Capacity management method, device, electronic device and storage medium of business system

Abstract

The present disclosure relates to a capacity management method, device, electronic device and storage medium for a business system, wherein the method includes: acquiring monitoring raw data and online stress test data from a capacity data source in real time, wherein the monitoring raw data includes business monitoring raw data of the business dimension, and the online stress test data includes business limit data of the business dimension; comparing the business monitoring raw data with the business limit data to obtain a limit comparison result, and judging whether the business monitoring raw data complies with the service level agreement to obtain a protocol judgment result; and performing capacity management on the business system according to the limit comparison result, the protocol judgment result and the alarm threshold. The online stress test data disclosed in the present disclosure does not rely on human decision-making, and the online stress test data can provide a more accurate limit data basis for capacity management. Real-time acquisition of monitoring raw data and online stress test data, and a series of operations such as comparison and judgment of the monitoring raw data and the online stress test data improve the timeliness of capacity management.

Description

Technical Field

[0001] The present disclosure relates to the field of Internet technology, and in particular to a capacity management method, device, electronic device and storage medium for a business system. Background Art

[0002] With the rapid development of the Internet, various Internet services are emerging one after another. In order to support the normal operation of the services, it is necessary to manage the capacity of the business systems.

[0003] In the related art, in the existing capacity management solutions, on the one hand, the business capacity is judged by the resource indicators of human decision-making. For example, online computing business mainly focuses on the online CPU usage. If the CPU utilization rate is lower than 30%, the capacity utilization is judged to be insufficient; if the CPU utilization rate is higher than 30%, the capacity utilization is judged to be reasonable. This solution that relies on resource indicators of human decision-making to judge business capacity is simple and crude, and the accuracy of capacity management is not high. On the other hand, stress testing is performed on the business in a specific period to judge the business capacity. For example, full-link stress testing is performed on the eve of a large-scale business event, but it is stopped after the event ends. The timeliness of capacity management is low.

[0004] In short, the existing capacity management solutions for business systems are not accurate and have low timeliness. Summary of the invention

[0005] The present disclosure provides a capacity management method, device, electronic device and storage medium for a business system, so as to at least solve the problem that the capacity management solution in the related art is not accurate and has low timeliness. The technical solution of the present disclosure is as follows:

[0006] According to a first aspect of an embodiment of the present disclosure, a capacity management method for a business system is provided, comprising: acquiring monitoring raw data and online stress testing data from a capacity data source in real time, the monitoring raw data comprising business monitoring raw data of a business dimension, and the online stress testing data comprising business limit data of the business dimension; comparing the business monitoring raw data with the business limit data to obtain a limit comparison result, and judging whether the business monitoring raw data complies with a service level agreement to obtain an agreement judgment result; and performing capacity management on the business system according to the limit comparison result, the agreement judgment result and a preset alarm threshold.

[0007] Optionally, the capacity management of the business system based on the limit comparison result, the protocol judgment result and the preset alarm threshold includes: analyzing the limit comparison result and the protocol judgment result to obtain an analysis result; comparing the analysis result with the alarm threshold to obtain a data comparison result, and performing capacity management of the business system based on the data comparison result.

[0008] Optionally, the comparing the service monitoring original data with the service limit data to obtain the limit comparison result includes: comparing the query rate per second of the service monitoring original data with the maximum query rate per second of the service limit data to obtain the limit comparison result.

[0009] Optionally, the determining whether the original business monitoring data complies with the service level agreement to obtain the agreement determination result includes: determining whether the average response time and the business success rate of the original business monitoring data both comply with the service level agreement to obtain the agreement determination result.

[0010] Optionally, the monitoring raw data also includes resource monitoring raw data of the resource dimension, and the online stress testing data also includes resource limit data of the resource dimension; the analysis of the limit comparison result and the protocol judgment result to obtain an analysis result includes: when the limit comparison result indicates that the query rate per second is equal to the maximum query rate per second, the protocol judgment result indicates that the average response time and the business success rate both comply with the service level agreement, and the resource monitoring raw data corresponds to the resource limit data, the resource monitoring raw data, the query rate per second, the average response time and the business success rate are used as the analysis result.

[0011] Optionally, the analysis result is compared with the alarm threshold to obtain a data comparison result, and capacity management of the business system is performed according to the data comparison result, including: comparing at least one of the resource monitoring original data, the query rate per second, the average response time and the business success rate with the corresponding alarm threshold to obtain the data comparison result; when the data comparison result indicates that at least one of the resource monitoring original data, the query rate per second, the average response time and the business success rate is greater than the corresponding alarm threshold, generating and outputting a capacity overload alarm prompt information.

[0012] Optionally, the method further comprises: performing regression iterative processing on the resource limit data and / or the service limit data according to the capacity overload alarm prompt information and the actual resource occupancy information.

[0013] Optionally, the method further includes: generating capacity increase prompt information of the business system according to the capacity overload alarm prompt information.

[0014] Optionally, the real-time acquisition of monitoring raw data and online stress testing data from the capacity data source includes: acquiring the resource monitoring raw data in real time from the web server file security monitoring platform based on the data acquisition interface, acquiring the business monitoring raw data in real time from at least one of a remote procedure call monitor, a performance analysis tool and a reverse proxy server, and acquiring the online stress testing data in real time from a stress testing platform.

[0015] According to a second aspect of an embodiment of the present disclosure, a capacity management device for a business system is provided, comprising: a data acquisition module, configured to acquire monitoring raw data and online stress testing data from a capacity data source in real time, the monitoring raw data comprising business monitoring raw data of a business dimension, and the online stress testing data comprising business limit data of the business dimension; a comparison and judgment module, configured to compare the business monitoring raw data with the business limit data to obtain a limit comparison result, and to judge whether the business monitoring raw data complies with a service level agreement to obtain an agreement judgment result; and a capacity management module, configured to perform capacity management on the business system according to the limit comparison result, the protocol judgment result and a preset alarm threshold.

[0016] Optionally, the capacity management module includes: a result analysis module, configured to analyze the limit comparison result and the protocol judgment result to obtain an analysis result; a threshold comparison module, configured to compare the analysis result with the alarm threshold to obtain a data comparison result, and perform capacity management on the business system according to the data comparison result.

[0017] Optionally, the comparison and judgment module is configured to compare the query rate per second of the service monitoring original data with the maximum query rate per second of the service limit data to obtain the limit comparison result.

[0018] Optionally, the comparison and judgment module is further configured to judge whether the average response time and the service success rate of the service monitoring original data are both in compliance with the service level agreement to obtain the agreement judgment result.

[0019] Optionally, the monitoring raw data also includes resource monitoring raw data of the resource dimension, and the online stress testing data also includes resource limit data of the resource dimension; the result analysis module is configured to use the resource monitoring raw data, the query rate per second, the average response time and the business success rate as the analysis result when the limit comparison result indicates that the query rate per second is equal to the maximum query rate per second, the protocol judgment result indicates that the average response time and the business success rate both comply with the service level agreement, and the resource monitoring raw data corresponds to the resource limit data.

[0020] Optionally, the threshold comparison module is configured to compare at least one of the resource monitoring raw data, the query rate per second, the average response time and the business success rate with the corresponding alarm threshold to obtain the data comparison result; when the data comparison result indicates that at least one of the resource monitoring raw data, the query rate per second, the average response time and the business success rate is greater than the corresponding alarm threshold, a capacity overload alarm prompt information is generated and output.

[0021] Optionally, the device further includes: a regression iteration module configured to perform regression iteration processing on the resource limit data and / or the service limit data according to the capacity overload alarm prompt information and the actual resource occupancy information.

[0022] Optionally, the device further includes: a capacity prompt module configured to generate capacity increase prompt information of the service system according to the capacity overload alarm prompt information.

[0023] Optionally, the data acquisition module is configured to acquire the resource monitoring raw data in real time from the web server file security monitoring platform based on the data acquisition interface, acquire the business monitoring raw data in real time from at least one of the remote procedure call monitor, the performance analysis tool and the reverse proxy server, and acquire the online stress testing data in real time from the stress testing platform.

[0024] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the capacity management method of the business system as described in the first aspect.

[0025] According to a fourth aspect of an embodiment of the present disclosure, a storage medium is provided. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the capacity management method of the business system as described in the first aspect.

[0026] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements the capacity management method of the business system as described in the first aspect.

[0027] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects:

[0028] The capacity management solution for the business system provided by the embodiment of the present disclosure obtains the monitoring raw data and online stress test data from the capacity data source in real time. Among them, the monitoring raw data may include the business monitoring raw data of the business dimension, and the online stress test data may include the business limit data of the business dimension. Then, the business monitoring raw data is compared with the business limit data to obtain the limit comparison result, and it is judged whether the business monitoring raw data complies with the service level agreement to obtain the agreement judgment result, and then the capacity management of the business system is performed according to the limit comparison result, the agreement judgment result and the preset alarm threshold.

[0029] The embodiments of the present disclosure obtain online stress testing data and monitoring raw data in real time. The online stress testing data contains business limit data, and the monitoring raw data contains business monitoring raw data. The business limit data and business monitoring raw data, both of which belong to the business dimension, are compared to obtain the limit comparison result, and it is judged whether the business monitoring raw data complies with the service level agreement to obtain the agreement judgment result, and then capacity management is performed according to the limit comparison result, the agreement judgment result and the alarm threshold. Among them, the online stress testing data does not rely on human decision-making, and the business limit data of the online stress testing data can provide a more accurate limit data basis for capacity management. Moreover, a series of operations such as obtaining the monitoring raw data and the online stress testing data in real time, and comparing and judging the monitoring raw data and the online stress testing data can perform capacity management on the business system in real time, thereby improving the timeliness of capacity management.

[0030] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0032] Figure 1 The present invention is a flowchart of a method for capacity management of a business system according to an exemplary embodiment.

[0033] Figure 2 The diagram is a schematic diagram of an architecture of a capacity management solution according to an exemplary embodiment.

[0034] Figure 3 The figure is a schematic diagram of a design concept of a capacity management solution according to an exemplary embodiment.

[0035] Figure 4 The present invention is a schematic diagram of a capacity indicator system of a capacity management solution according to an exemplary embodiment.

[0036] Figure 5It is a block diagram of a capacity management device for a business system according to an exemplary embodiment.

[0037] Figure 6 The present invention is a block diagram of a capacity management electronic device of a business system according to an exemplary embodiment.

[0038] Figure 7 The figure is a block diagram of another capacity management electronic device of a business system according to an exemplary embodiment. DETAILED DESCRIPTION

[0039] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0040] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0041] Figure 1 is a flow chart of a capacity management method for a business system according to an exemplary embodiment. Figure 1 As shown, the method may specifically include the following steps.

[0042] In step S11, raw monitoring data and online stress testing data are acquired in real time from a capacity data source.

[0043] In an embodiment of the present disclosure, raw monitoring data and online stress testing data can be obtained in real time from multiple capacity data sources. Among them, raw monitoring data can include raw business monitoring data of business dimensions, and online stress testing data can include business limit data of business dimensions. Raw monitoring data can come from a monitoring system, and the monitoring system can be used to monitor the operating status of a business system. Online stress testing data can come from a safety stress testing system or a rated stress testing system, and the safety stress testing system or the rated stress testing system can be used to explore the maximum throughput capacity of a business system for online business in a lossless state.

[0044] In step S12, the service monitoring original data is compared with the service limit data to obtain a limit comparison result, and it is determined whether the service monitoring original data complies with the service level agreement to obtain an agreement determination result.

[0045] In an embodiment of the present disclosure, the limit comparison result may include that the service monitoring original data is greater than the service limit data, the service monitoring original data is equal to the service limit data, or the service monitoring original data is less than the service limit data. The protocol judgment result may indicate that the service monitoring original data complies with the service level agreement, or that the service monitoring original data does not comply with the service level agreement.

[0046] In step S13, capacity management is performed on the service system according to the limit comparison result, the protocol judgment result and the preset alarm threshold.

[0047] In the embodiments of the present disclosure, the limit comparison results and the protocol judgment results can be analyzed, and then the capacity management of the business system can be performed according to the analysis results and the alarm thresholds corresponding to the analysis results. In practical applications, capacity management can include but is not limited to capacity expansion, early warning, overload prevention suggestions, etc. for the business system.

[0048] The capacity management solution of the business system provided by the embodiment of the present disclosure is as follows:

[0049] The original monitoring data and online stress test data are obtained from the capacity data source in real time. The original monitoring data may include the original business monitoring data of the business dimension, and the online stress test data may include the business limit data of the business dimension. Then, the original business monitoring data is compared with the business limit data to obtain the limit comparison result, and it is determined whether the original business monitoring data complies with the service level agreement to obtain the agreement judgment result, and then the capacity management of the business system is performed according to the limit comparison result, the agreement judgment result and the preset alarm threshold.

[0050] The embodiments of the present disclosure obtain online stress testing data and monitoring raw data in real time. The online stress testing data contains business limit data, and the monitoring raw data contains business monitoring raw data. The business limit data and business monitoring raw data, both of which belong to the business dimension, are compared to obtain the limit comparison result, and it is judged whether the business monitoring raw data complies with the service level agreement to obtain the agreement judgment result, and then capacity management is performed according to the limit comparison result, the agreement judgment result and the alarm threshold. Among them, the online stress testing data does not rely on human decision-making, and the business limit data of the online stress testing data can provide a more accurate limit data basis for capacity management. Moreover, a series of operations such as obtaining the monitoring raw data and the online stress testing data in real time, and comparing and judging the monitoring raw data and the online stress testing data can perform capacity management on the business system in real time, thereby improving the timeliness of capacity management.

[0051] In an exemplary embodiment of the present disclosure, a server cluster composed of multiple business servers may be deployed in the business system. The source of capacity data may be a server cluster, a capacity database, or a customized capacity data acquisition interface. The above step S11 can obtain the monitoring raw data and online stress test data of the single machine, single module (multiple servers or containers), business interface (product latitude, multiple modules constitute a business), combination level (a collection of multiple business APIs) in real time. In practical applications, the monitoring raw data may include resource monitoring raw data of resource dimension (such as server resources consumed by the business, converted into corresponding computing power or storage capacity) and business monitoring raw data of business dimension (such as business indicators that can intuitively feedback business capabilities), and the online stress test data may include resource limit data of resource dimension and business limit data of business dimension. Specifically, the above step S11 can obtain resource monitoring raw data from the web server file security monitoring platform (Faclon) in real time based on the data acquisition interface, obtain business monitoring raw data from at least one of the remote procedure call monitor (Rpcmonitor), performance analysis tool (Perf) and reverse proxy server (Nginx) in real time, and obtain online stress test data from the stress test platform in real time. Resource monitoring raw data may include but not limited to CPU data, memory data, storage data, etc., business monitoring raw data may include but not limited to query rate per second, average response time, business success rate, etc., and online stress test data may include but not limited to maximum query rate per second, etc. The embodiments of the present disclosure may utilize a data acquisition interface to obtain monitoring raw data and online stress test data from multiple capacity data sources, and the acquired monitoring raw data and online stress test data may both include data that meets actual capacity management requirements. The data acquisition interface may be used to obtain various data from different capacity data sources, meeting the targeted capacity management requirements of different business systems.

[0052] In an exemplary embodiment of the present disclosure, during the execution of the above step S12, when comparing the service monitoring raw data with the service limit data to obtain the limit comparison result, the query rate per second of the service monitoring raw data can be compared with the maximum query rate per second of the service limit data to obtain the limit comparison result. Therefore, the limit comparison result can indicate that the query rate per second is greater than the maximum query rate per second, the query rate per second is equal to the maximum query rate per second, and the query rate per second is less than the maximum query rate per second. When comparing the service monitoring raw data with the service limit data, the embodiment of the present disclosure can compare the query rate per second with the maximum query rate per second, and use the comparison result of the query rate per second with the maximum query rate per second as the comparison result of the service monitoring raw data with the service limit data. The embodiment of the present disclosure uses the query rate per second and the maximum query rate per second as representatives of the service monitoring raw data and the service limit data, compares the service data and the limit data of the same service type in the service monitoring raw data and the service limit data, and can specifically compare the service data and the limit data of each service type, and further refines the comparison of the service monitoring raw data with the service limit data.

[0053] In an exemplary embodiment of the present disclosure, during the execution of the above-mentioned step S12, when judging whether the original business monitoring data complies with the service level agreement to obtain the agreement judgment result, it is possible to judge whether the average response time and the business success rate of the original business monitoring data both comply with the service level agreement to obtain the agreement judgment result. Therefore, the agreement judgment result can indicate that the average response time and the business success rate both comply with the service level agreement, the average response time and the business success rate do not comply with the service level agreement, the average response time complies with the service level agreement but the business success rate does not comply with the service level agreement, and the average response time does not comply with the service level agreement but the business success rate complies with the service level agreement. The embodiment of the present disclosure uses the average response time and the business success rate as representatives of the original business monitoring data to judge whether the average response time and the business success rate both comply with the service level agreement, and further refines the judgment of whether the original business monitoring data complies with the service level agreement.

[0054] In an exemplary embodiment of the present disclosure, during the execution of the above step S13, the limit comparison result and the protocol judgment result can be analyzed to obtain an analysis result, and then the analysis result can be compared with the alarm threshold to obtain a data comparison result, and then the capacity management of the business system is performed according to the data comparison result. Among them, the limit comparison result and the protocol judgment result are analyzed to obtain the analysis result, that is, when the limit comparison result indicates that the query rate per second is equal to the maximum query rate per second, the protocol judgment result indicates that the average response time and the business success rate are in compliance with the service level agreement, and the resource monitoring original data corresponds to the resource limit data, the resource monitoring original data, the query rate per second, the average response time and the business success rate are used as the analysis result. Among them, the resource monitoring original data corresponds to the resource limit data, and can indicate that the resource type corresponding to the resource monitoring original data is the same as the resource type corresponding to the resource limit data. For example, the resource monitoring original data is the memory utilization rate, and the resource limit data is the maximum memory utilization rate.

[0055] The analysis result is compared with the alarm threshold to obtain a data comparison result. When the capacity of the business system is managed according to the data comparison result, at least one of the resource monitoring original data, query rate per second, average response time and business success rate can be compared with the corresponding alarm threshold to obtain a data comparison result. When the data comparison result indicates that at least one of the resource monitoring original data, query rate per second, average response time and business success rate is greater than the corresponding alarm threshold, a capacity overload alarm prompt information is generated and output. Among them, the capacity overload alarm prompt information can represent resource alarm prompt information, such as memory utilization alarm prompt information, and the capacity overload alarm prompt information can also represent query rate per second alarm prompt information, average response time alarm prompt information, and business success rate alarm prompt information.

[0056] The embodiment of the present disclosure can first analyze the limit comparison result and the protocol judgment result, then compare the analysis result and the alarm threshold to obtain the data comparison result, and then perform capacity management according to the data comparison result. Among them, analyzing the limit comparison result is to analyze the size relationship between each data in the service monitoring original data and the corresponding data in the service limit data, such as analyzing the query rate per second in the service monitoring original data is equal to the maximum query rate per second in the service limit data. Analyzing the protocol judgment result is to analyze whether each data in the service monitoring original data meets the service level agreement, such as analyzing whether the average response time and the service success rate in the service monitoring original data meet the service level agreement. Moreover, the analysis result is the resource monitoring original data and the service monitoring original data when the above-mentioned analysis protocol judgment result and the analysis protocol judgment result meet certain conditions. The above-mentioned certain conditions can be that the query rate per second is equal to the maximum query rate per second, the average response time and the service success rate are in compliance with the service level agreement, and the resource monitoring original data corresponds to the resource limit data. That is to say, the obtained analysis result can be the resource monitoring original data and the service monitoring original data. The analysis results are compared with the corresponding alarm thresholds, that is, the resource monitoring raw data is compared with the corresponding alarm threshold, and the business monitoring raw data is compared with the corresponding alarm threshold. When the resource monitoring raw data is greater than the corresponding alarm threshold, and / or the business monitoring raw data is greater than the corresponding alarm threshold, a capacity overload alarm prompt message is generated to remind the administrator of the business system that the capacity of the business system exceeds the limit.

[0057] In an exemplary embodiment of the present disclosure, after generating the capacity overload alarm prompt information, the resource limit data and / or the business limit data may be regressed and iterated based on the capacity overload alarm prompt information and the actual resource occupancy information. For example, if the capacity overload alarm prompt information is a memory utilization alarm prompt information, and the actual resource occupancy information is the current memory occupancy, the maximum memory utilization in the resource limit data may be increased. The business system in the embodiment of the present disclosure may change with product modifications, updates, and iterations. Therefore, it is necessary to regress and iterate the resource limit data and / or the business limit data based on the capacity overload alarm prompt information and the actual resource occupancy information to adapt to the capacity management of business systems in different situations or with different needs.

[0058] In an exemplary embodiment of the present disclosure, after generating the capacity overload alarm prompt information, the capacity increase prompt information of the business system may also be generated. For example, if the capacity overload alarm prompt information is the memory utilization alarm prompt information, the memory increase prompt information may be generated, such as the increased memory size. The embodiment of the present disclosure generates the capacity increase prompt information according to the capacity overload alarm prompt information, and may suggest the business system administrator to upgrade or adjust the business system according to the capacity overload alarm prompt information.

[0059] Based on the above description of an embodiment of a capacity management method for a business system, a capacity management solution based on real-time analysis and stress test feedback is introduced below. Figure 2 As shown, Figure 2 The schematic diagram of the architecture of the capacity management solution is shown. The capacity management solution can be applied to the capacity management platform. The capacity management platform can include a cluster layer, a development tool layer (DevTools), a data management layer, and a scenario management layer from bottom to top. Among them, the cluster layer can include multiple server clusters running business systems. The development tool layer can include a capacity management data acquisition interface and a capacity data source, wherein the capacity data source can include but is not limited to: Faclon, Rpc monitor, Perf, Nginx, and a stress testing platform. The data management layer can include capacity data metadata and a capacity status calculation model. Among them, the capacity data metadata can include service information, status information, and indicator information. The scenario management layer can include capacity water level / warning, overload protection, resource planning, anti-degradation, and deep analysis.

[0060] This capacity management solution is based on business needs and builds a capacity management platform to support online business needs. Combined with the current business status, multi-dimensional monitoring and real-time analysis are carried out to obtain comprehensive capacity data for online business, support availability, capacity planning, monitoring and early warning capabilities, and drive business stability improvement and resource cost optimization through capacity-related data. Through this capacity management solution, capacity can be measured and indicators can be built. Capacity can be planned to optimize stability, cost, and efficiency. Capacity can be audited, and a business system capacity model can be built to guide cost and business optimization.

[0061] Figure 3The schematic diagram of the design idea of ​​the capacity management solution is shown. The capacity management platform can obtain stress testing data from the stress testing system and obtain raw monitoring data from the monitoring platform. Among them, the stress testing system can include routine stress testing such as extreme stress testing, rated stress testing and offline stress testing. Extreme stress testing means applying pressure to the business system until the business system crashes. Extreme stress testing is harmful to the business system. Rated stress testing is a safe stress testing that explores the maximum throughput of each functional module of the business system without damaging the online business. Offline stress testing can be a baseline performance configuration. Extreme stress testing, rated stress testing and offline stress testing can be selected according to the actual business system. Raw monitoring data can include but is not limited to: CPU information, memory information, network card information, query rate per second, number of available instances, etc. After collecting stress testing data and raw monitoring data, the capacity management platform performs real-time analysis on the stress testing data and raw monitoring data to obtain data calculation results. On the one hand, the data calculation results can be used for capacity monitoring, and capacity warnings can be issued for data that exceeds the monitoring indicators, which plays a role in overload protection. In the process of capacity warning, capacity overload warning information can be generated and output based on the data calculation results (load reference data). On the other hand, the data calculation results can be used for performance optimization, computer room layout planning, data operation and resource optimization for specific business systems. Moreover, capacity overload warning information and stress test data can also perform regression and iterative processing on capacity warning indicators (resource limit data and business limit data), realizing a data feedback assistance mechanism for the capacity management platform.

[0062] In an exemplary embodiment of the present disclosure, at least one key resource can be set for each business system to feedback business dependent resources. For example, computing business can use CPU utilization as a key resource and measure the current load through CPU utilization. Storage business can use memory utilization or disk utilization as a key resource. Resource indicators in capacity management must be business bottlenecks that gradually appear as traffic increases (manifested as reduced availability or longer average response time). Resource consumption, query rate per second growth, and availability must be in a relatively balanced state. In a relatively balanced state, if the query rate per second is at the system maximum value and the availability meets the service level agreement, then resource consumption will become a limit of the business system. The query rate per second at this time is also the limit capacity of the business system throughput. Based on the above conditions, the main purpose of capacity management is to grasp the limit capacity of the balance state of the three. The capacity of the business system will always be in a dynamic change process with the changes online. For the business system, the resource indicators that are of more concern are the maximum value of the resource business (feedback of the maximum online occupancy, hot spot effect), the minimum value (resource utilization trough, mixed distribution or optimization point), the average value (mean measures the overall use), the standard value (standard deviation, feedback capacity balance, dispersion), etc. Moreover, single-dimensional data is more prone to deviation during capacity management, and deviation data or jitter of a certain indicator data may lead to misjudgment, so the embodiment of the present disclosure introduces multi-dimensional evaluation to ensure that resources, traffic, and availability comprehensively feedback the throughput capacity of the business system.

[0063] Therefore, the raw monitoring data and online stress test data can be analyzed in real time to obtain resource consumption reference data, traffic reference data, and availability reference data. That is, when the query rate per second is equal to the maximum query rate per second in the business limit data, the average response time and the business success rate are in line with the service level agreement, and the raw resource monitoring data corresponds to the resource limit data, the raw resource monitoring data is used as the resource consumption reference data, the query rate per second is used as the traffic reference data, and the average response time and the business success rate are used as the availability reference data.

[0064] Figure 4The diagram of the capacity indicator system of the capacity management solution is shown. The capacity indicator system is a data system related to capacity management, which may include algorithms, data sources, dimensions, indicators, etc. involved in the capacity management solution. Among them, the algorithm may include the peak algorithm, the mean algorithm, and the 995th percentile algorithm. The data source may include Falcon, Rpc monitor / Perf Log / Nginx Log, and stress testing (stress testing system). Among them, Falcon may include CPU utilization and memory utilization. Rpc monitor / Perf Log / Nginx Log may include query rate per second (QPS), and the stress testing system may include indicator thresholds. Dimensions may include business lines, services / modules, Internet Data Centers (IDC), single machines, time, AZ (combination), etc. Indicators may include horizontal capacity indicators and vertical capacity indicators. Horizontal capacity indicators may include QPS and bandwidth usage. Vertical capacity indicators may include CPU utilization, memory utilization, and number of connections.

[0065] In actual applications, indicators (resource limit data and business limit data) can be set according to the actual business system, and different maximum watermarks can be set for different business systems. The query rate per second indicator can be set by the business entrance and the core focus module. For example, the business entrance is the discovery page, and the discovery page has core modules such as index, recall, user information, and data storage. In addition, the indicator can be supplemented and corrected by the stress test data output by the stress test system in the future.

[0066] In actual applications, the service information in the capacity data metadata may at least include the keywords of the service name or module name, deployment type, CPU, memory, query rate per second limit, and query rate per second source. The service name or module name + deployment type can be used to batch query CPU data, memory data, and storage data from Falcon. The keywords of QPS source include Rpc monitor (service name, kafka), Perf, Nginx, etc.

[0067] Table 1 shows the comparative effects of stress testing and calculation in the capacity management solution:

[0068]

[0069] As shown in the table above, the capacity management solution itself needs to have the characteristics of high timeliness, high accuracy, no data pollution, available module capacity and low cost. Therefore, considering the above factors, real-time analysis and online stress testing are selected as the core content of the capacity management solution.

[0070] Figure 5The present invention is a block diagram of a capacity management device for a business system according to an exemplary embodiment. The device may specifically include the following modules.

[0071] The data acquisition module 51 is configured to acquire monitoring raw data and online stress testing data from the capacity data source in real time, wherein the monitoring raw data includes business monitoring raw data of the business dimension, and the online stress testing data includes business limit data of the business dimension;

[0072] The comparison and judgment module 52 is configured to compare the service monitoring original data with the service limit data to obtain a limit comparison result, and to judge whether the service monitoring original data complies with the service level agreement to obtain an agreement judgment result;

[0073] The capacity management module 53 is configured to perform capacity management on the service system according to the limit comparison result, the protocol judgment result and the preset alarm threshold.

[0074] In an exemplary embodiment of the present disclosure, the capacity management module 53 includes:

[0075] A result analysis module, configured to analyze the limit comparison result and the protocol judgment result to obtain an analysis result;

[0076] The threshold comparison module is configured to compare the analysis result with the alarm threshold to obtain a data comparison result, and perform capacity management on the business system according to the data comparison result.

[0077] In an exemplary embodiment of the present disclosure, the comparison and judgment module 52 is configured to compare the query rate per second of the service monitoring raw data with the maximum query rate per second of the service limit data to obtain the limit comparison result.

[0078] In an exemplary embodiment of the present disclosure, the comparison and judgment module 52 is further configured to judge whether the average response time and the service success rate of the service monitoring original data are both in compliance with the service level agreement to obtain the agreement judgment result.

[0079] In an exemplary embodiment of the present disclosure, the monitoring raw data further includes resource monitoring raw data in a resource dimension, and the online stress testing data further includes resource limit data in the resource dimension;

[0080] The result analysis module is configured to use the resource monitoring original data, the query rate per second, the average response time and the business success rate as the analysis result when the limit comparison result indicates that the query rate per second is equal to the maximum query rate per second, the protocol judgment result indicates that the average response time and the business success rate both comply with the service level agreement, and the resource monitoring original data corresponds to the resource limit data.

[0081] In an exemplary embodiment of the present disclosure, the threshold comparison module is configured to compare at least one of the resource monitoring raw data, the query rate per second, the average response time and the business success rate with the corresponding alarm threshold to obtain the data comparison result; when the data comparison result indicates that at least one of the resource monitoring raw data, the query rate per second, the average response time and the business success rate is greater than the corresponding alarm threshold, a capacity overload alarm prompt information is generated and output.

[0082] In an exemplary embodiment of the present disclosure, the device further includes:

[0083] The regression iteration module is configured to perform regression iteration processing on the resource limit data and / or the service limit data according to the capacity overload alarm prompt information and the actual resource occupancy information.

[0084] In an exemplary embodiment of the present disclosure, the device further includes:

[0085] The capacity prompt module is configured to generate capacity increase prompt information of the business system according to the capacity overload alarm prompt information.

[0086] In an exemplary embodiment of the present disclosure, the data acquisition module 51 is configured to acquire the resource monitoring raw data in real time from the web server file security monitoring platform based on the data acquisition interface, acquire the business monitoring raw data in real time from at least one of the remote procedure call monitor, the performance analysis tool and the reverse proxy server, and acquire the online stress testing data in real time from the stress testing platform.

[0087] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0088] Figure 6 6 is a block diagram of a capacity management electronic device for a business system according to an exemplary embodiment. For example, the electronic device 600 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0089] Reference Figure 6 , the electronic device 600 may include one or more of the following components: a processing component 602 , a memory 604 , a power component 606 , a multimedia component 608 , an audio component 610 , an input / output (I / O) interface 612 , a sensor component 614 , and a communication component 616 .

[0090] The processing component 602 generally controls the overall operation of the electronic device 600, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 602 may include one or more processors 620 to execute instructions to complete all or part of the steps of the capacity management method of the business system described above. In addition, the processing component 602 may include one or more modules to facilitate the interaction between the processing component 602 and other components. For example, the processing component 602 may include a multimedia module to facilitate the interaction between the multimedia component 608 and the processing component 602.

[0091] The memory 604 is configured to store various types of data to support operations on the electronic device 600. Examples of such data include instructions for any application or method operating on the electronic device 600, contact data, phone book data, messages, images, videos, etc. The memory 604 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0092] The power supply component 606 provides power to the various components of the electronic device 600. The power supply component 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 600.

[0093] The multimedia component 608 includes a screen that provides an output interface between the electronic device 600 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 608 includes a front camera and / or a rear camera. When the electronic device 600 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0094] The audio component 610 is configured to output and / or input audio signals. For example, the audio component 610 includes a microphone (MIC), and when the electronic device 600 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 604 or sent via the communication component 616. In some embodiments, the audio component 610 also includes a speaker for outputting audio signals.

[0095] I / O interface 612 provides an interface between processing component 602 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0096] The sensor assembly 614 includes one or more sensors for providing various aspects of status assessment for the electronic device 600. For example, the sensor assembly 614 can detect the open / closed state of the electronic device 600, the relative positioning of the components, such as the display and keypad of the electronic device 600, and the sensor assembly 614 can also detect the position change of the electronic device 600 or a component of the electronic device 600, the presence or absence of contact between the user and the electronic device 600, the orientation or acceleration / deceleration of the electronic device 600, and the temperature change of the electronic device 600. The sensor assembly 614 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 614 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 614 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0097] The communication component 616 is configured to facilitate wired or wireless communication between the electronic device 600 and other devices. The electronic device 600 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 616 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 616 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0098] In an exemplary embodiment, the electronic device 600 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the capacity management method of the above-mentioned business system.

[0099] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory 604 including instructions, and the instructions can be executed by a processor 620 of an electronic device 600 to complete the capacity management method of the business system. Optionally, the storage medium can be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0100] In an exemplary embodiment, a computer program product is also provided, the computer program product includes a readable program code, the readable program code can be executed by the processor 620 of the electronic device 600 to complete the capacity management method of the above-mentioned business system. Optionally, the program code can be stored in a storage medium of the electronic device 600, and the storage medium can be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0101] Figure 7 7 is a block diagram of another electronic device for capacity management of a business system according to an exemplary embodiment. For example, the electronic device 700 may be provided as a server. Figure 7The electronic device 700 includes a processing component 722, which further includes one or more processors, and a memory resource represented by a memory 732 for storing instructions executable by the processing component 722, such as an application. The application stored in the memory 732 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 722 is configured to execute instructions to perform the capacity management method of the business system.

[0102] The electronic device 700 may also include a power supply component 726 configured to perform power management of the electronic device 700, a wired or wireless network interface 750 configured to connect the electronic device 700 to a network, and an input / output (I / O) interface 758. The electronic device 700 may operate based on an operating system stored in the memory 732, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0103] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0104] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A capacity management method for a business system, characterized in that: include: Acquire monitoring raw data and online stress testing data from a capacity data source in real time, wherein the monitoring raw data includes business monitoring raw data of a business dimension, and the online stress testing data includes business limit data of the business dimension; Compare the query rate per second of the service monitoring raw data with the maximum query rate per second of the service limit data to obtain a limit comparison result, and determine whether the average response time and the service success rate of the service monitoring raw data are in compliance with the service level agreement to obtain an agreement determination result, wherein the monitoring raw data further includes resource monitoring raw data of the resource dimension, and the online stress testing data further includes resource limit data of the resource dimension; When the limit comparison result indicates that the query rate per second is equal to the maximum query rate per second, the protocol judgment result indicates that the average response time and the business success rate both meet the service level agreement, and the resource monitoring original data corresponds to the resource limit data, the resource monitoring original data, the query rate per second, the average response time and the business success rate are taken as analysis results; The resource monitoring original data, the query rate per second, the average response time and the business success rate in the analysis result are compared with the corresponding alarm threshold to obtain a data comparison result. When the data comparison result indicates that at least one of the resource monitoring original data, the query rate per second, the average response time and the business success rate is greater than the corresponding alarm threshold, a capacity overload alarm prompt information is generated and output.

2. The method according to claim 1, characterized in that The method further comprises: The resource limit data and / or the service limit data are subjected to regression and iteration processing according to the capacity overload alarm prompt information and the actual resource occupancy information.

3. The method according to claim 1, characterized in that The method further comprises: Capacity increase prompt information of the service system is generated according to the capacity overload alarm prompt information.

4. The method according to claim 1, characterized in that: The real-time acquisition of raw monitoring data and online stress testing data from the capacity data source includes: Based on the data acquisition interface, the resource monitoring raw data is obtained in real time from the web server file security monitoring platform, the business monitoring raw data is obtained in real time from at least one of the remote procedure call monitor, the performance analysis tool and the reverse proxy server, and the online stress testing data is obtained in real time from the stress testing platform.

5. A capacity management device for a business system, characterized in that: include: A data acquisition module is configured to acquire monitoring raw data and online stress testing data from a capacity data source in real time, wherein the monitoring raw data includes business monitoring raw data of a business dimension, and the online stress testing data includes business limit data of the business dimension; A comparison and judgment module compares the query rate per second of the service monitoring raw data with the maximum query rate per second of the service limit data to obtain a limit comparison result, and judges whether the average response time and the service success rate of the service monitoring raw data are in compliance with the service level agreement to obtain an agreement judgment result, characterized in that the monitoring raw data also includes resource monitoring raw data of the resource dimension, and the online stress testing data also includes resource limit data of the resource dimension; A result analysis module is configured to use the resource monitoring original data, the query rate per second, the average response time and the business success rate as analysis results when the limit comparison result indicates that the query rate per second is equal to the maximum query rate per second, the protocol judgment result indicates that the average response time and the business success rate both meet the service level agreement, and the resource monitoring original data corresponds to the resource limit data; The threshold comparison module is configured to compare the resource monitoring raw data, the query rate per second, the average response time and the business success rate in the analysis result with the corresponding alarm threshold to obtain a data comparison result. When the data comparison result indicates that at least one of the resource monitoring raw data, the query rate per second, the average response time and the business success rate is greater than the corresponding alarm threshold, a capacity overload alarm prompt information is generated and output.

6. The device according to claim 5, characterized in that The device also includes: The regression iteration module is configured to perform regression iteration processing on the resource limit data and / or the service limit data according to the capacity overload alarm prompt information and the actual resource occupancy information.

7. The device according to claim 6, characterized in that The device also includes: The capacity prompt module is configured to generate capacity increase prompt information of the business system according to the capacity overload alarm prompt information.

8. The device according to claim 5, characterized in that The data acquisition module is configured to acquire the resource monitoring raw data in real time from the web server file security monitoring platform based on the data acquisition interface, acquire the business monitoring raw data in real time from at least one of the remote procedure call monitor, the performance analysis tool and the reverse proxy server, and acquire the online stress testing data in real time from the stress testing platform.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the capacity management method of the business system according to any one of claims 1 to 4.

10. A storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the capacity management method of a business system as claimed in any one of claims 1 to 4.

11. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the capacity management method of the business system according to any one of claims 1 to 4 is implemented.

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