Business system peak guarantee method, device, equipment, medium and program product

Abstract

The present disclosure provides a business system peak guarantee method, which can be applied to the technical field of information security. The business system peak guarantee method comprises: obtaining a business peak guarantee value, wherein the business peak guarantee value is an estimated peak value of transactions that the business system will bear in a first time interval in the future; obtaining actual production data saved in a pre-deployed application portrait tool, wherein the actual production data comprises data based on index description of a production environment of infrastructure and batch job classes; and based on the business peak guarantee value and the actual production data, adjusting a to-be-adjusted object through a peak model matching tool to meet business system peak guarantee, wherein the to-be-adjusted object comprises an object based on the infrastructure class and the batch job class. The present disclosure also provides a business system peak guarantee device, equipment, storage medium and program product.

Description

Technical Field

[0001] This disclosure relates to the field of information security technology, specifically to a method, apparatus, device, medium, and program product for ensuring peak performance of a business system. Background Technology

[0002] Business systems play a crucial role in the financial industry, supporting various business operations. Some banking systems are characterized by high concurrency and high transaction volume. During certain time periods, their transaction volume can reach several times the usual peak. Therefore, for the sake of business system stability, it is necessary to configure appropriate peak-load protection logic.

[0003] However, due to the high complexity of the system and the large number of functional modules involved, it is difficult for manual analysis to achieve accurate assessment in the short term, and the workload is huge, resulting in high implementation risks. Summary of the Invention

[0004] In view of the above problems, this disclosure provides methods, apparatus, equipment, media and program products for peak-hour business system assurance to improve the assurance capabilities of business systems.

[0005] According to a first aspect of this disclosure, a method for ensuring peak performance of a business system is provided, comprising: obtaining a business peak performance guarantee value, wherein the business peak performance guarantee value is an estimated business peak that the business system will withstand in a future first time interval; obtaining actual production data stored in a pre-deployed application profiling tool, wherein the actual production data includes data describing production environment indicators based on infrastructure class and batch operation class; and adjusting objects to be adjusted using a peak model matching tool based on the business peak performance guarantee value and the actual production data to meet the peak performance guarantee requirements of the business system, wherein the objects to be adjusted include objects based on the infrastructure class and the batch operation class.

[0006] According to embodiments of this disclosure, the actual production data in the application profiling tool is obtained by processing streaming data in node logs and persisting it to a specified location. The step of obtaining the actual production data stored in the pre-deployed application profiling tool includes obtaining the actual production data based on the specified location.

[0007] According to embodiments of this disclosure, the step of adjusting the object to be adjusted using a peak model matching tool based on the business peak guarantee value and the actual production data to meet the peak guarantee requirements of the business system includes: calculating the setting parameter value of the object to be adjusted based on the business peak guarantee value, a first time interval, and the actual production data; generating a technology call instruction based on the setting parameter value; and calling the corresponding technology based on the technology call instruction.

[0008] According to embodiments of this disclosure, the step of calculating the setting parameter value of the object to be adjusted based on the business peak guarantee value, the first time interval, and the actual production data includes: for the infrastructure class, extracting container parameters from the actual production data, the container parameters including the actual number of containers and the container capacity; calculating the expected number of containers based on the business peak guarantee value and the container capacity; calculating the number of expansion containers based on the actual number of containers and the expected number of containers; and forming an expansion instruction based on the number of expansion containers.

[0009] According to an embodiment of this disclosure, the step of calculating the setting parameter value of the object to be adjusted based on the business peak guarantee value, the first time interval, and the actual production data further includes: for the batch operation type, extracting batch operation parameters from the actual production data, the batch operation parameters including batch operation time and batch operation running status; and generating an operation time window adjustment instruction, the time window adjustment instruction causing the batch operation time and the batch operation running status to be delayed until the first time interval before execution.

[0010] According to embodiments of this disclosure, the actual production data further includes data describing the production environment indicators based on online service classes, and the objects to be adjusted further include objects based on the online service classes. The adjustment of the objects to be adjusted using a peak model matching tool based on the business peak guarantee value and the actual production data further includes: for the online service class, receiving an online service adjustment instruction; generating an online service window deactivation instruction based on the online service adjustment instruction; and deactivating the online service within the first time interval based on the online service window deactivation instruction.

[0011] A second aspect of this disclosure provides a business peak guarantee device, comprising: a guarantee value acquisition module for acquiring a business peak guarantee value, wherein the business peak guarantee value is the estimated peak transaction volume that the business system will withstand in a future first time interval; an actual production data acquisition module for acquiring actual production data stored in a pre-deployed application profiling tool, wherein the actual production data includes data describing the production environment based on infrastructure and batch operation indicators; and an object adjustment module for adjusting objects to be adjusted based on the business peak guarantee value and the actual production data, using a peak model matching tool to meet the business system peak guarantee requirements, wherein the objects to be adjusted include objects based on the infrastructure and batch operation categories.

[0012] According to an embodiment of this disclosure, the actual production data in the application profiling tool is obtained by processing streaming data in node logs and persisting it to a specified location. The actual production data acquisition module is also used to acquire the actual production data based on the specified location.

[0013] According to embodiments of this disclosure, the object adjustment module is further configured to calculate setting parameter values ​​for the object to be adjusted based on the business peak guarantee value, the first time interval, and the actual production data; generate a technology call instruction based on the setting parameter values; and

[0014] The corresponding technology is invoked based on the aforementioned technology invocation instruction.

[0015] According to an embodiment of this disclosure, the object adjustment module is further configured to, for the infrastructure class, extract container parameters from the actual production data, the container parameters including the actual number of containers and the container capacity; calculate the expected number of containers based on the business peak guarantee value and the container capacity; calculate the number of expansion containers based on the actual number of containers and the expected number of containers; and generate an expansion instruction based on the number of expansion containers.

[0016] According to an embodiment of this disclosure, the object adjustment module is further configured to extract batch operation parameters from the actual production data for the batch operation class, the batch operation parameters including batch operation time and batch operation running status; and generate a job time window adjustment instruction, the time window adjustment instruction causing the batch operation time and the batch operation running status to be delayed until the first time interval before execution.

[0017] According to an embodiment of this disclosure, the object adjustment module is further configured to receive an online service adjustment instruction for the online service class; generate an online service window deactivation instruction based on the online service adjustment instruction; and deactivate the online service within the first time interval based on the online service window deactivation instruction.

[0018] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the aforementioned peak traffic assurance method.

[0019] A fourth aspect of this disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the aforementioned peak traffic protection method.

[0020] A fifth aspect of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned business peak protection method.

[0021] The embodiments disclosed herein offer the following advantages: 1. By combining application profiling tools and peak model matching tools, the problem of frequently needing to manually assess and adjust system performance capacity based on transaction forecast data during peak promotional events at various payment institutions is solved, thus improving reusability. Specifically, the peak model matching tool can analyze system performance capacity bottlenecks based on dynamic transaction data and automatically implement adjustments, reducing the operational risks associated with manual adjustments. 2. Adjustments are made to infrastructure and batch operation objects to meet peak business system requirements and maximize the support capabilities for major promotional events. Attached Figure Description

[0022] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0023] Figure 1 This diagram illustrates an application scenario of a peak-hour service guarantee method for a business system according to an embodiment of the present disclosure.

[0024] Figure 2 A flowchart illustrating a peak-hour service guarantee method for a business system according to an embodiment of the present disclosure is shown schematically.

[0025] Figure 3 A flowchart illustrating a peak model adjustment method according to an embodiment of the present disclosure is shown schematically.

[0026] Figure 4 A flowchart illustrating an infrastructure class adjustment method according to an embodiment of this disclosure is shown schematically;

[0027] Figure 5 A flowchart illustrating a batch job class adjustment method according to an embodiment of the present disclosure is shown schematically;

[0028] Figure 6 A flowchart illustrating an online service class adjustment method according to an embodiment of the present disclosure is shown schematically.

[0029] Figure 7 A schematic diagram illustrating the structural block diagram of a peak-load protection device for a business system according to an embodiment of the present disclosure is shown; and

[0030] Figure 8 A block diagram of an electronic device suitable for implementing a peak-load protection method for a business system, according to an embodiment of the present disclosure, is shown schematically. Detailed Implementation

[0031] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0032] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0033] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0034] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0035] The business systems cover all areas of finance. Taking payment systems as an example, developers have found that payment systems are characterized by high concurrency and high transaction volume. With the development of online payment business, payment institutions are launching various promotional activities. For the sake of system stability and transaction continuity, before major promotional events, regulatory agencies, banks, and payment institutions will remove specific safeguard indicators based on the peak business volume. Under the requirements of these safeguard indicators, each indicator will be manually analyzed to make corresponding adjustments.

[0036] Specifically, based on the protection requirements, resource deployment, transaction chain changes, operational dependencies, and impacts are analyzed manually item by item. Conventional methods have the following problems: 1. As system complexity increases, manual analysis struggles to provide accurate assessments in a short time, leading to a high risk of omissions. Furthermore, the protection involves numerous functional modules, resulting in a massive workload for assessment. 2. Manual processing struggles to create standardized implementation plans, leading to repetitive work during each protection effort. It also lacks the ability to dynamically adjust online in real time, making it difficult to flexibly adapt to changes in protection objectives. These two issues indicate that current peak protection solutions for high-sensitivity payment systems have low levels of automation, online capabilities, and capability reusability, resulting in large implementation workloads and high implementation risks.

[0037] To address the technical problem of insufficient support capabilities in existing technologies when dealing with business scenarios characterized by significant business growth and sudden peak increases, embodiments of this disclosure provide a peak-load support method for a business system, comprising: obtaining a business peak support value, wherein the business peak support value is an estimated business peak that the business system will withstand in a future first time interval; obtaining actual production data stored in a pre-deployed application profiling tool, wherein the actual production data includes data describing production environment indicators based on infrastructure and batch operation categories; and adjusting objects to be adjusted using a peak model matching tool based on the business peak support value and the actual production data to meet the peak-load support requirements of the business system, wherein the objects to be adjusted include objects based on the infrastructure and batch operation categories.

[0038] The embodiments disclosed herein offer the following advantages: 1. By combining application profiling tools and peak model matching tools, the problem of frequently needing to manually assess and adjust system performance capacity based on transaction forecast data during peak promotional events at various payment institutions is solved, thus improving reusability. Specifically, the peak model matching tool can analyze system performance capacity bottlenecks based on dynamic transaction data and automatically implement adjustments, reducing the operational risks associated with manual adjustments. 2. Adjustments are made to infrastructure and batch operation objects to meet peak business system requirements and maximize the support capabilities for major promotional events.

[0039] Figure 1 The diagram illustrates an application scenario of a peak-hour support method for a business system according to an embodiment of this disclosure.

[0040] like Figure 1 As shown, application scenario 100 according to this embodiment may include terminal devices 101, 102, and 103, network 104, and server 105. Network 104 is used as a medium to provide a communication link between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0041] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0042] Terminal devices 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0043] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using terminal devices 101, 102, and 103 (for example only). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0044] It should be noted that the peak-hour service protection method for the business system provided in this disclosure embodiment can generally be executed by server 105. Correspondingly, the peak-hour service protection device for the business system provided in this disclosure embodiment can generally be located in server 105. The peak-hour service protection method for the business system provided in this disclosure embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105. Correspondingly, the peak-hour service protection device for the business system provided in this disclosure embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105.

[0045] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0046] The following will be based on Figure 1 The described scene, through Figures 2-6 The peak-hour protection method for business systems according to the disclosed embodiments is described in detail.

[0047] Figure 2 A flowchart illustrating a peak-load protection method for a business system according to an embodiment of this disclosure is shown.

[0048] like Figure 2As shown, the peak traffic protection method for the business system in this embodiment includes operations S210 to S230, and the peak traffic protection method for the business system can be executed by server 105.

[0049] In operation S210, the peak service guarantee value is obtained, wherein the peak service guarantee value is the estimated peak service value that the service system will bear in the first time interval in the future.

[0050] Specifically, when business systems are operating, in response to events that cause a surge in business volume, anomalies may occur at some point in the future; these anomalies are the business peak values. To ensure normal business operation, relevant departments will calculate and obtain the guaranteed business peak value for the first time interval to guide operations and maintenance personnel in making advance deployments.

[0051] Taking a transaction scenario as an example, during the Double Eleven promotion period, relevant calculations indicate that the peak transaction protection target is 25,000 transactions per second, with the first time interval being 20:00:00-21:00:00. That is, within this time interval, the peak transaction volume may reach 25,000 transactions per second.

[0052] In operation S220, the actual production data stored in the pre-deployed application profiling tool is obtained, wherein the actual production data includes data describing the production environment based on infrastructure and batch operation indicators.

[0053] According to embodiments of this disclosure, the actual production data in the application profiling tool is obtained by processing streaming data in node logs and persisting it to a specified location. The step of obtaining the actual production data stored in the pre-deployed application profiling tool includes obtaining the actual production data based on the specified location.

[0054] Specifically, application profiling tools are a technology based on log data collection that describes the resource configuration, deployment, and operation of business systems. Furthermore, application profiling tools can enable visualized remote transaction monitoring of the system, monitor business differences between different zones, support topology display of the health status of individual nodes, and quickly correlate abnormal data protocols for analysis and location.

[0055] The application profiling tool contains three components: a data acquisition component, a data processing component, and a data storage component. Among them, (1) the data acquisition component: through component deployment, it completes the operation of the system's basic equipment, monitors the operation of batch and online jobs, and completes the collection of relevant data in a specified format. The data acquisition mainly collects node logs in the form of streaming data and transmits them to the profiling device, and then calls the data storage component to complete the data storage. (2) the data processing component: mainly formats and standardizes the collected data. The data processing component: formats and standardizes the collected data, and constructs a link topology diagram of system resources, job configuration, operation status, and interrelationships. It adapts to the peak model configuration device's needs in terms of display and calculation. (3) the data storage component: storage mainly realizes the persistence of collected data in the form of tables or files, and stores the collected data in a specified location, mainly in database tables.

[0056] Therefore, it's understandable that actual production data is obtained by processing raw data collected from logs, and it contains multiple different categories of information describing the production environment. For this reason, only infrastructure and batch job-related actual production data are selected. Of course, in some scenarios, online service-related actual production data can be further selected.

[0057] According to embodiments of this disclosure, the actual production data further includes data describing the production environment based on online service classes, and the objects to be adjusted further include objects based on the online service classes.

[0058] In operation S230, based on the business peak guarantee value and the actual production data, the objects to be adjusted are adjusted using a peak model matching tool to meet the peak guarantee requirements of the business system. The objects to be adjusted include objects based on the infrastructure class and the batch operation class.

[0059] By calculating the peak business guarantee value and actual production data, the specific parameters that should be adjusted for the corresponding objects (i.e., parameters) of infrastructure and batch operation classes are calculated. These adjusted parameters are then used to implement parameter adjustments through a peak model matching tool.

[0060] The embodiments disclosed herein offer the following advantages: 1. By combining application profiling tools and peak model matching tools, the problem of frequently needing to manually assess and adjust system performance capacity based on transaction forecast data during peak promotional events at various payment institutions is solved, thus improving reusability. Specifically, the peak model matching tool can analyze system performance capacity bottlenecks based on dynamic transaction data and automatically implement adjustments, reducing the operational risks associated with manual adjustments. 2. Adjustments are made to infrastructure and batch operation objects to meet peak business system requirements and maximize the support capabilities for major promotional events.

[0061] It should be noted that the aforementioned application profiling tool and peak model matching tool refer to programs with pre-encapsulated logic. Specifically, the peak model matching tool encapsulates components for parameter value calculation, generating technology call instructions, and making the technology call itself. The operational mechanism of the peak model matching tool will be explained in detail below:

[0062] Figure 3 A flowchart illustrating a peak model adjustment method according to an embodiment of the present disclosure is shown schematically.

[0063] like Figure 3 As shown, the peak model adjustment method of this embodiment includes operations S310 to S330. Operations S310 to S330 can at least partially perform the above-described operation S230.

[0064] In operation S310, the setting parameter value of the object to be adjusted is calculated based on the business peak guarantee value, the first time interval, and the actual production data.

[0065] Specifically, by using the corresponding adjustment rules for different categories, relevant data is extracted from actual production data for calculation to obtain the parameter values ​​to be adjusted.

[0066] In operation S320, a technical invocation instruction is generated based on the set parameter value.

[0067] In operation S330, the corresponding technology is invoked based on the technology invocation instruction.

[0068] Specifically, assuming the application profiling device has completed system deployment and collected relevant data, the system peak load protection device reads the protection targets and rule parameters set by the peak model configuration device and completes system performance capacity adjustments. Different invocation commands correspond to different invocation technologies, which include at least: template resource domain tags, graceful container scrolling, integrated batch job command technology, distributed service framework, and hot reloading technology. Template resource domain tags and graceful container scrolling are applied to infrastructure, integrated batch job command technology is applied to batch jobs, and distributed service framework and hot reloading technology are applied to online services.

[0069] Figure 4 A flowchart illustrating an infrastructure class adjustment method according to an embodiment of this disclosure is shown schematically.

[0070] like Figure 4 As shown, the infrastructure adjustment method of this embodiment includes operations S410 to S440. Operations S410 to S440 can at least partially perform the above-described operation S320.

[0071] In operation S410, for the infrastructure class, container parameters are extracted from the actual production data, including the actual number of containers and container capacity.

[0072] Specifically, the infrastructure class refers to infrastructure such as containers. When setting the corresponding parameter objects of the infrastructure class, the hardware-level configuration of the infrastructure needs to be considered.

[0073] In operation S420, the expected number of containers is calculated based on the peak service guarantee value and the container capacity.

[0074] In operation S430, the number of expansion containers is calculated based on the actual number of containers and the expected number of containers.

[0075] In operation S440, an expansion command is generated based on the number of expansion containers.

[0076] Specifically, different containers correspond to different container specifications, which characterize the maximum number of transactions per second (DPS) for that container. Container specifications include: number of threads, number of connections, number of CPUs, memory, operating system, and the maximum number of DPS for a single container. Of course, only the maximum number of DPS for a single container is needed for calculation.

[0077] For example, with a peak service capacity of 25,000 transactions per second and an estimated peak time (first time interval) of 20:00:00-21:00:00, based on single-server container device performance estimates, 100 containers of model "4C6G" are required. Currently, 50 containers are deployed in production, so an additional 50 need to be added. The specifications of the "4C6G" container are: 200 threads, 200 connections, 4 CPUs, 6GB of memory, SUSE 12 SP5 operating system, and a maximum of 250 transactions per second per container.

[0078] Then, an expansion command requiring 50 additional units is generated. This command invokes template resource domain tags and container graceful scrolling techniques to achieve container expansion.

[0079] Figure 5 A flowchart illustrating a batch job class adjustment method according to an embodiment of the present disclosure is shown schematically.

[0080] like Figure 5 As shown, the batch job adjustment method of this embodiment includes operations S510 to S520. Operations S510 to S520 can at least partially execute the above-described operation S320.

[0081] In operation S510, for the batch job class, the batch job parameters are extracted from the actual production data. The batch job parameters include the batch job time and the batch job running status.

[0082] Specifically, the batch job dimension refers to some elements involved in batch jobs. Among them, batch job time refers to the time period during which batch job tasks are executed, and batch job running status includes being started and stopped (e.g., the running status of data lake entry jobs).

[0083] In operation S520, a job time window adjustment instruction is generated, which causes the batch job time and the batch job running status to be delayed until the first time interval before execution.

[0084] For example, if the original batch job overlaps with the first time interval, the execution time of the host data import job within the first time interval (i.e., 20:00:00-21:00:00) is postponed to 21:00:00 (this includes a customizable parameter N, where N is the postponement time). (Alternatively, it could be any other time period that does not overlap with the first time interval). The execution status of the transaction entry into the lake job (i.e., the batch job execution status) is then adjusted to FALSE to avoid peak periods. This time window adjustment scheme is based on integrated batch job instruction technology to achieve batch job execution time window adjustment.

[0085] Figure 6 A flowchart illustrating an online service class adjustment method according to an embodiment of this disclosure is shown schematically.

[0086] like Figure 6 As shown, the online service adjustment method of this embodiment includes operations S610 to S630. Operations S610 to S630 can at least partially perform the above-described operation S220.

[0087] In operation S610, for the online service class, an online service adjustment instruction is received.

[0088] Specifically, online services refer to some elements involved in online services.

[0089] In operation S620, an online service window deactivation command is generated based on the online service adjustment command.

[0090] In operation S630, the online service is disabled within the first time interval based on the online service window disable instruction.

[0091] Specifically, this can be achieved by using distributed service frameworks and hot reloading technologies to enable the starting, stopping, and invoking of service windows, as well as adjustments to service degradation strategies.

[0092] Specifically, online services that consume high resources but do not affect core processes will be suspended during a designated period (e.g., the first time interval "20:00:00-21:00:00"). For example, for installment payment services that do not involve core processes, the following parameters can be adjusted: "Installment payment service concurrent traffic = 5 transactions / second; Installment payment service suspension time = 20:00:00-21:00:00; Installment payment service timeout = 10 milliseconds".

[0093] It should be noted that operations S610 to S630 are different from operations S310 to S330 and / or operations S410 to S440 and / or operations S510 to S520. Operations S310 to S330 and / or operations S410 to S440 and / or operations S510 to S520 are executed automatically, while adjustments to online services require receiving corresponding adjustment instructions, which can be sent manually.

[0094] Based on the above-mentioned peak-load protection method for business systems, this disclosure also provides a peak-load protection device for business systems. The following will combine... Figure 7 The device is described in detail.

[0095] Figure 7 A schematic block diagram of a peak-load protection device for a business system according to an embodiment of the present disclosure is shown.

[0096] like Figure 7 As shown, the business system peak guarantee device 700 in this embodiment includes a guarantee value acquisition module 710, an actual production data acquisition module 720, and an object adjustment module 730.

[0097] The guarantee value acquisition module 710 is used to acquire the business peak guarantee value, wherein the business peak guarantee value is the estimated peak value of transactions that the business system will withstand in a first time interval in the future. In one embodiment, the guarantee value acquisition module 710 can be used to perform the operation S210 described above, which will not be repeated here.

[0098] The actual production data acquisition module 720 is used to acquire actual production data stored in a pre-deployed application profiling tool. This actual production data includes data describing production environment indicators based on infrastructure and batch operation categories. In one embodiment, the actual production data acquisition module 720 can be used to perform the operation S220 described above, which will not be repeated here.

[0099] The object adjustment module 730 is used to adjust the objects to be adjusted based on the business peak guarantee value and the actual production data using a peak model matching tool to meet the peak guarantee requirements of the business system. The objects to be adjusted include objects based on the infrastructure class and the batch operation class. In one embodiment, the object adjustment module 730 can be used to perform the operation S230 described above, which will not be repeated here.

[0100] The embodiments disclosed herein offer the following advantages: 1. By combining application profiling tools and peak model matching tools, the problem of frequently needing to manually assess and adjust system performance capacity based on transaction forecast data during peak promotional events at various payment institutions is solved, thus improving reusability. Specifically, the peak model matching tool can analyze system performance capacity bottlenecks based on dynamic transaction data and automatically implement adjustments, reducing the operational risks associated with manual adjustments. 2. Adjustments are made to infrastructure and batch operation objects to meet peak business system requirements and maximize the support capabilities for major promotional events.

[0101] According to an embodiment of this disclosure, the actual production data in the application profiling tool is obtained by processing streaming data in node logs and persisting it to a specified location. The actual production data acquisition module is also used to acquire the actual production data based on the specified location.

[0102] According to embodiments of this disclosure, the object adjustment module is further configured to calculate setting parameter values ​​for the object to be adjusted based on the business peak guarantee value, the first time interval, and the actual production data; generate a technology call instruction based on the setting parameter values; and

[0103] The corresponding technology is invoked based on the aforementioned technology invocation instruction.

[0104] According to an embodiment of this disclosure, the object adjustment module is further configured to, for the infrastructure class, extract container parameters from the actual production data, the container parameters including the actual number of containers and the container capacity; calculate the expected number of containers based on the business peak guarantee value and the container capacity; calculate the number of expansion containers based on the actual number of containers and the expected number of containers; and generate an expansion instruction based on the number of expansion containers.

[0105] According to an embodiment of this disclosure, the object adjustment module is further configured to extract batch operation parameters from the actual production data for the batch operation class, the batch operation parameters including batch operation time and batch operation running status; and generate a job time window adjustment instruction, the time window adjustment instruction causing the batch operation time and the batch operation running status to be delayed until the first time interval before execution.

[0106] According to an embodiment of this disclosure, the object adjustment module is further configured to receive an online service adjustment instruction for the online service class; generate an online service window deactivation instruction based on the online service adjustment instruction; and deactivate the online service within the first time interval based on the online service window deactivation instruction.

[0107] According to embodiments of this disclosure, any plurality of modules among the guaranteed value acquisition module 710, the actual production data acquisition module 720, and the object adjustment module 730 can be merged into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the guaranteed value acquisition module 710, the actual production data acquisition module 720, and the object adjustment module 730 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented by any other reasonable means of integrating or packaging the circuitry, or implemented in any one of software, hardware, and firmware methods, or in a suitable combination of any of these. Alternatively, at least one of the guaranteed value acquisition module 710, the actual production data acquisition module 720, and the object adjustment module 730 can be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

[0108] Figure 8 A block diagram of an electronic device suitable for implementing a peak-load protection method for a business system, according to an embodiment of the present disclosure, is shown schematically.

[0109] like Figure 8 As shown, an electronic device 800 according to an embodiment of this disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage portion 808 into a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this disclosure.

[0110] RAM 803 stores various programs and data required for the operation of electronic device 800. Processor 801, ROM 802, and RAM 803 are interconnected via bus 804. Processor 801 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 802 and / or RAM 803. It should be noted that the programs may also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0111] According to embodiments of this disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to a bus 804. The electronic device 800 may also include one or more of the following components connected to the I / O interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 810 as needed so that computer programs read from it can be installed into the storage section 808 as needed.

[0112] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0113] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 802 and / or RAM 803 and / or one or more memories other than ROM 802 and RAM 803 described above.

[0114] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this disclosure.

[0115] When the computer program is executed by the processor 801, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0116] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 809, and / or installed from a removable medium 811. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0117] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 809, and / or installed from removable medium 811. When the computer program is executed by processor 801, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0118] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0119] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0120] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0121] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A method for ensuring smooth operation of a business system during peak periods, comprising: Obtain the peak service guarantee value, wherein the peak service guarantee value is the estimated peak service value that the business system will withstand in the first time interval in the future; Obtain actual production data stored in pre-deployed application profiling tools, wherein the actual production data includes data describing production environment indicators based on infrastructure and batch operation categories; and Based on the business peak guarantee value and the actual production data, the objects to be adjusted are adjusted using a peak model matching tool to meet the peak guarantee requirements of the business system. The objects to be adjusted include objects based on the infrastructure class and the batch operation class. The step of calculating the setting parameter value of the object to be adjusted based on the business peak guarantee value, the first time interval, and the actual production data includes: for the batch operation type, extracting the batch operation parameters from the actual production data, the batch operation parameters including the batch operation time and the batch operation running status; and generating an operation time window adjustment instruction, the time window adjustment instruction causing the batch operation time and the batch operation running status to be delayed until the first time interval before execution, wherein the time window adjustment instruction delays the batch operation by adjusting the running status of the lake entry operation.

2. The method according to claim 1, wherein, The actual production data in the application profiling tool is obtained by processing streaming data in node logs and persisting it to a specified location. The process of obtaining the actual production data stored in the pre-deployed application profiling tool includes: obtaining the actual production data based on the specified location.

3. The method according to claim 1, wherein, The adjustment of the target object based on the business peak guarantee value and the actual production data, using a peak model matching tool, to meet the peak guarantee requirements of the business system includes: The setting parameter values ​​of the object to be adjusted are calculated based on the business peak guarantee value, the first time interval, and the actual production data. Instructions are invoked based on the parameter value generation technology described above; and The corresponding technology is invoked based on the aforementioned technology invocation instruction.

4. The method according to claim 3, wherein, The calculation of the setting parameter value of the object to be adjusted based on the business peak guarantee value, the first time interval, and the actual production data includes: For the infrastructure class, extract the container parameters from the actual production data. The container parameters include the actual number of containers and the container capacity. Based on the peak service guarantee value and the container capacity, calculate the expected number of containers; Calculate the number of expansion containers based on the actual number of containers and the projected number of containers; and An expansion instruction is generated based on the number of expansion containers.

5. The method according to any one of claims 1 to 4, wherein, The actual production data also includes data describing the production environment based on online service classes, and the objects to be adjusted also include objects based on the online service classes. The adjustment of the target object based on the peak service guarantee value and the actual production data using a peak model matching tool also includes: For the aforementioned online service class, receive online service adjustment instructions; Based on the online service adjustment instruction, a command to disable the online service window is generated; and The online service is disabled within the first time interval based on the online service window disable instruction.

6. A peak-hour protection device for a business system, comprising: The guarantee value acquisition module is used to acquire the business peak guarantee value, wherein the business peak guarantee value is the estimated peak value of transactions that the business system will bear in the first time interval in the future; The actual production data acquisition module is used to acquire actual production data stored in a pre-deployed application profiling tool. This actual production data includes data describing production environment indicators based on infrastructure and batch operation categories; and The object adjustment module is used to adjust the objects to be adjusted based on the business peak guarantee value and the actual production data using a peak model matching tool to meet the peak guarantee of the business system. The objects to be adjusted include objects based on the infrastructure class and the batch operation class. The step of calculating the setting parameter value of the object to be adjusted based on the business peak guarantee value, the first time interval, and the actual production data includes: for the batch operation type, extracting the batch operation parameters from the actual production data, the batch operation parameters including the batch operation time and the batch operation running status; and generating an operation time window adjustment instruction, the time window adjustment instruction causing the batch operation time and the batch operation running status to be delayed until the first time interval before execution, wherein the time window adjustment instruction delays the batch operation by adjusting the running status of the lake entry operation.

7. An electronic device, comprising: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 5.

9. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 5.

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