System capacity expansion method, device, equipment, medium and program product

Abstract

This disclosure provides a system expansion method applicable to the field of computer technology. The method includes: cyclically acquiring transaction data generated by a target system within each preset time interval; for the i-th time interval, determining that the i-th transaction data corresponding to the i-th transaction duration generated by the target system within the i-th time interval meets a first preset condition, identifying the i-th transaction data as questionable transaction data, and determining a processing score for the i-th transaction within the i-th time interval based on the system weight of the target system; updating the system capacity score of the target system based on the i-th transaction processing score to obtain an updated system capacity score; and expanding the system capacity of the target system if the updated system capacity score is greater than or equal to a first threshold. This disclosure also provides a system expansion device, equipment, storage medium, and program product.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more specifically to a system expansion method, apparatus, device, medium, and program product. Background Technology

[0002] System capacity refers to the number of services a system can support and return normally. System capacity can be determined by the number of cluster nodes and the processing capacity of each node. In practical applications, system capacity can be configured based on the number of business requests in historical records. After configuration, the system's processing resources are fixed, and therefore the system capacity is also fixed. However, as the number of business requests increases, the system capacity becomes insufficient to efficiently handle a large number of requests, leading to the unavailability of the entire service chain and causing large-scale failures.

[0003] In related technologies, the need for system expansion is typically determined by assessing whether the system capacity is less than a preset threshold during idle periods. However, this method does not incorporate comprehensive analysis of business requests, making it difficult to accurately determine system resource usage and prone to sluggish or overly sensitive responses to expansion and contraction. Summary of the Invention

[0004] In view of the above problems, this disclosure provides a system expansion method, apparatus, device, medium and program product.

[0005] According to the first aspect of this disclosure, a system expansion method is provided, comprising:

[0006] According to a preset time interval, the transaction data generated by the target system in each time interval is obtained cyclically, wherein the transaction data includes the transaction duration;

[0007] For the i-th time interval in each time interval, if the duration of the i-th transaction data corresponding to the i-th transaction data generated by the target system in the i-th time interval meets the first preset condition, the i-th transaction data is determined to be questionable transaction data, and the i-th transaction processing score for the i-th time interval is determined according to the system weight of the target system, where i is a positive integer.

[0008] The system capacity score of the target system is updated based on the transaction processing score of the i-th time interval to obtain the updated system capacity score. This updated system capacity score is determined based on the transaction processing scores of the i-1 time intervals preceding the start time of the i-th time interval, and represents the system capacity utilization rate of the target system.

[0009] If the updated system capacity score is greater than or equal to the first threshold, the system capacity of the target system is expanded.

[0010] According to embodiments of this disclosure, the i-th transaction data mentioned above includes multiple entries;

[0011] The above methods also include:

[0012] Based on the i-th transaction duration of each of the i-th transaction data points mentioned above, determine the i-th average transaction duration for the i-th time interval mentioned above.

[0013] If the average transaction duration of the i-th time interval meets the first preset condition, the i-th transaction score is determined according to the system weight of the target system.

[0014] According to embodiments of this disclosure, the above method further includes:

[0015] Based on the historical transaction data generated before the start time of the i-th time interval, determine the historical average transaction duration;

[0016] If the ratio of the i-th average transaction duration to the historical average transaction duration is greater than the second threshold, then the i-th average transaction duration is determined to satisfy the first preset condition.

[0017] According to embodiments of this disclosure, the transaction data also includes transaction status;

[0018] Before determining the i-th transaction data as questionable transaction data, the following steps are also included:

[0019] Based on the historical transaction data generated before the start time of the i-th time interval, determine the historical transaction success rate;

[0020] Based on the i-th transaction status of each of the i-th transaction data points mentioned above, determine the i-th transaction success rate for the i-th time interval;

[0021] If the ratio of the success rate of the i-th transaction to the historical transaction success rate is less than the third threshold, the i-th transaction data is determined to be questionable transaction data.

[0022] According to embodiments of this disclosure, the target system includes multiple system nodes; wherein, determining the i-th transaction score for the i-th time interval based on the system weight of the target system includes:

[0023] For the j-th system node among the multiple system nodes mentioned above, determine the j-th transaction sub-data corresponding to the j-th system node from the i-th transaction data;

[0024] If the duration of the jth transaction corresponding to the jth transaction sub-data satisfies the first preset condition, the weight of the jth node of the jth system node is determined based on the historical transaction data for the jth system node generated before the start time of the ith time interval.

[0025] The score for the i-th transaction processing is determined based on the node weights of the aforementioned system nodes and the system weight of the aforementioned target system.

[0026] According to embodiments of this disclosure, the above method further includes:

[0027] If the duration of the jth transaction in the jth transaction sub-data meets the first preset condition, the jth transaction sub-data is marked as abnormal transaction sub-data.

[0028] The determination of the weight of the j-th system node based on the historical transaction data generated before the start time of the i-th time interval includes:

[0029] If the historical transaction data for the j-th system node generated before the start time of the i-th time interval contains abnormal transaction sub-data, the weight of the j-th system node is determined to be the first value.

[0030] If the historical transaction data for the j-th system node generated before the start time of the i-th time interval does not contain abnormal transaction sub-data, the weight of the j-th system node is determined to be the second value.

[0031] According to embodiments of this disclosure, the above method further includes:

[0032] After expanding the system capacity of the aforementioned target system, adjust the system capacity score of the aforementioned target system to zero; or;

[0033] According to a preset time period, the system capacity score of the above target system is repeatedly cleared to zero, so that the system capacity score of the above target system at the end of each preset time period is zero.

[0034] According to embodiments of this disclosure, before expanding the system capacity of the target system when the updated system capacity score meets the first threshold, the method further includes:

[0035] Obtain a preset whitelist, and if the preset whitelist does not contain the target system, expand the system capacity of the target system.

[0036] A second aspect of this disclosure provides a system expansion device, comprising:

[0037] The first acquisition module is used to cyclically acquire transaction data generated by the target system within each time interval according to a preset time interval, wherein the transaction data includes the transaction duration;

[0038] The first determining module is used to determine, for the i-th time interval in each time interval, if the i-th transaction duration corresponding to the i-th transaction data generated by the target system in the i-th time interval meets the first preset condition, to determine the i-th transaction data as questionable transaction data, and to determine the i-th transaction processing score for the i-th time interval according to the system weight of the target system, where i is a positive integer.

[0039] The update module is used to update the system capacity score of the target system based on the i-th transaction processing score, thereby obtaining the updated system capacity score. The system capacity score is determined based on the transaction processing scores corresponding to the i-1 time intervals prior to the start time of the i-th time interval, and the system capacity score represents the system capacity utilization rate of the target system.

[0040] The expansion module is used to expand the system capacity of the target system when the updated system capacity score is greater than or equal to the first threshold.

[0041] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0042] A fourth aspect of this disclosure also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0043] The fifth aspect of this disclosure also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0044] According to embodiments of this disclosure, a technical solution is implemented by: cyclically acquiring transaction data containing transaction durations generated by the target system within each time interval according to a preset time interval; then, for the i-th time interval, determining that the i-th transaction duration corresponding to the i-th transaction data generated by the target system within the i-th time interval meets a first preset condition, identifying the i-th transaction data as questionable transaction data, and determining the i-th transaction processing score for the i-th time interval based on the system weight of the target system; updating the system capacity score of the target system based on the i-th transaction processing score to obtain an updated system capacity score; and expanding the system capacity of the target system if the updated system capacity score is greater than or equal to a first threshold. Since this disclosure determines whether the transaction duration within a time interval meets the preset condition based on the transaction duration, and if not, identifies the transaction data as questionable transaction data, calculates the system's transaction processing score for the system corresponding to the questionable transaction data, and then superimposes the transaction processing scores of each time interval to obtain the system capacity score, and expands the system capacity when the system capacity score reaches a certain threshold, this approach can fully combine the comprehensive analysis of the system's business request data to more accurately understand the system resource usage. Attached Figure Description

[0045] 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:

[0046] Figure 1 The illustrations depict application scenarios of system expansion methods, apparatus, devices, media, and program products according to embodiments of the present disclosure.

[0047] Figure 2 A flowchart illustrating a system expansion method according to an embodiment of the present disclosure is shown schematically.

[0048] Figure 3 A schematic diagram illustrating the determination of an updated system capacity score according to an embodiment of the present disclosure is shown.

[0049] Figure 4 A flowchart illustrating the determination of questionable data according to an embodiment of this disclosure is shown schematically;

[0050] Figure 5 A flowchart illustrating the determination of a transaction processing score according to an embodiment of this disclosure is shown schematically;

[0051] Figure 6 A schematic diagram illustrating the structure of a system expansion device according to an embodiment of the present disclosure is shown; and

[0052] Figure 7 A block diagram schematically illustrates an electronic device suitable for implementing the above-described method according to an embodiment of the present disclosure. Detailed Implementation

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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 those 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.).

[0057] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0058] It should be noted that the system expansion method and apparatus disclosed herein can be used in the financial field, or in any field other than the financial field. The application field of the system expansion method and apparatus disclosed herein is not limited.

[0059] Figure 1 The illustration schematically depicts application scenarios of system expansion methods, apparatus, devices, media, and program products according to embodiments of the present disclosure.

[0060] like Figure 1As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0061] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0062] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0063] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). 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.

[0064] It should be noted that the system expansion method provided in this embodiment can generally be executed by server 105. Correspondingly, the system expansion device provided in this embodiment can generally be located in server 105. The system expansion method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the system expansion device provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0065] 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.

[0066] The following will be based on Figure 1 The described scene, through Figures 2-5 The system expansion method of the disclosed embodiments is described in detail.

[0067] Figure 2 A flowchart illustrating a system expansion method according to an embodiment of the present disclosure is shown schematically.

[0068] like Figure 2 As shown, the system expansion in this embodiment includes operations S210 to S240, and the system expansion method can be executed by the server.

[0069] In operation S210, the transaction data generated by the target system in each time interval is obtained cyclically according to the preset time interval. The transaction data includes the transaction duration.

[0070] According to embodiments of this disclosure, a scheduled task can be set to periodically retrieve transaction data generated by the target system from the message data of the transaction log.

[0071] The preset time interval can be 1 hour, 10 hours, 24 hours, 48 ​​hours, etc.

[0072] Transaction data can include system information, node information, transaction response and duration, and transaction status, such as whether the transaction was successful or failed.

[0073] The acquired transaction data can be concatenated into SQL statements and stored in a database for persistence.

[0074] In operation S220, for the i-th time interval in each time interval, if the duration of the i-th transaction data corresponding to the i-th transaction data generated by the target system in the i-th time interval meets the first preset condition, the i-th transaction data is determined to be suspicious transaction data, and the i-th transaction processing score for the i-th time interval is determined according to the system weight of the target system, where i is a positive integer.

[0075] According to embodiments of this disclosure, the first preset condition may be that the ratio of the average transaction duration of the i-th transaction data to the historical transaction duration is greater than a second threshold, such as 1.5; or the average transaction duration of the i-th transaction data may be greater than a preset transaction duration.

[0076] According to embodiments of this disclosure, determining the i-th transaction processing score for the i-th time interval based on the system weight of the target system may include: determining the system weight of the target system as the i-th transaction processing score.

[0077] The system weights of the target system can be pre-configured based on the type and level of the target system. Different target systems can have the same or different system weights.

[0078] It should be noted that in determining the transaction processing score for each time interval, the transaction processing score for each time interval is determined sequentially according to the time order.

[0079] For example, first determine the transaction processing score corresponding to the first time interval, then determine the transaction processing score corresponding to the second time interval, and so on, until the transaction processing score corresponding to the i-th time interval is determined.

[0080] According to the embodiments of this disclosure, if the duration of the i-th transaction corresponding to the i-th transaction data generated by the target system within the i-th time interval does not meet the first preset condition, the i-th transaction processing score corresponding to the i-th time interval can be determined to be a preset value, such as zero.

[0081] In operation S230, the system capacity score of the target system is updated according to the i-th transaction processing score to obtain the updated system capacity score. The system capacity score is determined based on the transaction processing scores corresponding to the i-1 time intervals before the start time of the i-th time interval. The system capacity score represents the system capacity utilization rate of the target system.

[0082] According to embodiments of this disclosure, updating the system capacity score of the target system based on the i-th transaction processing score may include: adding the system capacity scores of the target system updated by the i-th transaction processing score to obtain the updated system capacity score.

[0083] In operation S240, if the updated system capacity score is greater than or equal to the first threshold, the system capacity of the target system is expanded.

[0084] According to an implementation of this disclosure, the first threshold can be pre-configured.

[0085] According to embodiments of this disclosure, expanding the system capacity of a target system may include node expansion and service expansion. When expanding nodes, the system uses the latest node snapshot to add node instances and automatically starts the corresponding service processes after the nodes start. When expanding services, the system deploys a new service instance directory based on the instance number of the current service after auto-incrementing, and starts the corresponding service processes after completion. After the above services are started, they are automatically added to the scheduling pool, which enables cluster load balancing and high availability.

[0086] It should be noted that existing expansion methods can be used to expand the target system, and this disclosure will not elaborate further.

[0087] According to embodiments of this disclosure, a technical solution is implemented by: cyclically acquiring transaction data containing transaction durations generated by the target system within each time interval according to a preset time interval; then, for the i-th time interval, determining that the i-th transaction duration corresponding to the i-th transaction data generated by the target system within the i-th time interval meets a first preset condition, identifying the i-th transaction data as questionable transaction data, and determining the i-th transaction processing score for the i-th time interval based on the system weight of the target system; updating the system capacity score of the target system based on the i-th transaction processing score to obtain an updated system capacity score; and expanding the system capacity of the target system if the updated system capacity score is greater than or equal to a first threshold. Since this disclosure determines whether the transaction duration within a time interval meets the preset condition based on the transaction duration, and if not, identifies the transaction data as questionable transaction data, calculates the system's transaction processing score for the system corresponding to the questionable transaction data, and then superimposes the transaction processing scores of each time interval to obtain the system capacity score, and expands the system capacity when the system capacity score reaches a certain threshold, this approach can fully combine the comprehensive analysis of the system's business request data to more accurately understand the system resource usage.

[0088] According to embodiments of this disclosure, the method further includes: after expanding the system capacity of the target system, adjusting the system capacity score of the target system to zero.

[0089] According to embodiments of this disclosure, the above method may further include: cyclically clearing the system capacity score of the target system according to a preset time period, so that the system capacity score of the target system at the end of each preset time period is zero.

[0090] For example, the system capacity score of the target system is adjusted to zero every month, so that the accumulation of the system capacity score of the target system starts from zero again.

[0091] According to embodiments of this disclosure, by adjusting the system capacity score of the target system to zero, the system capacity score of the target system is prevented from being inconsistent with the actual value due to the long-term accumulation of transaction processing scores.

[0092] Figure 3 A schematic diagram illustrating the determination of an updated system capacity score according to an embodiment of the present disclosure is shown.

[0093] like Figure 3As shown, in this embodiment, i is 2. It should be noted that the first time interval refers to the first time interval after the initial system capacity score of the target system is adjusted to zero. First, the first transaction data 311 generated by the target system within the first time interval 310 is obtained; then, 312 is executed to determine whether the first transaction data 311 is suspicious transaction data according to the first preset condition; if the first transaction data 311 is suspicious transaction data, the first transaction processing score 314 is determined according to the system weight of the target system; then, the initial system capacity score 315 is updated according to the first transaction processing score 314, for example, to zero, to obtain the updated system capacity score, i.e., the first system capacity score 316; if the first transaction data 311 is not suspicious transaction data, the preset value 313 is directly obtained, and the preset value can be zero; then, the initial system capacity score 315 is updated according to the preset value 313, to obtain the updated system capacity score, i.e., the first system capacity score 316. Then, the second transaction data 321 generated by the target system within the second time interval 320 is obtained; then, 322 is executed to determine whether the second transaction data 321 is suspicious transaction data according to the first preset condition; if the second transaction data 321 is suspicious transaction data, the second transaction processing score 323 is determined according to the system weight of the target system; then, the system capacity score of the target system, i.e., the first system capacity score 316, is updated according to the second transaction processing score 323, and the updated system capacity score, i.e., the second system capacity score 325, is obtained; if the second transaction data 321 is not suspicious transaction data, the preset value 324 is directly obtained, and the preset value can be zero; then, the system capacity score of the target system, i.e., the first system capacity score 316, is updated according to the preset value 324, and the updated system capacity score, i.e., the second system capacity score 325, is obtained.

[0094] According to an embodiment of this disclosure, the i-th transaction data includes multiple entries; the method further includes: determining the i-th average transaction duration for the i-th time interval based on the i-th transaction duration of each of the multiple i-th transaction data; and determining the i-th transaction score for the i-th time interval based on the system weight of the target system when the i-th average transaction duration meets a first preset condition.

[0095] According to an embodiment of this disclosure, the first preset condition may be that the ratio of the average transaction duration of the i-th transaction data to the historical transaction duration is greater than a second threshold.

[0096] According to an embodiment of this disclosure, the average transaction duration of the i-th transaction can be obtained by adding the i-th transaction durations of each of the multiple i-th transaction data and dividing by the number of the first transaction data.

[0097] According to an embodiment of this disclosure, the method further includes: determining the historical average transaction duration based on historical transaction data generated before the start time of the i-th time interval; and determining that the i-th average transaction duration satisfies a first preset condition if the ratio of the i-th average transaction duration to the historical average transaction duration is greater than a second threshold.

[0098] According to the embodiment of this invention, when the start time of the i-th time interval is t, the historical transaction data generated before the start time of the i-th time interval can be the historical transaction data generated before time t.

[0099] According to embodiments of this disclosure, the historical transaction data generated before the start time of the i-th time interval can be historical transaction data generated within a preset time period before the start time of the i-th time interval. For example, historical transaction data generated within one month before the start time of the i-th time interval can be obtained.

[0100] The historical average transaction duration can be determined by dividing the sum of the transaction durations of all historical transaction data by the number of historical transaction records.

[0101] According to embodiments of this disclosure, the transaction data also includes transaction status; before determining the i-th transaction data as questionable transaction data, the method further includes: determining the historical transaction success rate based on historical transaction data generated before the start time of the i-th time interval; determining the i-th transaction success rate for the i-th time interval based on the i-th transaction status of each of the multiple i-th transaction data; and determining the i-th transaction data as questionable transaction data if the ratio of the i-th transaction success rate to the historical transaction success rate is less than a third threshold.

[0102] According to embodiments of this disclosure, the third threshold may be, for example, 0.8.

[0103] According to embodiments of this disclosure, the transaction status may include transaction success or transaction failure.

[0104] According to embodiments of this disclosure, the historical transaction success rate can be determined as the ratio of the number of historical transaction records with a transaction status of "transaction successful" to the total number of all historical transaction records.

[0105] According to embodiments of this disclosure, the success rate of the i-th transaction can be the ratio of the number of i-th transaction data items with a successful transaction status to the total number of i-th transaction data items. For example, if the i-th transaction data includes M items, then the number of i-th transaction data items with a successful transaction status among the M items is m, where m ≤ M, and the success rate of the i-th transaction is m / M.

[0106] Figure 4 A flowchart illustrating the determination of questionable data according to an embodiment of this disclosure is shown schematically.

[0107] like Figure 4 As shown, this embodiment includes operations S410 to S480.

[0108] In operation S410, based on the i-th transaction duration of each of the i-th transaction data, the i-th average transaction duration for the i-th time interval is determined.

[0109] In operation S420, the historical average transaction duration is determined based on the historical transaction data generated before the start time of the i-th time interval.

[0110] In operation S430, determine whether the ratio of the average transaction duration of the i-th transaction to the historical average transaction duration is greater than the second threshold; if yes, then execute operation S440; otherwise, execute operation S480.

[0111] In operation S440, the historical transaction success rate is determined based on the historical transaction data generated before the start time of the i-th time interval.

[0112] In operation S450, based on the i-th transaction status of each of the i-th transaction data, the success rate of the i-th transaction for the i-th time interval is determined.

[0113] In operation S460, determine whether the ratio of the success rate of the i-th transaction to the historical transaction success rate is less than the third threshold. If yes, proceed to operation S470; otherwise, proceed to operation S480.

[0114] In operation S470, the i-th transaction data is determined to be questionable transaction data.

[0115] In operation S480, the i-th transaction data is determined to be normal transaction data.

[0116] According to embodiments of this disclosure, judging whether data is questionable based on both average transaction duration and transaction success rate helps improve the accuracy of the judgment.

[0117] According to embodiments of this disclosure, the target system includes multiple system nodes.

[0118] Figure 5 A flowchart illustrating the determination of a transaction processing score according to an embodiment of this disclosure is shown schematically.

[0119] like Figure 5 As shown, this embodiment includes operations S510 to S530.

[0120] In operation S510, for the j-th system node among multiple system nodes, the j-th transaction sub-data corresponding to the j-th system node is determined from the i-th transaction data.

[0121] In operation S520, if the duration of the j-th transaction corresponding to the j-th transaction sub-data satisfies the first preset condition, the weight of the j-th node of the j-th system node is determined based on the historical transaction data for the j-th system node generated before the start time of the i-th time interval.

[0122] According to embodiments of this disclosure, the method further includes: marking the j-th transaction sub-data as abnormal transaction sub-data when the j-th transaction duration in the j-th transaction sub-data meets a first preset condition; determining the j-th node weight of the j-th system node based on historical transaction data generated before the start time of the i-th time interval includes: determining the j-th node weight of the j-th system node as a first value when the historical transaction data generated before the start time of the i-th time interval contains abnormal transaction sub-data; and determining the j-th node weight of the j-th system node as a second value when the historical transaction data generated before the start time of the i-th time interval does not contain abnormal transaction sub-data.

[0123] In operation S530, the i-th transaction processing score is determined based on the node weights of multiple system nodes and the system weight of the target system.

[0124] For example, when determining that the i-th transaction data is questionable, the database flag field is set from 0 to 1. Then, the weight score, i.e., the transaction processing score, is calculated based on the system level to which the questionable data belongs and the number of times the node to which it belongs has been marked in the past. The system can be divided into three levels: A, B, and C, with weight scores of 4, 2, and 1 respectively. The weight score of the node can be 1 (historically marked) or 0 (historically not marked). For example, if the system to which the questionable data belongs is level A and the corresponding node has been marked in the past, then the weight score of the questionable data, i.e., the transaction processing score, is 4+1=5.

[0125] According to embodiments of this disclosure, determining the i-th transaction processing score based on the node weights of multiple system nodes and the system weight of the target system may include: adding the node weights of multiple system nodes and the system weight of the target system to obtain the i-th transaction processing score.

[0126] According to embodiments of this disclosure, the above method may further include: after each collection cycle, sorting the data in reverse order according to the system capacity score, recording the results in memory for further analysis, and if suspicious situations are found at the same time as historical daily data, then marking the system as a whitelist.

[0127] According to embodiments of this disclosure, before expanding the system capacity of the target system if the updated system capacity score meets a first threshold, the following steps are included:

[0128] Obtain a preset whitelist, and if the target system is not included in the preset whitelist, expand the system capacity of the target system.

[0129] According to embodiments of this disclosure, by setting a whitelist, situations where the system capacity score exceeds a first threshold due to special reasons can be avoided.

[0130] Based on the above system expansion method, this disclosure also provides a system expansion device. The following will be combined with... Figure 6 The device is described in detail.

[0131] Figure 6 A schematic block diagram of a system expansion device according to an embodiment of the present disclosure is shown.

[0132] like Figure 6 As shown, the system expansion device 600 in this embodiment includes a first acquisition module 610, a first determination module 620, an update module 630, and an expansion module 640.

[0133] The first acquisition module 610 is used to cyclically acquire transaction data generated by the target system within each preset time interval, wherein the transaction data includes the transaction duration. In one embodiment, the first acquisition module 610 can be used to perform the operation S210 described above, which will not be repeated here.

[0134] The first determining module 620 is configured to, for the i-th time interval in each time interval, determine that the i-th transaction data corresponding to the i-th transaction duration generated by the target system within the i-th time interval meets a first preset condition, determine that the i-th transaction data is questionable transaction data, and determine the i-th transaction processing score for the i-th time interval according to the system weight of the target system, where i is a positive integer. In one embodiment, the first determining module 620 may be used to execute the operation S220 described above, which will not be repeated here.

[0135] The update module 630 is used to update the system capacity score of the target system according to the i-th transaction processing score, thereby obtaining an updated system capacity score. The system capacity score is determined based on the transaction processing scores corresponding to the i-1 time intervals prior to the start time of the i-th time interval, and the system capacity score represents the system capacity utilization rate of the target system. In one embodiment, the update module 630 can be used to perform the operation S230 described above, which will not be repeated here.

[0136] The capacity expansion module 640 is used to expand the system capacity of the target system when the updated system capacity score is greater than or equal to a first threshold. In one embodiment, the capacity expansion module 640 can be used to perform the operation S240 described above, which will not be repeated here.

[0137] According to embodiments of this disclosure, the i-th transaction data includes multiple entries.

[0138] According to embodiments of this disclosure, the apparatus further includes a second determining module and a third determining module.

[0139] The second determining module is used to determine the average transaction duration for the i-th time interval based on the i-th transaction duration of each of the multiple i-th transaction data.

[0140] The third determining module is used to determine the i-th transaction score for the i-th time interval based on the system weight of the target system, provided that the i-th average transaction duration meets the first preset condition.

[0141] According to embodiments of this disclosure, the above-described apparatus further includes a fourth determining module and a fifth determining module.

[0142] The fourth determination module is used to determine the historical average transaction duration based on the historical transaction data generated before the start time of the i-th time interval.

[0143] The fifth determining module is used to determine that the i-th average transaction duration meets the first preset condition when the ratio of the i-th average transaction duration to the historical average transaction duration is greater than the second threshold.

[0144] According to embodiments of this disclosure, the transaction data also includes the transaction status.

[0145] According to embodiments of this disclosure, the above-described apparatus further includes: a sixth determining module, a seventh determining module, and an eighth determining module.

[0146] The sixth determination module is used to determine the historical transaction success rate based on the historical transaction data generated before the start time of the i-th time interval.

[0147] The seventh determination module is used to determine the success rate of the i-th transaction for the i-th time interval based on the i-th transaction status of each of the multiple i-th transaction data.

[0148] The eighth determination module is used to determine the i-th transaction data as questionable transaction data when the ratio of the i-th transaction success rate to the historical transaction success rate is less than the third threshold.

[0149] According to embodiments of this disclosure, the target system includes multiple system nodes.

[0150] According to embodiments of this disclosure, the first determining module includes: a first determining submodule, a second determining submodule, a third determining submodule, and a fourth determining submodule.

[0151] The first determination submodule is used to determine the j-th transaction sub-data corresponding to the j-th system node from the i-th transaction data for the j-th system node among multiple system nodes.

[0152] The second determining submodule is used to determine the weight of the jth system node based on the historical transaction data for the jth system node generated before the start time of the i-th time interval, provided that the duration of the jth transaction corresponding to the jth transaction subdata meets the first preset condition.

[0153] The third determination submodule is used to determine the i-th transaction processing score based on the node weights of multiple system nodes and the system weight of the target system.

[0154] According to embodiments of this disclosure, the above-described apparatus further includes a marking module.

[0155] The marking module is used to mark the j-th transaction sub-data as abnormal transaction sub-data when the j-th transaction duration in the j-th transaction sub-data meets the first preset condition.

[0156] According to embodiments of this disclosure, the second determining submodule includes: a first determining unit and a second determining unit.

[0157] The first determining unit is used to determine the weight of the j-th system node as a first value when the historical transaction data for the j-th system node generated before the start time of determining the i-th time interval contains abnormal transaction sub-data.

[0158] The second determining unit is used to determine the weight of the j-th system node as a second value when the historical transaction data generated before the start time of the i-th time interval does not contain abnormal transaction sub-data.

[0159] According to embodiments of this disclosure, the above-described apparatus further includes an adjustment module and a zeroing processing module.

[0160] The adjustment module is used to adjust the system capacity score of the target system to zero after expanding the system capacity of the target system.

[0161] The zeroing module is used to repeatedly zero out the system capacity score of the target system according to a preset time period, so that the system capacity score of the target system at the end of each preset time period is zero.

[0162] According to embodiments of this disclosure, the apparatus further includes a second acquisition module.

[0163] The second acquisition module is used to acquire a preset whitelist and, if it is determined that the preset whitelist does not contain the target system, to expand the system capacity of the target system.

[0164] According to embodiments of this disclosure, any plurality of modules among the first acquisition module 610, the first determination module 620, the update module 630, and the expansion module 640 can be combined 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 first acquisition module 610, the first determination module 620, the update module 630, and the expansion module 640 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 in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in a suitable combination of any of these. Alternatively, at least one of the first acquisition module 610, the first determination module 620, the update module 630, and the expansion module 640 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0165] Figure 7 A block diagram schematically illustrates an electronic device suitable for implementing the above-described method according to an embodiment of the present disclosure.

[0166] like Figure 7 As shown, an electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage portion 708 into a random access memory (RAM) 703. The processor 701 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 701 may also include onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0167] RAM 703 stores various programs and data required for the operation of electronic device 700. Processor 701, ROM 702, and RAM 703 are interconnected via bus 704. Processor 701 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 702 and / or RAM 703. It should be noted that the programs may also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 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.

[0168] According to embodiments of this disclosure, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to a bus 704. The electronic device 700 may also include one or more of the following components connected to the input / output (I / O) interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0169] 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.

[0170] According to embodiments of this disclosure, the computer-readable storage medium can 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 can 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 702 and / or RAM 703 and / or one or more memories other than ROM 702 and RAM 703 described above.

[0171] 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.

[0172] When the computer program is executed by the processor 701, 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.

[0173] 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 709, and / or installed from a removable medium 711. 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.

[0174] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, 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.

[0175] 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 a user's computing device, partially on a 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).

[0176] 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.

[0177] Those skilled in the art will understand that the features described in the various embodiments of this disclosure can be combined and / 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 of this disclosure can be combined and / 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.

[0178] 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. 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 system expansion method, comprising: According to a preset time interval, the transaction data generated by the target system in each time interval is obtained cyclically, wherein the transaction data includes the transaction duration; For the i-th time interval in each time interval, if the duration of the i-th transaction data corresponding to the i-th transaction data generated by the target system in the i-th time interval meets the first preset condition, the i-th transaction data is determined to be questionable transaction data, and the i-th transaction processing score for the i-th time interval is determined according to the system weight of the target system, where i is a positive integer; The system capacity score of the target system is updated based on the i-th transaction processing score to obtain the updated system capacity score. The system capacity score is determined based on the transaction processing scores corresponding to the i-1 time intervals preceding the start time of the i-th time interval, and the system capacity score represents the system capacity utilization rate of the target system. If the updated system capacity score is greater than or equal to the first threshold, the system capacity of the target system is expanded.

2. The method according to claim 1, wherein, The i-th transaction data includes multiple entries; The method further includes: Based on the i-th transaction duration of each of the i-th transaction data, determine the i-th average transaction duration for the i-th time interval; If the average transaction duration of the i-th time interval meets the first preset condition, the i-th transaction score is determined according to the system weight of the target system for the i-th time interval.

3. The method according to claim 2, further comprising: The historical average transaction duration is determined based on the historical transaction data generated before the start time of the i-th time interval; If the ratio of the i-th average transaction duration to the historical average transaction duration is greater than the second threshold, then the i-th average transaction duration is determined to satisfy the first preset condition.

4. The method according to claim 2, wherein, The transaction data also includes the transaction status; Before determining the i-th transaction data as questionable transaction data, the method further includes: The historical transaction success rate is determined based on the historical transaction data generated before the start time of the i-th time interval. Based on the i-th transaction status of each of the i-th transaction data, determine the i-th transaction success rate for the i-th time interval; If the ratio of the success rate of the i-th transaction to the historical transaction success rate is less than a third threshold, the i-th transaction data is determined to be questionable transaction data.

5. The method according to claim 1, wherein, The target system includes multiple system nodes; wherein, determining the i-th transaction score for the i-th time interval based on the system weight of the target system includes: For the j-th system node among the multiple system nodes, determine the j-th transaction sub-data corresponding to the j-th system node from the i-th transaction data; If the duration of the jth transaction corresponding to the jth transaction sub-data satisfies the first preset condition, the weight of the jth node of the jth system node is determined based on the historical transaction data for the jth system node generated before the start time of the i-th time interval. The i-th transaction processing score is determined based on the node weights of the multiple system nodes and the system weight of the target system.

6. The method according to claim 5, further comprising: If the duration of the j-th transaction in the j-th transaction sub-data satisfies the first preset condition, the j-th transaction sub-data is marked as abnormal transaction sub-data. The step of determining the weight of the j-th system node based on historical transaction data generated before the start time of the i-th time interval includes: If the historical transaction data for the j-th system node generated before the start time of the i-th time interval contains abnormal transaction sub-data, the weight of the j-th system node is determined to be a first value. If the historical transaction data for the j-th system node generated before the start time of the i-th time interval does not contain abnormal transaction sub-data, the weight of the j-th system node is determined to be the second value.

7. The method according to claim 1, further comprising: After expanding the system capacity of the target system, the system capacity score of the target system is adjusted to zero; or; The system capacity score of the target system is repeatedly reset to zero according to a preset time period, so that the system capacity score of the target system at the end of each preset time period is zero.

8. The method according to claim 1, wherein, Before expanding the system capacity of the target system if the updated system capacity score meets the first threshold, the following steps are also included: Obtain a preset whitelist, and if the target system is not included in the preset whitelist, expand the system capacity of the target system.

9. A system expansion device, comprising: The first acquisition module is used to cyclically acquire transaction data generated by the target system within each time interval according to a preset time interval, wherein the transaction data includes transaction duration; The first determining module is used to determine, for the i-th time interval in each time interval, if the duration of the i-th transaction data corresponding to the i-th transaction data generated by the target system in the i-th time interval meets the first preset condition, to determine the i-th transaction data as questionable transaction data, and to determine the i-th transaction processing score for the i-th time interval according to the system weight of the target system, where i is a positive integer; An update module is configured to update the system capacity score of the target system based on the i-th transaction processing score, thereby obtaining an updated system capacity score. The system capacity score is determined based on the transaction processing scores corresponding to the i-1 time intervals preceding the start time of the i-th time interval, and the system capacity score characterizes the system capacity utilization rate of the target system. The expansion module is used to expand the system capacity of the target system when the updated system capacity score is greater than or equal to a first threshold.

10. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 8.

12. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 8.

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