An application container extension engine platform

Abstract

The present invention provides an application container expansion engine platform, which includes: a quantity determination module, which is used to obtain the current running load data of the server and determine the expansion copy quantity based on the current running load data; an expansion module, which is used to copy and expand the initial application container according to the expansion copy quantity through the expansion engine to obtain a target application container; a detection module, which is used to detect the running data of the target application container and adjust the target application container based on the detection result to complete the expansion of the initial application container. By quickly expanding or reducing the application container according to the load running data of the server monitored in real time, and deploying the internal resources in time, a lot of time is saved, which provides a guarantee for the availability of the application.

Description

Technical Field

[0001] The present invention relates to the technical field of application container extension, and in particular to an application container extension engine platform. Background Art

[0002] Currently, the extension engine can split an application into multiple independent services with business attributes. Each service runs in an independent process. The services collaborate with each other through a lightweight communication mechanism to provide business value to end users.

[0003] As an open source application container engine, Docker container enables developers to encapsulate applications and their dependencies into portable Docker containers, and then publish Docker containers to Linux machines with Docker container environments.

[0004] The Docker container cluster built based on the existing Docker container orchestration and deployment technology cannot automatically adjust resources according to the real-time Docker container load, resulting in insufficient load capacity of the Docker container cluster during operation. The process of deploying Docker containers includes a series of operations such as downloading images, deploying images, and starting Docker containers. When it is found that the current Docker container resources cannot meet the load requirements, resources are applied for. Since operations such as downloading images will make the process of deploying new Docker containers very time-consuming, the availability of the application cannot be guaranteed during this period.

[0005] Therefore, the present invention provides an application container expansion engine platform, which can quickly expand or reduce the application container according to the load operation data of the server monitored in real time, and deploy internal resources in time, saving a lot of time and providing guarantee for the availability of the application. Summary of the invention

[0006] The present invention provides an application container expansion engine platform, which is used to quickly expand or reduce the application container according to the load operation data of the server monitored in real time, and deploy internal resources in time, thereby saving a lot of time and providing guarantee for the availability of the application.

[0007] The present invention provides an application container extension engine platform, comprising:

[0008] A quantity determination module, used to obtain current operation load data of the server and determine the quantity of extended replicas based on the current operation load data;

[0009] An expansion module, configured to replicate and expand the initial application container according to the expansion copy quantity through an expansion engine to obtain a target application container;

[0010] The detection module is used to detect the running data of the target application container and adjust the target application container based on the detection result to complete the expansion of the initial application container.

[0011] Preferably, an application container extension engine platform, a quantity determination module, comprises:

[0012] An instruction acquisition unit, used to acquire a data collection instruction sent by a management terminal, wherein the data collection instruction includes a target server type and a type of data to be collected;

[0013] An operation status determination unit, configured to obtain the operation status of the target server based on the data collection instruction, and determine whether the target server is operating normally based on the operation status;

[0014] The data collection unit is used to collect the operation load data of the target server based on the data collection instruction when it is determined that the target server is running normally, otherwise, it is determined that the operation load data collection fails.

[0015] Preferably, an application container extension engine platform, the quantity determination module further includes:

[0016] A data acquisition unit, used to acquire current operation load data of the server, and divide the current operation load data into a test set and a training set;

[0017] A model building unit, used to build a neural network model, and train the neural network model based on the training set to obtain a target neural network model;

[0018] The load value determination unit is used to analyze and process the test set based on the target neural network model to obtain the load value corresponding to the test set, wherein the load value is used to characterize the access volume of the current request to access the server.

[0019] Preferably, an application container extension engine platform, a load value determination unit, comprises:

[0020] A load value comparison unit, used to obtain the obtained load value, and at the same time, obtain the maximum load value of the initial application container in the server, and compare the obtained load value with the maximum load value;

[0021] an expansion determination unit, configured to determine a magnitude relationship between the obtained load value and the maximum load value based on the comparison result;

[0022] When the obtained load value is greater than the maximum load value, determining that the initial application container in the server needs to be expanded;

[0023] Otherwise, keep the current number of initial application containers unchanged;

[0024] A replica quantity calculation unit is used to determine the quantity of replicas that need to be expanded for the initial application container based on a target difference between the obtained load value and the maximum load value when it is determined that the initial application container in the server needs to be expanded, wherein the quantity of replicas is used to represent the number of expansions of the initial application container.

[0025] Preferably, an application container extension engine platform, a replica amount calculation unit, includes:

[0026] A replica quantity verification unit, used to obtain the replica quantity for expanding the initial application container, and compare the replica quantity with a preset threshold;

[0027] If the number of replicas is greater than the preset threshold, it is determined that the number of replicas for expanding the initial application container exceeds the limit, and the preset threshold is used as the target number of replicas for expanding the initial application container;

[0028] Otherwise, it is determined that the number of replicas for expanding the initial application container is qualified.

[0029] Preferably, an application container extension engine platform, an extension module, comprises:

[0030] An extension instruction acquisition unit, configured to receive an extension instruction sent by a server based on the extension engine, and the extension engine analyzes the extension instruction to obtain an extension copy quantity corresponding to the replication and extension of the initial application container;

[0031] An extended task determining unit, configured to create an application container extended task based on the extended replica quantity, obtain service configuration parameters of the initial application container based on the extended task, and determine a storage path for the initial application container after expansion;

[0032] an expansion unit, configured to expand the initial application container based on the service configuration parameters of the initial application container to obtain a target application container, and store the target application container based on the storage path, wherein the number of the target application containers is consistent with the number of the expanded copies;

[0033] A resource deployment unit, configured to obtain resource configuration information inside the initial application container, and select a target resource deployment mode from a preset resource deployment mode library based on the resource configuration information;

[0034] The resource deployment unit is configured to deploy the resources in the initial application container in the target application container based on the target resource deployment mode, wherein the resources in the target application container belong to the initial application container;

[0035] A resource scheduling determination unit, configured to monitor the user access volume of the target application container after resource deployment is completed, and determine whether the target application container needs to be rescheduled based on the user access volume;

[0036] A resource scheduling unit, configured to formulate a resource rescheduling strategy based on the user access volume when it is determined that resource rescheduling is required, and determine a resource set for each target application container based on the resource rescheduling strategy;

[0037] The resource scheduling unit is used to determine the target scheduling amount of resources for each target application container based on the resource set of each target application container, and reschedule resources for each target application container based on the target scheduling amount to obtain a final target application container, wherein each application container corresponds to a target scheduling amount.

[0038] Preferably, an application container extension engine platform, a resource scheduling determination unit, comprises:

[0039] a resource utilization determination unit, configured to monitor the resource load coefficient of the target application container in real time, and determine the resource utilization of the target application container based on the resource load coefficient;

[0040] an idle application container determining unit, configured to compare the resource utilization with a preset resource utilization, and when the resource utilization is less than the preset resource utilization, determine that an idle application container exists in the target application container; otherwise, determine that no idle application container exists in the target application container;

[0041] a container reduction unit, configured to, when determining that there is an idle application container in the target application container, determine a target idle application container based on the resource utilization rate, and stop a service process of the target idle application container;

[0042] The container reduction unit is further used to recycle the target idle application container after the service process is completely stopped, thereby completing the reduction of the idle application container.

[0043] Preferably, an application container extension engine platform, a resource deployment unit, comprises:

[0044] A resource detection unit is used to obtain a target application container where resources have been deployed, and to obtain a resource layer inside the target application container to generate a corresponding record file;

[0045] The resource detection unit is used to detect the resource layers inside the target application container layer by layer according to the record file based on a preset resource deployment detection method, and obtain a detection result;

[0046] A rationality judgment unit, used to judge whether the resource deployment inside the target container is reasonable based on the detection result;

[0047] If the resource deployment is determined to be reasonable, the deployment of internal resources of the target application container is completed;

[0048] Otherwise, the resources inside the target application container are redeployed until it is determined that the resource deployment inside the target application container is reasonable.

[0049] Preferably, an application container extension engine platform, a detection module, comprises:

[0050] a data acquisition unit, configured to acquire the operating data of the target application container, and input the operating data into a label value determination model to obtain a label value corresponding to the operating data, wherein the label value includes a normal label value and an abnormal label value;

[0051] A training data determination unit, configured to divide the operation data into a training data set and a test data set based on the label value, and train a preset data cleaning model based on the training data set;

[0052] A data cleaning model verification unit, used to test the trained preset data cleaning model based on the test data, and when the test result meets the preset conditions, determine that the preset data cleaning model is qualified;

[0053] A data cleaning unit, configured to process the running data of the target application container based on the preset data cleaning model, remove error data in the running data, and obtain data to be analyzed, wherein the error data includes null values ​​and non-data type data;

[0054] A fault detection unit, configured to detect the data to be analyzed based on a preset fault detection method, and determine whether the target application container has a fault;

[0055] A fault type detection unit, configured to analyze and process the data to be analyzed based on a preset fault type detection model to determine the fault type of the target application container when it is determined that the target application container has a fault;

[0056] A fault handling solution determination unit, used to determine whether the fault type is in a locally pre-stored list of solvable faults;

[0057] If the fault type is in the solvable fault list, obtaining a target solution corresponding to the fault type, and resolving the fault of the target application container based on the target solution;

[0058] Otherwise, a solution is newly formulated based on the fault type until the fault of the target application container is completely resolved.

[0059] Preferably, an application container extension engine platform, a fault handling solution determination unit, comprises:

[0060] A performance inspection unit, configured to detect a performance value of the target application container in real time after the fault existing in the target application container is resolved, and compare the performance value with a preset performance value;

[0061] If the performance value is greater than or equal to the preset performance value, it is determined that the target application container has a fault solution that meets the target requirement;

[0062] Otherwise, it is determined that the target application container fault solution does not meet the target requirement, and a target solution is re-formulated until it is determined that the target application container fault solution meets the target requirement, thereby completing the resolution of the target application container fault.

[0063] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0064] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0066] Figure 1 This is a structural diagram of an application container extension engine platform in an embodiment of the present invention;

[0067] Figure 2 It is a first structural diagram of a quantity determination module in an application container extension engine platform in an embodiment of the present invention;

[0068] Figure 3 This is a second structural diagram of a quantity determination module in an application container extension engine platform in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0070] Embodiment 1:

[0071] This embodiment provides an application container extension engine platform, such as Figure 1 As shown, including:

[0072] A quantity determination module, used to obtain current operation load data of the server and determine the quantity of extended replicas based on the current operation load data;

[0073] An expansion module, configured to replicate and expand the initial application container according to the expansion copy quantity through an expansion engine to obtain a target application container;

[0074] The detection module is used to detect the running data of the target application container and adjust the target application container based on the detection result to complete the expansion of the initial application container.

[0075] In this embodiment, the current operation load data refers to the current operation data of the server when the server is accessed or used by the user.

[0076] In this embodiment, the extended copy quantity refers to the quantity of the initial application container to be copied and extended according to the access volume of the user.

[0077] In this embodiment, the extension engine is set in advance and is used to copy and extend the initial application container.

[0078] In this embodiment, the initial application container refers to an application container that exists as a reference in the server and exists as a replication and extension source of the application container.

[0079] The beneficial effect of the above technical solution is: by quickly expanding or reducing the application container according to the load operation data of the server monitored in real time, and deploying the internal resources in time, a lot of time is saved and the availability of the application is guaranteed.

[0080] Embodiment 2:

[0081] Based on the above embodiment 1, this embodiment provides an application container extension engine platform, such as Figure 2 As shown, the quantity determination module includes:

[0082] An instruction acquisition unit, used to acquire a data collection instruction sent by a management terminal, wherein the data collection instruction includes a target server type and a type of data to be collected;

[0083] An operation status determination unit, configured to obtain the operation status of the target server based on the data collection instruction, and determine whether the target server is operating normally based on the operation status;

[0084] The data collection unit is used to collect the operation load data of the target server based on the data collection instruction when it is determined that the target server is running normally, otherwise, it is determined that the operation load data collection fails.

[0085] In this embodiment, the target server refers to the server corresponding to the application container expansion operation that needs to be performed.

[0086] In this embodiment, the running status includes whether the target server is running normally or abnormally.

[0087] The beneficial effect of the above technical solution is: by determining the corresponding server type in the data collection instruction and the data type of the data to be collected, it is convenient to accurately collect data from the server, thereby providing convenience for accurately determining the number of expanded copies of the initial application container, and facilitating the rapid expansion or reduction of the application container.

[0088] Embodiment 3:

[0089] Based on the above embodiment 1, this embodiment provides an application container extension engine platform, such as Figure 3 As shown, the quantity determination module also includes:

[0090] A data acquisition unit, used to acquire current operation load data of the server, and divide the current operation load data into a test set and a training set;

[0091] A model building unit, used to build a neural network model, and train the neural network model based on the training set to obtain a target neural network model;

[0092] The load value determination unit is used to analyze and process the test set based on the target neural network model to obtain the load value corresponding to the test set, wherein the load value is used to characterize the access volume of the current request to access the server.

[0093] In this embodiment, the target neural network model refers to a neural network model obtained after training and testing the constructed neural network model. The neural network model can directly analyze the operation data of the server to determine the number of currently accessed servers.

[0094] In this embodiment, the load value refers to the operation load value of the server after the server currently receives the user access request. The value can be used to accurately calculate the number of clients currently accessing the server.

[0095] The beneficial effect of the above technical solution is: by accurately analyzing the running load data of the server, the number of users accessing the server is accurately determined, which facilitates the determination of the number of extended copies of the initial application container.

[0096] Embodiment 4:

[0097] Based on the above embodiment 3, this embodiment provides an application container extension engine platform, a load value determination unit, including:

[0098] A load value comparison unit, used to obtain the obtained load value, and at the same time, obtain the maximum load value of the initial application container in the server, and compare the obtained load value with the maximum load value;

[0099] an expansion determination unit, configured to determine a magnitude relationship between the obtained load value and the maximum load value based on the comparison result;

[0100] When the obtained load value is greater than the maximum load value, determining that the initial application container in the server needs to be expanded;

[0101] Otherwise, keep the current number of initial application containers unchanged;

[0102] A replica quantity calculation unit is used to determine the quantity of replicas that need to be expanded for the initial application container based on a target difference between the obtained load value and the maximum load value when it is determined that the initial application container in the server needs to be expanded, wherein the quantity of replicas is used to represent the number of expansions of the initial application container.

[0103] In this embodiment, the maximum load value of the initial application container refers to the maximum access amount allowed by the initial application container.

[0104] In this embodiment, the method for determining the amount of extended replicas may also be that the staff codes the background program and manually sets the amount of the initial application container that needs to be extended.

[0105] The beneficial effect of the above technical solution is: by comparing the maximum load value of the initial application container with the load value obtained by analysis, it is accurately determined whether the initial application container needs to be expanded, and when expansion is required, the initial application container is quickly and accurately expanded according to the number of expanded copies, which saves a lot of time and provides a guarantee for the availability of the application.

[0106] Embodiment 5:

[0107] Based on the above-mentioned embodiment 4, this embodiment provides an application container extension engine platform, a replica amount calculation unit, including:

[0108] A replica quantity verification unit, used to obtain the replica quantity for expanding the initial application container, and compare the replica quantity with a preset threshold;

[0109] If the number of replicas is greater than the preset threshold, it is determined that the number of replicas for expanding the initial application container exceeds the limit, and the preset threshold is used as the target number of replicas for expanding the initial application container;

[0110] Otherwise, it is determined that the number of replicas for expanding the initial application container is qualified.

[0111] In this embodiment, the preset threshold is set in advance and is used to measure whether the expansion amount of the initial application container exceeds the set requirement. If it exceeds the preset threshold, it is determined that the expansion amount of the initial application container exceeds the requirement, which is likely to cause malicious occupation of resources.

[0112] In this embodiment, the target copy quantity refers to when the expansion quantity of the initial application container is greater than a preset threshold, the preset threshold is used as the expansion quantity of the initial application container.

[0113] The beneficial effect of the above technical solution is: by verifying the expansion quantity of the obtained initial application container, when the expansion amount is greater than a preset threshold, the preset threshold is used as the expansion quantity of the initial application container, which is conducive to preventing malicious occupation of resources and improving the availability of the application.

[0114] Embodiment 6:

[0115] Based on the above embodiment 1, this embodiment provides an application container extension engine platform, an extension module, including:

[0116] An extension instruction acquisition unit, configured to receive an extension instruction sent by a server based on the extension engine, and the extension engine analyzes the extension instruction to obtain an extension copy quantity corresponding to the replication and extension of the initial application container;

[0117] An extended task determining unit, configured to create an application container extended task based on the extended replica quantity, obtain service configuration parameters of the initial application container based on the extended task, and determine a storage path for the initial application container after expansion;

[0118] an expansion unit, configured to expand the initial application container based on the service configuration parameters of the initial application container to obtain a target application container, and store the target application container based on the storage path, wherein the number of the target application containers is consistent with the number of the expanded copies;

[0119] A resource deployment unit, configured to obtain resource configuration information inside the initial application container, and select a target resource deployment mode from a preset resource deployment mode library based on the resource configuration information;

[0120] The resource deployment unit is configured to deploy the resources in the initial application container in the target application container based on the target resource deployment mode, wherein the resources in the target application container belong to the initial application container;

[0121] A resource scheduling determination unit, configured to monitor the user access volume of the target application container after resource deployment is completed, and determine whether the target application container needs to be rescheduled based on the user access volume;

[0122] A resource scheduling unit, configured to formulate a resource rescheduling strategy based on the user access volume when it is determined that resource rescheduling is required, and determine a resource set for each target application container based on the resource rescheduling strategy;

[0123] The resource scheduling unit is used to determine the target scheduling amount of resources for each target application container based on the resource set of each target application container, and reschedule resources for each target application container based on the target scheduling amount to obtain a final target application container, wherein each application container corresponds to a target scheduling amount.

[0124] In this embodiment, the application container expansion task refers to determining a corresponding expansion task according to the required expansion quantity when receiving an expansion instruction, and the expansion task includes checking the expansion quantity during the expansion process.

[0125] In this embodiment, the service configuration parameters of the initial application container refer to the size of the initial application container and the permissions set by the access program for user access when docking with the client.

[0126] In this embodiment, the storage path is used to guide the storage location of the target application container obtained after the initial application container is expanded, so as to facilitate the location locking of the expanded application container and facilitate the user to access it according to the path.

[0127] In this embodiment, the target application container refers to an application container obtained by expanding an initial application container in the server, wherein the target application container may be one or more.

[0128] In this embodiment, the resource configuration information refers to the application corresponding data information stored in the initial application container, such as the storage location and storage method of the data in the initial application container.

[0129] In this embodiment, the preset resource deployment mode library is set in advance and stores a variety of resource deployment modes internally.

[0130] In this embodiment, the target resource deployment mode is one or more combinations in a preset resource deployment mode library, and is used to deploy the resources in the initial application container in the target application container.

[0131] In this embodiment, the resource rescheduling strategy is used to adjust the resources in the target application container when there is a lack or excess of resources in the target application container after resource deployment is completed.

[0132] In this embodiment, the resource set refers to the number of resources stored in each target application container.

[0133] In this embodiment, the target scheduling amount refers to the quantity value that needs to be adjusted for resources of each target application container.

[0134] In this embodiment, the resources in the target application container belong to a part or all of the initial application container.

[0135] The beneficial effects of the above technical solution are: by determining the number of initial application containers that need to be expanded, the initial application container can be quickly and accurately expanded, and at the same time, the deployment strategy for deploying resources in the target application container is determined according to the resource configuration information in the initial application container, which is conducive to accurately expanding the target application container and improving the availability of the application. At the same time, after the resource deployment is completed, the resource operation status of each application container is monitored in real time, and when adjustment is needed, the resources in the target application container are scheduled in time, providing a guarantee for ensuring the availability of the application.

[0136] Embodiment 7:

[0137] Based on the above-mentioned embodiment 6, this embodiment provides an application container extension engine platform, a resource scheduling determination unit, including:

[0138] a resource utilization determination unit, configured to monitor the resource load coefficient of the target application container in real time, and determine the resource utilization of the target application container based on the resource load coefficient;

[0139] an idle application container determining unit, configured to compare the resource utilization with a preset resource utilization, and when the resource utilization is less than the preset resource utilization, determine that an idle application container exists in the target application container; otherwise, determine that no idle application container exists in the target application container;

[0140] a container reduction unit, configured to, when determining that there is an idle application container in the target application container, determine a target idle application container based on the resource utilization rate, and stop a service process of the target idle application container;

[0141] The container reduction unit is further used to recycle the target idle application container after the service process is completely stopped, thereby completing the reduction of the idle application container.

[0142] In this embodiment, the resource load coefficient is used to describe the resources deployed in the target application container, and the number of resources accessed by users in the same time period when the users access the resources.

[0143] In this embodiment, the preset resource utilization is set in advance and is used to measure whether the resource utilization inside the target application container meets the preset requirement.

[0144] In this embodiment, the idle application containers refer to the redundant application containers after expansion, that is, the current number of application containers cannot be used by the current user access volume.

[0145] In this embodiment, the target idle application container refers to an idle application container among the plurality of target application containers.

[0146] In this embodiment, the service process refers to an application program that provides access services when the idle application container is accessed by a client.

[0147] In this embodiment, real-time monitoring of the resource load factor of the target application container and determining the resource utilization of the target application container based on the resource load factor include:

[0148] Acquire the client access volume of the target application server based on a preset time interval, wherein the client access volume is in multiple groups;

[0149] Calculating the resource load coefficient of the target application container based on the customer access volume, and calculating the resource utilization rate in the target application container within a preset time period based on the resource load coefficient, the specific steps include:

[0150] The resource load factor of the target application container is calculated according to the following formula:

[0151]

[0152] in, represents the resource load factor of the target application container; n represents the number of groups of customer visits obtained; i represents the number of groups of customer visits currently obtained; q i represents the customer visit value in the i-th group of customer visits; Q represents the customer visit value with the largest value in the n-group customer visits;

[0153] The resource utilization rate in the target application container within the preset time period is calculated according to the following formula:

[0154]

[0155] Wherein, η represents the resource utilization rate in the target application container, and its value range is (0, 1); represents the resource load factor of the target application container; z represents the current user accessing the target application container, and the value of z is [1, m]; m represents the maximum number of users accessing the target application container; x z Indicates the number of resources that the zth user needs to access within a preset time period, and The value of is less than s; s represents the total value of resources in the target application container; r represents the error coefficient, and its value range is (0.05, 0.15);

[0156] comparing the calculated resource utilization with a preset threshold;

[0157] If the resource utilization rate is greater than or equal to the preset threshold, it is determined that the resources in the target application container are fully utilized;

[0158] Otherwise, it is determined that the resources in the target application container are not fully utilized, and the resource utilization rate of each target application container is recorded;

[0159] A target application container that needs to be reduced in the target application container is determined based on the recording result, and the target application container that needs to be reduced is reduced and recycled.

[0160] The above-mentioned preset time period is set in advance, for example, it can be 1 hour, 2 hours, etc.

[0161] The above preset thresholds are set in advance and are used to measure whether the calculated resource utilization meets the expected set requirements, and can be modified manually.

[0162] The above formula In the example, if n is 3, q1 is 5, q2 is 6, q3 is 2, and Q is 6, then the calculated value is It is 72.2%.

[0163] The above formula In, if The value is 72.2%, m is 2, x1 is 15, x2 is 18, and s is 35, then the calculated η is 61.3%.

[0164] The beneficial effect of the above technical solution is: by real-time monitoring of the resource utilization inside the target application container, and when the resource utilization does not meet the standard, it is determined whether there are idle application containers inside the expanded application container, and when there are idle application containers, the expanded application container is reduced, thereby improving the purpose of real-time adjustment of the application container, saving a lot of time, and improving the availability of the application.

[0165] Embodiment 8:

[0166] Based on the above-mentioned embodiment 6, this embodiment provides an application container extension engine platform, a resource deployment unit, including:

[0167] A resource detection unit is used to obtain a target application container where resources have been deployed, and to obtain a resource layer inside the target application container to generate a corresponding record file;

[0168] The resource detection unit is used to detect the resource layers inside the target application container layer by layer according to the record file based on a preset resource deployment detection method, and obtain a detection result;

[0169] A rationality judgment unit, used to judge whether the resource deployment inside the target container is reasonable based on the detection result;

[0170] If the resource deployment is determined to be reasonable, the deployment of internal resources of the target application container is completed;

[0171] Otherwise, the resources inside the target application container are redeployed until it is determined that the resource deployment inside the target application container is reasonable.

[0172] In this embodiment, the resource layer refers to a data layer inside the application container for storing application data.

[0173] In this embodiment, the record file is used to record the data type and data volume of each resource layer in the application container.

[0174] In this embodiment, the preset resource deployment verification method is set in advance and is used to verify whether the resource deployment inside the container is reasonable. For example, the integrity of the data can be verified according to the function to be implemented.

[0175] The beneficial effect of the above technical solution is: by verifying the resources deployed inside the application container, it is convenient to ensure the normal operation of the resources inside the application container, which provides convenience for ensuring that no errors occur during the operation of the application container, and at the same time provides a guarantee for ensuring the availability of the application.

[0176] Embodiment 9:

[0177] Based on the above embodiment 1, this embodiment provides an application container extension engine platform, a detection module, including:

[0178] a data acquisition unit, configured to acquire the operating data of the target application container, and input the operating data into a label value determination model to obtain a label value corresponding to the operating data, wherein the label value includes a normal label value and an abnormal label value;

[0179] A training data determination unit, configured to divide the operation data into a training data set and a test data set based on the label value, and train a preset data cleaning model based on the training data set;

[0180] A data cleaning model verification unit, used to test the trained preset data cleaning model based on the test data, and when the test result meets the preset conditions, determine that the preset data cleaning model is qualified;

[0181] A data cleaning unit, configured to process the running data of the target application container based on the preset data cleaning model, remove error data in the running data, and obtain data to be analyzed, wherein the error data includes null values ​​and non-data type data;

[0182] A fault detection unit, configured to detect the data to be analyzed based on a preset fault detection method, and determine whether the target application container has a fault;

[0183] A fault type detection unit, configured to analyze and process the data to be analyzed based on a preset fault type detection model to determine the fault type of the target application container when it is determined that the target application container has a fault;

[0184] A fault handling solution determination unit, used to determine whether the fault type is in a locally pre-stored list of solvable faults;

[0185] If the fault type is in the solvable fault list, obtaining a target solution corresponding to the fault type, and resolving the fault of the target application container based on the target solution;

[0186] Otherwise, a solution is newly formulated based on the fault type until the fault of the target application container is completely resolved.

[0187] In this embodiment, the running data refers to the running parameters of the target application container obtained after the initial application container is expanded when receiving the user access.

[0188] In this embodiment, the label value determination model is set in advance and is used to determine the value size of the specific running data of the target application container when it is running.

[0189] In this embodiment, the label value corresponding to the operating data refers to a specific value used to represent the operating data.

[0190] In this embodiment, the preset data cleaning model is set in advance and is used to clean erroneous data in the target running data to ensure accurate analysis of the running status of the target application container.

[0191] In this embodiment, the preset condition is set in advance, for example, the cleaning accuracy of the preset data cleaning model measured by test data reaches more than 95%.

[0192] In this embodiment, the preset fault detection method is set in advance and is used to determine whether there is a fault in the target application container by analyzing the operating data of the target application container. For example, the current operating data can be compared with the operating data of the target application container when there is no fault, so as to determine whether there is a fault in the target application container.

[0193] In this embodiment, the preset fault type detection model is set in advance.

[0194] In this embodiment, the target solution refers to a fault resolution method corresponding to a fault problem existing in the target application container.

[0195] The beneficial effect of the above technical solution is: by cleaning and analyzing the running data of the target application container, it is convenient to accurately judge whether there is a fault in the target application container based on the running data. At the same time, when a fault exists, the fault type is accurately judged, thereby accurately formulating a fault solution, improving the efficiency of solving the target application container fault, and providing a guarantee for ensuring the application availability.

[0196] Embodiment 10:

[0197] Based on the above-mentioned Embodiment 9, this embodiment provides an application container extension engine platform, a fault handling solution determination unit, including:

[0198] A performance inspection unit, configured to detect a performance value of the target application container in real time after the fault existing in the target application container is resolved, and compare the performance value with a preset performance value;

[0199] If the performance value is greater than or equal to the preset performance value, it is determined that the target application container has a fault solution that meets the target requirement;

[0200] Otherwise, it is determined that the target application container fault solution does not meet the target requirement, and a target solution is re-formulated until it is determined that the target application container fault solution meets the target requirement, thereby completing the resolution of the target application container fault.

[0201] In this embodiment, the performance value is used to describe the working performance of the target application container when it is working after the fault existing in the target application container is processed.

[0202] In this embodiment, the preset performance value is set in advance and is used to measure whether the working performance value of the target application container after the fault is handled meets the expected requirements, and can be manually modified.

[0203] In this embodiment, the target requirement refers to the required value of the working performance of the target application container after the fault existing in the target application container is resolved.

[0204] The beneficial effect of the above technical solution is: after resolving the fault of the target application container, the working performance value of the target application container is verified to ensure that the problems existing in the target application container are thoroughly solved through the target solution, which provides a guarantee for ensuring the normal operation of the target application container obtained after the initial application container is expanded, and ensures the availability of the application.

[0205] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An application container extension engine platform, characterized in that: include: A quantity determination module, used to obtain current operation load data of the server and determine the quantity of extended replicas based on the current operation load data; An expansion module, configured to replicate and expand the initial application container according to the expansion copy quantity through an expansion engine to obtain a target application container; A detection module, used to detect the running data of the target application container, and adjust the target application container based on the detection result to complete the expansion of the initial application container; The detection module includes: a data acquisition unit, configured to acquire the operating data of the target application container, and input the operating data into a label value determination model to obtain a label value corresponding to the operating data, wherein the label value includes a normal label value and an abnormal label value; A training data determination unit, configured to divide the operation data into a training data set and a test data set based on the label value, and train a preset data cleaning model based on the training data set; A data cleaning model verification unit, used to test the trained preset data cleaning model based on the test data, and when the test result meets the preset conditions, determine that the preset data cleaning model is qualified; A data cleaning unit, configured to process the running data of the target application container based on the preset data cleaning model, remove error data in the running data, and obtain data to be analyzed, wherein the error data includes null values ​​and non-data type data; A fault detection unit, configured to detect the data to be analyzed based on a preset fault detection method, and determine whether the target application container has a fault; A fault type detection unit, configured to analyze and process the data to be analyzed based on a preset fault type detection model to determine the fault type of the target application container when it is determined that the target application container has a fault; A fault handling solution determination unit, used to determine whether the fault type is in a locally pre-stored list of solvable faults; If the fault type is in the solvable fault list, obtaining a target solution corresponding to the fault type, and resolving the fault of the target application container based on the target solution; Otherwise, a solution is newly formulated based on the fault type until the fault of the target application container is resolved; Among them, the extension modules include: An extension instruction acquisition unit, configured to receive an extension instruction sent by a server based on the extension engine, and the extension engine analyzes the extension instruction to obtain an extension copy quantity corresponding to the replication and extension of the initial application container; An extended task determining unit, configured to create an application container extended task based on the extended replica quantity, obtain service configuration parameters of the initial application container based on the extended task, and determine a storage path for the initial application container after expansion; an expansion unit, configured to expand the initial application container based on the service configuration parameters of the initial application container to obtain a target application container, and store the target application container based on the storage path, wherein the number of the target application containers is consistent with the number of the expanded copies; A resource deployment unit, configured to obtain resource configuration information inside the initial application container, and select a target resource deployment mode from a preset resource deployment mode library based on the resource configuration information; The resource deployment unit is configured to deploy the resources in the initial application container in the target application container based on the target resource deployment mode, wherein the resources in the target application container belong to the initial application container; A resource scheduling determination unit, configured to monitor the user access volume of the target application container after resource deployment is completed, and determine whether the target application container needs to be rescheduled based on the user access volume; A resource scheduling unit, configured to formulate a resource rescheduling strategy based on the user access volume when it is determined that resource rescheduling is required, and determine a resource set for each target application container based on the resource rescheduling strategy; The resource scheduling unit is used to determine the target scheduling amount of resources for each target application container based on the resource set of each target application container, and reschedule resources for each target application container based on the target scheduling amount to obtain a final target application container, wherein each application container corresponds to a target scheduling amount.

2. The application container extension engine platform according to claim 1, characterized in that: Quantity determination module, including: An instruction acquisition unit, used to acquire a data collection instruction sent by a management terminal, wherein the data collection instruction includes a target server type and a type of data to be collected; An operation status determination unit, configured to obtain the operation status of the target server based on the data collection instruction, and determine whether the target server is operating normally based on the operation status; The data collection unit is used to collect the operation load data of the target server based on the data collection instruction when it is determined that the target server is running normally, otherwise, it is determined that the operation load data collection fails.

3. The application container extension engine platform according to claim 1, characterized in that: The quantity determination module also includes: A data acquisition unit, used to acquire current operation load data of the server, and divide the current operation load data into a test set and a training set; A model building unit, used to build a neural network model, and train the neural network model based on the training set to obtain a target neural network model; The load value determination unit is used to analyze and process the test set based on the target neural network model to obtain the load value corresponding to the test set, wherein the load value is used to characterize the access volume of the current request to access the server.

4. The application container extension engine platform according to claim 3, characterized in that: A load value determination unit, comprising: A load value comparison unit, used to obtain the obtained load value, and at the same time, obtain the maximum load value of the initial application container in the server, and compare the obtained load value with the maximum load value; an expansion determination unit, configured to determine a magnitude relationship between the obtained load value and the maximum load value based on the comparison result; When the obtained load value is greater than the maximum load value, determining that the initial application container in the server needs to be expanded; Otherwise, keep the current number of initial application containers unchanged; A replica quantity calculation unit is used to determine the quantity of replicas that need to be expanded for the initial application container based on a target difference between the obtained load value and the maximum load value when it is determined that the initial application container in the server needs to be expanded, wherein the quantity of replicas is used to represent the number of expansions of the initial application container.

5. The application container extension engine platform according to claim 4, characterized in that: The replica calculation unit includes: A replica quantity verification unit, used to obtain the replica quantity for expanding the initial application container, and compare the replica quantity with a preset threshold; If the number of replicas is greater than the preset threshold, it is determined that the number of replicas for expanding the initial application container exceeds the limit, and the preset threshold is used as the target number of replicas for expanding the initial application container; Otherwise, it is determined that the number of replicas for expanding the initial application container is qualified.

6. The application container extension engine platform according to claim 1, characterized in that: The resource scheduling determination unit includes: a resource utilization determination unit, configured to monitor the resource load coefficient of the target application container in real time, and determine the resource utilization of the target application container based on the resource load coefficient; an idle application container determining unit, configured to compare the resource utilization with a preset resource utilization, and when the resource utilization is less than the preset resource utilization, determine that an idle application container exists in the target application container; otherwise, determine that no idle application container exists in the target application container; a container reduction unit, configured to, when determining that there is an idle application container in the target application container, determine a target idle application container based on the resource utilization rate, and stop a service process of the target idle application container; The container reduction unit is further used to recycle the target idle application container after the service process is completely stopped, thereby completing the reduction of the idle application container.

7. The application container extension engine platform according to claim 1, characterized in that: Resource deployment unit, including: A resource detection unit is used to obtain a target application container where resources have been deployed, and to obtain a resource layer inside the target application container to generate a corresponding record file; The resource detection unit is used to detect the resource layers inside the target application container layer by layer according to the record file based on a preset resource deployment detection method, and obtain a detection result; A rationality judgment unit, used to judge whether the resource deployment inside the target application container is reasonable based on the detection result; If the resource deployment is determined to be reasonable, the deployment of internal resources of the target application container is completed; Otherwise, the resources inside the target application container are redeployed until it is determined that the resource deployment inside the target application container is reasonable.

8. The application container extension engine platform according to claim 1, characterized in that: The fault handling solution determination unit includes: A performance inspection unit, configured to detect a performance value of the target application container in real time after the fault existing in the target application container is resolved, and compare the performance value with a preset performance value; If the performance value is greater than or equal to the preset performance value, it is determined that the target application container has a fault solution that meets the target requirement; Otherwise, it is determined that the target application container fault solution does not meet the target requirement, and a target solution is re-formulated until it is determined that the target application container fault solution meets the target requirement, thereby completing the resolution of the target application container fault.

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