Application expansion and contraction method and device

Abstract

The present invention provides an application scaling method and device. The application scaling method provided in this embodiment includes: obtaining a call log output when a first application calls a target object; determining the total number of calls of the first application according to the call log, the total number of calls is used to represent the cumulative number of times the first application calls the target object; determining the scaling number of the first application according to the total number of calls and a preset processing rule; and performing scaling operations on the first application according to the scaling number. The application scaling method provided in the embodiment of the present invention can realize scaling operations on the application while ensuring its timeliness and accuracy and reducing the deviation of application resources caused by the scaling operations.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and device for scaling an application. Background Art

[0002] With the rapid development of information and mobile Internet, the architecture of application systems generally adopts a combination of distributed system architecture and microservice containerized system architecture; at the same time, in order to cope with the arrival of massive traffic, general application systems have high elastic scaling capabilities. For example, when business calls reach a peak, applications can be quickly scaled horizontally to ensure the normal operation of the business; when business calls drop to a low point, applications can be quickly scaled down to release resources for other applications. In a large-scale distributed, Internet, containerized, microservice-based system, automatic scaling of applications is a necessary capability of the system. Currently, automatic scaling is mainly used in the Kubernetes environment used for containerization and container orchestration.

[0003] The existing scaling methods mainly include manual and automatic methods. The manual method is to manually scale up and down according to the calls of each application and the resource consumption; the automatic method is to preset a threshold for the resource occupancy of the application container, and when the resource occupancy reaches the preset threshold, the scaling mechanism of the application is automatically triggered. Both methods have their own shortcomings. The manual method has problems with timeliness and accuracy, and the automatic method has problems with deviation.

[0004] Therefore, how to ensure the timeliness and accuracy of the application while scaling it up and down and reduce the application resource deviation caused by the scaling operations is an urgent problem to be solved. Summary of the invention

[0005] The present invention provides an application scaling method to achieve scaling operations on an application while ensuring its timeliness and accuracy and reducing the application resource deviation caused by the scaling operations.

[0006] In a first aspect, the present invention provides a method for scaling an application, comprising:

[0007] Obtaining the call log output when the first application calls the target object;

[0008] Determine the total number of calls of the first application according to the call log, where the total number of calls is used to represent the cumulative number of times the first application calls the target object;

[0009] Determine the number of expansion and contraction of the first application according to the total number of calls and preset processing rules;

[0010] The first application is scaled up or down according to the scaled up or down number.

[0011] In one possible design, the target object is a target service or a second application.

[0012] In a possible design, determining the total number of calls of the first application according to the call log includes:

[0013] The call logs are classified according to the name of the first application to determine the call information of the first application, where the call information includes: the name of the first application and the total number of calls of the first application.

[0014] In a possible design, the call information further includes: the current number of nodes on which the first application is running, a preset concurrency benchmark of the first application, a preset node quantity benchmark of the first application, a preset minimum number of target nodes for the first application, and a preset maximum number of target nodes for the first application; determining the number of expansion and contraction of the first application according to the total number of calls and the preset processing rules includes:

[0015] Divide the concurrency benchmark by the node number benchmark to get the single-node concurrency benchmark;

[0016] Divide the total number of calls by the single-node concurrency benchmark to get the target number of nodes;

[0017] When the target number of nodes is greater than or equal to the minimum number of target nodes and less than or equal to the maximum number of target nodes, the number of expansions and contractions is the difference between the target number of nodes and the current number of nodes.

[0018] In a possible design, the total number of calls is divided by the single-node concurrency benchmark, and before obtaining the target number of nodes, the following is also included:

[0019] Determine the current single-node concurrency based on the total number of calls and the current number of nodes;

[0020] According to the current single-node concurrency and the single-node concurrency benchmark, determine the relative error between the current single-node concurrency and the single-node concurrency benchmark;

[0021] When the absolute value of the relative error is greater than the first threshold, the number of target nodes is calculated.

[0022] In one possible design, when the number of target nodes is less than the minimum number of target nodes, the value of the target node number is the minimum number of target nodes;

[0023] When the number of target nodes is greater than the maximum number of target nodes, the value of the target node number is the maximum number of target nodes.

[0024] In a second aspect, the present invention further provides an application expansion and contraction device, comprising:

[0025] The acquisition module is used to obtain the call log output when the first application calls the target object

[0026] A first determination module, used to determine the total number of calls of the first application according to the call log, where the total number of calls is used to represent the cumulative number of times the first application calls the target object;

[0027] A second determination module, used to determine the number of expansion and contraction of the first application according to the total number of calls and preset processing rules;

[0028] The processing module is used to perform scaling operations on the first application according to the scaling number.

[0029] In one possible design, the target object is a target service or a second application.

[0030] In one possible design, the first determining module has a function for:

[0031] The call logs are classified according to the name of the first application to determine the call information of the first application, where the call information includes: the name of the first application and the total number of calls of the first application.

[0032] In a possible design, the call information further includes: the current number of nodes on which the first application is running, a preset concurrency benchmark of the first application, a preset node quantity benchmark of the first application, a preset minimum number of target nodes for the first application, and a preset maximum number of target nodes for the first application; the second determination module is specifically used to:

[0033] Divide the concurrency benchmark by the node number benchmark to get the single-node concurrency benchmark;

[0034] Divide the total number of calls by the single-node concurrency benchmark to get the target number of nodes;

[0035] When the target number of nodes is greater than or equal to the minimum number of target nodes and less than or equal to the maximum number of target nodes, the number of expansions and contractions is the difference between the target number of nodes and the current number of nodes.

[0036] In a possible design, the second determination module is further used to:

[0037] Determine the current single-node concurrency based on the total number of calls and the current number of nodes;

[0038] According to the current single-node concurrency and the single-node concurrency benchmark, determine the relative error between the current single-node concurrency and the single-node concurrency benchmark;

[0039] When the absolute value of the relative error is greater than the first threshold, the number of target nodes is calculated.

[0040] In one possible design, when the number of target nodes is less than the minimum number of target nodes, the value of the target node number is the minimum number of target nodes;

[0041] When the number of target nodes is greater than the maximum number of target nodes, the value of the target node number is the maximum number of target nodes.

[0042] In a third aspect, the present invention further provides a database, comprising:

[0043] processor; and,

[0044] A memory for storing executable instructions of the processor;

[0045] The processor is configured to execute the scaling method of any application in the first aspect by executing executable instructions.

[0046] In a fourth aspect, an embodiment of the present invention further provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for scaling up and down of any one of the applications in the first aspect.

[0047] The present invention provides an application scaling method and device, which obtains a call log output when a first application calls a target object; determines the total number of calls of the first application according to the call log, and the total number of calls is used to represent the cumulative number of times the first application calls the target object; determines the scaling number of the first application according to the total number of calls and preset processing rules; and performs scaling operations on the first application according to the scaling number, thereby achieving scaling operations on the application while ensuring its timeliness and accuracy and reducing application resource deviations caused by the scaling operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0049] Figure 1 This is an application scenario diagram of a method for scaling an application according to an exemplary embodiment of the present invention;

[0050] Figure 2 A schematic flow chart of a method for scaling application capacity according to an exemplary embodiment of the present invention;

[0051] Figure 3 A schematic diagram of a flow chart of calling the total amount in a method for scaling an application according to an exemplary embodiment of the present invention;

[0052] Figure 4 A schematic diagram of a process of scaling capacity in a method for scaling capacity of an application according to an exemplary embodiment of the present invention;

[0053] Figure 5 It is a structural schematic diagram of an application expansion and contraction device according to an exemplary embodiment of the present invention;

[0054] Figure 6 The diagram is a schematic diagram of the structure of a database according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0057] The following is a detailed description of the technical solution of the present invention and how the technical solution of the present application solves the above technical problems with specific embodiments. The following is a description of the embodiments of the present invention in conjunction with the accompanying drawings.

[0058] Figure 1 FIG. 1 is an application scenario diagram of an application scaling method according to an exemplary embodiment of the present invention. Figure 1As shown, it includes a Kubernetes platform 101, an application Pod 102, a call record or log file storage file 103, a call volume calculation module 104, a call volume storage database 105, and a scaling control module 106. The Kubernetes platform 101 is the external interface of the application Pod 102, which is used for monitoring and scaling operations such as the application Pod 102. When the application in the application Pod 102 performs a call operation, the call record or call log is output, and these call records or call logs are collected in the storage file 103. The call volume calculation module 104 calculates and processes the call record or call log to obtain the call information of the application and stores it in the call volume storage database 105. The scaling control module 106 performs scaling judgment processing according to the call information of the application and the preset processing rules. When it is determined to perform scaling operations on the application, the application programming interface (Application Programming Interface, API) on the Kubernetes platform 101 is called to perform scaling operations on the corresponding application.

[0059] Figure 2 FIG. 1 is a flow chart of a method for scaling an application according to an exemplary embodiment of the present invention; Figure 2 As shown, the method for scaling up and down an application provided in this embodiment includes:

[0060] Step 201: Obtain a call log output when the first application calls a target object;

[0061] Specifically, during the operation of the system, the target object called by the first application can be the target business or the second application; that is, when a business call is implemented in the first application or when a business call is implemented between the first application and the second application, the corresponding call log or call record is output synchronously; and the call log or call record is collected in a storage file or a storage database. The following embodiments all take the call log as an example, and other situations are not repeated here.

[0062] Step 202: Determine the total number of calls of the first application according to the call log, where the total number of calls is used to represent the cumulative number of times the first application calls the target object;

[0063] Specifically, the call logs are classified according to the name of the first application to determine the call information of the first application, where the call information includes: the name of the first application and the total number of calls to the first application.

[0064] Further specifically, the call information also includes: the current number of nodes running the first application, the preset concurrency benchmark of the first application, the preset node quantity benchmark of the first application, the preset minimum number of target nodes of the first application, and the preset maximum number of target nodes of the first application.

[0065] Figure 3 This is a flow chart of the total amount of calls in the method for scaling up and down an application according to an exemplary embodiment of the present invention. As shown in the figure, a fixed time is set for statistics, and the fixed time can be 1 minute, 5 minutes or 10 minutes, etc. According to the set fixed time, the call log corresponding to the fixed time period is read from the storage file or storage database, and the call log is classified according to the name of the first application to determine the call information of the first application, and the call information is stored in the call volume storage database. The specific fields of the call information are shown in Table 1, which is a detailed table data structure of the call information.

[0066] Table 1

[0067]

[0068] (1) Time: The statistical time of a single application calling a service.

[0069] (2) Application name: The name of the application service in the system, which facilitates the identification and operation of subsequent application services.

[0070] (3) Total number of calls: The cumulative number of times a single application service calls a business.

[0071] (4) Current number of nodes: The number of node Pods where the current application service is running.

[0072] (5) Concurrency benchmark: The total concurrency supported by the application under normal circumstances, the preset value.

[0073] (6) Node quantity benchmark. Under normal circumstances, the number of node Pods that the application runs on, the preset value.

[0074] (7) Single-node concurrency benchmark. Under normal circumstances, the concurrency supported by a single node of the application. Usually, this value is "concurrency benchmark / node number benchmark".

[0075] (8) Minimum number of target nodes: The minimum number of nodes to ensure the normal operation of the application is preset.

[0076] (9) Maximum number of target nodes. To ensure that the entire system resources are not exhausted, the maximum number of nodes that the application can run on is set to a preset value.

[0077] Among them, the minimum target node number <= the node number benchmark <= the maximum target node number.

[0078] Step 203: Determine the number of expansion and contraction of the first application according to the total number of calls and preset processing rules;

[0079] Specifically, according to the fields of the call information in Table 1, the concurrency benchmark is divided by the node number benchmark to obtain the single-node concurrency benchmark; then the total number of calls is divided by the single-node concurrency benchmark to obtain the target number of nodes; when the target number of nodes is greater than or equal to the minimum number of target nodes and less than or equal to the maximum number of target nodes, the number of expansions and contractions is the difference between the target number of nodes and the current number of nodes.

[0080] To be more specific, when the target number of nodes is less than the minimum number of target nodes, the value of the target number of nodes is the minimum number of target nodes; when the target number of nodes is greater than the maximum number of target nodes, the value of the target number of nodes is the maximum number of target nodes; the number of expansions and contractions is the difference between the current value of the target number of nodes and the current number of nodes.

[0081] Figure 4 FIG. 1 is a schematic diagram of a process of scaling in a method for scaling an application according to an exemplary embodiment of the present invention. Figure 4 As shown, a fixed time is set to periodically read the call information of the application from the call volume storage database, wherein the fixed time is set with reference to the time when the call log is read and corresponds to the same time; based on the call information, the target node number of the first application is calculated and determined according to the preset processing rules, and compared with the preset minimum number of target nodes and the maximum number of target nodes to determine whether the target nodes are within a reasonable range. For example, the preset minimum number of target nodes is 30, and the maximum number of target nodes is 200. If the target number of nodes is 50, it is judged that the target number of nodes is within a reasonable range; if the target number of nodes is 20, the target number of nodes is taken as the minimum number of target nodes, 30; if the target number of nodes is 210, the target number of nodes is taken as the maximum number of target nodes, 200.

[0082] More specifically, the total number of calls is divided by the current number of nodes to obtain the current single-node concurrency; based on the current single-node concurrency and the single-node concurrency benchmark, the relative error between the current single-node concurrency and the single-node concurrency benchmark, that is, the deviation of the node concurrency, is determined; when the absolute value of the relative error is greater than the first threshold, the target number of nodes is calculated. For example, the first threshold is set to 10%, and when the relative error obtained by (current single-node concurrency - single-node concurrency benchmark) * 100 / single-node concurrency benchmark is a positive number and the absolute value is greater than 10%, the application needs to be expanded; when the relative error is negative and the absolute value is greater than 10%, the application needs to be reduced; when the absolute value of the relative error is less than or equal to 10%, the deviation of the node concurrency is still within the loadable range, and the application is not expanded or reduced.

[0083] Step 204: perform scaling operations on the first application according to the scaling number.

[0084] Specifically, after subtracting the current number of nodes from the target number of nodes to obtain the number of expansions and contractions of the first application, the API provided by the Kubernetes platform is called to perform expansion and contraction operations on the first application according to the number of expansions and contractions.

[0085] Through the application scaling method of steps 201-204 above, a call log output when the first application calls the target object is obtained; the total number of calls of the first application is determined according to the call log, and the total number of calls is used to represent the cumulative number of times the first application calls the target object; the scaling number of the first application is determined according to the total number of calls and preset processing rules; and the first application is scaled up or down according to the scaling number, so as to achieve scaling of the application while ensuring its timeliness and accuracy and reducing the deviation of application resources caused by the scaling operation.

[0086] Figure 5 FIG. 1 is a schematic diagram of a structure of an application expansion and contraction device according to an exemplary embodiment of the present invention. Figure 5 As shown, the application expansion and contraction device 50 provided in this embodiment includes:

[0087] The acquisition module 501 is used to acquire the call log output when the first application calls the target object;

[0088] A first determining module 502 is used to determine the total number of calls of the first application according to the call log, where the total number of calls is used to represent the cumulative number of times the first application calls the target object;

[0089] The second determination module 503 is used to determine the number of expansion and contraction of the first application according to the total number of calls and a preset processing rule;

[0090] The processing module 504 is used to perform scaling operations on the first application according to the scaling number.

[0091] In one possible design, the target object is a target service or a second application.

[0092] In one possible design, the first determining module 502 is configured to:

[0093] The call logs are classified according to the name of the first application to determine the call information of the first application, where the call information includes: the name of the first application and the total number of calls of the first application.

[0094] In a possible design, the call information further includes: the current number of nodes on which the first application is running, a preset concurrency benchmark of the first application, a preset node quantity benchmark of the first application, a preset minimum number of target nodes of the first application, and a preset maximum number of target nodes of the first application; the second determination module 503 is specifically used to:

[0095] Divide the concurrency benchmark by the node number benchmark to get the single-node concurrency benchmark;

[0096] Divide the total number of calls by the single-node concurrency benchmark to get the target number of nodes;

[0097] When the target number of nodes is greater than or equal to the minimum number of target nodes and less than or equal to the maximum number of target nodes, the number of expansions and contractions is the difference between the target number of nodes and the current number of nodes.

[0098] In a possible design, the second determining module 503 is further configured to:

[0099] Determine the current single-node concurrency based on the total number of calls and the current number of nodes;

[0100] According to the current single-node concurrency and the single-node concurrency benchmark, determine the relative error between the current single-node concurrency and the single-node concurrency benchmark;

[0101] When the absolute value of the relative error is greater than the first threshold, the number of target nodes is calculated.

[0102] In one possible design, when the number of target nodes is less than the minimum number of target nodes, the value of the target node number is the minimum number of target nodes;

[0103] When the number of target nodes is greater than the maximum number of target nodes, the value of the target node number is the maximum number of target nodes.

[0104] Figure 6 FIG. 1 is a schematic diagram of a database structure according to an exemplary embodiment of the present invention. Figure 6 As shown, this embodiment provides a database 60, including:

[0105] Processor 601; and

[0106] A memory 602, used to store executable instructions of the processor, and the memory may also be a flash memory;

[0107] The processor 601 is configured to execute the various steps in the above method by executing executable instructions. For details, please refer to the relevant description in the above method embodiment.

[0108] Optionally, the memory 602 may be independent or integrated with the processor 601 .

[0109] When the memory 602 is a device independent of the processor 601, the database 60 may further include:

[0110] The bus 603 is used to connect the processor 601 and the memory 602 .

[0111] In addition, an embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When at least one processor of a user device executes the computer-executable instructions, the user device executes the above-mentioned various possible methods.

[0112] Among them, computer-readable media include computer storage media and communication media, wherein the communication media include any media that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general or special-purpose computer. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also be present in a communication device as discrete components.

[0113] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for scaling an application, characterized in that: include: Obtaining the call log output when the first application calls the target object; The call logs are classified according to the name of the first application to determine the call information of the first application, the call information including: the name of the first application, the total number of calls of the first application, the current number of nodes on which the first application is running, a preset concurrency benchmark of the first application, a preset node number benchmark of the first application, a preset minimum number of target nodes of the first application, and a preset maximum number of target nodes of the first application; wherein the total number of calls is used to represent the cumulative number of times the first application calls the target object; Divide the concurrency benchmark by the node quantity benchmark to obtain a single-node concurrency benchmark; Divide the total number of calls by the single-node concurrency benchmark to obtain the target number of nodes; When the target number of nodes is greater than or equal to the minimum number of target nodes and less than or equal to the maximum number of target nodes, the number of expansions and contractions is the difference between the target number of nodes and the current number of nodes; Scaling the first application is performed according to the scaling number.

2. The method for scaling an application according to claim 1, characterized in that: The target object is a target service or a second application.

3. The method for scaling an application according to claim 1, characterized in that: Before obtaining the target number of nodes by dividing the total number of calls by the single-node concurrency benchmark, the method further includes: Determine the current single-node concurrency based on the total number of calls and the current number of nodes; Determine a relative error between the current single-node concurrency and the single-node concurrency benchmark according to the current single-node concurrency and the single-node concurrency benchmark; When the absolute value of the relative error is greater than a first threshold, the target node number is calculated.

4. The method for scaling an application according to claim 3, characterized in that: When the target node number is less than the minimum target node number, the target node number is taken as the minimum target node number; When the target node number is greater than the maximum target node number, the value of the target node number is the maximum target node number.

5. An application expansion and contraction device, characterized in that: include: An acquisition module, used to acquire a call log output when the first application calls the target object; a first determination module, configured to classify the call log according to the name of the first application, and determine the call information of the first application, wherein the call information includes: the name of the first application, the total number of calls of the first application, the current number of nodes on which the first application is running, a preset concurrency benchmark of the first application, a preset node number benchmark of the first application, a preset minimum number of target nodes of the first application, and a preset maximum number of target nodes of the first application; wherein the total number of calls is used to represent the cumulative number of times the first application calls the target object; The second determination module is used to divide the concurrency benchmark by the node quantity benchmark to obtain a single-node concurrency benchmark; divide the total number of calls by the single-node concurrency benchmark to obtain a target number of nodes; when the target number of nodes is greater than or equal to the minimum number of target nodes and less than or equal to the maximum number of target nodes, the number of expansions and contractions is the difference between the target number of nodes and the current number of nodes; A processing module is used to perform scaling operations on the first application according to the scaling number.

6. The expansion and contraction device for application according to claim 5, characterized in that: The target object is a target service or a second application.

7. A database, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; The processor is configured to execute the method for scaling the application described in any one of claims 1 to 4 by executing the executable instructions.

8. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for scaling the application described in any one of claims 1 to 4 is implemented.

9. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the scaling method of the application as claimed in any one of claims 1 to 4 is implemented.

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