CN113010576A - Method, device, equipment and storage medium for capacity evaluation of cloud computing system - Google Patents
Method, device, equipment and storage medium for capacity evaluation of cloud computing system Download PDFInfo
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Abstract
The application provides a method, a device, equipment and a storage medium for evaluating the capacity of a cloud computing system, wherein the method comprises the steps of obtaining historical operation data of a server in the cloud computing system, wherein the historical operation data specifically comprises a CPU (Central processing Unit) utilization rate peak value and a data volume peak value of the server in a historical time period; for an analyzable server (a server successfully acquiring historical operating data), reading a utilization rate interval set according to the type of the server to which the analyzable server belongs and a data volume interval set according to the total storage space; utilizing historical operation data of an analyzable server, and carrying out resource information evaluation on a utilization rate interval and a data volume interval to obtain an evaluation result of the server; and counting the proportion of the servers with normal capacity as an evaluation result to obtain a normal rate, and determining the capacity evaluation result of the cloud computing system according to the normal rate. According to the scheme, different types are divided based on different purposes of the server, different capacity evaluation standards are set for the servers of different types and storage spaces, and the accuracy of the capacity evaluation result is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method, a device, equipment and a storage medium for capacity evaluation of a cloud computing system.
Background
The cloud computing system is a large computing system composed of a plurality of servers and can be used for executing analysis processing tasks of large-scale data. During the actual operation of the cloud computing system, the amount of data that actually needs to be processed may fluctuate, for example, the amount of data may be large in a certain month and then significantly decrease in the following months.
In order to cope with the fluctuation of the data volume, the capacity evaluation of the cloud computing system is required periodically to check whether the total amount of resources (generally including processor resources and storage resources) of the cloud computing system meets the recent processing requirement, so as to adjust the total amount of resources of the cloud computing system according to the evaluation result.
In the existing capacity evaluation method, a uniform resource threshold value is generally set, and when the amount of resources recently used by most servers in the cloud computing system exceeds the resource threshold value, the cloud computing system is considered to need capacity expansion.
Each server in the cloud computing system often has different purposes, so that the use conditions of resources by each server are different, and the existing method for evaluating all the servers of the cloud computing system according to the unified standard is low in accuracy.
Disclosure of Invention
In view of the above drawbacks of the prior art, the present invention provides a method, an apparatus, a device, and a storage medium for capacity evaluation of a cloud computing system, so as to improve the accuracy of capacity evaluation of the cloud computing system.
A first aspect of the present application provides a method for capacity evaluation of a cloud computing system, the cloud computing system being composed of a plurality of servers, the method comprising:
acquiring historical operating data of each server in the cloud computing system; the historical operation data of the server comprises a CPU utilization rate peak value and a data volume peak value of the server in a preset historical time period;
reading a utilization rate interval set according to the server category to which the analyzable server belongs and a data volume interval set according to the total storage space of the analyzable server for each analyzable server; the analyzable server refers to a server which successfully acquires historical operating data in the cloud computing system; the server category comprises a network server, a database server, an online application server, an offline application server and a data analysis server;
for each analyzable server, utilizing historical operation data, a utilization rate interval and a data volume interval of the analyzable server to evaluate resource information of the analyzable server to obtain an evaluation result of the analyzable server; wherein the evaluation result comprises the excess capacity, the normal capacity and the insufficient capacity;
counting the proportion of the servers with normal capacity in all analyzable servers of the cloud computing system to obtain the normal rate of the cloud computing system;
and determining a capacity evaluation result of the cloud computing system according to the normal rate of the cloud computing system.
Optionally, the evaluating resource information of the analyzable server according to the historical operating data, the usage interval and the data volume interval of the analyzable server to obtain an evaluation result of the analyzable server includes:
judging whether the CPU utilization rate peak value of the analyzable server is in the utilization rate interval or not, and judging whether the data volume peak value of the analyzable server is in the data volume interval or not;
if the CPU utilization rate peak value of the analyzable server is in the utilization rate interval and the data volume peak value of the analyzable server is in the data volume interval, determining that the evaluation result of the analyzable server is normal in capacity;
if the CPU utilization rate peak value of the analyzable server is larger than the upper limit of the utilization rate interval and the data volume peak value of the analyzable server is larger than the upper limit of the data volume interval, determining that the evaluation result of the analyzable server is insufficient in capacity;
and if the CPU utilization rate peak value of the analyzable server is smaller than the lower limit of the utilization rate interval and the data volume peak value of the analyzable server is smaller than the lower limit of the data volume interval, determining that the evaluation result of the analyzable server is capacity surplus.
Optionally, after obtaining the historical operating data of each server in the cloud computing system, the method further includes:
for each analyzable server, calculating a load index of the analyzable server by using historical operation data of the analyzable server;
for each analyzable server, calculating a load weight and a load redundancy value of the analyzable server according to the load index and the performance index of the analyzable server; and the load weight value and the load redundancy value of the analyzable server are used as the basis for distributing the request in the cloud computing system.
Optionally, for each of the analyzable servers, after the resource information of the analyzable server is evaluated by using the historical operating data, the usage interval, and the data volume interval of the analyzable server, and the evaluation result of the analyzable server is obtained, the method further includes:
identifying an analyzable server with an evaluation result of excess capacity, and outputting recovery prompt information; the recovery prompt information is used for indicating that resource recovery operation is executed on the analyzable server with the evaluation result of capacity surplus;
identifying an analyzable server with an evaluation result of insufficient capacity, and outputting capacity expansion prompt information; the capacity expansion prompting information is used for indicating that resource capacity expansion operation is executed on the analyzable server with the capacity surplus evaluation result.
A second aspect of the present application provides an apparatus for capacity evaluation of a cloud computing system, the cloud computing system being composed of a plurality of servers, the apparatus comprising:
the acquisition unit is used for acquiring historical operating data of each server in the cloud computing system; the historical operation data of the server comprises a CPU utilization rate peak value and a data volume peak value of the server in a preset historical time period;
a reading unit configured to read, for each analyzable server, a usage section set according to a server class to which the analyzable server belongs and a data amount section set according to a total storage space of the analyzable server; the analyzable server refers to a server which successfully acquires historical operating data in the cloud computing system; the server category comprises a network server, a database server, an online application server, an offline application server and a data analysis server;
the evaluation unit is used for evaluating the resource information of each analyzable server by utilizing the historical operating data, the utilization rate interval and the data volume interval of the analyzable server to obtain the evaluation result of the analyzable server; wherein the evaluation result comprises the excess capacity, the normal capacity and the insufficient capacity;
the statistical unit is used for counting the proportion of the servers with normal capacity in all analyzable servers of the cloud computing system to obtain the normal rate of the cloud computing system;
the determining unit is used for determining the capacity evaluation result of the cloud computing system according to the normal rate of the cloud computing system.
Optionally, the evaluation unit is configured to perform resource information evaluation on the analyzable server according to the historical operating data, the usage interval, and the data volume interval of the analyzable server, and when obtaining an evaluation result of the analyzable server, specifically:
judging whether the CPU utilization rate peak value of the analyzable server is in the utilization rate interval or not, and judging whether the data volume peak value of the analyzable server is in the data volume interval or not;
if the CPU utilization rate peak value of the analyzable server is in the utilization rate interval and the data volume peak value of the analyzable server is in the data volume interval, determining that the evaluation result of the analyzable server is normal in capacity;
if the CPU utilization rate peak value of the analyzable server is larger than the upper limit of the utilization rate interval and the data volume peak value of the analyzable server is larger than the upper limit of the data volume interval, determining that the evaluation result of the analyzable server is insufficient in capacity;
and if the CPU utilization rate peak value of the analyzable server is smaller than the lower limit of the utilization rate interval and the data volume peak value of the analyzable server is smaller than the lower limit of the data volume interval, determining that the evaluation result of the analyzable server is capacity surplus.
Optionally, the apparatus further comprises:
the first calculation unit is used for calculating a load index of each analyzable server by using historical operation data of the analyzable server;
the second calculation unit is used for calculating a load weight and a load redundancy value of each analyzable server according to the load index and the performance index of the analyzable server; and the load weight value and the load redundancy value of the analyzable server are used as the basis for distributing the request in the cloud computing system.
Optionally, the evaluation unit is further configured to:
identifying an analyzable server with an evaluation result of excess capacity, and outputting recovery prompt information; the recovery prompt information is used for indicating that resource recovery operation is executed on the analyzable server with the evaluation result of capacity surplus;
identifying an analyzable server with an evaluation result of insufficient capacity, and outputting capacity expansion prompt information; the capacity expansion prompting information is used for indicating that resource capacity expansion operation is executed on the analyzable server with the capacity surplus evaluation result.
A third aspect of the present application provides a computer storage medium for storing a computer program, which when executed is particularly adapted to implement the method for capacity assessment of a cloud computing system as provided in any of the first aspects of the present application.
A fourth aspect of the present application provides an electronic device comprising a memory and a processor;
wherein the memory is for storing a computer program;
the processor is configured to execute the computer program, and in particular, to implement the method for cloud computing system capacity assessment as provided in any of the first aspects of the present application.
The application provides a method, a device, equipment and a storage medium for capacity evaluation of a cloud computing system, which are suitable for the cloud computing system, wherein the cloud computing system consists of a plurality of servers; the historical operation data of the server comprises a CPU utilization rate peak value and a data volume peak value of the server in a preset historical time period; reading a utilization rate interval set according to the type of the server to which the analyzable server belongs and a data volume interval set according to the total storage space of the analyzable server for each analyzable server; the analyzable server refers to a server which successfully acquires historical operating data in the cloud computing system; the server category comprises a network server, a database server, an online application server, an offline application server and a data analysis server; for each analyzable server, evaluating resource information of the analyzable server by using historical operating data, a utilization rate interval and a data volume interval of the analyzable server to obtain an evaluation result of the analyzable server; wherein the evaluation result comprises the excess capacity, the normal capacity and the insufficient capacity; counting the proportion of the servers with normal capacity in all analyzable servers of the cloud computing system to obtain the normal rate of the cloud computing system; and determining the capacity evaluation result of the cloud computing system according to the normal rate of the cloud computing system. According to the scheme, different types are divided based on different purposes of the servers, and different capacity evaluation standards are set for the servers of different types and different storage spaces, so that the accuracy of the capacity evaluation result is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic architecture diagram of a cloud computing system according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for capacity evaluation of a cloud computing system according to an embodiment of the present disclosure;
fig. 3 is a flowchart of a method for capacity estimation of a cloud computing system according to another embodiment of the present application;
fig. 4 is a schematic structural diagram of an apparatus for capacity evaluation of a cloud computing system according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
With the development of computer technology, cloud computing systems (cloud computing platforms) are widely used in various fields related to large-scale data processing in society, for example, data centers of large banks are configured with cloud computing systems.
In the operation process of a cloud computing system of a large-scale bank data center, the problems of high energy consumption and low resource utilization rate can be caused because the distribution of computing resources and storage resources is unbalanced in part of the system. In order to solve the problem, capacity evaluation needs to be performed on the cloud computing system frequently, and load balancing needs to be performed on the cloud computing system based on a capacity evaluation result of the cloud computing system.
The existing method for evaluating the capacity of the cloud computing system generally regards all servers in computing resources as a same deployment unit, so that a unified evaluation standard is set, for example, a capacity threshold is set, then resource information evaluation is performed on all servers of the cloud computing system according to the capacity threshold, and finally the capacity evaluation result of the whole cloud computing system is obtained by combining the evaluation results of all servers, but servers in different scenes such as online scenes, offline scenes, data analysis and the like are not distinguished, meanwhile, file systems with different storage space sizes of different servers are not distinguished, and the problems of poor adaptability, low accuracy and the like exist, so that the method cannot be well applied to multi-system service scenes.
In order to solve the problems, the invention provides a method for evaluating the capacity of a cloud computing system, which can provide an accurate and rapid capacity analysis scheme for operation and maintenance personnel by extracting the peak value number of the resource utilization rate of each server in the system within one month and utilizing three modules of resource information acquisition, resource information evaluation and resource load balancing to evaluate the capacity, expand or recycle the resources of the cloud computing system at the server level and the system level, so that the method can reasonably distribute the resources of the servers and the systems under different service scenes.
Compared with the traditional load balancing algorithm based on the CPU and the disk utilization rate, the method for evaluating the capacity of the cloud computing system can quickly generate a capacity evaluation result, and is convenient for operation and maintenance personnel to expand or recycle resources.
The method for capacity evaluation provided by the present application may be applied to a cloud computing System as shown in fig. 1, and as shown in fig. 1, the System is divided into three layers, namely, a CVM (cloud virtual machine), a System (System), and a Service, from bottom to top, and is divided into three modules, namely, a resource information acquisition module, a resource information evaluation module, and a resource load balancing module, from left to right, wherein the CVM layer and the System layer constitute a main body part of the cloud computing System, each computing task received by the cloud computing System and required to be processed is processed by a cloud virtual machine (i.e., C1, C2 … … Ci, which is equivalent to a server in the cloud computing System as shown in fig. 1) in the CVM layer, and a plurality of cloud virtual machines constitute a computer cluster in the cloud computing System, i.e., S1, S2 … … Si, as shown in fig. 1. The resource information evaluation module specifically comprises a server module and a system module.
In the process of carrying out capacity analysis on a plurality of systems of the cloud computing system, the method is divided into four steps. Firstly, a resource information acquisition module acquires historical operating data of each server of the cloud virtual machine layer. And secondly, selecting the CPU utilization rate and the disk utilization rate as capacity indexes by using an index threshold value method according to the types of the servers (mainly determined according to the functions of the servers and the levels of the servers in the network) by a server module in the resource information evaluation module, and obtaining the capacity evaluation result of each server by combining the index threshold value range of the server. Further, a system module in the resource information evaluation module analyzes all servers governed by the system, summarizes the server capacity evaluation results, and obtains the system capacity evaluation results according to the index threshold range evaluated by the system. And finally, expanding or recycling the calculation resources and the storage resources of the server through a resource load balancing module.
The capacity analysis model for evaluating the server and the system can be rapidly generated by organically combining the three layers and the three modules. However, the existing load balancing-based algorithm cannot perform reasonable capacity evaluation on the system, and has some unreasonable problems in the aspect of server resource scheduling, so that the server resources cannot be effectively utilized.
The method adopts an improved load balancing algorithm to carry out fine-grained capacity assessment on servers and systems with different purposes, and finally forms a capacity analysis model framework which is convenient to operate and simple in process. As a back-end algorithm module, the method can display results on a front-end interface in real time for operation and maintenance personnel to refer to by only acquiring peak data of the utilization rate of a CPU and a disk in one month of a server governed by the system, dividing the server into different deployment units such as online units, offline units, data analysis units and the like according to different applications, and determining corresponding index threshold ranges.
The following describes a specific implementation process of the method for evaluating the capacity of the cloud computing system provided by the present application, with reference to the system architecture shown in fig. 1.
Referring to fig. 2, a method for evaluating capacity of a cloud computing system provided in an embodiment of the present application may specifically include the following steps:
s201, obtaining historical operation data of each server in the cloud computing system.
The historical operation data of the server comprises a CPU utilization rate peak value and a data volume peak value of the server in a preset historical time period. The preset history period may be the last month, that is, step S201 may be to acquire a CPU usage peak value and a data volume peak value of each server in the cloud computing system in the past month.
Step S201 may be executed by the resource information collection module, which is a basic module of the cloud computing platform, and provides peak data of the utilization rates of the CPU and the disk of the server, and analyzes the data.
The CPU utilization peak value refers to the highest CPU utilization within a preset history period. The data volume refers to the data volume stored by the server, namely the disk space used by the server, and the data volume peak value is the maximum value of the disk space used by the server in the preset historical time period.
For example, in the last month, the CPU utilization rate of the server a reaches 70% at the highest, and when the amount of stored data reaches 3TB at the highest, the peak value of the CPU utilization rate is 70%, and the peak value of the data amount is 3 TB.
The resource information Collection module may specifically include three components, namely, Request (Request), Collection (Collection), and Analysis (Analysis).
Specifically, in step S201, the operation and maintenance staff may send a data obtaining request to each server in the cloud computing system through the request component, so as to obtain a CPU usage peak value and a data volume peak value of the server in the last month by using a related Shell script command or by using a visual monitoring billboard, that is, obtain historical operating data of the server. Subsequently, the acquisition component may provide a relevant interface for data operation, so that operation and maintenance personnel can view the acquired historical operation data, and simultaneously, the data acquired by the request module can be stored in a database.
The analysis component can perform preliminary screening and cleaning on the acquired historical operating data of each server so as to prepare for subsequent resource information evaluation.
For example, when historical operating data is acquired, some servers may not acquire corresponding historical operating data due to lack of records and the like, and the analysis module may divide the servers in the cloud computing system into an analyzable server and an unanalyzable server by analyzing whether the historical operating data exists, where the analyzable server is a server that successfully acquires the corresponding historical operating data, and the unanalyzable server is a server that fails to acquire the corresponding historical operating data.
Furthermore, the analysis module may further identify the acquired historical operating data based on a preset abnormal data identification model, such as a pre-trained artificial neural network model, so as to find and remove abnormal historical operating data.
That is to say, the request component can send a request to the server, store the obtained peak data of the utilization rates of the CPU and the disk of the server within one month into the database of the acquisition component, analyze the data in the database by using the analysis component, remove the data with missing values, obtain the data of the utilization rates of the CPU and the disk respectively, and form a complete data acquisition and analysis flow.
S202, reading a utilization rate interval set according to the type of the server to which the analyzable server belongs and a data volume interval set according to the total storage space of the analyzable server.
It should be noted that step S202 and subsequent step S203 are performed for each analyzable server, that is, for each server of the cloud computing system, as long as the historical operating data of the server is successfully acquired in step S201, the corresponding section is read, and resource information evaluation is performed on the section.
The analyzable server refers to a server which successfully acquires historical operating data in the cloud computing system; the server category includes web servers, database servers, online application servers, offline application servers, and data analysis servers.
The server in the cloud computing system specifically comprises three purposes of a network agent, an Application and a Database, so that the server in the cloud computing system can be divided into three types of a network (WEB) server, an Application (AP) server and a Database (DB) server, wherein the Application server can be further divided into three types of an online Application server, an offline Application server and a data analysis server, and the use conditions of the servers with different purposes to the CPU and the disk storage space are different, so that different data volume intervals and different use rate intervals are set for the servers with different categories based on the classification basis.
Generally, in terms of CPU usage, the CPU usage rate of the application server is the highest, the network server is slightly lower, the database server is generally only used for accessing data, and the CPU usage rate is the lowest. The usage intervals of the servers of different classes can be set accordingly.
For example, the usage interval of the online application server may be set to 85% to 95%, the usage interval of the offline application server may be set to 80% to 90%, the usage interval of the data analysis server may be set to 85% to 95%, and the usage interval of the web server may be set to 65% to 80%. The usage interval of the database server is set to 55% to 70%. Of course, the usage intervals of the respective categories may be set according to specific situations, and are not limited to the above examples.
The data size interval is set according to the size of the file system of the server, that is, according to the size of the total storage space of the server, specifically, the size of the total storage space may be divided into four gears, which are respectively smaller than 2TB, 2TB to 5TB, 5TB to 10TB, and larger than 10TB, and a corresponding data size interval is set for each gear, for example:
the data volume interval is 1TB to 1.5TB for the gear less than 2 TB;
the data volume interval of the gears from 2TB to 5TB is from 3.5TB to 4 TB;
the data volume interval of the gears from 5TB to 10TB is from 8TB to 9 TB;
the data volume interval is 15TB to 16TB for the gear position larger than 10 TB.
On the basis, for each analyzable server, the gear to which the total storage space of the server belongs can be determined, and then the data volume interval corresponding to the gear to which the total storage space of the server belongs is used as the data volume interval used when the resource information of the server is evaluated.
Similarly, the division of the gear positions and the division of the data amount intervals for each gear position may be adjusted according to actual conditions.
S203, evaluating the resource information of the analyzable server by using the historical operating data, the utilization rate interval and the data volume interval of the analyzable server to obtain an evaluation result of the analyzable server.
Wherein the evaluation result comprises the excess capacity, the normal capacity and the insufficient capacity.
The resource information evaluation module of the present application may set an Accept () function, receive, through the function, the historical operating data of each server acquired by the resource information acquisition module, and perform resource information evaluation by using the historical operating data.
That is, for each analyzable server, after the resource information evaluation, it may be determined that the server is over-capacity, normal-capacity or under-capacity.
Generally, if a peak in the historical operating data of a server is within a corresponding section, the capacity of the server is considered to be normal, if the historical operating data is lower than the lower limit of the corresponding section, the capacity of the server is considered to be excessive, and if the historical operating data is higher than the upper limit of the corresponding section, the capacity of the server is considered to be insufficient.
It can be seen that the usage rate interval and the data volume interval are equivalent to evaluation criteria for evaluating resource information of one server, that is, different evaluation criteria are actually set for different types of servers according to the scheme.
Based on the intervals set for the servers with different purposes and the different storage spaces of the servers in step S202, in step S203, if one analyzable server belongs to the database server and the total storage space belongs to the gear from 2TB to 5TB, the resource information is evaluated by using the usage interval from 55% to 70% and the data amount interval from 3.5TB to 4TB, and if one analyzable server belongs to the online application server and the total storage space belongs to the gear from 5TB to 10TB, the resource information is evaluated by using the usage interval from 85% to 95% and the data amount interval from 8TB to 9 TB.
And S204, counting the proportion of the servers with normal capacity in all analyzable servers of the cloud computing system to obtain the normal rate of the cloud computing system.
For example, in step S201, if the historical operating data of 100 servers in the cloud computing system is successfully acquired, that is, the number of the analysis servers is 100, and the resource information evaluation in step S203 shows that the evaluation result of 70 servers is that the capacity is normal, and the percentage of all analyzable servers is 70%, then the normal rate of the cloud computing system is 70%.
And S205, determining a capacity evaluation result of the cloud computing system according to the normal rate of the cloud computing system.
As shown in fig. 1, the resource information evaluation module of the present application may specifically include a server module and a system module.
The capacity evaluation result of the cloud computing system may specifically include good, medium, and poor, and generally, the capacity evaluation result of the cloud computing system may be defined as poor when the normality rate is less than 60%, the capacity evaluation result of the cloud computing system may be medium when the normality rate is between 60% and 70%, the capacity evaluation result of the cloud computing system may be good when the normality rate is between 70% and 80%, and the capacity evaluation result of the cloud computing system may be good when the normality rate is greater than 80%.
The determination criteria for each capacity estimation result may be changed according to actual conditions.
As shown in fig. 1, the resource information evaluation module specifically includes two sub-modules, namely, a server sub-module and a system sub-module, and in the above steps, steps S202 and S203 may be executed by the server sub-module, and steps S204 and S205 may be executed by the system sub-module.
In the resource information evaluation process, the servers with different purposes are distinguished, different thresholds are specified, and the calculation resources and the storage resources of each server can be quickly evaluated. And aiming at the servers with normal capacity evaluation, the percentage of the servers in the total number of the instances is used as a normal rate, a threshold value is specified to carry out capacity analysis at a system level, and the related flow is simple and easy to operate. In addition, different intervals are set by combining actual use conditions of the servers with different purposes on the CPU and the storage space, so that the resource information evaluation result of each server can be more accurate, and the accuracy of the capacity evaluation result of the cloud computing system is improved.
In the above embodiment, step S203 is to perform resource information evaluation on the analyzable server, and the specific implementation process is as follows:
judging whether the CPU utilization rate peak value of the analyzable server is in a utilization rate interval or not, and judging whether the data volume peak value of the analyzable server is in a data volume interval or not;
if the CPU utilization rate peak value of the analyzable server is in the utilization rate interval and the data volume peak value of the analyzable server is in the data volume interval, determining that the evaluation result of the analyzable server is normal in capacity;
if the CPU utilization rate peak value of the analyzable server is larger than the upper limit of the utilization rate interval and the data volume peak value of the analyzable server is larger than the upper limit of the data volume interval, determining that the evaluation result of the analyzable server is insufficient in capacity;
and if the CPU utilization rate peak value of the analyzable server is smaller than the lower limit of the utilization rate interval and the data volume peak value of the analyzable server is smaller than the lower limit of the data volume interval, determining that the evaluation result of the analyzable server is the capacity surplus.
For example, assuming that the usage interval of one server is 80% to 90%, and the data amount interval is 8TB to 9TB, in the historical operation data of the server, the CPU usage peak value is 85%, and the data amount peak value is 8.4TB, both of which are within the corresponding interval, so that it is determined that the server capacity is normal, if the CPU usage peak value is 75%, the data amount peak value is 6.4TB, both of which are smaller than the lower limit of the corresponding interval, it is determined that the server capacity is excessive, and if the CPU usage peak value is 96%, the data amount peak value is 9.6TB, both of which are larger than the upper limit of the corresponding interval, it is determined that the server capacity is insufficient.
As shown in fig. 1, the system provided in the present application may further include a resource load balancing module, and based on this module, an embodiment of the present application further provides the following method for evaluating the capacity of the cloud computing system, please refer to fig. 3, where this method may include the following steps:
s301, obtaining historical operating data of each server in the cloud computing system.
S302, reading a utilization rate interval set according to the type of the server to which the analyzable server belongs and a data volume interval set according to the total storage space of the analyzable server.
And S303, evaluating the resource information of the analyzable server by utilizing the historical operating data, the utilization rate interval and the data volume interval of the analyzable server to obtain an evaluation result of the analyzable server.
And S304, counting the proportion of the servers with normal capacity in all analyzable servers of the cloud computing system to obtain the normal rate of the cloud computing system.
S305, determining a capacity evaluation result of the cloud computing system according to the normal rate of the cloud computing system.
The processes described in step S301 to step S305 are the same as those described in step S201 to step S205, and are not described in detail here.
And S306, calculating the load index of the analyzable server by using the historical operation data of the analyzable server.
And S307, calculating according to the load index and the performance index of the analyzable server to obtain a load weight and a load redundancy value of the analyzable server.
The load weight value and the load redundancy value of the server can be analyzed to serve as the basis for distributing requests in the cloud computing system.
It should be noted that step S306 and step S307 are also performed for each analyzable server, that is, each analyzable server can calculate a corresponding load weight value and a corresponding load redundancy value.
Step S306 and step S307 may be performed by a resource load balancing module in the system of the present application.
Specifically, the resource load balancing module may receive the CPU utilization peak value and the data volume peak value of each server acquired by the resource information acquisition module through the Accept () function, then perform the calculation process described in steps S306 and S307 by using Update (), calculate to obtain a load weight and a load redundancy value of an analyzable server, and finally perform load balancing according to the load weight and the load redundancy value of the server by the Judge () function.
Suppose a cloud computing system includes servers C1, C2, Cn, for the ith server Ci, p (Ci) represents the performance index of the server, f (Ci) represents the load index of the server, where p (Ci) can be calculated by the following formula:
P(Ci)＝[α1×PCPU(Ci)+α2×PMem(Ci)+(1-α1)×PCPU(Ci)2+(1-α2)×PCPU(Ci)2]÷2
in the above formula, PCPU(Ci) denotes CPU frequency, P, of server CiMem(Ci) represents the disk size of server Ci, i.e. the total storage space of server Ci, α1And alpha2Is a preset specific gravity coefficient, and the square of the sum of the two specific gravity coefficients is equal to 1.
The server load index f (ci) may be calculated according to the following formula:
F(Ci)＝[β1×FCPU(Ci)+β2×FMem(Ci)+(1-β1)×FCPU(Ci)2+(1-β2)×FCPU(Ci)2]÷2
in the above formula, FCPU(Ci) represents the CPU utilization peak of server Ci obtained in the previous step, FMem(Ci) represents a data amount peak value, β, of the server Ci acquired in the above step1And beta2Is a preset specific gravity coefficient, and the square of the sum of the two specific gravity coefficients is equal to 1.
The load index and the performance index obtained by utilizing the CPU utilization rate and the high-order term of the disk storage space can reflect the load capacity of the server more accurately.
The load weight is calculated according to the server performance and the load capacity, and assuming that the load weight of the server Ci is w (Ci), the calculation formula is:
W(Ci)＝F(Ci)÷P(Ci)。
the load weight w (Ci) of the server Ci may represent the load capacity of the server, and the larger the value w (Ci) is, the larger the load amount borne by the current server Ci is, so the resource load balancing module always allocates a new request to the server with the smaller value w (Ci) for processing.
Calculation of load redundancy value:
for a server Ci, a load redundancy value of the server is represented by r (Ci), the load redundancy value is introduced, and the load redundancy value is used to represent r (Ci) to represent the capacity of the server Ci capable of newly increasing load, and the calculation formula is as follows: judging the capacity of a newly added load of a certain node at a certain moment, wherein the calculation formula is as follows:
R(Ci)＝P(Ci)÷F(Ci)。
in a system, a minimum load redundancy value R is generally setminWhen R (Ci) is greater than RminIt is said that the server Ci has the ability to handle newly arriving requests.
Optionally, in any embodiment of the present application, after obtaining the evaluation result of each analyzable server in the cloud computing system through resource information evaluation, the following steps may be further performed:
identifying an analyzable server with an evaluation result of excess capacity, and outputting recovery prompt information; and the recovery prompt information is used for indicating that resource recovery operation is executed on the analyzable server with the capacity surplus as the evaluation result. Specifically, the recovery prompt may prompt the operation and maintenance personnel to reduce the total storage space of the server with the excess capacity and reduce the CPU frequency thereof.
Identifying an analyzable server with an evaluation result of insufficient capacity, and outputting capacity expansion prompt information; and the capacity expansion prompt information is used for indicating that resource capacity expansion operation is executed on the analyzable server with the evaluation result of capacity surplus. Specifically, the capacity expansion prompting message can prompt the operation and maintenance personnel to increase the total storage space of the server with insufficient capacity and increase the CPU frequency of the server.
According to the method, three modules of resource information acquisition, resource information evaluation and resource load balancing are adopted, data acquisition, data analysis and threshold value designation are carried out on a system server cluster, capacity analysis of a server level and a system level is achieved, and finally capacity expansion or recovery is carried out on resources according to an evaluation result of the server.
In the resource information evaluation process, different types of servers are distinguished, different thresholds are specified, and rapid evaluation on computing resources and storage resources can be realized. And aiming at the servers with normal capacity evaluation, the percentage of the servers in the total number of the instances is used as a normal rate, a threshold value is specified to carry out capacity analysis at a system level, and the related flow is simple and easy to operate.
The resource load balancing utilizes the server load redundancy value as the basis of resource expansion or recovery, has strong timeliness and takes effect automatically, can reduce the times of resource change, avoids unnecessary on-off operation of an instance, and improves the overall performance and the resource utilization rate of the system.
The cloud computing platform generates unnecessary resource waste when the system runs, and how to efficiently utilize the resources of the cloud computing platform is one of key research objects in current cloud computing development. In a traditional cloud computing multi-tenant application mode, due to the fact that dynamic load changes can affect tenant resource allocation, resource competition or occupation can occur due to too high load, and quality of computing services provided externally is reduced. The general load balancing algorithm cannot well solve the problem of a multi-system server cluster, the existing work result is poor in self-adaptability, and the timeliness of the work result needs to be improved.
However, the innovative method of the patent is a capacity analysis model based on a cloud computing platform. On the basis of collecting peak data of CPU and disk utilization rates of different system servers within one month, capacity analysis is carried out on computing resources and storage resources through three modules of resource information collection, resource information evaluation and resource load balancing, resource distribution is adjusted according to a server load redundancy value, and management of the computing resources and the storage resources of the servers is achieved. The test result shows that the capacity analysis model can effectively realize the resource adjustment of a multi-system and multi-server cluster, can adapt to the size of a task set, dynamically improve the resource utilization rate of the cloud computing platform and reduce the energy consumption, and provides an effective solution for the cloud computing platform to meet the ever-increasing service requirements.
The method and the device can accurately and quickly provide capacity analysis schemes for operation and maintenance personnel, so that resources of the server can be reasonably distributed under different service scenes, the operation and maintenance efficiency can be improved, the service quality of the cloud computing platform for tenants can be effectively guaranteed, and a solid foundation is laid for multi-system capacity analysis.
With reference to fig. 4, the apparatus may specifically include the following units:
the obtaining unit 401 is configured to obtain historical operation data of each server in the cloud computing system.
The historical operation data of the server comprises a CPU utilization rate peak value and a data volume peak value of the server in a preset historical time period.
A reading unit 402, configured to read, for each analyzable server, a usage interval set according to a server class to which the analyzable server belongs, and a data size interval set according to a total storage space of the analyzable server.
The analyzable server refers to a server which successfully acquires historical operating data in the cloud computing system; the server category includes web servers, database servers, online application servers, offline application servers, and data analysis servers.
The evaluation unit 403 is configured to, for each analyzable server, perform resource information evaluation on the analyzable server by using the historical operating data, the usage interval, and the data volume interval of the analyzable server, so as to obtain an evaluation result of the analyzable server.
Wherein the evaluation result comprises the excess capacity, the normal capacity and the insufficient capacity.
The counting unit 404 is configured to count a proportion of the servers with the normal capacity in all analyzable servers of the cloud computing system to obtain a normal rate of the cloud computing system.
A determining unit 405, configured to determine a capacity evaluation result of the cloud computing system according to a normality rate of the cloud computing system.
Optionally, the evaluation unit 403 evaluates the resource information of the analyzable server according to the historical operating data, the usage interval and the data volume interval of the analyzable server, and when obtaining the evaluation result of the analyzable server, is specifically configured to:
judging whether the CPU utilization rate peak value of the analyzable server is in a utilization rate interval or not, and judging whether the data volume peak value of the analyzable server is in a data volume interval or not;
if the CPU utilization rate peak value of the analyzable server is in the utilization rate interval and the data volume peak value of the analyzable server is in the data volume interval, determining that the evaluation result of the analyzable server is normal in capacity;
if the CPU utilization rate peak value of the analyzable server is larger than the upper limit of the utilization rate interval and the data volume peak value of the analyzable server is larger than the upper limit of the data volume interval, determining that the evaluation result of the analyzable server is insufficient in capacity;
and if the CPU utilization rate peak value of the analyzable server is smaller than the lower limit of the utilization rate interval and the data volume peak value of the analyzable server is smaller than the lower limit of the data volume interval, determining that the evaluation result of the analyzable server is the capacity surplus.
Optionally, the apparatus further comprises:
a first calculating unit 406, configured to calculate, for each analyzable server, a load index of the analyzable server by using historical operating data of the analyzable server;
a second calculating unit 407, configured to calculate, for each analyzable server, a load weight and a load redundancy value of the analyzable server according to the load index and the performance index of the analyzable server; the load weight value and the load redundancy value of the server can be analyzed to serve as the basis for distributing requests in the cloud computing system.
Optionally, the evaluation unit 403 is further configured to:
identifying an analyzable server with an evaluation result of excess capacity, and outputting recovery prompt information; the recovery prompt information is used for indicating that resource recovery operation is executed on the analyzable server with the evaluation result of capacity surplus;
identifying an analyzable server with an evaluation result of insufficient capacity, and outputting capacity expansion prompt information; and the capacity expansion prompt information is used for indicating that resource capacity expansion operation is executed on the analyzable server with the evaluation result of capacity surplus.
The specific working principle of the device for cloud computing system capacity evaluation provided by the embodiment of the present application may refer to the relevant steps of the method for cloud computing system capacity evaluation provided by the embodiment of the present application, and details are not described here.
The application provides a device for evaluating the capacity of a cloud computing system, which is suitable for the cloud computing system, wherein the cloud computing system consists of a plurality of servers, and in the device, an acquisition unit 401 acquires historical operating data of each server in the cloud computing system; the historical operation data of the server comprises a CPU utilization rate peak value and a data volume peak value of the server in a preset historical time period; the reading unit 402 reads, for each analyzable server, a usage interval set according to a server class to which the analyzable server belongs and a data amount interval set according to a total storage space of the analyzable server; the analyzable server refers to a server which successfully acquires historical operating data in the cloud computing system; the server category comprises a network server, a database server, an online application server, an offline application server and a data analysis server; the evaluation unit 403 evaluates the resource information of each analyzable server by using the historical operating data, the usage interval and the data volume interval of the analyzable server to obtain an evaluation result of the analyzable server; wherein the evaluation result comprises the excess capacity, the normal capacity and the insufficient capacity; the statistical unit 404 counts the proportion of the servers with normal capacity in all analyzable servers of the cloud computing system to obtain the normal rate of the cloud computing system; the determination unit 405 determines a capacity evaluation result of the cloud computing system according to the normality rate of the cloud computing system. According to the scheme, different types are divided based on different purposes of the servers, and different capacity evaluation standards are set for the servers of different types and different storage spaces, so that the accuracy of the capacity evaluation result is improved.
The embodiment of the present application further provides a computer storage medium, which is used for storing a computer program, and when the computer program is executed, the computer program is specifically used for implementing the method for capacity evaluation of a cloud computing system provided in any embodiment of the present application.
An electronic device is further provided in the embodiments of the present application, and as shown in fig. 5, the electronic device specifically includes a memory 501 and a processor 502.
Wherein, the memory 501 is used for storing computer programs;
the processor 502 is configured to execute the above computer program, and is specifically configured to implement the method for capacity estimation of a cloud computing system provided in any embodiment of the present application.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It should be noted that the terms "first", "second", and the like in the present invention are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
Those skilled in the art can make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for capacity assessment of a cloud computing system, the cloud computing system being comprised of a plurality of servers, the method comprising:
acquiring historical operating data of each server in the cloud computing system; the historical operation data of the server comprises a CPU utilization rate peak value and a data volume peak value of the server in a preset historical time period;
reading a utilization rate interval set according to the server category to which the analyzable server belongs and a data volume interval set according to the total storage space of the analyzable server for each analyzable server; the analyzable server refers to a server which successfully acquires historical operating data in the cloud computing system; the server category comprises a network server, a database server, an online application server, an offline application server and a data analysis server;
for each analyzable server, utilizing historical operation data, a utilization rate interval and a data volume interval of the analyzable server to evaluate resource information of the analyzable server to obtain an evaluation result of the analyzable server; wherein the evaluation result comprises the excess capacity, the normal capacity and the insufficient capacity;
counting the proportion of the servers with normal capacity in all analyzable servers of the cloud computing system to obtain the normal rate of the cloud computing system;
and determining a capacity evaluation result of the cloud computing system according to the normal rate of the cloud computing system.
2. The method of claim 1, wherein the evaluating resource information of the analyzable server according to the historical operation data, the usage interval and the data volume interval of the analyzable server to obtain the evaluation result of the analyzable server comprises:
judging whether the CPU utilization rate peak value of the analyzable server is in the utilization rate interval or not, and judging whether the data volume peak value of the analyzable server is in the data volume interval or not;
if the CPU utilization rate peak value of the analyzable server is in the utilization rate interval and the data volume peak value of the analyzable server is in the data volume interval, determining that the evaluation result of the analyzable server is normal in capacity;
if the CPU utilization rate peak value of the analyzable server is larger than the upper limit of the utilization rate interval and the data volume peak value of the analyzable server is larger than the upper limit of the data volume interval, determining that the evaluation result of the analyzable server is insufficient in capacity;
and if the CPU utilization rate peak value of the analyzable server is smaller than the lower limit of the utilization rate interval and the data volume peak value of the analyzable server is smaller than the lower limit of the data volume interval, determining that the evaluation result of the analyzable server is capacity surplus.
3. The method of claim 1, wherein after obtaining historical operating data of each server in the cloud computing system, further comprising:
for each analyzable server, calculating a load index of the analyzable server by using historical operation data of the analyzable server;
for each analyzable server, calculating a load weight and a load redundancy value of the analyzable server according to the load index and the performance index of the analyzable server; and the load weight value and the load redundancy value of the analyzable server are used as the basis for distributing the request in the cloud computing system.
4. The method according to claim 1, wherein for each of the analyzable servers, after evaluating resource information of the analyzable server by using historical operating data, a usage interval and a data volume interval of the analyzable server and obtaining an evaluation result of the analyzable server, the method further comprises:
identifying an analyzable server with an evaluation result of excess capacity, and outputting recovery prompt information; the recovery prompt information is used for indicating that resource recovery operation is executed on the analyzable server with the evaluation result of capacity surplus;
identifying an analyzable server with an evaluation result of insufficient capacity, and outputting capacity expansion prompt information; the capacity expansion prompting information is used for indicating that resource capacity expansion operation is executed on the analyzable server with the capacity surplus evaluation result.
5. An apparatus for capacity assessment of a cloud computing system, the cloud computing system being comprised of a plurality of servers, the apparatus comprising:
the acquisition unit is used for acquiring historical operating data of each server in the cloud computing system; the historical operation data of the server comprises a CPU utilization rate peak value and a data volume peak value of the server in a preset historical time period;
a reading unit configured to read, for each analyzable server, a usage section set according to a server class to which the analyzable server belongs and a data amount section set according to a total storage space of the analyzable server; the analyzable server refers to a server which successfully acquires historical operating data in the cloud computing system; the server category comprises a network server, a database server, an online application server, an offline application server and a data analysis server;
the evaluation unit is used for evaluating the resource information of each analyzable server by utilizing the historical operating data, the utilization rate interval and the data volume interval of the analyzable server to obtain the evaluation result of the analyzable server; wherein the evaluation result comprises the excess capacity, the normal capacity and the insufficient capacity;
the statistical unit is used for counting the proportion of the servers with normal capacity in all analyzable servers of the cloud computing system to obtain the normal rate of the cloud computing system;
the determining unit is used for determining the capacity evaluation result of the cloud computing system according to the normal rate of the cloud computing system.
6. The apparatus according to claim 5, wherein the evaluation unit is configured to perform resource information evaluation on the analyzable server according to historical operating data, a usage interval and a data volume interval of the analyzable server, and when obtaining an evaluation result of the analyzable server, specifically:
judging whether the CPU utilization rate peak value of the analyzable server is in the utilization rate interval or not, and judging whether the data volume peak value of the analyzable server is in the data volume interval or not;
if the CPU utilization rate peak value of the analyzable server is in the utilization rate interval and the data volume peak value of the analyzable server is in the data volume interval, determining that the evaluation result of the analyzable server is normal in capacity;
if the CPU utilization rate peak value of the analyzable server is larger than the upper limit of the utilization rate interval and the data volume peak value of the analyzable server is larger than the upper limit of the data volume interval, determining that the evaluation result of the analyzable server is insufficient in capacity;
and if the CPU utilization rate peak value of the analyzable server is smaller than the lower limit of the utilization rate interval and the data volume peak value of the analyzable server is smaller than the lower limit of the data volume interval, determining that the evaluation result of the analyzable server is capacity surplus.
7. The apparatus of claim 5, further comprising:
the first calculation unit is used for calculating a load index of each analyzable server by using historical operation data of the analyzable server;
the second calculation unit is used for calculating a load weight and a load redundancy value of each analyzable server according to the load index and the performance index of the analyzable server; and the load weight value and the load redundancy value of the analyzable server are used as the basis for distributing the request in the cloud computing system.
8. The apparatus of claim 5, wherein the evaluation unit is further configured to:
identifying an analyzable server with an evaluation result of excess capacity, and outputting recovery prompt information; the recovery prompt information is used for indicating that resource recovery operation is executed on the analyzable server with the evaluation result of capacity surplus;
identifying an analyzable server with an evaluation result of insufficient capacity, and outputting capacity expansion prompt information; the capacity expansion prompting information is used for indicating that resource capacity expansion operation is executed on the analyzable server with the capacity surplus evaluation result.
9. A computer storage medium storing a computer program which, when executed, is particularly adapted to implement the method of cloud computing system capacity assessment according to any one of claims 1 to 4.
10. An electronic device comprising a memory and a processor;
wherein the memory is for storing a computer program;
the processor is configured to execute the computer program, in particular to implement the method of cloud computing system capacity assessment according to any of claims 1 to 4.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108243030A (en) * | 2016-12-23 | 2018-07-03 | 航天星图科技（北京）有限公司 | A kind of backup server selects management method |
| CN113568759A (en) * | 2021-09-27 | 2021-10-29 | 睿至科技集团有限公司 | Cloud computing-based big data processing method and system |
| CN114401195A (en) * | 2022-01-14 | 2022-04-26 | 中国建设银行股份有限公司 | Server capacity adjustment method and device, storage medium and electronic device |
| CN116048821A (en) * | 2023-04-03 | 2023-05-02 | 深圳市昊源诺信科技有限公司 | High-utilization AI server and resource allocation method thereof |
| CN116566696A (en) * | 2023-05-22 | 2023-08-08 | 天津施维科技开发有限公司 | Security assessment system and method based on cloud computing |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108243030A (en) * | 2016-12-23 | 2018-07-03 | 航天星图科技（北京）有限公司 | A kind of backup server selects management method |
| CN113568759A (en) * | 2021-09-27 | 2021-10-29 | 睿至科技集团有限公司 | Cloud computing-based big data processing method and system |
| CN114401195A (en) * | 2022-01-14 | 2022-04-26 | 中国建设银行股份有限公司 | Server capacity adjustment method and device, storage medium and electronic device |
| CN116048821A (en) * | 2023-04-03 | 2023-05-02 | 深圳市昊源诺信科技有限公司 | High-utilization AI server and resource allocation method thereof |
| CN116048821B (en) * | 2023-04-03 | 2023-06-16 | 深圳市昊源诺信科技有限公司 | High-utilization AI server and resource allocation method thereof |
| CN116566696A (en) * | 2023-05-22 | 2023-08-08 | 天津施维科技开发有限公司 | Security assessment system and method based on cloud computing |
| CN116566696B (en) * | 2023-05-22 | 2024-03-29 | 深圳市众志天成科技有限公司 | Security assessment system and method based on cloud computing |
| CN116881106A (en) * | 2023-07-31 | 2023-10-13 | 招商基金管理有限公司 | Method, device, storage medium and equipment for analyzing and managing capacity operation of service system |
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