Server resource usage amount analysis method and device, equipment, medium and product

Abstract

The invention discloses a server resource usage amount analysis method and device, equipment, a medium and a product, relates to the field of distributed technologies, can be applied to the field of financial science and technology, and comprises the following steps: determining candidate servers included in a target server cluster, determining candidate service functions called by the candidate servers during service data processing, and storing the candidate service functions in the target server cluster; determining a server resource usage amount of candidate service functions in the candidate servers, and determining a hotspot service function corresponding to the target server cluster from the candidate service functions according to the server resource usage amount; and determining a target object having an association relationship with the hotspot service function, and generating a resource usage amount analysis report corresponding to the target server cluster according to the hotspot service function and the target object. According to the method, the server resource usage amount analysis on the global level of the server cluster is realized, the comprehensiveness and integrity of the server resource usage amount analysis are improved, and the global resource usage amount optimization on the server cluster is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of distributed technology, and in particular to a method, device, equipment, medium and product for analyzing server resource usage. Background Art

[0002] With the rapid development of cloud computing, big data and artificial intelligence technologies, the scale of servers has grown rapidly and can now meet the growing demand for computing power.

[0003] At the same time, with the increasing popularity and maturity of technical architectures such as cloud native and microservices, the technical architecture used by servers has become increasingly complex. As a result, the usage of server resources has increased exponentially. In order to ensure the normal operation of the server, it is necessary to monitor and analyze the usage of server resources.

[0004] Currently, the analysis of server resource usage is usually limited to a certain server, and the analysis results lack comprehensiveness and integrity, which is not conducive to global resource usage optimization of the server cluster. Summary of the invention

[0005] The present invention provides a method, device, equipment, medium and product for analyzing server resource usage to solve the problem that the existing analysis of server resource usage lacks comprehensiveness and integrity and is not conducive to global resource usage optimization of server clusters.

[0006] According to one aspect of the present invention, a method for analyzing server resource usage is provided, the method comprising:

[0007] Determine at least one candidate server included in the target server cluster, and determine at least one candidate service function called by each candidate server when processing service data;

[0008] Determine the server resource usage of each of the candidate service functions in each of the candidate servers, and determine the hot service function corresponding to the target server cluster from each of the candidate service functions according to the server resource usage;

[0009] Determine a target object associated with each of the hot business functions, and generate a resource usage analysis report corresponding to the target server cluster according to each of the hot business functions and each of the target objects;

[0010] The target object includes at least one of a function language, a function component, a function base library, a function container, a server room and a server processor.

[0011] According to another aspect of the present invention, there is provided a device for analyzing server resource usage, the device comprising:

[0012] A candidate business function determination module, used to determine at least one candidate server included in the target server cluster, and determine at least one candidate business function called by each candidate server when processing business data;

[0013] A hot business function determination module, used to determine the server resource usage of each candidate business function in each candidate server, and determine the hot business function corresponding to the target server cluster from each candidate business function according to the server resource usage;

[0014] An analysis report generating module, used to determine a target object associated with each of the hot business functions, and generate a resource usage analysis report corresponding to the target server cluster according to each of the hot business functions and each of the target objects;

[0015] The target object includes at least one of a function language, a function component, a function base library, a function container, a server room and a server processor.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the server resource usage analysis method described in any one of the present inventions.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement any one of the server resource usage analysis methods of the present invention when executed.

[0021] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method for analyzing the usage of server resources described in any one of the present invention is implemented.

[0022] The present invention determines at least one candidate server included in the target server cluster, determines at least one candidate business function called by each candidate server when processing business data, determines the server resource usage of each candidate business function in each candidate server, and determines the hot business function corresponding to the target server cluster from each candidate business function based on the server resource usage; determines a target object associated with each hot business function, and generates a resource usage analysis report corresponding to the target server cluster based on each hot business function and each target object; wherein the target object includes at least one of a function language, a function component, a function base library, a function container, a server room and a server processor, thereby realizing a global-level server resource usage analysis of the server cluster, improving the comprehensiveness and integrity of the server resource usage analysis, and facilitating global resource usage optimization of the server cluster.

[0023] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A flowchart of a method for analyzing server resource usage provided in Embodiment 1 of the present invention;

[0026] Figure 2 A flowchart of a method for analyzing server resource usage provided in Embodiment 2 of the present invention;

[0027] Figure 3 A flowchart of a method for analyzing server resource usage provided in Embodiment 3 of the present invention;

[0028] Figure 4 A schematic diagram of the structure of a device for analyzing server resource usage provided by a fourth embodiment of the present invention;

[0029] Figure 5 It is a structural schematic diagram of an electronic device for implementing the method for analyzing the server resource usage according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "candidate", "target", "hotspot", "first", "second", "third", "fourth", "fifth" and "sixth" in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Embodiment 1

[0033] Figure 1 This is a flow chart of a method for analyzing server resource usage provided in the first embodiment of the present invention. This embodiment is applicable to the case of performing a global analysis of server resource usage of a server cluster. The method can be executed by an analysis device for server resource usage, and the analysis device for server resource usage can be implemented in the form of hardware and / or software. Figure 1 As shown, the method includes:

[0034] S101. Determine at least one candidate server included in a target server cluster, and determine at least one candidate business function called by each candidate server when processing business data.

[0035] The target server cluster refers to a server cluster with server resource usage analysis requirements. Server cluster is a technology that uses multiple servers to work together to improve computing power, reliability, and availability. A server cluster connects multiple servers through a high-speed network to form a tightly coordinated server system that appears to be a single server. Each independent server in the cluster is called a node, and these nodes work together to provide system resources and services.

[0036] The servers included in the target server cluster are used as candidate servers, and the number of candidate servers is at least one. Business data of various business types are allocated to each candidate server according to business needs, so that each candidate server can process the allocated business data at runtime. When each candidate server processes business data, it will call different types of candidate business functions to process the business data to achieve the corresponding business needs. Business function refers to a function used to process business data (such as financial business data in the financial field, etc.). It can be understood that the candidate business function refers to the business function called by each candidate server when processing business data.

[0037] In one embodiment, a lightweight agent is pre-deployed in each candidate server included in the target server cluster. Agent, also known as an intelligent agent, refers to a computer program or system that can perceive the environment, make decisions and take actions. In this embodiment, the agent includes a data acquisition module and a data upload module. The data acquisition module is used to collect the operating data of the corresponding candidate server, and the data upload module is used to upload the operating data collected by the data acquisition module to the monitoring system.

[0038] Specifically, the data collection module collects server information corresponding to the candidate server, and the data uploading module uploads the server information to the monitoring system, and the monitoring system determines at least one candidate server included in the target server cluster based on the received server information. In addition, during the operation of each candidate server, if any candidate server calls at least one business function when processing business data, the data collection module will monitor the business function call event accordingly, and then collect the function identifier of the called business function as the candidate function identifier of the candidate business function. The data uploading module uploads the candidate function identifier to the monitoring system, and the monitoring system determines at least one candidate business function called by the candidate server when processing business data based on the received candidate function identifier.

[0039] For example, assuming that data collection module 1 and data uploading module 1 are deployed in candidate server 1, data collection module 1 collects server information A of candidate server 1, data uploading module 1 sends server information A to the monitoring system, and the monitoring system determines that the target server cluster includes candidate server 1 based on server information A.

[0040] For another example, assuming that the data collection module 1 and the data uploading module 1 are deployed in the candidate server 1, and the data collection module 1 monitors that the candidate server 1 calls the candidate business function X when processing the business data, the candidate function identifier Y of the candidate business function X is collected. The data uploading module 1 sends the candidate function identifier Y to the monitoring system, and the monitoring system determines that the candidate server 1 calls the candidate business function X when processing the business data based on the candidate function identifier Y.

[0041] S102: Determine the server resource usage of each candidate service function in each candidate server, and determine the hot service function corresponding to the target server cluster from each candidate service function according to the server resource usage.

[0042] Among them, the server resource usage refers to the amount of server resources used by the candidate business function during operation. Server resources refer to various devices and software owned by the server that can support its operation and provide services, including but not limited to CPU resources, memory resources, storage resources, communication resources and network resources, etc. This embodiment does not limit the specific types of server resources. Hot business functions refer to candidate business functions with more server resource usage. It can be understood that the more server resource usage of any candidate business function, the greater the probability that the candidate business function is a hot business function; correspondingly, the less server resource usage of any candidate business function, the smaller the probability that the candidate business function is a hot business function.

[0043] In one embodiment, the data collection module periodically collects the server resource usage of each candidate business function in the corresponding candidate server, wherein the collection time is dispersed as much as possible to avoid peak impact. The data uploading module uploads the server resource usage of each candidate business function to the monitoring system. The monitoring system determines the server resource usage of each candidate business function in each candidate server based on the server resource usage uploaded by the data uploading module in each candidate server.

[0044] Further, the monitoring system counts the server resource usage of each candidate business function in each candidate server to determine the total resource usage of each candidate business function. Further, according to the ranking result of the total resource usage of each candidate business function, the hot business function corresponding to the target server cluster is determined from each candidate business function.

[0045] Optionally, according to the ranking result of the total resource usage of each candidate business function, a preset proportion of candidate business functions are selected as hot business functions corresponding to the target server cluster. For example, assuming that the preset proportion is set to 10%, the top 10% of the candidate business functions in the total resource usage ranking are selected as hot business functions, that is, the top 10% of the candidate business functions with the largest total resource usage are selected as hot business functions.

[0046] Optionally, according to the ranking result of the total resource usage of each candidate business function, a preset number of candidate business functions are selected as hot business functions corresponding to the target server cluster. For example, assuming that the preset number is set to 10, the top 10 candidate business functions ranked by the total resource usage are selected as hot business functions, that is, the top 10 candidate business functions with the largest total resource usage are selected as hot business functions.

[0047] S103: Determine a target object associated with each hot business function, and generate a resource usage analysis report corresponding to the target server cluster according to each hot business function and each target object.

[0048] Among them, the target object refers to an object that has an association relationship with a hot business function, including at least one of a function language, a function component, a function basic library, a function container, a server room and a server processor.

[0049] Function language refers to the programming language used by hot business functions, such as C++, Java, etc. Function component refers to the component used by hot business functions, and component refers to a simple encapsulation of data and methods. Function base library refers to the base library to which the hot business function belongs, and the base library refers to a collection of functions, classes, data structures and type definitions. Function container refers to the container used to encapsulate the function code of hot business functions. The server room refers to the room where the candidate server that calls the hot business function is located. The server processor refers to the server CPU called by the hot business function at runtime. If the candidate server is a quad-core server, the hot business function may call one or more of the four CPUs at runtime.

[0050] In one embodiment, the data acquisition module collects the function identifier of the called business function as the candidate function identifier of the candidate business function. At the same time, the data acquisition module also collects the object identifier associated with the called business function. The data uploading module sends the candidate function identifier and the object identifier to the monitoring system. The monitoring system determines the candidate business function called by the candidate server when processing business data based on the received candidate function identifier, and determines the candidate object associated with the candidate business function based on the received object identifier. Furthermore, if any candidate business function is determined to be a hot business function, the candidate object associated with the candidate business function is used as a target object having an association relationship with the hot business function. Among them, the target object can be associated with the hot business function in the form of a tag.

[0051] Furthermore, the monitoring system jointly generates a resource usage analysis report corresponding to the target server cluster based on each hot business function and the target object associated with each hot business function, and visualizes the resource usage analysis report. Users can use the resource usage analysis report to intuitively discover the hot business functions that use more server resources in the target server cluster, as well as the function languages, function components, function base libraries, function containers, server computer rooms and server processors related to the hot business functions, so that users can optimize the resource usage of the target server cluster at the global level based on the hot business functions, function languages, function components, function base libraries, function containers, server computer rooms and server processors, so as to improve the server resource utilization efficiency of the target server cluster.

[0052] The embodiment of the present invention determines at least one candidate server included in the target server cluster, determines at least one candidate business function called by each candidate server when processing business data, determines the server resource usage of each candidate business function in each candidate server, and determines the hot business function corresponding to the target server cluster from each candidate business function based on the server resource usage; determines a target object associated with each hot business function, and generates a resource usage analysis report corresponding to the target server cluster based on each hot business function and each target object; wherein the target object includes at least one of a function language, a function component, a function base library, a function container, a server room and a server processor, thereby realizing a global server resource usage analysis of the server cluster, improving the comprehensiveness and integrity of the server resource usage analysis, and being conducive to global resource usage optimization of the server cluster, rather than optimizing the resource usage of a single server, thereby improving the resource usage efficiency of the entire server cluster.

[0053] Optionally, a resource usage analysis report corresponding to the target server cluster is generated according to each hot business function and each target object, including:

[0054] A1. Determine container identification information corresponding to each function container, and determine the target workload corresponding to each function container according to the container identification information.

[0055] The container ID is the unique identity information of the function container. The workload is also called workload, which refers to the application instance running on the cluster.

[0056] In one implementation, based on the association between container identification information and workload, container identification information corresponding to each function container is used to determine the workload corresponding to each function container as the target workload.

[0057] B1. According to the configuration information corresponding to each target workload, determine the business system corresponding to each function container as the target business system corresponding to the associated hot business function.

[0058] In one implementation, the configuration information corresponding to each target workload is parsed, and the business systems corresponding to the function containers recorded in the configuration information are determined as the target business systems corresponding to the associated hot business functions.

[0059] For example, assuming that the configuration information of the target workload 1 corresponding to the function container 1 records "function container 1 corresponds to business system A", then "business system A" is used as the target business system corresponding to the hot business function associated with the function container 1.

[0060] C1. Generate a resource usage analysis report corresponding to the target server cluster according to each hot business function and the target business system corresponding to each hot business function.

[0061] For example, assuming that the target business system corresponding to "Hotspot Business Function 1" is "Business System 1", the target business system corresponding to "Hotspot Business Function 2" is "Business System 2", and the target business system corresponding to "Hotspot Business Function 3" is "Business System 3", the styles of the resource usage analysis report include but are not limited to: 1. "Hotspot Business Function 1-Business System 1"; 2. "Hotspot Business Function 2-Business System 2"; 3. "Hotspot Business Function 3-Business System 3".

[0062] By determining the container identification information corresponding to each function container, and determining the target workload corresponding to each function container according to the container identification information; determining the business system corresponding to each function container according to the configuration information corresponding to each target workload, as the target business system corresponding to the associated hot business function; generating a resource usage analysis report corresponding to the target server cluster according to each hot business function and the target business system corresponding to each hot business function, so that users can intuitively observe the target business system corresponding to each hot business function, which is beneficial for users to optimize each target business system in a targeted manner according to the target business system corresponding to each hot business function, and further improve the resource utilization efficiency of the entire server cluster.

[0063] Optionally, the method further includes:

[0064] A2. Determine the current basic library version corresponding to each function basic library, and compare each current basic library version with the standard basic library version.

[0065] B2. If any current basic library version lags behind the standard basic library version, the function basic library corresponding to the current basic library version is used as the function basic library to be upgraded, and a version upgrade operation is performed on the function basic library to be upgraded.

[0066] The standard basic library version may be the latest basic library version or any pre-set basic library version.

[0067] For example, assuming that the current basic library version corresponding to any function base library is "V1.0.2", and assuming that the standard basic library version is set to "V1.0.4", that is, the current basic library version corresponding to the function base library lags behind the standard basic library version, then the function base library will be treated as the function base library to be upgraded, and a version upgrade operation will be performed on the function base library to be upgraded.

[0068] For example, assuming that the current basic library version corresponding to any function basic library is "V1.0.5", and assuming that the standard basic library version is set to "V1.0.4", that is, the current basic library version corresponding to the function basic library is not behind the standard basic library version, there is no need to perform a version upgrade operation on the function basic library.

[0069] By determining the current basic library version corresponding to each function basic library, and comparing each current basic library version with the standard basic library version; if any current basic library version lags behind the standard basic library version, the function basic library corresponding to the current basic library version is used as the function basic library to be upgraded, and the version upgrade operation is performed on the function basic library to be upgraded, thereby achieving the effect of automatically upgrading the old version of the function basic library to which the hot business function belongs, which is beneficial to ensuring the performance of the function basic library to which the hot business function belongs, further optimizing the function performance of the hot business function, and ensuring the smoothness and efficiency of the server cluster in business processing.

[0070] Embodiment 2

[0071] Figure 2 This is a flow chart of a method for analyzing server resource usage provided by Embodiment 2 of the present invention. This embodiment further optimizes and expands the above embodiment and can be combined with the above optional implementations. Figure 2 As shown, the method includes:

[0072] S201. Determine at least one candidate server included in the target server cluster, and determine at least one candidate business function called by each candidate server when processing business data.

[0073] S202: Determine the server resource usage of each candidate business function in each candidate server, classify and count the server resource usage of each candidate business function in each candidate server, and determine the total resource usage corresponding to each candidate business function in the target server cluster.

[0074] Exemplarily, assume that the target server cluster includes candidate server 1, candidate server 2 and candidate server 3, assume that candidate server 1 includes candidate business function A and candidate business function B, candidate server 2 includes candidate business function A and candidate business function C, and candidate server 3 includes candidate business function A and candidate business function B.

[0075] Assume that the server resource usage of candidate service function A on candidate server 1 is x, the server resource usage on candidate server 2 is x, and the server resource usage on candidate server 3 is 2x. Assume that the server resource usage of candidate service function B on candidate server 1 is 2x, and the server resource usage on candidate server 3 is 3x. Assume that the server resource usage of candidate service function C on candidate server 2 is 3x.

[0076] Therefore, the total resource usage corresponding to the candidate business function A in the target server cluster is 4x, the total resource usage corresponding to the candidate business function B is 5x, and the total resource usage corresponding to the candidate business function C is 3x.

[0077] S203 . Select a target number of candidate business functions as hot business functions corresponding to the target server cluster according to the ranking results of the total resource usage amounts corresponding to the candidate business functions.

[0078] Among them, the target quantity can be set and adjusted according to needs.

[0079] For example, assume that the total resource usage corresponding to candidate business function A is x, the total resource usage corresponding to candidate business function B is 2x, the total resource usage corresponding to candidate business function C is 3x, the total resource usage corresponding to candidate business function D is 4x, the total resource usage corresponding to candidate business function E is 5x, the total resource usage corresponding to candidate business function F is 6x, the total resource usage corresponding to candidate business function G is 7x, the total resource usage corresponding to candidate business function H is 8x, the total resource usage corresponding to candidate business function I is 9x, and the total resource usage corresponding to candidate business function J is 10x.

[0080] The sorting results (from large to small) are candidate business function J, candidate business function I, candidate business function H, candidate business function G, candidate business function F, candidate business function E, candidate business function D, candidate business function C, candidate business function B and candidate business function A.

[0081] Assuming that the target number is 5, the hot business functions corresponding to the target server cluster are determined to be candidate business function J, candidate business function I, candidate business function H, candidate business function G and candidate business function F.

[0082] By classifying and counting the server resource usage of each candidate business function in each candidate server, the total resource usage corresponding to each candidate business function in the target server cluster is determined; based on the sorting results of the total resource usage corresponding to each candidate business function, a target number of candidate business functions are selected as the hot business functions corresponding to the target server cluster, thereby realizing the determination of hot business functions based on the resource usage at the global level of the target server cluster, ensuring the comprehensiveness of the determination of hot business functions, and facilitating the global resource usage optimization of the server cluster based on the hot business functions.

[0083] S204: Determine a target object associated with each hot business function, and generate resource usage association information according to each hot business function and the sum of resource usages corresponding to each hot function.

[0084] The resource usage association information refers to information including hot business functions and the sum of their resource usage.

[0085] Exemplarily, assuming that the total resource usage corresponding to hotspot business function 1 is x, the total resource usage corresponding to hotspot business function 2 is y, and the total resource usage corresponding to hotspot business function 3 is z, then according to "hotspot business function 1" and "total resource usage x", the first resource usage association information "hotspot business function 1-total resource usage x" is generated; according to "hotspot business function 2" and "total resource usage y", the second resource usage association information "hotspot business function 2-total resource usage y" is generated; according to "hotspot business function 3" and "total resource usage z", the third resource usage association information "hotspot business function 3-total resource usage z" is generated.

[0086] S205: Generate a resource usage analysis report corresponding to the target server cluster according to the resource usage association information and the target object.

[0087] In one implementation, a resource usage analysis report is generated based on each resource usage association information and its corresponding target object.

[0088] For example, the resource usage analysis report includes but is not limited to:

[0089] 1. "Hot business function 1-total resource usage x-function language 1-function component 1-function basic library 1-function container 1-server room 1-server processor 1";

[0090] 2. "Hot business function 2-total resource usage y-function language 2-function component 2-function basic library 2-function container 2-server room 2-server processor 2";

[0091] 3. “Hot business function 3-total resource usage z-function language 3-function component 3-function basic library 3-function container 3-server room 3-server processor 3”.

[0092] By generating resource usage association information based on each hot business function and the total resource usage corresponding to each hot business function; generating a resource usage analysis report corresponding to the target server cluster based on the resource usage association information and the target object, the user can intuitively observe the total resource usage corresponding to each hot business function based on the resource usage association information, which is beneficial for the user to optimize each hot business function in a targeted manner based on the total resource usage corresponding to each hot business function, thereby further improving the resource utilization efficiency of the entire server cluster.

[0093] Embodiment 3

[0094] Figure 3 This is a flow chart of a method for analyzing server resource usage provided by Embodiment 3 of the present invention. This embodiment further optimizes and expands the above embodiment and can be combined with the above optional implementations. Figure 3 As shown, the method includes:

[0095] S301. Determine at least one candidate server included in a target server cluster, and determine at least one candidate business function called by each candidate server when processing business data.

[0096] S302: Determine the server resource usage of each candidate service function in each candidate server, and determine the hot service function corresponding to the target server cluster from each candidate service function according to the server resource usage.

[0097] S303: Count the function languages ​​associated with each hot business function, determine the first statistical quantity corresponding to each function language, and generate first statistical association information according to each function language and the first statistical quantity corresponding to each function language.

[0098] For example, assume that the function language associated with hot business function 1 is function language 1, the function language associated with hot business function 2 is function language 1, the function language associated with hot business function 3 is function language 1, the function language associated with hot business function 4 is function language 2, the function language associated with hot business function 5 is function language 2, the function language associated with hot business function 6 is function language 3, the function language associated with hot business function 7 is function language 3, the function language associated with hot business function 8 is function language 3, the function language associated with hot business function 9 is function language 4, and the function language associated with hot business function 10 is function language 4.

[0099] The first statistical quantity corresponding to function language 1 is "3", the first statistical quantity corresponding to function language 2 is "2", the first statistical quantity corresponding to function language 3 is "3", and the first statistical quantity corresponding to function language 4 is "2".

[0100] Further, based on “function language 1” and the first statistical quantity “3”, the first statistical association information “function language 1-first statistical quantity 3” of function language 1 is generated; based on “function language 2” and the first statistical quantity “2”, the first statistical association information “function language 2-first statistical quantity 3” of function language 2 is generated; based on “function language 3” and the first statistical quantity “3”, the first statistical association information “function language 3-first statistical quantity 3” of function language 3 is generated; based on “function language 4” and the first statistical quantity “2”, the first statistical association information “function language 4-first statistical quantity 2” of function language 4 is generated.

[0101] By counting the function languages ​​respectively associated with each hot business function, determining the first statistical quantity corresponding to each function language, and generating first statistical association information based on each function language and the first statistical quantity corresponding to each function language, the user can intuitively observe the number of times the function language of each hot business function appears based on the first statistical association information, which is beneficial for the user to optimize each hot business function in a targeted manner based on the number of times the function language of each hot business function appears, thereby further improving the resource utilization efficiency of the entire server cluster.

[0102] S304: Count the function components respectively associated with each hot business function, determine the second statistical quantity respectively corresponding to each function component, and generate second statistical association information according to each function component and the second statistical quantity respectively corresponding to each function component.

[0103] For example, assume that the function component associated with hotspot business function 1 is function component 1, the function component associated with hotspot business function 2 is function component 1, the function component associated with hotspot business function 3 is function component 1, the function component associated with hotspot business function 4 is function component 2, the function component associated with hotspot business function 5 is function component 2, the function component associated with hotspot business function 6 is function component 3, the function component associated with hotspot business function 7 is function component 3, the function component associated with hotspot business function 8 is function component 3, the function component associated with hotspot business function 9 is function component 4, and the function component associated with hotspot business function 10 is function component 4.

[0104] The second statistical quantity corresponding to function component 1 is "3", the second statistical quantity corresponding to function component 2 is "2", the second statistical quantity corresponding to function component 3 is "3", and the second statistical quantity corresponding to function component 4 is "2".

[0105] Further, based on “function component 1” and the second statistical quantity “3”, the second statistical association information “function component 1-second statistical quantity 3” of function component 1 is generated; based on “function component 2” and the second statistical quantity “2”, the second statistical association information “function component 2-second statistical quantity 3” of function component 2 is generated; based on “function component 3” and the second statistical quantity “3”, the second statistical association information “function component 3-second statistical quantity 3” of function component 3 is generated; based on “function component 4” and the second statistical quantity “2”, the second statistical association information “function component 4-second statistical quantity 2” of function component 4 is generated.

[0106] By counting the function components respectively associated with each hot business function, determining the second statistical quantity corresponding to each function component, and generating second statistical association information based on each function component and the second statistical quantity corresponding to each function component, the user can intuitively observe the number of times the function component used by each hot business function appears based on the second statistical association information, which is beneficial for the user to optimize each function component in a targeted manner based on the number of times the function component used by each hot business function appears, thereby further improving the resource utilization efficiency of the entire server cluster.

[0107] S305, counting the function base libraries respectively associated with each hot business function, determining the third statistical quantity corresponding to each function base library, and generating third statistical association information according to each function base library and the third statistical quantity corresponding to each function base library.

[0108] For example, assume that the function base library associated with hot business function 1 is function base library 1, the function base library associated with hot business function 2 is function base library 1, the function base library associated with hot business function 3 is function base library 1, the function base library associated with hot business function 4 is function base library 2, the function base library associated with hot business function 5 is function base library 2, the function base library associated with hot business function 6 is function base library 3, the function base library associated with hot business function 7 is function base library 3, the function base library associated with hot business function 8 is function base library 3, the function base library associated with hot business function 9 is function base library 4, and the function base library associated with hot business function 10 is function base library 4.

[0109] The third statistical quantity corresponding to function basic library 1 is "3", the third statistical quantity corresponding to function basic library 2 is "2", the third statistical quantity corresponding to function basic library 3 is "3", and the third statistical quantity corresponding to function basic library 4 is "2".

[0110] Further, based on “Function base library 1” and the third statistical quantity “3”, the third statistical association information “Function base library 1-third statistical quantity 3” of function base library 1 is generated; based on “Function base library 2” and the third statistical quantity “2”, the third statistical association information “Function base library 2-third statistical quantity 3” of function base library 2 is generated; based on “Function base library 3” and the third statistical quantity “3”, the third statistical association information “Function base library 3-third statistical quantity 3” of function base library 3 is generated; based on “Function base library 4” and the third statistical quantity “2”, the third statistical association information “Function base library 4-third statistical quantity 2” of function base library 4 is generated.

[0111] By counting the function base libraries associated with each hot business function, the third statistical quantities corresponding to each function base library are determined, and third statistical association information is generated based on each function base library and the third statistical quantities corresponding to each function base library. This allows users to intuitively observe the number of times the function base library to which each hot business function belongs appears based on the third statistical association information. This helps users to optimize each function base library in a targeted manner based on the number of times the function base library to which each hot business function belongs appears, thereby further improving the resource utilization efficiency of the entire server cluster.

[0112] S306: Count the function containers respectively associated with the hot business functions to determine the fourth statistical quantities respectively corresponding to the function containers, and generate fourth statistical association information according to the function containers and the fourth statistical quantities respectively corresponding to the function containers.

[0113] For example, assume that the function container associated with hot business function 1 is function container 1, the function container associated with hot business function 2 is function container 1, the function container associated with hot business function 3 is function container 1, the function container associated with hot business function 4 is function container 2, the function container associated with hot business function 5 is function container 2, the function container associated with hot business function 6 is function container 3, the function container associated with hot business function 7 is function container 3, the function container associated with hot business function 8 is function container 3, the function container associated with hot business function 9 is function container 4, and the function container associated with hot business function 10 is function container 4.

[0114] The fourth statistical quantity corresponding to function container 1 is "3", the fourth statistical quantity corresponding to function container 2 is "2", the fourth statistical quantity corresponding to function container 3 is "3", and the fourth statistical quantity corresponding to function container 4 is "2".

[0115] Further, based on “function container 1” and the fourth statistical quantity “3”, the fourth statistical association information “function container 1-fourth statistical quantity 3” of function container 1 is generated; based on “function container 2” and the fourth statistical quantity “2”, the fourth statistical association information “function container 2-fourth statistical quantity 3” of function container 2 is generated; based on “function container 3” and the fourth statistical quantity “3”, the fourth statistical association information “function container 3-fourth statistical quantity 3” of function container 3 is generated; based on “function container 4” and the fourth statistical quantity “2”, the fourth statistical association information “function container 4-fourth statistical quantity 2” of function container 4 is generated.

[0116] By counting the function containers respectively associated with each hot business function, the fourth statistical quantity corresponding to each function container is determined, and the fourth statistical association information is generated according to each function container and the fourth statistical quantity corresponding to each function container, so that the user can intuitively observe the number of times the function container used to encapsulate each hot business function appears based on the fourth statistical association information, which is beneficial for the user to optimize each function container in a targeted manner according to the number of times the function container used to encapsulate each hot business function appears, thereby further improving the resource utilization efficiency of the entire server cluster.

[0117] S307: Count the server processors respectively associated with each hot business function to determine the fifth statistical quantity corresponding to each server processor, and generate fifth statistical association information according to each server processor and the fifth statistical quantity corresponding to each server processor.

[0118] For example, assume that the server processor associated with hot business function 1 is server processor 1, the server processor associated with hot business function 2 is server processor 1, the server processor associated with hot business function 3 is server processor 1, the server processor associated with hot business function 4 is server processor 2, the server processor associated with hot business function 5 is server processor 2, the server processor associated with hot business function 6 is server processor 3, the server processor associated with hot business function 7 is server processor 3, the server processor associated with hot business function 8 is server processor 3, the server processor associated with hot business function 9 is server processor 4, and the server processor associated with hot business function 10 is server processor 4.

[0119] The fifth statistical number corresponding to server processor 1 is "3", the fifth statistical number corresponding to server processor 2 is "2", the fifth statistical number corresponding to server processor 3 is "3", and the fifth statistical number corresponding to server processor 4 is "2".

[0120] Further, based on “server processor 1” and the fifth statistical quantity “3”, the fifth statistical association information “server processor 1-fifth statistical quantity 3” of server processor 1 is generated; based on “server processor 2” and the fifth statistical quantity “2”, the fifth statistical association information “server processor 2-fifth statistical quantity 3” of server processor 2 is generated; based on “server processor 3” and the fifth statistical quantity “3”, the fifth statistical association information “server processor 3-fifth statistical quantity 3” of server processor 3 is generated; based on “server processor 4” and the fifth statistical quantity “2”, the fifth statistical association information “server processor 4-fifth statistical quantity 2” of server processor 4 is generated.

[0121] By counting the server processors associated with each hot business function, the fifth statistical quantity corresponding to each server processor is determined, and the fifth statistical association information is generated based on each server processor and the fifth statistical quantity corresponding to each server processor. This allows users to intuitively observe the number of times the server processor called by each hot business function at runtime appears based on the fifth statistical association information. This helps users to optimize each server processor in a targeted manner based on the number of times the server processor called by each hot business function at runtime, thereby further improving the resource utilization efficiency of the entire server cluster.

[0122] S308, counting the server rooms respectively associated with each hot business function, determining the sixth statistical quantity corresponding to each server room, and generating sixth statistical association information according to each server room and the sixth statistical quantity corresponding to each server room.

[0123] For example, assume that the server room associated with hot business function 1 is server room 1, the server room associated with hot business function 2 is server room 1, the server room associated with hot business function 3 is server room 1, the server room associated with hot business function 4 is server room 2, the server room associated with hot business function 5 is server room 2, the server room associated with hot business function 6 is server room 3, the server room associated with hot business function 7 is server room 3, the server room associated with hot business function 8 is server room 3, the server room associated with hot business function 9 is server room 4, and the server room associated with hot business function 10 is server room 4.

[0124] The sixth statistical number corresponding to server room 1 is "3", the sixth statistical number corresponding to server room 2 is "2", the sixth statistical number corresponding to server room 3 is "3", and the sixth statistical number corresponding to server room 4 is "2".

[0125] Further, based on “server room 1” and the sixth statistical quantity “3”, the sixth statistical association information “server room 1-sixth statistical quantity 3” of server room 1 is generated; based on “server room 2” and the sixth statistical quantity “2”, the sixth statistical association information “server room 2-sixth statistical quantity 3” of server room 2 is generated; based on “server room 3” and the sixth statistical quantity “3”, the sixth statistical association information “server room 3-sixth statistical quantity 3” of server room 3 is generated; based on “server room 4” and the sixth statistical quantity “2”, the sixth statistical association information “server room 4-sixth statistical quantity 2” of server room 4 is generated.

[0126] By counting the server rooms associated with each hot business function, the sixth statistical quantity corresponding to each server room is determined, and the sixth statistical association information is generated according to each server room and the sixth statistical quantity corresponding to each server room, so that the user can intuitively observe the number of times the server room that calls each hot business function appears based on the sixth statistical association information, which is beneficial for the user to optimize each server room in a targeted manner according to the number of times the server room that calls each hot business function appears, thereby further improving the resource utilization efficiency of the entire server cluster.

[0127] S309: Generate a resource usage analysis report corresponding to the target server cluster according to each hot business function, the first statistical association information, the second statistical association information, the third statistical association information, the fourth statistical association information, the fifth statistical association information and the sixth statistical association information.

[0128] By generating a resource usage analysis report corresponding to the target server cluster according to each hot business function, the first statistical association information, the second statistical association information, the third statistical association information, the fourth statistical association information, the fifth statistical association information and the sixth statistical association information, the user can optimize not only the hot business functions, but also the function components, function basic libraries, function containers, server rooms and server processors together, which is conducive to global resource usage optimization of the server cluster and improving the resource utilization efficiency of the entire server cluster.

[0129] Embodiment 4

[0130] Figure 4 The schematic diagram of the structure of a server resource usage analysis device provided in the fourth embodiment of the present invention is applicable to the situation of performing a global analysis on the server resource usage of a server cluster, such as Figure 4 As shown, the device comprises:

[0131] A candidate business function determination module 41 is used to determine at least one candidate server included in the target server cluster, and to determine at least one candidate business function called by each candidate server when processing business data;

[0132] A hot business function determination module 42 is used to determine the server resource usage of each candidate business function in each candidate server, and determine the hot business function corresponding to the target server cluster from each candidate business function according to the server resource usage;

[0133] An analysis report generating module 43 is used to determine a target object associated with each of the hot business functions, and generate a resource usage analysis report corresponding to the target server cluster according to each of the hot business functions and each of the target objects;

[0134] The target object includes at least one of a function language, a function component, a function base library, a function container, a server room and a server processor.

[0135] Optionally, the hotspot service function determination module 42 is specifically configured to:

[0136] Classify and count the server resource usage of each of the candidate business functions in each of the candidate servers to determine the total resource usage corresponding to each of the candidate business functions in the target server cluster;

[0137] According to the ranking results of the sums of resource usages corresponding to the candidate business functions, a target number of the candidate business functions are selected as hot business functions corresponding to the target server cluster.

[0138] Optionally, the analysis report generating module 43 is specifically used for:

[0139] Generate resource usage association information according to each of the hot business functions and the sum of the resource usages corresponding to each of the hot functions;

[0140] A resource usage analysis report corresponding to the target server cluster is generated according to the resource usage association information and the target object.

[0141] Optionally, the analysis report generating module 43 is further configured to:

[0142] Performing statistics on the function languages ​​respectively associated with the hot business functions, determining first statistical quantities respectively corresponding to the function languages, and generating first statistical association information according to the function languages ​​and the first statistical quantities respectively corresponding to the function languages;

[0143] Performing statistics on the function components respectively associated with each of the hot business functions, determining second statistical quantities respectively corresponding to each of the function components, and generating second statistical association information according to each of the function components and the second statistical quantities respectively corresponding to each of the function components;

[0144] Performing statistics on the function base libraries respectively associated with each of the hot business functions, determining third statistical quantities respectively corresponding to each of the function base libraries, and generating third statistical association information according to each of the function base libraries and the third statistical quantities respectively corresponding to each of the function base libraries;

[0145] Performing statistics on the function containers respectively associated with the hot business functions, determining fourth statistical quantities respectively corresponding to the function containers, and generating fourth statistical association information according to the function containers and the fourth statistical quantities respectively corresponding to the function containers;

[0146] Performing statistics on the server processors respectively associated with each of the hot business functions, determining fifth statistical quantities respectively corresponding to each of the server processors, and generating fifth statistical association information according to each of the server processors and the fifth statistical quantities respectively corresponding to each of the server processors;

[0147] Performing statistics on the server rooms respectively associated with the hot business functions, determining sixth statistical quantities respectively corresponding to the server rooms, and generating sixth statistical association information according to the server rooms and the sixth statistical quantities respectively corresponding to the server rooms;

[0148] A resource usage analysis report corresponding to the target server cluster is generated according to each of the hot business functions, the first statistical association information, the second statistical association information, the third statistical association information, the fourth statistical association information, the fifth statistical association information and the sixth statistical association information.

[0149] Optionally, the analysis report generating module 43 is further configured to:

[0150] Determine container identification information corresponding to each of the function containers, and determine the target workload corresponding to each of the function containers according to the container identification information;

[0151] According to the configuration information corresponding to each of the target workloads, determine the business system corresponding to each of the function containers as the target business system corresponding to the associated hot business function;

[0152] A resource usage analysis report corresponding to the target server cluster is generated according to each of the hot business functions and the target business systems corresponding to each of the hot business functions.

[0153] Optionally, the device further includes a basic library upgrade module, which is specifically used to:

[0154] Determine the current basic library version corresponding to each of the function basic libraries, and compare each of the current basic library versions with the standard basic library version;

[0155] If any of the current basic library versions lags behind the standard basic library version, the function basic library corresponding to the current basic library version is used as the function basic library to be upgraded, and a version upgrade operation is performed on the function basic library to be upgraded.

[0156] The server resource usage analysis device provided in the embodiment of the present invention can execute the server resource usage analysis method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0157] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0158] Embodiment 5

[0159] Figure 5A schematic diagram of an electronic device 50 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0160] like Figure 5 As shown, the electronic device 50 includes at least one processor 51, and a memory connected to the at least one processor 51 in communication, such as a read-only memory (ROM) 52, a random access memory (RAM) 53, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 51 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 52 or the computer program loaded from the storage unit 58 to the random access memory (RAM) 53. In the RAM 53, various programs and data required for the operation of the electronic device 50 can also be stored. The processor 51, the ROM 52, and the RAM 53 are connected to each other via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0161] A number of components in the electronic device 50 are connected to the I / O interface 55, including: an input unit 56, such as a keyboard, a mouse, etc.; an output unit 57, such as various types of displays, speakers, etc.; a storage unit 58, such as a disk, an optical disk, etc.; and a communication unit 59, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 59 allows the electronic device 50 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0162] The processor 51 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 51 executes the various methods and processes described above, such as a method for analyzing server resource usage.

[0163] In some embodiments, the server resource usage analysis method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 58. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 50 via the ROM 52 and / or the communication unit 59. When the computer program is loaded into the RAM 53 and executed by the processor 51, one or more steps of the server resource usage analysis method described above may be performed. Alternatively, in other embodiments, the processor 51 may be configured to execute the server resource usage analysis method in any other appropriate manner (e.g., by means of firmware).

[0164] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0165] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0166] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0167] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0168] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0169] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0170] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0171] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing server resource usage, characterized in that: The method comprises: Determine at least one candidate server included in the target server cluster, and determine at least one candidate service function called by each candidate server when processing service data; Determine the server resource usage of each of the candidate service functions in each of the candidate servers, and determine the hot service function corresponding to the target server cluster from each of the candidate service functions according to the server resource usage; Determine a target object associated with each of the hot business functions, and generate a resource usage analysis report corresponding to the target server cluster according to each of the hot business functions and each of the target objects; The target object includes at least one of a function language, a function component, a function base library, a function container, a server room and a server processor.

2. The method according to claim 1, characterized in that Determining the hot business function corresponding to the target server cluster from the candidate business functions according to the server resource usage includes: Classify and count the server resource usage of each of the candidate business functions in each of the candidate servers to determine the total resource usage corresponding to each of the candidate business functions in the target server cluster; According to the ranking results of the sums of resource usages corresponding to the candidate business functions, a target number of the candidate business functions are selected as hot business functions corresponding to the target server cluster.

3. The method according to claim 2, characterized in that Generating a resource usage analysis report corresponding to the target server cluster according to each of the hot business functions and each of the target objects includes: Generate resource usage association information according to each of the hot business functions and the sum of the resource usages corresponding to each of the hot functions; A resource usage analysis report corresponding to the target server cluster is generated according to the resource usage association information and the target object.

4. The method according to claim 1, characterized in that Generating a resource usage analysis report corresponding to the target server cluster according to each of the hot business functions and each of the target objects includes: Performing statistics on the function languages ​​respectively associated with the hot business functions, determining first statistical quantities respectively corresponding to the function languages, and generating first statistical association information according to the function languages ​​and the first statistical quantities respectively corresponding to the function languages; Performing statistics on the function components respectively associated with each of the hot business functions, determining second statistical quantities respectively corresponding to each of the function components, and generating second statistical association information according to each of the function components and the second statistical quantities respectively corresponding to each of the function components; Performing statistics on the function base libraries respectively associated with each of the hot business functions, determining third statistical quantities respectively corresponding to each of the function base libraries, and generating third statistical association information according to each of the function base libraries and the third statistical quantities respectively corresponding to each of the function base libraries; Performing statistics on the function containers respectively associated with the hot business functions, determining fourth statistical quantities respectively corresponding to the function containers, and generating fourth statistical association information according to the function containers and the fourth statistical quantities respectively corresponding to the function containers; Performing statistics on the server processors respectively associated with each of the hot business functions, determining fifth statistical quantities respectively corresponding to each of the server processors, and generating fifth statistical association information according to each of the server processors and the fifth statistical quantities respectively corresponding to each of the server processors; Performing statistics on the server rooms respectively associated with the hot business functions, determining sixth statistical quantities respectively corresponding to the server rooms, and generating sixth statistical association information according to the server rooms and the sixth statistical quantities respectively corresponding to the server rooms; A resource usage analysis report corresponding to the target server cluster is generated according to each of the hot business functions, the first statistical association information, the second statistical association information, the third statistical association information, the fourth statistical association information, the fifth statistical association information and the sixth statistical association information.

5. The method according to claim 1, characterized in that Generating a resource usage analysis report corresponding to the target server cluster according to each of the hot business functions and each of the target objects includes: Determine container identification information corresponding to each of the function containers, and determine the target workload corresponding to each of the function containers according to the container identification information; According to the configuration information corresponding to each of the target workloads, determine the business system corresponding to each of the function containers as the target business system corresponding to the associated hot business function; A resource usage analysis report corresponding to the target server cluster is generated according to each of the hot business functions and the target business systems corresponding to each of the hot business functions.

6. The method according to claim 1, further comprising: Determine the current basic library version corresponding to each of the function basic libraries, and compare each of the current basic library versions with the standard basic library version; If any of the current basic library versions lags behind the standard basic library version, the function basic library corresponding to the current basic library version is used as the function basic library to be upgraded, and a version upgrade operation is performed on the function basic library to be upgraded.

7. A device for analyzing server resource usage, characterized in that: The device comprises: A candidate business function determination module, used to determine at least one candidate server included in the target server cluster, and determine at least one candidate business function called by each candidate server when processing business data; A hot business function determination module, used to determine the server resource usage of each candidate business function in each candidate server, and determine the hot business function corresponding to the target server cluster from each candidate business function according to the server resource usage; An analysis report generating module, used to determine a target object associated with each of the hot business functions, and generate a resource usage analysis report corresponding to the target server cluster according to each of the hot business functions and each of the target objects; The target object includes at least one of a function language, a function component, a function base library, a function container, a server room and a server processor.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the server resource usage analysis method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the server resource usage analysis method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for analyzing the usage of server resources according to any one of claims 1 to 6.

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