CN117707782A - Server container specification adjustment method, system, computer equipment and storage medium - Google Patents
Server container specification adjustment method, system, computer equipment and storage medium Download PDFInfo
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Abstract
The invention provides a server container specification adjustment method, a system, computer equipment and a storage medium, wherein the method comprises the following steps: collecting resource use data matched with various configuration specifications in a plurality of server containers; carrying out statistical analysis on the resource usage data of each server container, and determining the average level of the resource usage of each server container; server containers with similar average levels of resource usage are grouped into a group; grouping, for each group, server containers within the group according to at least one resource usage data; and determining an abnormal server container according to the specification configuration difference of each server container in the same group, and adjusting the specification configuration of the abnormal server container. The invention can avoid the problems of subjective tendency, excessive standardization and the like possibly occurring during manual adjustment, and can realize the accurate management of container specification configuration, thereby improving the utilization rate of the whole resources.
Description
Technical Field
The invention relates to the technical field of container specification optimization, in particular to a method, a system, computer equipment and a storage medium for adjusting the specification of a server container.
Background
In various activities of the internet, how to use big data to improve the use efficiency of a server container is an important research topic. For enterprise service terminals, the server container is an important basic application architecture, and the resource configuration directly affects the service operation efficiency and the cost saving capability. However, conventionally, the container specification is set by a developer by experience, and there is a subjective factor, and there are unavoidable situations in which configuration imbalance and resource waste are caused.
Therefore, how to avoid resource waste caused by unreasonable server container specification configuration is a problem to be solved.
Disclosure of Invention
One of the purposes of the present invention is to provide a server container specification adjustment method, system, computer device and storage medium, which can avoid resource waste caused by unreasonable server container specification configuration. The invention can be realized as follows:
in a first aspect, the present invention provides a server container specification adjustment method, the method including: collecting resource use data matched with various configuration specifications in a plurality of server containers; performing a statistical analysis on the resource usage data of each of the server containers to determine an average level of resource usage for each of the server containers; server containers with similar average levels of resource usage are grouped into a group; grouping server containers within the group according to at least one of the resource usage data for each of the groups; and determining an abnormal server container according to the specification configuration difference of each server container in the same group, and adjusting the specification configuration of the abnormal server container.
In a second aspect, the present invention provides a server container specification adjustment system, including a business subsystem, a data processing subsystem, and a modeling and application subsystem; the service subsystem is used for collecting resource use data matched with various configuration specifications in a plurality of server containers; the data processing subsystem is used for carrying out statistical analysis on the resource usage data of each server container and determining the average level of the resource usage of each server container; the modeling and application subsystem is used for forming a group of server containers with similar average levels of resource use; the modeling and application subsystem is further configured to group, for each of the groups, server containers within the group according to at least one of the resource usage data; the modeling and application subsystem is further configured to determine an abnormal server container according to the specification configuration difference of each server container in the same group, and adjust the specification configuration of the abnormal server container.
In a third aspect, the present invention provides a computer device comprising a processor and a memory, the memory storing a computer program executable by the processor, the processor being executable by the computer program to implement the server container specification adjustment method of the first aspect.
In a fourth aspect, the present invention provides a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the server container specification adjustment method of the first aspect.
According to the server container specification adjustment method, system, computer equipment and storage medium, the resource usage data matched with various configuration specifications in the plurality of server containers are collected to provide basis for subsequent statistical analysis, then the resource usage data of each server container are subjected to statistical analysis to determine the average level of the resource usage of each server container, the accuracy can be ensured by utilizing the long-term load characteristics of each server container to carry out subsequent analysis, the server containers with similar average levels of the resource usage are grouped, and the server containers with similar running conditions are primarily identified. Next, within each group, further subdivisions are made based on at least one resource usage data, enabling better differentiation between server containers. Finally, according to the difference of the specification configuration of each server container in the same group, the abnormal server container is identified, the specification configuration is adjusted, the problems of subjective tendency, excessive standardization and the like possibly occurring during manual adjustment are avoided, the specification configuration of the container can be accurately managed, and therefore the overall resource utilization rate is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a server container specification adjustment method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a server container group according to an embodiment of the present invention;
FIG. 3 is a functional block diagram of a server container specification adjustment system according to an embodiment of the present invention;
fig. 4 is a schematic diagram of data interaction between a service subsystem 310 and a data processing subsystem 320 according to an embodiment of the present invention;
fig. 5 is a block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present invention, it should be noted that, if the terms "upper", "lower", "inner", "outer", and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or the azimuth or the positional relationship in which the inventive product is conventionally put in use, it is merely for convenience of describing the present invention and simplifying the description, and it is not indicated or implied that the apparatus or element referred to must have a specific azimuth, be configured and operated in a specific azimuth, and thus it should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, if any, are used merely for distinguishing between descriptions and not for indicating or implying a relative importance.
It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic flowchart of a server container specification adjustment method according to an embodiment of the present invention, where the method may include the following steps:
s101, collecting resource use data matched with various configuration specifications in a plurality of server containers.
In the embodiment of the present invention, the configuration specification is various technical parameters and performance indexes of the value server container, which may include, but are not limited to: cpu, memory, disk, network, etc. The resource usage data refers to cpu usage, memory occupancy, disk io, network io, and the like.
S102, carrying out statistical analysis on the resource usage data of each server container, and determining the average level of the resource usage of each server container;
in an embodiment of the invention, the average level of resource usage characterizes an average of the resource usage data over a period of time.
S103, server containers with similar average levels of resource use are formed into a group;
s104, grouping server containers in the groups according to at least one resource use data for each group;
s105, determining an abnormal server container according to the specification configuration difference of each server container in the same group, and adjusting the specification configuration of the abnormal server container.
In the steps S101 to S105, the resource usage data matched with the multiple configuration specifications in the multiple server containers are collected, a basis is provided for subsequent statistical analysis, then the resource usage data of each server container is statistically analyzed, the average level of the resource usage of each server container is determined, the long-term load characteristic of each server container is utilized to perform subsequent analysis, accuracy can be ensured, server containers with similar average levels of the resource usage are grouped, and server containers with similar running conditions are primarily identified. Next, within each group, further subdivisions are made based on at least one resource usage data, enabling better differentiation between server containers. Finally, according to the difference of the specification configuration of each server container in the same group, the abnormal server container is identified, the specification configuration is adjusted, the problems of subjective tendency, excessive standardization and the like possibly occurring during manual adjustment are avoided, the specification configuration of the container can be accurately managed, and therefore the overall resource utilization rate is improved.
The above steps are described in detail below.
In step S101, resource usage data of each server container may be collected according to a preset data collection frequency. For example, the data collection frequency may be set according to actual requirements, such as every minute, every hour, or every day, and the cpu usage, the memory occupancy, the disk io, and the network io are collected every one minute, one hour, or one day when the server container is running.
Thus, an embodiment of step S101 may be to determine a data acquisition frequency; and acquiring the resource use data matched with various configuration specifications in each server container according to the data acquisition frequency.
In embodiments of the present invention, the resource usage data for each acquisition may be stored in a database for later recall.
In step S102, tasks may be timed, all collected resource usage data are processed, and average values of cpu usage, memory occupancy, disk io, network io, etc. are calculated in an accumulated manner. For example, the timing task counts cpu usage, memory occupancy, disk io, average value of network io, etc. per server per day.
Thus, the implementation of step S102 may be: carrying out data preprocessing on the resource usage data collected each time; and according to the preset time interval, carrying out statistical average on the resource use data of each server container to obtain the average level of the resource use.
In the embodiment of the present invention, the preset time interval may be set according to actual needs, such as daily, weekly, etc. The data preprocessing may include any one and combination of the following: null value processing; outlier processing; homogenizing; missing value interpolation, which aims to change the data into a format more suitable for model processing, has a great influence on the improvement of analysis and modeling quality.
In step S103, server containers with similar average levels of resource usage are grouped into a group, and specifically, all server containers may be clustered according to the average level of resource usage of each server container, to obtain a group.
In the embodiment of the present invention, the clustering method may, but is not limited to: K-Means, K-Medoids, clarans, birch, etc., without limitation. The following description regards K-Means as an example of the present invention to obtain a group, which specifically includes the following steps:
step a1: determining a preset number of target server containers from all the server containers, and taking the average level of resource use of the target servers as an initial clustering center; the preset number is smaller than the total number;
step a2: calculating the average level of the resource usage of each server container and the distance between each clustering center, and distributing the server container corresponding to the average level of the resource usage closest to the clustering center and the target server corresponding to the clustering center into the same group;
step a3: determining a new clustering center according to the average level of the resource use of each server container in the same group, and returning to the step a2;
step a4: and when the cluster centers are not changed, taking the group corresponding to each cluster center as a final group.
Assuming that there are N server containers, the resource usage data of the ith server container is x i ，i＝1，2，…N，x i An array of average levels of use of each resource may be possible. For example, x i May include average levels of resource usage by the cpu, memory usage, disk io, and network io, respectively.
Determining K target server containers from N server containers, taking the average level of the corresponding resource use as an initial clustering center, and recording as u 1 To u K . Then calculate x i Respectively with u 1 To u K Will x i The corresponding server containers are assigned to the same group as the server containers corresponding to the cluster centers closest thereto. Finally, calculating a new cluster center u 'by using the average level of the resource use of each server container in the same group' 1 To u' K Performing cluster calculation based on the updated cluster center again, and repeatedly executing the steps until u' 1 To u' K No change occurs, and the final group is obtained. As shown in FIG. 2, FIG. 2 is a schematic diagram of a server container group clustering result according to an embodiment of the present invention, wherein x is i Is composed of cpu usage and disk io, just one example.
After the groups of the server containers are obtained through the embodiment, the groups can be stored in a database for subsequent calling.
In step S104, the server containers in each group may be grouped at regular time according to the resource usage data, so as to determine server containers with abnormal specification configuration and adjust the server containers.
In an alternative embodiment, for each group obtained by the clustering algorithm, resource usage data of each server container in the group in a certain time may be collected, server containers with similar resource usage data are used as the same group, and server containers in the group are subdivided.
Resource usage data such as cpu usage is selected: grouping server containers with cpu utilization rate of 50-60% into one sub-group; classifying cpu utilization rate of 60-70% into one sub-packet; the cpu usage is classified into one sub-packet with 70-80%.
The grouping manner based on the remaining resource usage data, such as the memory occupancy, the disk io and the network io, is similar and will not be described here again. Such a server container group is subdivided into multiple groupings of different cpu usage levels, such that the secondary subdivision within each group for different operational metrics better identifies the server container-to-container differences.
For example: there are 6 servers in a group: server 1: cpu usage 50%; the server 2: cpu usage 60%; server 3: cpu usage 65%; server 4: cpu usage 72%; server 5: cpu usage 75%; server 6: cpu use 78%; grouping according to cpu usage can be divided into: 50-60% group: a server 1, a server 2;60-70% group: a server 3;70-80% group: server 4, server 5, server 6.
In another alternative embodiment, multiple kinds of resource usage data may be grouped together as a combination condition, for example, "cpu usage+memory occupancy", which is not described herein.
After each group is obtained by the above embodiment, the server container having abnormal specification configuration in each group can be adjusted. See step S105.
In step S105, for each server container in the same group, the minimum value of their configuration specifications (e.g., cpu, memory) is taken. The corresponding configuration specification of the server containers within the group is then adjusted to the minimum value. Thus, the implementation of step S105 may be: determining a minimum value of configuration specifications corresponding to at least one kind of operation data in the same group; and adjusting the configuration specifications of all the server containers except the server container corresponding to the minimum value in the same group to the minimum value.
For example: assume that two groupings are obtained after grouping 10 servers according to the memory occupancy rate, wherein the grouping 1 comprises a server container 1 and a server container 2, and the memories are 128G and 256G respectively; packet 2 includes server 3, server 4, and server 5, with memories 128G, 64G, and 256G, respectively. Then: for server 1 and server 2 in group 1, their memory minimum is taken to be 128G, and then the memory of server container 2 is adjusted to 128G. Similarly, for the group 2, the memories of the server 3 and the server 5 are adjusted to 64G, so that the specification configuration of the server container in each group is uniformly adjusted to the minimum value of the specification in the same group, the uniformity of the specification is realized, and the resource waste is avoided.
In another alternative embodiment, server containers having the same specification configuration may be grouped together at regular intervals, and server containers having a smaller grouping ratio may be determined to be abnormal server containers. And then the specification of the abnormal server container is adjusted to correspond to the server container with more groups. For example: there are a total of 4 server containers, with 3 server containers having 4 cores for cpu and 1 server having 8 cores for cpu. The grouping is performed according to the cpu specification and is divided into two groups, with the server container of 4 cores being group 1, and the server container of 8 cores being group 2. The number of server containers for group 1 is 3. The number of server containers for group 2 is 1, less than the number of server containers for group 1. The system determines that the server container in group 2 is an abnormal server container, and therefore modifies the cpu core number of 1 server container of 8 cores to 4. The remaining specifications are similarly processed and will not be described again here.
According to the server container specification adjustment method, the server container can be classified according to the running condition of the server container, and then the server container with abnormal specification is adjusted, so that the purpose of accurately managing container specification configuration is finally achieved, the container resource utilization rate is improved, and the server cost is saved.
Based on the same inventive concept as fig. 1, the embodiment of the present invention further provides a server container specification adjustment system, and referring to fig. 3, fig. 3 is a schematic structural diagram of a server container specification adjustment system 300 according to the embodiment of the present invention, including a service subsystem 310, a data processing subsystem 320, and a modeling and application subsystem 330.
A service subsystem 310, configured to collect resource usage data in a plurality of server containers that matches a plurality of configuration specifications;
a data processing subsystem 320 for performing a statistical analysis on the resource usage data of each server container to determine an average level of resource usage of each server container;
a modeling and application subsystem 330 for grouping server containers with similar average levels of resource usage into a group;
the modeling and application subsystem 330 is further configured to group, for each group, server containers within the group according to at least one resource usage data;
modeling and application subsystem 330 is also configured to determine an anomalous server container based on the difference in the specification configuration of the individual server containers within the same group and to adjust the specification configuration of the anomalous server container.
It will be appreciated that business subsystem 310, data processing subsystem 320, and modeling and application subsystem 330 may cooperatively perform the various steps of fig. 1 to achieve corresponding technical effects.
In order to facilitate an understanding of the functions of the above-described respective subsystems, a detailed description is provided below in connection with examples.
As shown in fig. 3, the service subsystem 310 is mainly configured to collect cpu usage, memory occupancy, disk io, network io, etc. when the server container runs, and store resource usage data generated by the server container in a database, that is, execute step S101 in the embodiment of the present invention.
In an alternative embodiment, the service subsystem 310 is specifically configured to determine a data acquisition frequency; and acquiring the resource use data matched with various configuration specifications in each server container according to the data acquisition frequency.
With continued reference to FIG. 3, the data processing subsystem 320 may include a data acquisition module, a data processing module, and a feature engineering module.
Referring to fig. 4, fig. 4 is a schematic diagram of data interaction between a service subsystem 310 and a data processing subsystem 320 provided by an embodiment of the present invention, where a data acquisition module acquires resource usage data from the service subsystem 310, and a data processing module processes all the resource usage data in a unit time according to a configured timing task, and calculates average values of cpu usage, memory occupation, disk io, network io, etc. in an accumulated manner. For example, the timing task counts the daily memory occupation, the number of the disk io and the network io of each server, and the like, obtains the average level of the resource use as the characteristic data, and then the characteristic engineering module carries out processing treatment such as null value, homogenization, abnormal value and the like on the characteristic data.
In combination with the foregoing, the data processing subsystem 320 is specifically configured to perform statistical averaging on the resource usage data of each server container according to the preset time interval, so as to obtain an average level of resource usage. The method is also specifically used for: carrying out data preprocessing on the average level of resource use; the data preprocessing comprises any one of the following and combination: null value processing; outlier processing; homogenizing; missing value interpolation.
With continued reference to fig. 3, the modeling and application subsystem 330 may include a clustering module, a container adjustment module, which cooperates with S103 to S105 to implement specification adjustment of server containers configured for abnormal specifications.
Thus, in an alternative embodiment, modeling and application subsystem 330 is specifically configured to determine a minimum value of the configuration specification corresponding to at least one operational data within the same group; and adjusting the configuration specifications of all the server containers except the server container corresponding to the minimum value in the same group to the minimum value.
In an alternative embodiment, modeling and application subsystem 330 is also specifically configured to: clustering all server containers according to the average level of resource usage of each server container to obtain a group, including: determining a preset number of target server containers from all the server containers, and taking the average level of resource use of the target servers as an initial clustering center; the preset number is smaller than the total number; calculating the average level of the resource usage of each server container and the distance between each clustering center, and distributing the server container corresponding to the average level of the resource usage closest to the clustering center and the target server corresponding to the clustering center into the same group; determining a new clustering center according to the average level of the resource usage of each server container in the same group, and returning to the step of calculating the distance between the average level of the resource usage of each server container and each clustering center; and when the cluster centers are not changed, taking the group corresponding to each cluster center as a final group.
It should be noted that, in the above embodiments of the present application, the division of the system is merely schematic, and there may be another division manner in actual implementation, and in addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or may exist separately and physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all or part of the technical solution contributing to the prior art or in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Referring to fig. 5, fig. 5 is a block diagram of a computer device according to an embodiment of the present invention, where the computer device is configured to execute a server container specification adjustment method according to an embodiment of the present invention, and computer device 500 includes: the memory 501, the processor 502, the communication interface 503, and the bus 504 are electrically connected to each other directly or indirectly, so as to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.
Alternatively, bus 504 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, or the like. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 5, but not only one bus or one type of bus.
In an embodiment of the present invention, the processor 502 may be a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, where the methods, steps, and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present invention may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in the processor for execution. The software modules may be located in a memory 501 and a processor 502 reads program instructions from the memory 501 to perform the steps of the methods described above in connection with the hardware thereof.
In the embodiment of the present invention, the memory 501 may be a nonvolatile memory, such as a hard disk (HDD) or a Solid State Drive (SSD), or may be a volatile memory (RAM). The memory may also be any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory in embodiments of the present invention may also be circuitry or any other device capable of performing memory functions for storing instructions and/or data.
The memory 501 may be used to store software programs and modules, such as instructions/modules of the server container specification adjustment system 300 provided in the embodiments of the present invention, and may be stored in the memory 501 in the form of software or firmware (firmware) or be solidified in an Operating System (OS) of the computer device 500, so that the processor 502 executes the software programs and modules stored in the memory 501 to perform various functional applications and data processing. The communication interface 503 may be used for communication of signaling or data with other node devices.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus and units described above may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
It is to be understood that the configuration shown in fig. 5 is merely illustrative, and that the computer device 500 may also include more or fewer components than shown in fig. 5, or have a different configuration than shown in fig. 5. The components shown in fig. 5 may be implemented in hardware, software, or a combination thereof.
Based on the above embodiments, the present application further provides a storage medium in which a computer program is stored, which when executed by a computer, causes the computer to execute the server container specification adjustment method provided in the above embodiments.
Based on the above embodiments, the present invention also provides a computer program, which when run on a computer, causes the computer to execute the server container specification adjustment method provided in the above embodiments.
Based on the above embodiments, the present invention further provides a chip, where the chip is configured to read a computer program stored in a memory, and is configured to execute the server container specification adjustment method provided in the above embodiments.
Embodiments of the present invention also provide a computer program product comprising instructions that, when executed on a computer, cause the computer to perform the server container specification adjustment method provided in the above embodiments.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by instructions. These instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A server container specification adjustment method, the method comprising:
collecting resource use data matched with various configuration specifications in a plurality of server containers;
performing a statistical analysis on the resource usage data of each of the server containers to determine an average level of resource usage for each of the server containers;
server containers with similar average levels of resource usage are grouped into a group;
grouping server containers within the group according to at least one of the resource usage data for each of the groups;
and determining an abnormal server container according to the specification configuration difference of each server container in the same group, and adjusting the specification configuration of the abnormal server container.
2. The server container specification adjustment method according to claim 1, wherein determining an abnormal server container based on a specification configuration difference of each server container in the same group, and adjusting the specification configuration of the abnormal server container, comprises:
determining the minimum value of the configuration specification corresponding to at least one kind of the resource use data in the same group;
and adjusting the configuration specifications of all the server containers except the server container corresponding to the minimum value in the same group to the minimum value.
3. The server container specification adjustment method according to claim 1, wherein grouping server containers having similar average levels of resource usage into a group comprises:
and clustering all the server containers according to the average level of the resource use of each server container to obtain the group.
4. The server container specification adjustment method according to claim 1, wherein clustering all the server containers according to the average level of resource usage of each of the server containers to obtain the group includes:
determining a preset number of target server containers from all the server containers, and taking the average level of resource use of the target servers as an initial clustering center; the preset number is less than the total number of server containers;
calculating the average level of the resource use of each server container and the distance between each clustering center, and distributing the server container corresponding to the average level of the resource use closest to the clustering center and the target server corresponding to the clustering center into the same group;
determining a new clustering center according to the average level of the resource usage of each server container in the same group, and returning to the step of calculating the distance between the average level of the resource usage of each server container and each clustering center;
and when the cluster centers are not changed, taking the group corresponding to each cluster center as a final group.
5. The server container specification adjustment method of claim 1, wherein collecting resource usage data in a plurality of server containers that matches a plurality of configuration specifications comprises:
determining data acquisition frequency;
and acquiring resource use data matched with various configuration specifications in each server container according to the data acquisition frequency.
6. The server container specification adjustment method according to claim 1, wherein performing a statistical analysis on the resource usage data of each of the server containers, determining an average level of resource usage of each of the server containers, comprises:
and according to a preset time interval, carrying out statistical average on the resource use data of each server container to obtain the average level of the resource use.
7. The server container specification adjustment method of claim 6, further comprising:
carrying out data preprocessing on the average level of the resource usage; the data preprocessing comprises any one of the following and combination: null value processing; outlier processing; homogenizing; missing value interpolation.
8. The server container specification adjustment system is characterized by comprising a service subsystem, a data processing subsystem and a modeling and application subsystem;
the service subsystem is used for collecting resource use data matched with various configuration specifications in a plurality of server containers;
the data processing subsystem is used for carrying out statistical analysis on the resource usage data of each server container and determining the average level of the resource usage of each server container;
the modeling and application subsystem is used for forming a group of server containers with similar average levels of resource use;
the modeling and application subsystem is further configured to group, for each of the groups, server containers within the group according to at least one of the resource usage data;
the modeling and application subsystem is further configured to determine an abnormal server container according to the specification configuration difference of each server container in the same group, and adjust the specification configuration of the abnormal server container.
9. A computer device comprising a processor and a memory, the memory storing a computer program executable by the processor, the processor executable by the computer program to implement the server container specification adjustment method of any one of claims 1 to 7.
10. A storage medium having stored thereon a computer program which, when executed by a processor, implements the server container specification adjustment method according to any one of claims 1 to 7.
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| CN202311832110.XA CN117707782A (en) | 2023-12-27 | 2023-12-27 | Server container specification adjustment method, system, computer equipment and storage medium |
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