane is used to manage policies and configurations related to traffic routing and forwarding; the management plane obtains the latest traffic configuration information through the standard API; the data plane forwards and processes traffic based on the configuration rules issued by the control plane.

[0023] In an optional implementation, in order to reduce the system resource usage of the host in the computer cluster, the DPU can be connected to the computing node, and the business traffic processing of the microservices in the computing node can be offloaded to the DPU for execution, that is, Envoy is deployed in the DPU. Here, the dedicated data processor DPU is not in the computer cluster.

[0024] Traditional service grids provide some open source log monitoring methods, such as Prometheus, which can collect, count and display the processing logs of the traffic processing agent of each computing node. However, this method can only be used to troubleshoot and solve the problem after the fault has occurred, and it cannot avoid the occurrence of the fault, and therefore cannot avoid the loss of users caused by the failure of business traffic processing. In view of this, the embodiments of the present application provide a traffic processing fault prediction method, device, storage medium and electronic device to solve the above technical problems.

[0025] Reference Figure 2 , shows a flow chart of the steps of a flow processing fault prediction method according to an exemplary embodiment of the present application. As shown in the figure, this embodiment mainly includes the following steps:

[0026] S202: Obtain operation log data of the traffic processing agent.

[0027] Exemplarily, the traffic processing agent may be Envoy, and the operation log data of Envoy may be obtained. The operation log data may include operation indicators. For example, the operation indicator may be the session delay of the business traffic.

[0028] In an optional implementation, obtaining the operation log data of the traffic processing agent includes: obtaining the operation log data of the traffic processing agent according to a preset acquisition frequency; and / or obtaining the operation log data of the traffic processing agent in response to a received acquisition instruction.

[0029] Exemplarily, a timing module may be provided in the computing device, and the timing module may be used to set the acquisition frequency of the operation log data of the traffic processing agent, that is, the preset acquisition frequency. For example, the preset acquisition frequency may be 24 hours, 36 hours, or a week, etc., and this embodiment does not limit this. In addition, the operation log data of the traffic processing agent may be acquired based on the acquisition instruction by detecting the acquisition operation of the user and receiving the acquisition instruction corresponding to the acquisition operation.

[0030] Furthermore, a data configuration module can be set up in the computing node, and the user can configure a configuration parameter file of a preset log classification through the data configuration module; when obtaining the operation log data, the operation log data of the traffic processing agent can also be obtained according to the preset log classification of the operation log data. For example, the user can classify the operation log data in advance, and can set the classification according to the type or importance of the operation indicators contained in the operation log data. The importance refers to the influence of the operation indicator on whether the business traffic processing can run normally. The greater the influence, the higher the importance. The target level of the operation log data to be obtained is set. When obtaining the operation log data of the traffic processing agent, only the operation log data corresponding to the target level is obtained, so as to reduce the amount of data obtained and subsequently processed, and improve data processing efficiency.

[0031] In this implementation, the operation log data of the traffic processing agent can be obtained according to a preset acquisition frequency; and / or, in response to a received acquisition instruction, the operation log data of the traffic processing agent can be obtained. Thus, the user can control the frequency and timing of log analysis according to needs, which is highly flexible.

[0032] S204: Based on the operation log data and a preset predicted fault database, determine the predicted fault log data in the operation log data and the predicted fault details corresponding to the predicted fault data.

[0033] Exemplarily, the predicted fault database includes multiple preset parameter ranges and predicted fault details corresponding to each preset parameter range. The preset parameter range refers to a parameter range set with reference to the target fault log data for pre-warning. The predicted fault details include the fault cause and solution corresponding to the target fault log data. Among them, the target fault log data may be historically processed fault log data, and the predicted fault details may include the fault cause and solution corresponding to the historically processed fault log data. The target fault log data may also be pre-inferred fault log data, as well as the corresponding fault cause and solution, etc. This embodiment does not limit this. The target fault log data may include a fault indicator and a corresponding fault threshold. For example, the fault indicator may be that the session delay of the business traffic exceeds the delay threshold. The delay threshold may be a value flexibly set by a technician in this field according to actual conditions.

[0034] The preset parameter range for pre-warning can be set based on the fault indicator in the target fault log data as a reference. Specifically, the fault indicator can be used as the maximum threshold or minimum threshold of the preset parameter range. For example, if the fault indicator is that the session delay exceeds the delay threshold of 10ms, then the preset parameter range corresponding to the session delay can be that the session delay is greater than 7ms and less than 10ms. The preset parameter range is a parameter range close to the fault indicator. When it is found that the operation log data falls into the preset parameter range, it can be indicated that the operation log data is about to reach the target fault log data, thereby indicating that the data processing of the traffic processing agent is about to encounter a fault corresponding to the target fault log data.

[0035] When determining the predicted fault log data in the operation log data, and the predicted fault details corresponding to the predicted fault data, the operation index in the operation log data can be used as a matching object, and the corresponding preset parameter range can be matched from the predicted fault database. When the operation log data falls into at least a certain preset parameter range, the operation log data is determined to be the predicted fault log data, and the predicted fault details corresponding to the preset parameter range are determined to be the predicted fault details of the predicted fault log data. It should be noted that each preset parameter range may correspond to one or more predicted fault details. For example, when the preset parameter range is a session delay greater than 7ms and less than 10ms, the corresponding fault cause may be a communication network problem, or it may be a system memory problem of the computing device, such as excessive memory usage.

[0036] S206: Display the predicted fault log data and predicted fault details.

[0037] Exemplarily, if the computing device is a computing node, the predicted fault details can be displayed, and the predicted fault log data can also be displayed, and this embodiment does not limit this. In an optional implementation, the computing device is a dedicated data processor DPU, and the dedicated data processor DPU is coupled to the computing node in the target computer cluster. The dedicated data processor DPU is used to process the business traffic sent and received by the computing node, and display the predicted fault log data and predicted fault details, including: sending the predicted fault log data and predicted fault details to the computing node, so as to display the predicted fault log data and predicted fault details through the computing node. It can be seen that the method of this embodiment can be applied to computing nodes, and can also be applied to dedicated data processors DPUs coupled to computing nodes, and has a wide range of applications. In addition, the functions of traditional Prometheus need to be based on a computer cluster network, that is, the control plane and data plane of the service grid must be in a cluster network, and the DPU is not in the computer cluster network. Therefore, the Prometheus software cannot collect and process the log data in the DPU. The method of this embodiment can be directly applied to the DPU, and after completing the entire analysis process in the DPU, the results are sent to the computing node, further reducing the occupancy of the host's CPU resources.

[0038] The form of display is not limited to text, charts, etc., so that users can efficiently and intuitively understand the predicted fault causes and solutions. Furthermore, the predicted fault log data can also be sent to the computing node, which is not limited in this embodiment. The data configuration module set by the computing node can be used to configure the configuration parameter file of the preset alarm classification. The target alarm level of the predicted fault details can be determined according to the preset alarm classification. When the predicted fault details are displayed, the predicted fault details can be warned according to the target alarm level. Among them, the preset alarm classification can be set according to the importance of the fault indicator, and different alarm levels can correspond to different alarm methods. The alarm method can include different color distinctions, different voice broadcasts, etc., which are not limited in this embodiment.

[0039] In an optional implementation, the computing device is a computing node in a target computer cluster, and the target computer cluster also includes a management node. The method of this embodiment also includes: obtaining address information of the computing device; generating a predictive failure analysis report for traffic processing by the computing device based on predicted failure log data, predicted failure details and address information; and sending the predicted failure analysis report to the management node so that the predicted failure analysis report can be displayed through the management node.

[0040] Exemplarily, a computer cluster includes multiple computing nodes, each of which runs a traffic processing agent. Each computing node can perform traffic processing fault prediction and obtain predicted fault details. The computing nodes need to send the predicted fault details to the management node for centralized processing. Therefore, before sending the predicted fault details to the management node, the address information of the computing node, such as the IP address, is obtained, and a predicted fault analysis report is generated based on the predicted fault details and address information, or based on the predicted fault log data, predicted fault details and address information; and then the predicted fault analysis report is sent to the management node.

[0041] In this implementation, by adding the address information of the computing node to the predicted fault analysis report, the predicted fault situation of the traffic processing of each computing node can be quickly and accurately distinguished when the predicted fault analysis report in the entire computer cluster is centrally processed.

[0042] In an optional implementation, the method of this embodiment further includes: obtaining update information for the predicted fault database, the update information including a newly added preset parameter range and corresponding predicted fault details; and updating the predicted fault database based on the update information.

[0043] Exemplarily, the predicted fault database can be updated regularly. For example, an update information template can be provided to the user through a data configuration module set by a computing node, so that the user can fill in the newly added preset parameter range and the corresponding predicted fault details as update information in the update information template, generate a configuration parameter file, and update the predicted fault database based on the updated information in the configuration parameter file to expand the predicted fault database; if the computing device is a DPU, the configuration parameter file is sent to the DPU through the computing node, and the DPU updates the predicted fault database based on the updated information in the configuration parameter file to expand the predicted fault database, thereby improving the data comprehensiveness of the predicted fault database and making the prediction results more accurate.

[0044] In addition, if the computing device is a DPU, data can be copied using the high-speed network transmission channel between the DPU and the computing node. The high-speed network transmission channel is a high-speed network port used for internal communication between the DPU and the computing node, such as a PCIe interface. It is implemented based on the hardware DMA of the DPU, which improves the data interaction efficiency between the computing node and the DPU during the entire data processing process.

[0045] This embodiment sets a predicted fault database in a computing device. The predicted fault database includes multiple preset parameter ranges and predicted fault details corresponding to each preset parameter range. The preset parameter range refers to a parameter range set with reference to the target fault log data and used for advance warning. The preset parameter range in the predicted fault database is used to analyze the operation log data, determine the predicted fault log data that meets the preset parameter range, and obtain the predicted fault details, which are then displayed so that the user can understand the possible fault information and corresponding solutions in advance, so that measures can be taken according to the predicted fault details before the fault occurs, which can effectively avoid the occurrence of the fault and the loss caused by the fault.

[0046] Reference Figure 3 , shows a flow chart of the steps of a flow processing fault prediction method according to another exemplary embodiment of the present application. As shown in the figure, this embodiment mainly shows a specific implementation scheme of step S204 of the above embodiment. This embodiment mainly includes the following steps:

[0047] S302: Obtain operation log data of the traffic processing agent.

[0048] It should be noted that step S302 can be implemented with reference to the specific implementation of step S202 above, which will not be described in detail here.

[0049] S304: Based on a preset predicted fault database, determine whether there is log data in the operation log data that falls within a preset parameter range.

[0050] Exemplarily, the operating indicators in the operation log data are matched with the preset parameter ranges in the predicted fault database to determine whether there is log data in the operation log data that falls within the preset parameter range. For example, the operating indicators of the operation log data include a session delay of 8ms, and the preset parameter range includes a session delay greater than 7ms and less than 10ms, which indicates that there is log data in the operation log data that falls within the preset parameter range.

[0051] S306: If so, the log data falling within the preset parameter range is determined as predicted fault log data, and predicted fault details corresponding to the predicted fault log data are determined.

[0052] Exemplarily, if there is log data in the operation log data that falls within one or more preset parameter ranges, such as a session delay of 8ms, it is determined to be predicted fault log data, and the predicted fault details corresponding to the preset parameter range are determined as the predicted fault details corresponding to the predicted fault log data. For example, a session delay of 8ms is predicted fault log data, and the corresponding predicted fault details may include the cause of the fault and a solution. The cause of the fault may include a communication network problem, or it may be a system memory problem of the computing device, such as excessive memory usage; the solution may include improving the network environment, releasing system memory, etc. In an optional implementation, if there is no log data in the operation log data that falls within the preset parameter range, it means that the traffic processing agent in the current computing device is running normally and no information is displayed; in addition, the operation log data may also be displayed so that the user can conduct a secondary investigation to avoid omissions. This embodiment does not limit this.

[0053] In an optional implementation, determining predicted fault details corresponding to predicted fault data includes: determining at least one fault cause corresponding to predicted fault log data based on a preset predicted fault database; if at least one fault cause includes a fault cause indicating a system failure of a computing device, obtaining system log data of the computing device; and determining, from the predicted fault database, the predicted fault details corresponding to the predicted fault log data based on the system log data and at least one fault cause.

[0054] Exemplarily, in the preset predicted fault database, each preset parameter range may correspond to one or more predicted fault details. For example, when the preset parameter range is that the session delay is greater than 7ms and less than 10ms, the corresponding fault cause may be a communication network problem or a system memory problem of the computing device, such as excessive memory usage.

[0055] Determine whether there is a fault cause indicating a system failure of the computing device among at least one fault cause corresponding to the predicted fault log data, for example, the fault cause is a system memory failure. If so, obtain the system log data of the computing device. For example, the system log data may include the operating status of the computing device's operating system, the memory occupancy of the computing device, and the occupancy of the computing cores on the computing device, etc. This embodiment does not limit this.

[0056] Multiple fault causes can be eliminated based on the system log data. For example, if it is determined based on the system log data that the computing device's system has not failed, the fault cause indicating a computing device system failure can be eliminated. Then, based on the remaining fault causes after elimination, the predicted fault details corresponding to the predicted fault log data can be determined from the predicted fault database.

[0057] In this implementation, by collecting system log data of the computing device, the system log data can be used as an aid to conduct in-depth analysis of the predicted fault details of the predicted fault log data, so as to more accurately determine the cause of the fault and the solution, thereby improving the accuracy of the analysis results.

[0058] In an optional implementation, predicted fault details corresponding to the predicted fault log data are determined from a predicted fault database based on system log data and at least one fault cause, including: determining a target fault cause from at least one fault cause based on the system log data; determining a target solution corresponding to the target fault cause from a predicted fault database based on the target fault cause; and generating predicted fault details corresponding to the predicted fault log data based on the target fault cause and the target solution.

[0059] Exemplarily, if the cause of the fault is a system memory failure, the memory usage of the current computing device can be determined based on the system log data. If it is found that the memory usage of the computing device exceeds a certain percentage threshold, the percentage threshold can be read from the predicted fault database, and the target fault cause can be determined to be a system memory failure. According to the target fault cause, the corresponding target solution is matched from the predicted fault database, and then the predicted fault details corresponding to the predicted fault log data are generated based on the target fault cause and the target solution.

[0060] In this implementation, based on the system log data, the target fault cause is determined from at least one fault cause, and then the target solution corresponding to the target fault cause is determined. Finally, based on the target fault cause and the target solution, the predicted fault details corresponding to the predicted fault log data are generated. By using the system log data, the accuracy of log analysis can be improved and the user's workload can be further reduced.

[0061] S08. The predicted fault log data and predicted fault details are displayed.

[0062] It should be noted that step S308 can be implemented with reference to the specific implementation of step S206, which will not be described in detail here.

[0063] In this embodiment, through the predicted fault database, it is determined whether there is log data falling within the preset parameter range in the operation log data. If so, the log data falling within the preset parameter range is determined as predicted fault log data, and the predicted fault details corresponding to the predicted fault log data are determined and displayed; otherwise, it indicates that the traffic processing agent is running normally. This embodiment only outputs the predicted fault log data and the corresponding predicted fault details, reducing the amount of data processing.

[0064] Reference Figure 4, shows a structural block diagram of a flow processing fault prediction device according to an exemplary embodiment of the present application.

[0065] The flow processing fault prediction device of this embodiment is applied to a computing device, in which a flow processing agent is deployed, and the flow processing agent is used to process the service flow received and sent by the computing device. The device of this embodiment of the application includes an acquisition module 402, a fault prediction module 406 and a display module 408.

[0066] Among them, the acquisition module 402 is used to obtain the operation log data of the traffic processing agent; the fault prediction module 406 is used to determine the predicted fault log data in the operation log data and the predicted fault details corresponding to the predicted fault data based on the operation log data and the preset predicted fault database. The predicted fault database includes multiple preset parameter ranges and predicted fault details corresponding to each preset parameter range. The preset parameter range refers to a parameter range set with reference to the target fault log data for advance warning. The predicted fault details include the fault cause and solution corresponding to the target fault log data; the display module 408 is used to display the predicted fault log data and the predicted fault details.

[0067] In an optional implementation, the fault prediction module 406 is also used to: based on a preset predicted fault database, determine whether there is log data in the operation log data that falls within a preset parameter range; if so, determine the log data that falls within the preset parameter range as predicted fault log data, and determine the predicted fault details corresponding to the predicted fault log data.

[0068] In an optional implementation, the fault prediction module 406 is also used to: determine at least one fault cause corresponding to the predicted fault log data based on a preset predicted fault database; if at least one fault cause includes a fault cause indicating a system failure of the computing device, obtain the system log data of the computing device; and determine the predicted fault details corresponding to the predicted fault log data from the predicted fault database based on the system log data and at least one fault cause.

[0069] In an optional implementation, the acquisition module 402 is further used to: acquire the operation log data of the traffic processing agent according to a preset acquisition frequency; and / or acquire the operation log data of the traffic processing agent in response to a received acquisition instruction.

[0070] In an optional implementation, the computing device is a computing node in a target computer cluster, and the target computer cluster also includes a management node. The apparatus of this embodiment also includes a sending module, which is used to: obtain address information of the computing device; generate a predicted fault analysis report for traffic processing by the computing device based on predicted fault log data, predicted fault details and address information; and send the predicted fault analysis report to the management node so that the predicted fault analysis report can be displayed through the management node.

[0071] In an optional implementation, the computing device is a dedicated data processor DPU, which is coupled to a computing node in a target computer cluster. The dedicated data processor DPU is used to process business traffic sent and received by the computing node. The display module 406 is also used to send predicted fault log data and predicted fault details to the computing node so as to display the predicted fault log data and predicted fault details through the computing node.

[0072] In an optional implementation, the device of this embodiment also includes an update module, which is used to: obtain update information for the predicted fault database, the update information including a newly added preset parameter range and corresponding predicted fault details; and update the predicted fault database based on the update information.

[0073] The flow processing fault prediction device of this embodiment is used to implement the corresponding flow processing fault prediction method in the aforementioned multiple method embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here. In addition, the functional implementation of each module in the flow processing fault prediction device of this embodiment can refer to the description of the corresponding part in the aforementioned method embodiments, which will not be repeated here.

[0074] Reference Figure 5 , shows a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. The specific embodiment of the present invention does not limit the specific implementation of the electronic device.

[0075] like Figure 5 As shown, the electronic device may include: a processor (processor) 501 , a memory (memory) 503 , a communication bus 504 , and a communication interface (Communications Interface) 505 .

[0076] Wherein: the processor 501 , the memory 503 and the communication interface 505 communicate with each other via the communication bus 504 .

[0077] The communication interface 505 is used to communicate with other electronic devices or servers.

[0078] The processor 501 is used to execute the program 502, and specifically can execute the steps of any one of the flow processing fault prediction methods in the above embodiments.

[0079] Specifically, the program 502 may include program codes, which include computer operation instructions.

[0080] The processor 501 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0081] The memory 503 is used to store the program 502. The memory 503 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0082] Program 502 can be specifically used to enable processor 501 to execute to implement the steps of any of the flow processing fault prediction methods described in the embodiments. The specific implementation of each step in program 502 can refer to the corresponding descriptions in the steps and units executed by any of the flow processing fault prediction methods in the above steps, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the aforementioned method embodiments.

[0083] An embodiment of the present application further provides a computer storage medium on which a computer program is stored. When the program is executed by a processor, the flow processing fault prediction method as described in any one of the above-mentioned multiple method embodiments is implemented.

[0084] An embodiment of the present application also provides a computer program product, including computer instructions, which instruct a computing device to execute operations corresponding to the traffic processing fault prediction method described in any one of the above-mentioned multiple method embodiments.

[0085] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.

[0086] The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or implemented as a computer code originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and to be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a special-purpose processor or programmable or special-purpose hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown here, the execution of the code converts the general-purpose computer into a special-purpose computer for executing the method shown here.

[0087] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present application.

[0088] The above implementation methods are only used to illustrate the embodiments of the present application, and are not limitations on the embodiments of the present application. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the embodiments of the present application. Therefore, all equivalent technical solutions also belong to the scope of the embodiments of the present application. The scope of patent protection of the embodiments of the present application should be limited by the claims.

Claims

1. A method for predicting flow processing failures, characterized in that: Applied to a computing device, wherein a traffic processing agent is deployed in the computing device, and the traffic processing agent is used to process the service traffic sent and received by the computing device, and the method includes: Obtaining operation log data of the traffic processing agent; Based on the operation log data and a preset predicted fault database, determine the predicted fault log data in the operation log data and the predicted fault details corresponding to the predicted fault data, the predicted fault database including a plurality of preset parameter ranges and predicted fault details corresponding to each of the preset parameter ranges, the preset parameter range refers to a parameter range set with reference to the target fault log data and used for pre-warning, and the predicted fault details include the fault cause and solution corresponding to the target fault log data; The predicted fault log data and the predicted fault details are displayed.

2. The method for predicting flow processing failures according to claim 1, characterized in that: The determining, based on the operation log data and a preset predicted fault database, the predicted fault details corresponding to the operation log data includes: Based on a preset predicted fault database, determining whether there is log data in the operation log data that falls within the preset parameter range; If so, the log data falling within the preset parameter range is determined as predicted fault log data, and the predicted fault details corresponding to the predicted fault log data are determined.

3. The method for predicting flow processing failures according to claim 2, characterized in that: The determining the predicted fault details corresponding to the predicted fault data includes: Based on a preset predicted fault database, determining at least one fault cause corresponding to the predicted fault log data; If the at least one fault cause includes a fault cause indicating a system fault of the computing device, obtaining system log data of the computing device; Determine predicted fault details corresponding to the predicted fault log data from the predicted fault database according to the system log data and the at least one fault cause.

4. The method for predicting flow processing failures according to claim 1, characterized in that: The obtaining of the operation log data of the traffic processing agent includes: Acquire the operation log data of the traffic processing agent according to a preset acquisition frequency; and / or, In response to the received acquisition instruction, the operation log data of the traffic processing agent is acquired.

5. The method for predicting flow processing failures according to claim 1, characterized in that: The computing device is a computing node in a target computer cluster, the target computer cluster also includes a management node, and the method further includes: Obtaining address information of the computing device; generating a predicted fault analysis report for traffic processing by the computing device according to the predicted fault log data, the predicted fault details and the address information; The predicted fault analysis report is sent to the management node so that the predicted fault analysis report is displayed through the management node.

6. The method for predicting flow processing failures according to claim 1, characterized in that: The computing device is a dedicated data processor DPU, the dedicated data processor DPU is coupled to a computing node in a target computer cluster, and the dedicated data processor DPU is used to process the service traffic sent and received by the computing node, and the displaying of the predicted fault log data and the predicted fault details includes: The predicted fault log data and the predicted fault details are sent to the computing node, so that the predicted fault log data and the predicted fault details are displayed through the computing node.

7. The method for predicting flow processing failures according to claim 1, characterized in that: The method further comprises: Obtaining update information for the predicted fault database, the update information including a newly added preset parameter range and corresponding predicted fault details; Based on the update information, the predicted fault database is updated.

8. A flow processing fault prediction device, characterized in that: Applied to a computing device, wherein a traffic processing agent is deployed in the computing device, and the traffic processing agent is used to process the service traffic sent and received by the computing device, and the device comprises: An acquisition module, used to acquire the operation log data of the traffic processing agent; a fault prediction module, for determining predicted fault log data in the operation log data and predicted fault details corresponding to the predicted fault data based on the operation log data and a preset predicted fault database, wherein the predicted fault database includes a plurality of preset parameter ranges and predicted fault details corresponding to each of the preset parameter ranges, wherein the preset parameter range refers to a parameter range set with reference to the target fault log data and used for pre-warning, and the predicted fault details include the fault cause and solution corresponding to the target fault log data; A display module is used to display the predicted fault log data and the predicted fault details.

9. An electronic device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform an operation corresponding to the method according to any one of claims 1 to 7.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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