CN117707889A - State determination method, device and storage medium - Google Patents

Abstract

The application discloses a state determining method, a state determining device and a storage medium, relates to the technical field of computers, and is used for solving the problem that the accuracy rate of determining the cause of abnormality of an application program is low in a general technology. The method comprises the following steps: after the key quality index of the first application, the configuration information of the first application and the plurality of flow information are obtained, the key quality index can be input into a target conversion model for conversion processing, so that a reliability index of the first application in operation is obtained, the plurality of first flow information is determined according to the plurality of flow information, and the operation state of the first application is determined according to the comparison result of the reliability index and the preset threshold value and the matching result of the plurality of first flow information and the preset condition.

Description

State determination method, device and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method and apparatus for determining a state, and a storage medium.

Background

With the development of cloud computing technology, application programs can be deployed in virtualized nodes of a cloud computing system in a container deployment mode and the like, so that the running of the application programs is realized.

However, the virtual resources for deploying the application programs can be flexibly configured, which easily causes a problem that it is difficult to monitor the running state of the application programs. When an application program runs abnormally, a worker is often required to check the abnormality in a large number of index parameters, so that the efficiency is low, and the reason of the abnormality is difficult to accurately determine.

Disclosure of Invention

The application provides a state determining method, a state determining device and a storage medium, which are used for solving the problem that the accuracy of determining the cause of the abnormality of an application program is low by a general technology and improving the accuracy of determining the cause of the abnormality of the application program.

In order to achieve the above purpose, the present application adopts the following technical scheme:

in a first aspect, a method for determining a state is provided, including: after the key quality index of the first application, the configuration information of the first application and the plurality of flow information are obtained, the key quality index can be input into a target conversion model for conversion processing, so that a reliability index of the first application in operation is obtained, the plurality of first flow information is determined according to the plurality of flow information, and the operation state of the first application is determined according to the comparison result of the reliability index and the preset threshold value and the matching result of the plurality of first flow information and the preset condition. The configuration information comprises information of a plurality of first containers used for deploying the first application and information of a first host corresponding to the first application, the flow information has a corresponding relation with the containers or has a corresponding relation with the host, the plurality of first flow information corresponds to the plurality of first containers deploying the first application one by one or corresponds to the first host deploying the first application, the running state is used for indicating that the first application is normal in running or the target container is abnormal in running or the first host is abnormal in running, and the target container is used for indicating that the first flow information is not matched with the first container of the preset condition.

Optionally, the key quality indicators include a first screen loading time length, a web page rendering speed and a web page byte loading speed, and the state determining method further includes: acquiring a plurality of sample quality indexes and a plurality of corresponding sample reliability indexes; the sample quality index comprises sample first screen loading time, sample webpage rendering speed and sample webpage byte loading speed; the sample reliability index comprises a sample failure rate, a sample message retransmission rate and a sample disconnection rate; and taking a plurality of sample quality indexes as input and a plurality of sample reliability indexes as training labels to train an initial model, so as to obtain a first conversion model.

Optionally, the state determining method further includes: acquiring a plurality of test quality indexes and a plurality of corresponding test reliability indexes; the test quality index comprises a test first screen loading time length, a test webpage rendering speed and a test webpage byte loading speed; the test reliability index comprises a sample failure rate, a sample message retransmission rate and a sample disconnection rate; inputting the multiple test quality indexes into a first conversion model to obtain multiple evaluation reliability indexes corresponding to the multiple test quality indexes; determining a test result according to the plurality of evaluation reliability indexes and the plurality of test reliability indexes; test results include test pass or test fail.

Optionally, the method for acquiring the configuration information of the first application specifically includes: acquiring application request information of a first application; the application request information comprises an application name of the first application; and determining configuration information of the first application according to the corresponding relation between the preset application names and the first containers or the corresponding relation between the preset application names and the first host.

Optionally, the traffic information includes throughput of a container or throughput of the host, and the method for obtaining multiple traffic information specifically includes: acquiring full-volume flow data through a plurality of data probes; the full flow data comprises flow data between different containers and flow data between the host and the containers; and determining the throughput of each container in the plurality of containers or the throughput of the host according to the configuration information of the first application, so as to obtain a plurality of flow information.

Optionally, the method for determining the running state of the first application according to the comparison result of the reliability index and the preset threshold value and the matching result of the plurality of first flow information and the preset condition specifically includes: when the comparison result is used for indicating that the reliability index is smaller than a preset threshold value, determining an operation state to be used for indicating that the first application is normal in operation; when the comparison result is used for indicating that the reliability index is larger than a preset threshold value and the matching result is used for indicating that the first flow information of the target container or the first host does not match the preset condition, the running state is determined to be used for indicating that the target container is abnormal in running or the first host is abnormal in running.

Optionally, the reliability index includes a service failure rate, a service message retransmission rate and a service disconnection rate, the preset threshold includes a first preset threshold, a second preset threshold and a third preset threshold, and the state determining method includes: the reliability index is larger than a preset threshold, including that the service failure rate is larger than the first preset threshold, the service message retransmission rate is larger than the second preset threshold, and the service disconnection rate is larger than the third preset threshold; the reliability index is smaller than a preset threshold, including that the service failure rate is smaller than a first preset threshold, and/or the service message retransmission rate is smaller than a second preset threshold, and/or the service disconnection rate is smaller than a third preset threshold.

In a second aspect, there is provided a state determining apparatus comprising: the device comprises an acquisition unit, a processing unit and a determination unit; acquiring a key quality index of a first application, configuration information of the first application and a plurality of flow information; the configuration information comprises information of a plurality of first containers for deploying the first application or information of a first host corresponding to the first application; the flow information has a corresponding relation with the container or the host; the processing unit is used for inputting the key quality index into the target conversion model for conversion processing to obtain a reliability index when the first application runs; a determining unit configured to determine a plurality of first flow information from the plurality of flow information; the first flow information corresponds to the first containers for deploying the first application one by one or corresponds to the first host for deploying the first application; the determining unit is further used for determining the running state of the first application according to the comparison result of the reliability index and the preset threshold value and the matching result of the plurality of first flow information and the preset condition; the running state is used for indicating that the first application runs normally, or the target container runs abnormally, or the first host runs abnormally; the target container is used for indicating a first container of which the first flow information does not match the preset condition.

Optionally, the state determining device further includes: a training unit; the acquisition unit is also used for acquiring a plurality of sample quality indexes and a plurality of corresponding sample reliability indexes; the sample quality index comprises sample first screen loading time, sample webpage rendering speed and sample webpage byte loading speed; the training unit is used for taking a plurality of sample quality indexes as input and a plurality of sample reliability indexes as training labels to train the initial model so as to obtain a first conversion model.

Optionally, the state determining device further includes: a test unit; the acquisition unit is also used for acquiring a plurality of test quality indexes and a plurality of corresponding test reliability indexes; the test quality index comprises a test first screen loading time length, a test webpage rendering speed and a test webpage byte loading speed; the testing unit is used for inputting the multiple testing quality indexes into the first conversion model to obtain multiple evaluation reliability indexes corresponding to the multiple testing quality indexes; the determining unit is also used for determining a test result according to the plurality of reliability indexes and the plurality of test reliability indexes; test results include test pass or test fail.

Optionally, the acquiring unit is specifically configured to: acquiring application request information of a first application; the application request information comprises an application name of the first application; and determining configuration information of the first application according to the corresponding relation between the preset application names and the first containers or the corresponding relation between the preset application names and the first host.

Optionally, the acquiring unit is specifically configured to: acquiring full-volume flow data through a plurality of data probes; the full traffic data comprises traffic data between different containers and traffic data between hosts; and determining the throughput of each container or the throughput of the host machine in the plurality of containers according to the configuration information of the first application to obtain a plurality of flow information.

Optionally, the determining unit is specifically configured to: when the comparison result is used for indicating that the reliability index is smaller than a preset threshold value, determining an operation state to be used for indicating that the first application is normal in operation; when the comparison result is used for indicating that the reliability index is larger than a preset threshold value and the matching result is used for indicating that the first flow information of the target container or the first host does not match the preset condition, the running state is determined to be used for indicating that the target container is abnormal in running or the first host is abnormal in running.

Optionally, the state determining device includes a service failure rate, a service message retransmission rate, and a service disconnection rate, the preset threshold includes a first preset threshold, a second preset threshold, and a third preset threshold, and further includes: the reliability index is larger than a preset threshold, including that the service failure rate is larger than the first preset threshold, the service message retransmission rate is larger than the second preset threshold, and the service disconnection rate is larger than the third preset threshold; the reliability index is smaller than a preset threshold, including that the service failure rate is smaller than a first preset threshold, and/or the service message retransmission rate is smaller than a second preset threshold, and/or the service disconnection rate is smaller than a third preset threshold.

In a third aspect, a state determining apparatus is provided, comprising a memory and a processor; the memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus; when the state determining device is operating, the processor executes computer-executable instructions stored in the memory to cause the state determining device to perform the state determining method according to the first aspect.

The state determining means may be a network device or may be a part of a device in a network device, such as a system-on-chip in a network device. The system-on-a-chip is configured to support the network device to implement the functions involved in the first aspect and any one of its possible implementations, e.g. to obtain, determine, send data and/or information involved in the above-mentioned state determination method. The chip system includes a chip, and may also include other discrete devices or circuit structures.

In a fourth aspect, there is provided a computer readable storage medium comprising computer executable instructions which, when run on a computer, cause the computer to perform the state determining method of the first aspect.

In a fifth aspect, there is also provided a computer program product comprising computer instructions which, when run on a state determining device, cause the state determining device to perform the state determining method as described in the first aspect above.

It should be noted that the above-mentioned computer instructions may be stored in whole or in part on the first computer readable storage medium. The first computer readable storage medium may be packaged together with the processor of the state determining device or may be packaged separately from the processor of the state determining device, which is not limited in this application.

The description of the second, third, fourth and fifth aspects of the present application may refer to the detailed description of the first aspect; the advantages of the second aspect, the third aspect, the fourth aspect and the fifth aspect may be referred to as analysis of the advantages of the first aspect, and will not be described here.

In the present application, the names of the above-mentioned state determining means do not constitute limitations on the devices or function modules themselves, and in actual implementations, these devices or function modules may appear under other names. Insofar as the function of each device or function module is similar to the present application, it is within the scope of the claims of the present application and the equivalents thereof.

These and other aspects of the present application will be more readily apparent from the following description.

The technical scheme provided by the application at least brings the following beneficial effects:

based on any one of the above aspects, the present application provides a state determining method, where after a state determining device may obtain a key quality index of a first application, configuration information of the first application, and a plurality of flow information, the key quality index may be input into a target conversion model to perform conversion processing, to obtain a reliability index when the first application operates, and determine a plurality of first flow information according to the plurality of flow information, so as to determine an operation state of the first application further according to a comparison result of the reliability index and a preset threshold value, and a matching result of the plurality of first flow information and a preset condition.

Based on the above, the application can determine the abnormal operation target container or host by acquiring the reliability index, the configuration information and the flow information of the application and analyzing the operation state of the application program. Compared with a mode of checking a large number of index parameters manually to determine the cause of the abnormality of the application program, the method and the device can accurately determine the target container or host machine causing the abnormal operation of the application program by analyzing the reliability index, the configuration information and the flow information of the application program, and effectively improve the accuracy of determining the cause of the abnormality of the application program. Therefore, the method and the device can be used for solving the problem that the accuracy of determining the cause of the abnormality of the application program is low in the general technology, and the accuracy of determining the cause of the abnormality of the application program is improved.

Drawings

Fig. 1 is a schematic structural diagram of a state determining system according to an embodiment of the present application;

fig. 2 is a schematic hardware structure of a state determining device according to an embodiment of the present application;

fig. 3 is a flow chart of a state determining method according to an embodiment of the present application;

FIG. 4 is a flowchart of another method for determining a state according to an embodiment of the present disclosure;

FIG. 5 is a flowchart of another method for determining a state according to an embodiment of the present disclosure;

FIG. 6 is a flowchart of another method for determining a state according to an embodiment of the present disclosure;

fig. 7 is a flowchart of another method for determining a state according to an embodiment of the present application;

fig. 8 is a schematic diagram of a state determining method according to an embodiment of the present application;

FIG. 9 is a flowchart of another method for determining a state according to an embodiment of the present disclosure;

fig. 10 is a schematic structural diagram of a state determining device according to an embodiment of the present application.

Detailed Description

The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.

In order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, the terms "first", "second", and the like are used to distinguish the same item or similar items having substantially the same function and effect, and those skilled in the art will understand that the terms "first", "second", and the like are not limited in number and execution order.

With the development of computer technology and internet technology, multiple virtual machines may be configured on one physical server through a virtualization technology, and multiple virtualization services may be implemented by multiple virtual machines, for example: the cloud computing service and the data storage service greatly improve the hardware utilization rate of the physical server, so that a plurality of users can share the resources of one server physical server, and higher performance is obtained. The user can send the virtualized service request to the cloud through the portable terminal, and then complex calculation and mass storage of data in the virtualized service request are realized through the virtual machine.

In order to realize complex cloud service, a multi-level, multi-unit and multi-node cloud service environment of public cloud, enterprise private cloud, container cloud, micro service and wireless network grid (mesh) can be constructed by configuring a multi-layer virtualization structure, but the architecture is completely based on continuously abstract multi-layer virtualization and software defined network (software defined network, SDN) technology, and a great deal of elastic resources and automatic arrangement are used, so that after a business accident happens, a cloud server cannot translate business language, a corresponding virtualization component cannot be determined according to the business characteristics of the accident, and because a monitoring system only monitors state indexes of CPU and memory occupation, disk Input and Output (IO), network card throughput, and when the state indexes change, alarm information is sent, but the corresponding cloud service is not represented, so that related staff cannot pay attention to the alarm. Therefore, only when the business is abnormal and the user initiates complaints, the related staff can search for the abnormality in the key performance indicators (key performance index, KPI) in a large number of Internet technology (internet technology, IT) indicators, so that the processing efficiency on the business abnormality is lower.

Currently, the problem of low processing efficiency of service exception can be solved by a related cloud computing management platform (e.g., openStack) traffic collection method based on a transmission control protocol (transmission control protocol, TCP), and the method may include: mirror image management is carried out on the flow to be collected through a data probe, the collected data packet of the local virtual port is added with service information and timestamp information to be repackaged into a result data packet, and then the result data packet is sent to a corresponding flow analysis application through a TCP protocol. The data probes are deployed on network nodes or computing nodes of the OpenStack, and the acquisition management center and the acquisition receiver are deployed on other physical devices or virtual machines. However, the method does not solve the problem that relevant staff does not pay attention to the alarm, and the existing data probe cannot realize fine granularity and blind spot-free flow data acquisition due to the defects of complex operation, delayed technology update and the like, so that the existing data probe cannot be used as an all-weather production-level data probe.

With the popularization of application mobility and the transition of agile development modes, the development of hybrid cloud and micro-service architecture technology, the application can detect and optimize an application system by adopting a pressure measurement and performance analysis tool through a performance verification function and a performance problem rapid discovery function, find out the current load capacity of the system, find out a potential performance bottleneck, guide the application system to optimize, realize the functions of rapidly positioning the performance problem of a service system and responding in time, ensure the normal operation of the service, reduce the failure rate of service positioning, reduce the failure recovery time of the service system, discover the performance hidden trouble of the service system in advance, and finally realize the effects of reducing cost, reducing personnel, improving efficiency and the like.

The embodiment of the application provides a state determining method, after acquiring a key quality index of a first application, configuration information of the first application and a plurality of flow information, the key quality index can be input into a target conversion model to be converted, a reliability index of the first application in operation is obtained, the plurality of first flow information is determined according to the plurality of flow information, and the running state of the first application is determined according to a comparison result of the reliability index and a preset threshold value and a matching result of the plurality of first flow information and a preset condition.

Based on the above, the application can determine the abnormal operation target container or host by acquiring the reliability index, the configuration information and the flow information of the application and analyzing the operation state of the application program. Compared with a mode of checking a large number of index parameters manually to determine the cause of the abnormality of the application program, the method and the device can accurately determine the target container or host machine causing the abnormal operation of the application program by analyzing the reliability index, the configuration information and the flow information of the application program, and effectively improve the accuracy of determining the cause of the abnormality of the application program. Therefore, the method and the device can be used for solving the problem that the accuracy of determining the cause of the abnormality of the application program is low in the general technology, and the accuracy of determining the cause of the abnormality of the application program is improved.

The state determination method is applicable to a state determination system. Fig. 1 shows a schematic configuration of a state determination system. As shown in fig. 1, the state determination system 100 includes: status determining means 101 and data acquisition means 102.

Wherein the state determining device 101 and the data acquisition device 102 can be in communication connection.

Alternatively, the connection between the state determining device 101 and the data acquisition device 102 may be a wired connection or a wireless connection.

In some embodiments, the state determining device 101 is configured to implement a state determining function, which may be a functional module on the data acquisition device 102, or may be an entity device that is configured independently of the data acquisition device 102.

It is easy to understand that when the state determining device 101 is a functional module on the data acquisition device 102, the interaction between the state determining device 101 and the data acquisition device 102 is an interaction between internal modules of the data acquisition device 102. In this case, the interaction flow between the two is the same as "in the case where the state determining apparatus 101 is a separately provided entity device".

For ease of understanding, fig. 1 illustrates an example of "the state determining device 101 and the data collecting device 102 are provided independently of each other".

The state determining apparatus 101 in fig. 1 may receive the key quality indicator of the first application, the configuration information of the first application, and the plurality of traffic information from the data acquisition apparatus 102, and may process the key quality indicator of the first application, the configuration information of the first application, and the plurality of traffic information from the data acquisition apparatus 102, so as to determine a target container or host with abnormal running state in the application.

The data acquisition device 102 in fig. 1 may be configured with a database, or may be connected to a device configured with the database, for acquiring a key quality index for transmitting the first application, configuration information of the first application, a plurality of traffic information, and the like, and may transmit the acquired information to the state determination device 101.

In some embodiments, when the state determining device 101 and the data collecting device 102 are entity devices that are disposed independently of each other, the state determining device 101 and the data collecting device 102 may be a separate server or other physical devices. The physical device may be one server in a server cluster (including a plurality of servers), may be a chip in the physical device, may also be a system on a chip in the physical device, or may be implemented by a virtual machine deployed on the physical machine, which is not limited in this embodiment of the present application.

In some embodiments, the state determining device 101 and the data collecting device 102 may be terminals for implementing man-machine interaction through the presentation page, where the terminals may be handheld devices with wireless connection functions, or wireless terminals connected to other processing devices of a wireless modem, and may also be wired terminals. For example, smart devices such as cell phones, personal computers (personal computer, PCs), desktop computers, tablet computers, notebook computers, netbooks, personal digital assistants (personal digital assistant, PDAs), and the like, which are not limited in this embodiment.

In connection with fig. 1, the state determining means 101 and the data acquisition means 102 in the state determining system may comprise the elements comprised by the state determining means shown in fig. 2. The hardware configuration of the state determining device 101 and the data acquisition device 102 will be described below taking the state determining device shown in fig. 2 as an example.

Fig. 2 is a schematic hardware structure of a state determining device according to an embodiment of the present application. The state determining means comprise a processor 21, a memory 22, a communication interface 23, a bus 24. The processor 21, the memory 22 and the communication interface 23 may be connected by a bus 24.

The processor 21 is a control center of the state determining apparatus, and may be one processor or a collective term of a plurality of processing elements. For example, the processor 21 may be a general-purpose central processing unit (central processing unit, CPU), or may be another general-purpose processor. Wherein the general purpose processor may be a microprocessor or any conventional processor or the like.

As one example, processor 21 may include one or more CPUs, such as CPU 0 and CPU 1 shown in fig. 2.

Memory 22 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), magnetic disk storage or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

In a possible implementation, the memory 22 may exist separately from the processor 21, and the memory 22 may be connected to the processor 21 by a bus 24 for storing instructions or program code. The processor 21, when calling and executing instructions or program code stored in the memory 22, is capable of implementing the state determination method provided in the following embodiments of the present invention.

In another possible implementation, the memory 22 may also be integrated with the processor 21.

A communication interface 23 for connecting the state determining means with other devices via a communication network, which may be an ethernet, a radio access network, a wireless local area network (wireless local area networks, WLAN) or the like. The communication interface 23 may include a receiving unit for receiving data, and a transmitting unit for transmitting data.

Bus 24 may be an industry standard architecture (industry standard architecture, ISA) bus, an external device interconnect (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in fig. 2, but not only one bus or one type of bus.

The following describes a state determining method provided in the embodiment of the present application in detail with reference to the accompanying drawings. As shown in fig. 3, the state determining method includes: S301-S304.

S301, a state determining device obtains a key quality index of a first application, configuration information of the first application and a plurality of flow information.

The key quality indicators (key quality indicators, KQI) may include, among other things, a first screen load duration, a web page rendering speed, and a web page byte load speed.

Specifically, the first screen loading time length is used for representing the time consumed from the beginning of loading to the situation that the browser page is fully paved in the display area of the terminal display and the area is provided with content display, the webpage loading time length is used for representing the time consumed in one webpage loading process of the target website, the webpage rendering speed is used for representing loading bytes of the display screen pixel points of the browser in unit time, the webpage byte loading speed is used for representing the speed of completing the byte loading in one webpage loading process, and the webpage loading speed can be calculated by the percentage of the number of bytes completing the loading in unit time relative to the total number of bytes in the webpage loading process.

In one implementation, a browser control may be included in the first application to enable loading and displaying of web page content through the first application. Browser controls are a software tool that can embed web browsing functionality in an application.

In one implementation manner, after receiving the application request message of the user terminal, the state determining device may send corresponding application data, i.e. a downlink data stream, to the user terminal through the network device. The downstream may include log information of the application. The downstream is used to represent the data sent by the state determining device to the user terminal.

Alternatively, the network device may be a router, switch, or the like. The network device may be configured to send an upstream data stream to the state determining means or to receive a downstream data stream from the state determining means. The upstream is used to represent data sent by the network device to the state determining means.

In one implementation manner, the state determining device may obtain a downlink data stream sent to the user terminal by the container corresponding to the application, obtain log information of the application from the downlink data stream, and analyze the log information to determine a key quality index of the first application. The log information may include attribute information and key performance indicators of the application.

In one implementation, the state determining device may input the key performance indicators into a pre-trained web page quality assessment model to determine the key quality indicators of the web page.

Optionally, the web page quality evaluation model may be obtained by performing model training through an artificial intelligence algorithm according to a plurality of key performance indexes and key quality indexes corresponding to the key quality indexes, and the web page quality evaluation model may be obtained by performing offline pre-training, or may be obtained by performing model training by extracting feature parameters according to log information of a downlink data stream collected by the first network device and key performance indexes corresponding to a web page loading process of a corresponding user device accessing a website.

In one implementation manner, the state determining device may determine an index system of quality and performance of an application corresponding to the downstream according to identification and calculation of a flag bit and content of a session for the downstream.

Alternatively, the downstream data stream may include data such as TCP, hypertext transfer protocol (hypertext transfer protocol, HTTP), domain name system (domain name system, DNS), structured query language database (structured query language server database, SQL), uniform resource locator (uniform resource locator, URL), user datagram protocol (user datagram protocol, UDP), extensible markup language (extensible markup language, XML), and JS key-value data (javascript object notation, JSON).

The quality and performance index system for the application is shown in table 1 below:

TABLE 1

The configuration information comprises information of a plurality of first containers for deploying the first application and information of a first host corresponding to the first application.

The flow information has a corresponding relation with the container or the host.

Alternatively, a Host operating system (Host operating system, host OS) may be configured in the first Host, and a plurality of first containers may be configured in each container.

Optionally, when the first application is deployed in a host operating system in the first host, the configuration information may further include resource usage information of the first application.

In one implementation, multiple Kubernetes clusters may be configured in the state determining device, where Pod (i.e., container) is the smallest scheduling unit in each Kubernetes cluster to run one or more dockers (i.e., first container).

In one implementation, a first container within a container may share the same network namespace and storage space and may communicate data via the shared IP address in the container. The purpose of the containers is to enable a set of first containers to share network and storage resources in an isolated environment, where the host operating system is the operating system run by the physical server hosting the containers, which is the underlying infrastructure of Kubernetes nodes.

In one implementation, containers run on nodes in the Kubernetes cluster, each running a host operating system and managing the containers, the first container deployed in the container being a process running on the host operating system, the container and the host operating system sharing the same resources. Thus, the container may affect the underlying host operating system, such as affecting network configuration through a shared network namespace, occupying a large amount of resources to affect host performance, and so on.

In one implementation manner, the state determining device may determine a corresponding cloud service baseline acquisition script UniScan according to each level of virtualized environment, system version, script authority and monitoring integrity, acquire resource usage information and baseline information of each level of virtualized environment, update the resource usage information and baseline information into an extensible markup language (extensible markup language, XML) or JS key value pair data (javascript object notation, JSON) format, and send the resource usage information and baseline information to a mongo db database in a cloud server, so as to facilitate matching with flow information acquired in a cloud primary data probe (UniProbe) in a subsequent manner, thereby realizing identification and measurement of each container.

In one implementation manner, the state determining device may connect to the target server according to a protocol such as a Secure Shell (SSH) protocol, and then run a cloud service baseline acquisition script to collect resource usage information of the host operating system.

In another implementation manner, the state determining device may integrate the cloud service baseline acquisition script into the cloud primary data probe, so as to obtain the configuration information and the resource usage information of the first application through the cloud primary data probe.

Specifically, after the state determining device obtains the data format and the communication mechanism of the cloud primary data probe, the cloud service baseline acquisition script is updated according to the data format and the communication mechanism of the cloud primary data probe, so that the cloud service baseline acquisition script can format and package the acquired data. After the cloud service baseline acquisition script can meet the data format and the communication mechanism of the cloud primary data probe, the state determining device can integrate the updated cloud service baseline acquisition script with the cloud primary data probe, so that the cloud service baseline acquisition script can further process, format and package the acquired resource use information so as to meet the requirements of the cloud primary data probe.

Alternatively, the resource usage information may include host information, system configuration information, process information, security configuration information, network configuration information, application information, and the like.

Specifically, the host information may include information such as a host name, a CPU, a memory, and a hard disk capacity. The system configuration information may include information on environment variables, service programs, open ports, configuration files, etc. The process information may include all process information running on the system, such as a process name, a process number, a process occupation resource, and the like. The security configuration information may include security configurations of the operating system and applications, and may include firewall configuration, password complexity, password policies, and the like. The network configuration information may include information such as IP addresses, domain name systems, routing tables, network device configurations, and network communication protocols. The application information may include information of an application installation location, version, configuration file, and the like.

In an implementation manner, the cloud native data probe is one of data probes, and compared with a traditional data probe, the cloud native data probe can achieve flow acquisition and local analysis of a host machine in various cloud service environments, has good processing performance, safety and stability, and can be used for achieving visualization of cloud communication.

In one implementation, the state determining device may configure one cloud primary data probe for each container, and when there is traffic transmission in the container, the cloud primary data probe in the container may acquire traffic information in the container to which the cloud primary data probe belongs. The cloud primary data probe is a software tool with functions of data acquisition, data analysis and the like.

S302, the state determining device inputs the key quality index into the target conversion model to perform conversion processing, and a reliability index of the first application in operation is obtained.

Alternatively, the reliability index may include an application call failure rate, an application message retransmission rate, and an application drop rate. The application call failure rate is used for representing the ratio of the call failure times of the application to the call times of the application, the application message retransmission rate is used for representing the ratio of the number of application messages which are failed to be sent and attempted to be retransmitted to the total number of application messages sent, and the application disconnection rate is used for representing the ratio of the number of times the application is disconnected to the number of times the application is attempted to be connected.

In one implementation, the state determining means may collect key quality indicators through a configured application performance monitoring tool or application developer tool.

In one implementation manner, the state determining device may perform statistical analysis on the key quality indexes, determine an average value, a median value, and a maximum value of each key quality index, and implement comprehensive analysis on the key quality indexes.

In one implementation manner, the state determining device may determine the relationship between the key quality index and the corresponding reliability index through correlation analysis, regression analysis, and other methods, and quantize the information such as the first screen loading time, the web page rendering speed, the web page byte loading speed, and the like into the login success rate, the message retransmission rate, and the disconnection rate index.

S303, the state determining device determines a plurality of first flow information from the plurality of flow information.

The first traffic information corresponds to the first containers for deploying the first application one by one or corresponds to the first hosts for deploying the first application.

In one implementation, the obtaining, by the state determining device, flow information of the first container may include: after acquiring an application request sent by a user terminal, the state determining device determines a corresponding container according to the service request, so as to further acquire flow information of the first container according to a cloud primary data probe in the container.

Alternatively, the application request may include an application name, application parameters, application configuration, and any other relevant requirements. The application request may be created using an appropriate tool or technique. For example, created using a command line tool, HTTP request library in a programming language, a graphical user interface, and the like.

In one implementation, the user terminal sends an application request to the state determining device, either by sending an HTTP request to an API endpoint of the state determining device, or by interacting with the state determining device using specific tools and interfaces.

Optionally, a target first container providing the target application is run on the container. The containers may be directly managed by Kubernete, and after one first container is run on the container, the container may be considered as a single packaged container, and after the target first container is in a normal running state, the traffic requesting the target application may be transferred to the target first container.

In one implementation manner, the cloud native data probe in the state determining device can monitor and collect traffic in a virtualized environment, in the virtualized environment, the cloud native data probe can collect traffic through a virtual switch at the same level as the host machine, and the virtual switch forwards traffic generated by the virtual machine to the cloud native data probe, so that traffic in the host machine is monitored. The virtualized environment may include a host operating system and a virtual machine operating system.

In another implementation, when the cloud native data probe is deployed in the host, the physical network card or the virtual network card of the host may be used for traffic monitoring and acquisition. In this manner, the cloud-native data probe needs to be installed in the host operating system and needs to have sufficient authority so that data collection and monitoring can be performed.

S304, the state determining device determines the running state of the first application according to the comparison result of the reliability index and the preset threshold value and the matching result of the plurality of first flow information and the preset condition.

The running state is used for indicating that the first application runs normally, or the target container runs abnormally, or the first host runs abnormally; the target container is used for indicating a first container of which the first flow information does not match the preset condition.

In one implementation, when the state determining device may be configured with a plurality of traffic monitors, the state determining device may determine the corresponding traffic monitor according to information such as application requirements and application traffic characteristics. The flow monitor is used for providing real-time flow data and monitoring statistical information, and can provide timely monitoring and fault positioning information for network administrators and application program developers. The traffic monitor is a tool or system for monitoring traffic information of a first application.

In one implementation manner, when the reliability indexes are all greater than the preset threshold, the state determining device may directly determine that the running state of the first application is not abnormal and the target container of the first application is not abnormal.

The application call failure rate, the application message retransmission rate and the application disconnection rate of the first application a are all larger than a preset threshold, the first flow information is not matched with the preset condition, and the state determining device can determine that the running state of the first application a is normal running.

The technical scheme provided by the method at least brings the following beneficial effects: as known from S301 to S304, after the state determining device obtains the key quality index of the first application, the configuration information of the first application, and the plurality of flow information, the key quality index may be input into the target conversion model to perform conversion processing, so as to obtain a reliability index when the first application operates, and determine the plurality of first flow information according to the plurality of flow information, so as to determine the operation state of the first application further according to a comparison result of the reliability index and the preset threshold value, and a matching result of the plurality of first flow information and the preset condition.

The method and the device can analyze the running state of the application program by acquiring the reliability index, the configuration information and the flow information of the application program, and determine the target container or the first host machine with abnormal running. Compared with a mode of checking a large number of index parameters manually to determine the cause of the abnormality of the application program, the method and the device can accurately determine the target container or the first host causing the abnormal operation of the application program by analyzing the reliability index, the configuration information and the flow information of the application program, and effectively improve the accuracy of determining the cause of the abnormality of the application program. Therefore, the method and the device can be used for solving the problem that the accuracy of determining the cause of the abnormality of the application program is low in the general technology, and the accuracy of determining the cause of the abnormality of the application program is improved.

In one embodiment, the key quality indicators include a first screen loading time length, a web page rendering speed and a web page byte loading speed, as shown in fig. 4, the application further provides a state determining method, which includes: S401-S402.

S401, the state determining device acquires a plurality of sample quality indexes and a plurality of corresponding sample reliability indexes.

The sample quality index comprises sample webpage first screen loading time, sample webpage rendering speed and sample webpage byte loading speed.

The sample reliability index comprises a sample application calling failure rate, a sample application message retransmission rate and a sample application disconnection rate.

In one possible implementation, the state determining device may quantize the plurality of sample quality indicators into a corresponding plurality of sample reliability indicators by a correlation analysis, a regression analysis, or the like.

S402, the state determining device takes a plurality of sample quality indexes as input and a plurality of sample reliability indexes as training labels to train an initial model, so as to obtain a first conversion model.

In one implementation manner, the state determining device may determine, according to the first application request message, different processing strategies for quantifying the multiple sample quality indexes into multiple sample reliability indexes, so as to obtain different multiple sample reliability indexes corresponding to the multiple sample quality indexes.

In one implementation manner, the state determining device may train the initial model according to different sample reliability indexes corresponding to the sample quality indexes of the first application, to obtain a plurality of first conversion models.

For example, the first application request message table is a picture query application, and the state determining device may determine a picture processing policy in which a plurality of sample quality indicators of the picture query application are quantized into a plurality of sample reliability indicators. The first application request message table is a video query application, and the state determining device may determine a video processing policy in which a plurality of sample quality indicators of the video query application are quantized into a plurality of sample reliability indicators.

The technical scheme provided by the method at least brings the following beneficial effects: as can be seen from S401 to S402, after the state determining device obtains the plurality of sample quality indexes and the corresponding plurality of sample reliability indexes, the plurality of sample quality indexes may be used as input, and the plurality of sample reliability indexes may be used as training labels to train the initial model, so as to obtain the first conversion model. Therefore, the method and the device can quantify the multiple sample quality indexes of the first application into the multiple sample reliability indexes, and facilitate the follow-up accurate determination of the running state of the first application.

In one embodiment, in conjunction with fig. 4, after S402, that is, the state determining device takes a plurality of sample quality indexes as input and a plurality of sample reliability indexes as training labels to train an initial model, and after obtaining a first conversion model, as shown in fig. 5, the present application further provides a state determining method, which includes: S501-S503.

S501, a state determining device acquires a plurality of test quality indexes and a plurality of corresponding test reliability indexes.

The plurality of test quality indexes comprise a test first screen loading time length, a test webpage rendering speed and a test webpage byte loading speed.

The plurality of test reliability indexes comprise a test application calling failure rate, a test application message retransmission rate and a test application disconnection rate.

In one implementation, the state determining means may determine a part of the plurality of sample quality indicators and the corresponding plurality of sample reliability indicators as the test quality indicator and the corresponding test reliability indicator.

For example, the state determining means may determine the plurality of sample quality indicators and the corresponding plurality of sample reliability indicators, one tenth of the sample quality indicators and the corresponding sample reliability indicators as the test quality indicators and the corresponding test reliability indicators.

S502, the state determining device inputs a plurality of test quality indexes into the first target conversion model to obtain a plurality of evaluation reliability indexes corresponding to the plurality of test quality indexes.

In one possible implementation, the state determining device may perform multiple tests on the conversion model according to different accuracy requirements of the reliability index.

S503, the state determining device determines a test result according to the plurality of evaluation reliability indexes and the plurality of test reliability indexes.

Wherein the test result includes test pass or test fail.

In one implementation manner, the state determining device may subtract the corresponding plurality of test reliability indexes from the plurality of evaluation reliability indexes to obtain a plurality of difference values, then sum the plurality of difference values to determine an error of the first conversion model, and when the error of the first conversion model is within a preset error range, the test result indicates that the test passes, otherwise, the test fails.

In another implementation, the state determining means may add a plurality of evaluation reliability indexes and add a plurality of test reliability indexes to further divide the value of the addition of the plurality of evaluation reliability indexes by the value of the addition of the plurality of test reliability indexes to obtain the error percentage. If the error percentage is within the preset error percentage range, the test result indicates that the test is passed, otherwise, the test is not passed.

In one implementation, if the test result indicates that the test does not pass, the state determining device may reenter the plurality of test quality indicators into the corresponding first conversion models to obtain a plurality of second conversion models. And repeating the steps when the test result of the second conversion model still indicates that the test fails. The first conversion model represents a model obtained through primary training, and the second conversion model represents a model obtained through secondary training.

The above technical solution at least brings the following advantages, and it is known from S501-S503 that after the state determining device obtains a plurality of test quality indexes and a plurality of corresponding test reliability indexes, the plurality of test quality indexes may be input into the first target conversion model to obtain a plurality of evaluation reliability indexes corresponding to the plurality of test quality indexes, and the test result is determined according to the plurality of evaluation reliability indexes and the plurality of test reliability indexes. Therefore, the method and the device can test the conversion model, and the test is stopped until the conversion model accords with the preset error condition, so that the accuracy of the conversion model can be improved, and the accuracy of the reliability index can be further improved.

In an embodiment, in conjunction with fig. 3, in S301, that is, when the state determining device obtains the configuration information of the first application, as shown in fig. 6, the application further provides a possible embodiment, including: S601-S602.

S601, the state determining device obtains application request information of a first application.

Wherein the application request information includes an application name of the first application.

In one implementation manner, the state determining device may start to monitor the resource usage information of the first application after receiving the application request message sent by the user terminal.

S602, the state determining device determines configuration information of the first application according to the corresponding relation between the preset application names and the first containers or the first hosts.

In one implementation manner, the state determining device may determine, in advance, a first container corresponding to each application name, and after receiving the application request message, determine a plurality of corresponding first containers according to the application names in the application request message.

In one implementation manner, the state determining device may further monitor resource usage information of the plurality of first containers or the first host after determining the plurality of first containers or the first host corresponding to the application according to the application name.

For example, after the state determining device obtains the application request message of the application a, the application name of the application a is a, and the corresponding plurality of first containers B, C, D may be determined according to a, and the resource usage information of the plurality of first containers B, C, D may be monitored. The state determining device may determine the configuration information and the resource usage information of the application a as a plurality of first containers B, C, D corresponding to the application a, where the first container B runs in the container E, the process resource occupies one percent, the first container C runs in the container E, the process resource occupies three percent, the first container D runs in the container F, and the process resource occupies five percent.

The above technical solution at least brings the following advantages, and it is known from S601-S602 that after the state determining device obtains the application request information of the first application, the configuration information of the first application can be determined according to the application name of the first application. Therefore, the method and the device can determine the first containers or the first hosts corresponding to the first application according to the relation between the predetermined application names and the first containers, so that the efficiency of determining the first containers or the first hosts can be improved, and the accuracy of determining the first containers or the first hosts can be improved.

In one embodiment, in conjunction with fig. 3, in S301, that is, when the state determining device acquires a plurality of pieces of traffic information, the traffic information includes throughput of the container, as shown in fig. 7, the application further provides a possible embodiment, including: S701-S702.

S701, the state determining device acquires full-volume flow data through a plurality of data probes.

Wherein the full volume flow data includes flow data between different containers, as well as flow data between containers and other devices.

For example, as shown in fig. 8, a schematic diagram of a state determining method may be shown, where a POD is a container, a dock is a first container, an ECS is a cloud Server, a Host-OS is a Host operating system, a NIC is a physical network card, and a Server is a physical Server, and in order to obtain a full amount of traffic data, a cloud primary data probe may be deployed in each container and in the Host operating system.

In one implementation, the method for the state determining device to obtain the flow information in each container through the cloud primary data probe may include: S1-S6.

S1, a state determining device determines a first target container.

The first target container is one of a plurality of containers corresponding to the first application.

The state determining means determines a plurality of first target containers from the application request message and ensures that the plurality of first target containers are running and accessible.

S2, the state determining device determines the network environment.

The state determining means determines a network connection manner between the first application and the plurality of first target containers, and according to the network connection manner, it is necessary to connect the first target containers to the network of the host, create the first target container network, or use other network schemes. The network environment is used for representing network connection modes between the plurality of first target containers and the application programs.

S3, the state determining device determines the target port.

The state determining means may map the first application to a specific port of the host for external access by a port mapping function of the first target container.

In one implementation, the state determining means may use the-p option of the dock run command in the host operating system to specify the port mapping rule or configure the port mapping rule in a dock composition file.

S4, the state determining device sends the flow to the first target container.

The state determining means may determine the traffic transmission rule according to the characteristics and the requirements of the first application.

In one implementation, the state determining device may perform traffic transmission between the user terminal and the first target container through a configured load balancer, a reverse proxy, and a network device.

S5, the state determining device tests and verifies the flow received by the first target container.

The state determining means may determine that the application request message can be accurately transmitted to the first target container by accessing the port of the first application, and obtain the expected response. Meanwhile, the state determining device can monitor and analyze the transmitted traffic to ensure that the traffic can be stably and efficiently transmitted.

S6, the state determining device acquires flow information of the first target container.

The state determining device can acquire flow information of all containers in the container through the cloud primary data probe in the container.

In one implementation, the state determining device may obtain traffic between the container and the host through a cloud-native data probe.

In another implementation manner, the state determining device may deploy a corresponding flow acquisition tool to a relevant node of the Kubernetes cluster according to the type of flow to be acquired and specific requirements, so as to achieve flow acquisition between the container and the host.

In one implementation, the state determining device may configure the monitoring and analysis environment of the flow collection tool for flow data according to the document corresponding to the flow collection tool, so as to view the flow data in real time, perform flow analysis, and generate reports.

In one implementation, the state determining device may deploy a traffic collection tool through functions such as DaemonSet, deployment or StatefulSet of Kubernetes, ensuring that each node installs the required tools and is able to operate and access data normally.

Alternatively, the flow collection means may comprise Prometheus, fluentd, istio or the like.

In particular, prometheus is an open source monitoring and alarm kit that can be used to collect and process time series data. Fluentd is an open source data collector that can centralize data from various sources to a central point for unified management, storage and retrieval. Istio is a service grid platform that can be used to manage and monitor traffic between micro-services.

In one implementation, the state determining device may analyze the acquired full volume flow data through Elasticsearch, kibana or Grafana flow analysis tools.

For example, the state determining device may configure the elastic search according to a configuration document of the elastic search, specify a data source and a data format according to an index template in the elastic search, and input source flow data into the elastic search to obtain a data analysis result of the flow data.

S702, the state determining device determines throughput of each container or throughput of the host machine in the plurality of containers according to the configuration information of the first application, and obtains a plurality of flow information.

In one implementation, when the configuration information of the first application indicates that the first application is located in a container, the state determining device may determine a throughput of the first container in each container according to a throughput of each container in the plurality of containers.

In another implementation, the state determining means may determine the throughput of the host operating system as the plurality of traffic messages when the configuration information of the first application indicates that the first application is configured in the host operating system.

The above technical solution at least brings the following advantages, and it is known from S701-S702 that after the state determining device obtains the full-volume flow data through the plurality of data probes, the throughput of each container or the throughput of the host in the plurality of containers can be determined according to the configuration information of the first application, so as to obtain a plurality of flow information. Therefore, after the full flow data is acquired, the throughput of each container or the throughput of the host corresponding to the first application can be analyzed, so that the running state of the first application can be analyzed conveniently, and the fault positioning efficiency can be improved.

In one embodiment, in conjunction with fig. 3, in S304, that is, when the state determining device determines the running state of the first application according to the comparison result of the reliability index and the preset threshold value and the matching result of the plurality of first flow information and the preset condition, as shown in fig. 9, the application further provides a possible embodiment, which includes: S901-S902.

And S901, when the comparison result is used for indicating that the reliability index is larger than a preset threshold value, the state determining device determines that the running state is used for indicating that the first application runs normally.

In one implementation manner, the state determining device may analyze the running state of the first application according to the configuration information and the plurality of traffic information of the first application, so as to implement fast processing of network traffic abnormality and application performance problems.

In one implementation manner, the state determining device may further perform comprehensive analysis on the first application by combining parameters such as the number of TCP connections, the client request time, the server response time, and the like of the first application after obtaining the configuration information and the plurality of traffic information of the first application.

S902, when the comparison result is used for indicating that the reliability index is smaller than a preset threshold value and the matching result is used for indicating that the first flow information of the target container or the first host does not match the preset condition, the state determining device determines that the operation state is used for indicating that the target container or the first host is abnormal in operation.

In one implementation, when the reliability index is smaller than the preset threshold, the state determining device determines that the running state of the first application is abnormal, and then may determine that the first target container or the first host has an abnormality according to a matching result of the first flow information and the preset condition.

The above technical solution at least brings the following advantages, and as can be seen from S901-S902, the state determining device may determine the running state of the first application by determining the comparison result of the reliability index and the preset threshold value and the matching result of the plurality of first flow information and the preset condition. Therefore, the fault location of the first application can be realized by combining the analysis results of various related information of the first application, the alarm generated when part of indexes are abnormal in the general technology is reduced, and the accuracy of the fault location of the first application is improved.

The foregoing description of the solution provided in the embodiments of the present application has been mainly presented in terms of a method. To achieve the above functions, it includes corresponding hardware structures and/or software modules that perform the respective functions. Those of skill in the art will readily appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The embodiment of the present application may divide the function modules of the state determining apparatus according to the above method example, for example, each function module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated modules may be implemented in hardware or in software functional modules. Optionally, the division of the modules in the embodiments of the present application is schematic, which is merely a logic function division, and other division manners may be actually implemented.

Fig. 10 is a schematic structural diagram of a state determining device according to an embodiment of the present application. The state determining device may be used to perform the state determining method shown in fig. 3, 4, 5, 6, 7, 9. The state determining apparatus shown in fig. 10 includes: an acquisition unit 1001, a processing unit 1002, and a determination unit 1003.

An obtaining unit 1001, configured to obtain a key quality indicator of a first application, configuration information of the first application, and a plurality of traffic information; the configuration information comprises information of a plurality of first containers for deploying the first application or information of a first host corresponding to the first application; the flow information has a corresponding relation with the container or the host. For example, in connection with fig. 3, the acquisition unit 1001 may be used to perform S301.

The processing unit 1002 inputs the key quality index into the target conversion model to perform conversion processing, so as to obtain a reliability index when the first application runs. For example, in connection with fig. 3, a processing unit 1002 may be used to perform S302.

A determining unit 1003 configured to determine a plurality of first traffic information from the plurality of traffic information; the plurality of first traffic information corresponds to the plurality of first containers deploying the first application one-to-one or to the first hosts deploying the first application. For example, in connection with fig. 3, the determining unit 1002 may be used to perform S303.

A determining unit 1003, configured to determine an operation state of the first application according to a comparison result of the reliability index and the preset threshold value, and a matching result of the plurality of first flow information and the preset condition; the running state is used for indicating that the first application runs normally, or the target container runs abnormally, or the first host runs abnormally; the target container is used for indicating a first container of which the first flow information does not match the preset condition. For example, in connection with fig. 3, the determination unit 1003 may be used to perform S304.

Optionally, the device further includes: training unit 1004. The acquiring unit 1001 is further configured to acquire a plurality of sample quality indexes and a corresponding plurality of sample reliability indexes; the sample quality index comprises sample first screen loading time length, sample webpage rendering speed and sample webpage byte loading speed. For example, in connection with fig. 4, the acquisition unit 1001 may be used to perform S401.

The training unit 1004 is configured to train the initial model by taking a plurality of sample quality indexes as input and a plurality of sample reliability indexes as training labels, so as to obtain a first conversion model. For example, in connection with fig. 4, training unit 1004 may be used to perform S402.

Optionally, the device further includes: a test unit 1005.

The acquiring unit 1001 is further configured to acquire a plurality of test quality indexes and a corresponding plurality of test reliability indexes; the test quality index comprises a test first screen loading time length, a test webpage rendering speed and a test webpage byte loading speed. For example, in connection with fig. 5, the acquisition unit 1001 may be used to perform S501.

The test unit 1005 is configured to input a plurality of test quality indexes into the first conversion model, and obtain a plurality of evaluation reliability indexes corresponding to the plurality of test quality indexes. For example, in connection with fig. 5, a test unit 1005 may be used to perform S502.

A determining unit 1003 further configured to determine a test result according to the plurality of reliability indexes and the plurality of test reliability indexes; test results include test pass or test fail. For example, in connection with fig. 5, the determination unit 1003 may be used to perform S503.

Optionally, the obtaining unit 1001 is specifically configured to:

acquiring application request information of a first application; the application request information includes an application name of the first application. For example, in connection with fig. 6, the acquisition unit 1001 may be used to perform S601.

And determining configuration information of the first application according to the corresponding relation between the preset application names and the first containers or the corresponding relation between the preset application names and the first host. For example, in connection with fig. 6, the acquisition unit 1001 may be used to perform S602.

Optionally, the obtaining unit 1001 is specifically configured to:

acquiring full-volume flow data through a plurality of data probes; the full traffic data includes traffic data between different containers, as well as traffic data between hosts. For example, in connection with fig. 7, the acquisition unit 1001 may be used to perform S701.

And determining the throughput of each container or the throughput of the host machine in the plurality of containers according to the configuration information of the first application to obtain a plurality of flow information. For example, in connection with fig. 7, the acquisition unit 1001 may be used to perform S702.

Optionally, the determining unit 1003 is specifically configured to:

and when the comparison result is used for indicating that the reliability index is smaller than the preset threshold value, determining the running state to be used for indicating that the first application runs normally. For example, in connection with fig. 9, the determination unit 1003 may be used to perform S901.

When the comparison result is used for indicating that the reliability index is larger than a preset threshold value and the matching result is used for indicating that the first flow information of the target container or the first host does not match the preset condition, the running state is determined to be used for indicating that the target container is abnormal in running or the first host is abnormal in running. For example, in connection with fig. 9, the determination unit 1003 may be used to perform S902.

Optionally, the device further includes: the reliability index is greater than a preset threshold, including that the service failure rate is greater than the first, the service message retransmission rate is greater than the second preset threshold, and the service disconnection rate is greater than the third preset threshold.

The reliability index is smaller than a preset threshold, including that the service failure rate is smaller than a first preset threshold, and/or the service message retransmission rate is smaller than a second preset threshold, and/or the service disconnection rate is smaller than a third preset threshold.

The present application also provides a computer-readable storage medium including computer-executable instructions that, when executed on a computer, cause the computer to perform the state determining method provided in the above embodiments.

The embodiment of the present application also provides a computer program, which can be directly loaded into a memory and contains software codes, and the computer program can implement the state determining method provided in the above embodiment after being loaded and executed by a computer.

Those skilled in the art will appreciate that in one or more of the examples described above, the functions described in the present invention may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, these functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer-readable storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and the division of modules or units, for example, is merely a logical function division, and other manners of division are possible when actually implemented. For example, multiple units or components may be combined or may be integrated into another device, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and the parts shown as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed in a plurality of different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units. The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application may be essentially or a part contributing to the general technology or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, where the software product includes several instructions to cause a device (may be a single-chip microcomputer, a chip or the like) or a processor (processor) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.

The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.

Claims (12)

1. A method of determining a state, comprising:

acquiring a key quality index of a first application, configuration information of the first application and a plurality of flow information; the configuration information comprises information of a plurality of first containers for deploying the first application and information of a first host corresponding to the first application; the flow information has a corresponding relation with the container or the host;

inputting the key quality index into a target conversion model for conversion treatment to obtain a reliability index of the first application in operation;

determining a plurality of first flow information according to the plurality of flow information; the first flow information is in one-to-one correspondence with the first containers for deploying the first application or in correspondence with the first host for deploying the first application;

determining the running state of the first application according to the comparison result of the reliability index and a preset threshold value and the matching result of the plurality of first flow information and preset conditions; the running state is used for indicating that the first application runs normally, or the target container runs abnormally, or the first host runs abnormally; the target container is used for indicating a first container of which the first flow information does not match a preset condition.

2. The state determination method according to claim 1, wherein the key quality indicators include a first screen loading time length, a web page rendering speed, and a web page byte loading speed; the method further comprises the steps of:

acquiring a plurality of sample quality indexes and a plurality of sample reliability indexes; the sample quality index comprises sample first screen loading time, sample webpage rendering speed and sample webpage byte loading speed;

and taking the plurality of sample quality indexes as input and the plurality of sample reliability indexes as training labels to train an initial model so as to obtain a first conversion model.

3. The state determination method according to claim 2, characterized by further comprising:

acquiring a plurality of test quality indexes and a plurality of test reliability indexes; the test quality index comprises a test first screen loading time length, a test webpage rendering speed and a test webpage byte loading speed;

inputting the multiple test quality indexes into a first target conversion model to obtain multiple evaluation reliability indexes corresponding to the multiple test quality indexes;

determining a test result according to the plurality of evaluation reliability indexes and the plurality of test reliability indexes; the test results include test pass or test fail.

4. The state determination method according to claim 1, wherein acquiring configuration information of the first application includes:

acquiring application request information of a first application; the application request information comprises an application name of a first application;

and determining configuration information of the first application according to the corresponding relation between the preset application names and the first containers or the corresponding relation between the preset application names and the host.

5. The state determination method according to claim 1, wherein the traffic information includes throughput of the container or the host; the obtaining a plurality of traffic information includes:

acquiring full-volume flow data through a plurality of data probes; the full flow data comprises flow data between different containers and flow data between a host and the containers;

and determining the throughput of each container or the throughput of the host machine in the plurality of containers according to the configuration information of the first application to obtain the plurality of flow information.

6. The state determining method according to claim 1, wherein the determining the running state of the first application according to the comparison result of the reliability index and a preset threshold value and the matching result of the plurality of first flow information and a preset condition includes:

When the comparison result is used for indicating that the reliability index is smaller than a preset threshold value, determining that the running state is used for indicating that the first application runs normally;

and when the comparison result is used for indicating that the reliability index is larger than a preset threshold value and the matching result is used for indicating that the first flow information of the target container or the first host is not matched with a preset condition, determining the running state to be used for indicating that the target container is abnormal in running or the first host is abnormal in running.

7. The state determination method according to claim 1, wherein the reliability index includes a service failure rate, a service message retransmission rate, and a service drop rate, and the preset thresholds include a first preset threshold, a second preset threshold, and a third preset threshold; comprising

The reliability index is larger than a preset threshold, and the reliability index comprises that the service failure rate is larger than the first preset threshold, the service message retransmission rate is larger than the second preset threshold, and the service disconnection rate is larger than the third preset threshold;

the reliability index is smaller than a preset threshold, including that the service failure rate is smaller than a first preset threshold, and/or the service message retransmission rate is smaller than a second preset threshold, and/or the service disconnection rate is smaller than a third preset threshold.

8. A state determining apparatus, comprising: the device comprises an acquisition unit, a processing unit and a determination unit;

the acquiring unit is used for acquiring key quality indexes of a first application, configuration information of the first application and a plurality of flow information; the configuration information comprises information of a plurality of first containers for deploying the first application and information of a first host corresponding to the first application; the flow information has a corresponding relation with the container or the host;

the processing unit is used for inputting the key quality index into a target conversion model for conversion processing to obtain a reliability index when the first application runs;

the determining unit is used for determining a plurality of first flow information according to the plurality of flow information; the first flow information is in one-to-one correspondence with the first containers of the first application or in correspondence with the first hosts of the first application;

the determining unit is further configured to determine an operation state of the first application according to a comparison result of the reliability index and a preset threshold value and a matching result of the plurality of first flow information and preset conditions; the running state is used for indicating that the first application runs normally or that a target container or a first host of the first application runs abnormally; the target container is used for indicating a first container of which the first flow information does not match a preset condition.

9. The state determination apparatus according to claim 8, further comprising: a training unit;

the acquisition unit is further used for acquiring a plurality of sample quality indexes and a plurality of corresponding sample reliability indexes; the sample quality index comprises sample first screen loading time, sample webpage rendering speed and sample webpage byte loading speed;

the training unit is used for taking the plurality of sample quality indexes as input and the plurality of sample reliability indexes as training labels to train the initial model so as to obtain a first conversion model.

10. The state determination apparatus according to claim 9, further comprising: test unit:

the acquisition unit is further used for acquiring a plurality of test quality indexes and a plurality of corresponding test reliability indexes; the test quality index comprises a test first screen loading time length, a test webpage rendering speed and a test webpage byte loading speed;

the test unit is used for inputting the plurality of test quality indexes into a first conversion model to obtain a plurality of evaluation reliability indexes corresponding to the plurality of test quality indexes;

The determining unit is further configured to determine a test result according to the multiple reliability indexes and the multiple test reliability indexes; the test results include test pass or test fail.

11. A state determining apparatus comprising a memory and a processor; the memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus; the processor executing the computer-executable instructions stored in the memory when the state determining device is operating, to cause the state determining device to perform the state determining method of any of claims 1-7.

12. A computer readable storage medium comprising computer executable instructions which, when run on a state determining device, cause the state determining device to perform the state determining method according to any of claims 1-7.