CN117215589A

CN117215589A - Cloud primary state evaluation method, device, equipment and storage medium

Info

Publication number: CN117215589A
Application number: CN202311176550.4A
Authority: CN
Inventors: 饶琛琳; 梁玫娟
Original assignee: Beijing Youtejie Information Technology Co ltd
Current assignee: Beijing Youtejie Information Technology Co ltd
Priority date: 2023-09-12
Filing date: 2023-09-12
Publication date: 2023-12-12

Abstract

The invention discloses a cloud primary state evaluation method, a device, equipment and a storage medium, which comprise the following steps: establishing a multidimensional index system corresponding to the cloud native platform according to the evaluation requirement corresponding to the cloud native platform; the multi-dimensional index system comprises at least one of an application data dependent dimension, an infrastructure operation dependent dimension and a cluster instance parallel dimension; acquiring a plurality of index data corresponding to a cloud native platform, and establishing an association relationship between the plurality of index data and a multidimensional index system; according to the association relation, determining dimension characteristics corresponding to each data dimension in the multidimensional index system, and determining a target state evaluation result corresponding to the cloud native platform according to each dimension characteristic. According to the technical scheme, the cloud native state can be comprehensively and accurately evaluated, the abnormal condition of the cloud native platform can be conveniently and timely processed, and the stability and usability of the cloud native platform are improved.

Description

Cloud primary state evaluation method, device, equipment and storage medium

Technical Field

The present invention relates to the field of cloud native technologies, and in particular, to a method, an apparatus, a device, and a storage medium for evaluating a cloud native state.

Background

Cloud protogenesis is a modern software development and deployment method oriented to a cloud computing environment, and by designing, constructing, deploying and managing an application program in the cloud computing environment, the scalability, reliability and flexibility of the application program can be improved. Along with the gradual complexity and continuous expansion of the scale of the design of the application program, whether the accurate evaluation of the cloud primary state can be realized becomes an important factor affecting the performance of the application program.

In the prior art, cloud raw state evaluation is generally performed in the following three ways. First, various indexes (such as central processing unit utilization rate, memory utilization rate, network traffic and the like) of the cloud native application and the infrastructure are monitored, log information of the cloud native application and the infrastructure is collected, and then the cloud native state is estimated according to the various indexes and the log information. Secondly, the cloud native state is evaluated by monitoring the service level indicators (Service Level Indicator, SLI) and setting a threshold. Thirdly, the stability and influence of the new codes are evaluated in real time through an automatic test and continuous integration mode, so that the cloud primary state evaluation is realized.

However, the existing cloud native state evaluation method cannot cope with the dynamic increase of the number of indexes based on fixed indexes and dimensions, so that the cloud native state evaluation result is inaccurate.

Disclosure of Invention

The invention provides a cloud native state evaluation method, device, equipment and storage medium, which can realize comprehensive and accurate evaluation of the cloud native state and improve the stability and usability of a cloud native platform.

In a first aspect, an embodiment of the present invention provides a cloud native state evaluation method, including:

establishing a multidimensional index system corresponding to the cloud native platform according to the evaluation requirement corresponding to the cloud native platform;

the multi-dimensional index system comprises at least one of an application data dependent dimension, an infrastructure operation dependent dimension and a cluster instance parallel dimension;

acquiring a plurality of index data corresponding to a cloud native platform, and establishing an association relationship between the plurality of index data and a multidimensional index system;

according to the association relation, determining dimension characteristics corresponding to each data dimension in the multidimensional index system, and determining a target state evaluation result corresponding to the cloud native platform according to each dimension characteristic.

Optionally, establishing an association relationship between the plurality of index data and the multidimensional index system includes:

acquiring prefixes of the data names according to the data names of the index data;

and mapping each index data to a multidimensional index system according to the prefix of each data name to obtain the association relation between a plurality of index data and the multidimensional index system.

Optionally, if the data dimension is an application data dependent dimension or an infrastructure operation dependent dimension, determining, according to the association relationship, a dimension feature corresponding to each data dimension in the multidimensional index system, including:

according to the association relation, determining SLI corresponding to each data dimension in the multidimensional index system;

and determining dimension characteristics corresponding to each data dimension in the multidimensional index system according to the SLI corresponding to each data dimension.

Optionally, determining the dimension feature corresponding to each data dimension in the multidimensional index system according to the SLI corresponding to each data dimension includes:

according to the association relation, constructing dependency trees respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension;

acquiring the root node semantic information in each dependency tree according to SLI (client side interface) respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension;

and determining dimension characteristics respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension according to the root node semantic information in each dependency tree.

Optionally, determining dimension features corresponding to the application data dependency dimension and the infrastructure operation dependency dimension respectively according to the root node semantic information in each dependency tree includes:

If the root node semantic information in the dependency tree is text information, converting the root node semantic information into semantic vectors;

extracting target semantic features included in each semantic vector, and carrying out aggregation processing on the target semantic features respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension to obtain dimension features respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension.

Optionally, if the data dimension is a parallel dimension of the cluster instance, determining, according to the association relationship, a dimension feature corresponding to each data dimension in the multidimensional index system, including:

according to the association relation, adding index data corresponding to different clusters respectively to obtain a plurality of index data addition results corresponding to each cluster;

and obtaining all index data addition results corresponding to all clusters, and taking the average value of all index data addition results as the dimension characteristic corresponding to the parallel dimension of the cluster instance.

In a second aspect, an embodiment of the present invention further provides a cloud native state evaluation apparatus, including:

the multi-dimensional index system establishing module is used for establishing a multi-dimensional index system corresponding to the cloud native platform according to the evaluation requirement corresponding to the cloud native platform; the multi-dimensional index system comprises at least one of an application data dependent dimension, an infrastructure operation dependent dimension and a cluster instance parallel dimension;

The association relation establishing module is used for acquiring a plurality of index data corresponding to the cloud native platform and establishing association relation between the plurality of index data and the multidimensional index system;

the evaluation result determining module is used for determining dimension characteristics corresponding to each data dimension in the multi-dimensional index system according to the association relation and determining a target state evaluation result corresponding to the cloud primary platform according to each dimension characteristic.

In a third aspect, an embodiment of the present invention further provides an electronic device, including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the cloud native state evaluation method provided by any of the embodiments of the present invention.

In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, where the computer readable storage medium stores computer instructions, where the computer instructions are configured to cause a processor to implement the cloud native state evaluation method provided by any embodiment of the present invention when executed.

In a fifth aspect, embodiments of the present invention further provide a computer program product, which includes a computer program, which when executed by a processor implements the cloud native state evaluation method provided by any of the embodiments of the present invention.

According to the technical scheme provided by the embodiment of the invention, a multidimensional index system corresponding to the cloud native platform is established according to the evaluation requirement corresponding to the cloud native platform; the multi-dimensional index system comprises at least one of an application data dependent dimension, an infrastructure operation dependent dimension and a cluster instance parallel dimension; acquiring a plurality of index data corresponding to a cloud native platform, and establishing an association relationship between the plurality of index data and a multidimensional index system; according to the association relation, the dimension characteristics corresponding to each data dimension in the multidimensional index system are determined, and the target state evaluation result corresponding to the cloud native platform is determined according to each dimension characteristic, so that the cloud native state can be comprehensively and accurately evaluated, abnormal conditions of the cloud native platform can be conveniently and timely processed, and the stability and usability of the cloud native platform are improved.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flowchart of a cloud native state evaluation method according to a first embodiment of the present invention;

FIG. 2 is a flowchart of another method for evaluating cloud native state according to a second embodiment of the present invention;

FIG. 3 is a flowchart of another cloud native state evaluation method according to a third embodiment of the present invention;

fig. 4 is a schematic structural diagram of a cloud native state evaluation apparatus according to a fourth embodiment of the present invention;

fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a cloud native state evaluation method according to an embodiment of the present invention, where the method may be performed by a cloud native state evaluation device, and the cloud native state evaluation device may be implemented in hardware and/or software, and the cloud native state evaluation device may be configured in an electronic device such as a computer.

As shown in fig. 1, the cloud native state evaluation method disclosed in this embodiment includes:

s110, establishing a multidimensional index system corresponding to the cloud native platform according to the evaluation requirement corresponding to the cloud native platform.

The multidimensional index system comprises at least one of an application data dependent dimension, an infrastructure operation dependent dimension and a cluster instance parallel dimension.

In this embodiment, the application data dependency dimension may represent a data dependency of an application program. The infrastructure operational dependency dimension may represent an operational dependency of the infrastructure. The cluster instance parallel dimension may represent a parallel relationship between cluster instances.

In a specific embodiment, if the states of the application programs, the infrastructure and the clusters of the cloud native platform need to be evaluated, a three-dimensional index system corresponding to the Yun Yuansheng platform can be established. The infrastructure may include servers, networks, databases, and the like. If the number of indexes in the evaluation requirement corresponding to the cloud native platform is expanded, the states of the application program, the infrastructure and the cluster can be considered to be evaluated, and at the moment, the data dimension corresponding to the index system can be increased according to the number of the newly increased index data and the association relation between the newly increased index data.

S120, acquiring a plurality of index data corresponding to the cloud native platform, and establishing an association relationship between the plurality of index data and the multidimensional index system.

In this embodiment, the index data corresponding to the application data dependency dimension may include a data source, a data interface, and data transmission. Index data corresponding to the infrastructure run-dependent dimensions may include servers, networks, databases, and the like. Index data corresponding to the parallel dimension of the cluster instance can comprise load balancing, concurrent connection number and the like.

In this step, optionally, an association relationship between a plurality of index data and a multidimensional index system may be established according to the applicable scenario and effect of the index data. For example, since the data transmission index may be used to evaluate the call state of the application, the data transmission index may be mapped to the application data dependency dimension, i.e. an association between the data transmission index and the multidimensional index system is established.

In a specific embodiment, if there are multiple index data that do not match with the application data dependent dimension, the infrastructure running dependent dimension, and the cluster instance parallel dimension, the same label may be set for the associated index data, and the index data set with the same label may be mapped to the same data dimension. The data dimension may be a newly added dimension in the index system. There are various ways of determining the correlation of the index data, for example, if there is a dependency relationship between a plurality of index data, the plurality of index data can be considered to be correlated.

S130, determining dimension characteristics corresponding to each data dimension in the multidimensional index system according to the association relation, and determining a target state evaluation result corresponding to the cloud native platform according to each dimension characteristic.

In this embodiment, dimension features may be used to evaluate cloud native platform states. The target state evaluation result may be a result obtained by evaluating the state of the cloud native platform. The target state evaluation result may include a cloud native platform state normal, a cloud native platform state abnormal, and the like.

In a specific embodiment, according to the association relationship, the index data corresponding to the application data dependency dimension, the infrastructure operation dependency dimension, and the cluster instance parallel dimension respectively may be determined. Then, according to each index data and the association relation between the index data, the dimension characteristics corresponding to each data dimension can be determined. And then, determining a target state evaluation result according to the values corresponding to the data dimension characteristics and a preset value range.

For example, if the dimension feature value corresponding to the application data dependency dimension exceeds the preset value range, but the dimension feature value corresponding to the infrastructure operation dependency dimension does not exceed the preset value range, it may be considered that the abnormal state of the application program may be caused by the surge of the user quantity, and the overall state of the cloud native platform is not affected, and at this time, the normal state of the cloud native platform may be output. Or if the dimension characteristic value corresponding to the application data dependent dimension exceeds the preset value range, the dimension characteristic value corresponding to the infrastructure operation dependent dimension and the cluster instance parallel dimension also exceeds the preset value range, the overall state of the cloud native platform can be considered to be abnormal, and the state of the cloud native platform can be output at the moment.

Compared with the existing static evaluation model, the technical scheme of the embodiment expands the dimension of the multidimensional index system, namely establishes the multidimensional index system which can be updated in real time, so as to cope with the condition of the pointer data swelling in the evaluation requirement corresponding to the cloud native platform, comprehensively and accurately evaluate the cloud native state, and timely process the abnormal condition of the cloud native platform if the state of the cloud native platform is abnormal, thereby improving the stability and usability of the cloud native platform.

According to the technical scheme of the embodiment, a multidimensional index system corresponding to the cloud native platform is established according to the evaluation requirement corresponding to the cloud native platform; the multi-dimensional index system comprises at least one of an application data dependent dimension, an infrastructure operation dependent dimension and a cluster instance parallel dimension; acquiring a plurality of index data corresponding to a cloud native platform, and establishing an association relationship between the plurality of index data and a multidimensional index system; according to the association relation, the dimensional characteristics corresponding to each data dimension in the multidimensional index system are determined, and the target state evaluation result corresponding to the cloud native platform is determined according to each dimensional characteristic.

Example two

Fig. 2 is a flowchart of another cloud native state evaluation method according to a second embodiment of the present invention, where the present embodiment is based on further optimization and expansion of the foregoing embodiments, and may be combined with various optional technical solutions in the foregoing embodiments.

As shown in fig. 2, another cloud native state evaluation method disclosed in this embodiment includes:

s210, establishing a multidimensional index system corresponding to the cloud native platform according to the evaluation requirement corresponding to the cloud native platform.

S220, acquiring a plurality of index data corresponding to the cloud native platform.

At this step, the index data may optionally be obtained by a monitoring tool. The monitoring tools may include Prometheus, visualization panel Grafana and protocol (ELK) stacks, etc.

Exemplary, table 1 is a service analysis index data table provided according to an embodiment of the present invention. As shown in table 1, the relevant indexes of the cloud native platform business analysis include: service effort, service failure rate, service failure, average processing time, service success rate, and 95 split time. The relevant indexes of the cloud native platform service call quantity comprise: service call volume, 95 split time consumption, service effort volume, 80 split time consumption, service failure volume, service success rate, average call time consumption and service failure rate. Relevant indexes for analyzing the process state of the cloud native platform can include call volume, memory usage, success volume, active thread number, failure volume, maximum thread number, average time consumption, dump file number, garbage collection times, starting time, garbage collection average time consumption, running state, application instance starting time, central processing unit (Central Processing Unit, CPU) utilization rate and the like. The related indexes for analyzing the application clusters of the cloud native platform can comprise cluster call quantity, cluster success rate, cluster work quantity, cluster average time consumption, cluster failure quantity, 95 bit time consumption and the like.

TABLE 1

Table 2 is a device monitoring index data table provided according to an embodiment of the present invention. As shown in table 2, the relevant indexes of the cloud native platform network device or the firewall may include total packet number, connection number, throughput, response delay, newly-built connection number, CPU utilization, and the like. The host related indexes of the cloud native platform can comprise the read data amount per second of a host disk, the dead number of the host, the write request number per second of the host disk, the number of the host system processes, the total size of a host storage space, the number of host processes, the residual usable size of the storage space, the restart time of the residual usable size of a host memory, the number of CPUs, the use percentage of the host storage space, the memory size, the number of processes of a host waiting resource, the use rate of the host memory and the like. The storage related indexes of the cloud native platform can comprise the number of interruption per second of a disk, the average service time of input and output operations of the disk, the transmission times per second of the disk, the number of read bytes per second of the disk, the total read data amount of the disk, the busy rate of the disk, the number of processes waiting for input and output of the disk, the read data amount per second of the disk, the number of read and write requests of the disk, the read data amount per second of the disk, the waiting time of input and output of the disk, the throughput per second of input and output of the disk, the total write data amount of the disk and the number of blocks written by the disk.

TABLE 2

Table 3 is a micro service index data table provided according to an embodiment of the present invention.

TABLE 3 Table 3

Table 4 is a database index data table provided according to an embodiment of the present invention.

TABLE 4 Table 4

Table 5 is a middleware index data table provided according to an embodiment of the present invention.

TABLE 5

The method has the advantages that through establishing each index data table, a plurality of index data corresponding to the cloud native platform can be obtained rapidly, the index data are mapped to a multidimensional index system, the determination efficiency of the target state evaluation result is improved, and the method is convenient to process abnormal conditions of the cloud native platform in time.

S230, acquiring prefixes of the data names according to the data names of the index data.

In the present embodiment, since each data name is generally divided by a dot, a prefix of each data name can be acquired.

S240, mapping each index data to a multidimensional index system according to the prefix of each data name, and obtaining the association relation between the plurality of index data and the multidimensional index system.

In a specific embodiment, each index data may be mapped to each preset category according to a prefix of each data name. The preset categories may include infrastructure, operating systems, middleware, databases, services, and others. Wherein the categories of infrastructure, operating system, middleware, and databases may correspond to infrastructure runtime dependency dimensions.

For example, assuming that the prefix of the data name is os, os may be mapped to an operating system class, i.e., os to an infrastructure runtime dependency dimension. Alternatively, assuming that the prefix of the data name is idc, aws, or azure, idc, aws, azure may be mapped to the infrastructure class, i.e., idc, aws, azure to the infrastructure operation dependency dimension.

At this step, the index data that is not in configuration or mapped to the application data dependent dimension, the infrastructure run dependent dimension, and the cluster instance parallel dimension may optionally be mapped into other categories. Then, a dimension corresponding to the other category may be newly added in the multidimensional index system. The newly added dimension may correspond to other categories.

S250, if the data dimension is an application data dependency dimension or an infrastructure operation dependency dimension, determining SLI corresponding to each data dimension in the multidimensional index system according to the association relation.

In this embodiment, the SLI may include request response time, error rate, availability, and the like.

In this step, optionally, SLI corresponding to each data dimension may be determined according to the association relationship and the user requirement.

S260, determining dimension characteristics corresponding to each data dimension in the multidimensional index system according to SLI corresponding to each data dimension.

In this embodiment, dimension features corresponding to each data dimension may be determined according to an application scenario and a target of the SLI mode. For example, the error rate of application data calls may be detected based on the application data dependency dimension.

The advantage of this arrangement is that by defining SLI, the performance of the cloud native platform in different data dimensions can be measured.

And S270, if the data dimension is a cluster instance parallel dimension, adding index data corresponding to different clusters according to the association relation to obtain a plurality of index data addition results corresponding to each cluster.

In the present embodiment, the index data addition result may be a result of addition of index data corresponding to each cluster.

In a specific embodiment, according to the association relationship between the index data and the parallel dimensions of the cluster instances, the index data corresponding to each cluster may be added to obtain a plurality of index data addition results.

S280, obtaining all index data addition results corresponding to all clusters, and taking the average value of all index data addition results as the dimension characteristic corresponding to the parallel dimension of the cluster instance.

Optionally, a cloud primitive state curve corresponding to the parallel dimension of the cluster instance can be established according to all clusters and all index data addition results corresponding to all clusters. And then, taking the up-and-down fluctuation condition of the cloud primitive state curve as the dimension characteristic corresponding to the parallel dimension of the cluster instance. And finally, according to the dimension characteristics and a preset fluctuation range, evaluating the state of the cloud native platform from the parallel dimension of the cluster instance. For example, if the up-down fluctuation range of the cloud native state curve exceeds the preset fluctuation range, the cluster parallel state of the cloud native platform may be considered abnormal.

The cloud native state evaluation method has the advantages that the cloud native platform can correspond to a plurality of clusters, so that the accuracy and the reliability of the cloud native state evaluation result can be improved by considering the parallel relation among the cluster instances.

S290, determining a target state evaluation result corresponding to the cloud native platform according to the dimension characteristics.

According to the technical scheme of the embodiment, a multidimensional index system corresponding to the cloud native platform is established according to the evaluation requirement corresponding to the cloud native platform; acquiring a plurality of index data corresponding to a cloud native platform; acquiring prefixes of the data names according to the data names of the index data; mapping each index data to a multidimensional index system according to the prefix of each data name to obtain the association relation between a plurality of index data and the multidimensional index system; if the data dimension is an application data dependent dimension or an infrastructure operation dependent dimension, determining SLI corresponding to each data dimension in the multidimensional index system according to the association relation; determining dimension characteristics corresponding to each data dimension in the multi-dimensional index system according to SLI corresponding to each data dimension; if the data dimension is the parallel dimension of the cluster instances, according to the association relation, adding the index data corresponding to different clusters respectively to obtain a plurality of index data adding results corresponding to each cluster; acquiring all index data addition results corresponding to all clusters, and taking the average value of all index data addition results as the dimension characteristic corresponding to the parallel dimension of the cluster instance; the technical means for determining the target state evaluation result corresponding to the cloud native platform according to the dimensional characteristics solves the problem that the cloud native state evaluation result is inaccurate due to the fact that the existing cloud native state evaluation method cannot cope with the dynamic increase of the number of indexes based on fixed indexes and dimensions, can comprehensively and accurately evaluate the cloud native state, is convenient to process abnormal conditions of the cloud native platform in time, and improves the stability and usability of the cloud native platform.

Example III

Fig. 3 is a flowchart of another cloud native state evaluation method according to a third embodiment of the present invention, where the present embodiment is further optimized and expanded based on the foregoing embodiments, and may be combined with various optional technical solutions in the foregoing embodiments.

As shown in fig. 3, another cloud native state evaluation method disclosed in this embodiment includes:

s310, establishing a multidimensional index system corresponding to the cloud native platform according to the evaluation requirement corresponding to the cloud native platform.

S320, acquiring a plurality of index data corresponding to the cloud native platform, and establishing an association relationship between the plurality of index data and the multidimensional index system.

Alternatively, semantically similar index data may be mapped to the same data dimension. Optionally, whether the semantics of each index data are similar or not may be determined according to the name of each index data and/or the association relationship between each index data. The association relation between the index data can be obtained through a log of the cloud native platform. By way of example, assuming that the names of the respective index data are server 1, server 2, and server 3, server 1, server 2, and server 3 can be regarded as semantically similar.

In an alternative embodiment, services and entities may be created according to preset logic and then the index data may be dynamically updated according to the services and entities. An entity may be a concrete thing, such as a database.

S330, if the data dimension is an application data dependency dimension or an infrastructure operation dependency dimension, determining SLI corresponding to each data dimension in the multidimensional index system according to the association relation.

And S340, constructing dependency trees respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension according to the association relation.

In this step, specifically, the data call relationship between each application program and the call relationship between each infrastructure may be obtained according to the association relationship and the cloud native platform log. Then, a dependency tree corresponding to the application data dependency dimension can be generated according to the data call relationship between the application programs. And generating a dependency tree corresponding to the running dependency dimension of the infrastructure according to the calling relation among the infrastructures.

In an optional embodiment, a calling relationship between index data corresponding to the application data dependency dimension and a dependency relationship between index data corresponding to the infrastructure operation dependency dimension may be obtained according to the association relationship and the cloud native platform log. Then, a dependency tree corresponding to the application data dependency dimension may be generated according to the dependency relationship between the application data dependency dimension and each index data. And generating a dependency tree corresponding to the infrastructure operation dependency dimension according to the calling relation among the index data corresponding to the infrastructure operation dependency dimension.

S350, acquiring the root node semantic information in each dependency tree according to SLI respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension.

In a specific embodiment, first, the type and semantic attribute of the root node in each dependency tree may be determined according to the application scenario and the target of the SLI mode. The type of root node may include clusters, systems, services, applications, etc. The semantic attributes of the root node may include the function, role, business objectives, etc. of the root node. The semantic information of the root node may then be extracted from the corresponding data source by text processing techniques or metadata parsing, based on the type and semantic attributes of the root node. Alternatively, the text processing technique may be a natural language processing technique.

S360, according to the semantic information of the root node in each dependency tree, determining dimension characteristics respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension.

In this step, specifically, first, semantic features for describing the root node may be defined according to the root node semantic information. The semantic features may include identifiers of root nodes, functional descriptions, keywords, and the like. The dimension characteristics may then be determined from the semantic characteristics of the root node.

In an optional implementation manner of the embodiment of the present invention, determining dimension features corresponding to application data dependency dimensions and infrastructure operation dependency dimensions according to root node semantic information in each dependency tree includes: if the root node semantic information in the dependency tree is text information, converting the root node semantic information into semantic vectors; extracting target semantic features included in each semantic vector, and carrying out aggregation processing on the target semantic features respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension to obtain dimension features respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension.

In one particular embodiment, text embedding techniques may be employed to convert root node semantic information into semantic vectors. Then, a feature extraction method may be employed to extract target semantic features included in each semantic vector according to the defined semantic features. The feature extraction method may include a statistical method, a machine learning method, a knowledge graph, and the like.

In this step, optionally, since the semantic information of the root node may be dynamically changed, timely and accurate assessment of the cloud native state can be achieved by updating the target semantic features in real time. Specifically, the target semantic features may be updated periodically or according to a trigger event set by a user.

In this step, optionally, the target semantic features may be updated periodically or according to a trigger event set by the user.

The advantage of this arrangement is that, because the semantic information of the root node may be dynamically changed, by updating the target semantic features in real time, the cloud native state can be evaluated timely and accurately.

And S370, if the data dimension is a cluster instance parallel dimension, adding index data corresponding to different clusters according to the association relation to obtain a plurality of index data addition results corresponding to each cluster.

S380, obtaining all index data addition results corresponding to all clusters, and taking the average value of all index data addition results as the dimension characteristic corresponding to the parallel dimension of the cluster instance.

S390, determining a target state evaluation result corresponding to the cloud native platform according to the dimension characteristics.

In this step, specifically, first, a state evaluation model may be built according to any one of the dimensional features. Alternatively, the state evaluation model described above may be applied to the SLI mode. Then, according to the state evaluation model, a state evaluation result corresponding to each data dimension can be determined. And then, training the state evaluation model through the characteristics of each dimension to obtain a target state evaluation model. Finally, a target state evaluation model can be adopted to evaluate the cloud native platform, so that a target state evaluation result is obtained.

For example, a state evaluation model may be built based on the dimension characteristics corresponding to the application data dependent dimensions. Then, whether the application data call state of the cloud native platform is abnormal or not can be evaluated through the state evaluation model.

At this step, optionally, the state estimation model may be trained using at least one dimension feature to obtain a target state estimation model. The target state evaluation model may evaluate the cloud native platform for at least one data dimension.

For example, if the cloud native monitoring object is from the application data dependency dimension and the infrastructure operation dependency dimension, the target state evaluation model generated by the dimension feature corresponding to the application data dependency dimension and the dimension feature training corresponding to the infrastructure operation dependency dimension can be obtained. Then, the cloud native monitoring object can be evaluated by adopting the target state evaluation model, and a result of evaluating the cloud native monitoring object is obtained.

Optionally, a data dimension selected for evaluating the cloud native state may be determined according to an application scenario and a target of the SLI mode. For example, for error count detection in SLI mode, error analysis may be performed in conjunction with request data correlation. For detection in aggregation mode, the performance index of all cluster instances needs to be considered laterally.

According to the technical scheme of the embodiment, a multidimensional index system corresponding to the cloud native platform is established according to the evaluation requirement corresponding to the cloud native platform; acquiring a plurality of index data corresponding to a cloud native platform, and establishing an association relationship between the plurality of index data and a multidimensional index system; according to the association relation, determining SLI corresponding to each data dimension in the multidimensional index system; according to the association relation, constructing dependency trees respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension; acquiring the root node semantic information in each dependency tree according to SLI (client side interface) respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension; determining dimension characteristics respectively corresponding to application data dependency dimensions and infrastructure operation dependency dimensions according to the root node semantic information in each dependency tree; according to the association relation, adding index data corresponding to different clusters respectively to obtain a plurality of index data addition results corresponding to each cluster; the method comprises the steps of obtaining all index data addition results corresponding to all clusters, taking an average value of all index data addition results as a technical means of dimension characteristics corresponding to parallel dimensions of cluster examples, solving the problem that the existing cloud native state assessment method cannot cope with dynamic increase of the number of indexes based on fixed indexes and dimensions, so that the cloud native state assessment result is inaccurate, comprehensively and accurately assessing the cloud native state can be realized, and the method is convenient for timely processing abnormal conditions of the cloud native platform, so that stability and usability of the cloud native platform are improved.

In a preferred implementation of the embodiment of the present invention, a multidimensional index system corresponding to the cloud native platform may be established. The multidimensional index system at least comprises an application data dependency dimension, an infrastructure operation dependency dimension, a cluster instance parallel dimension and a semantic dimension. Then, a prefix mapping method can be adopted to establish association relations between a plurality of index data and application data dependent dimensions, infrastructure operation dependent dimensions and cluster instance parallel dimensions. Based on the semantics of each index data, the association relation between a plurality of index data and semantic dimensions is established. And then, constructing a dependency tree by using index data respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension, giving different weight values to each index data of the dependency tree, and determining dimension characteristics respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension by carrying out weighted summation on each index data and the corresponding weight values. The dimension characteristics corresponding to the parallel dimension of the cluster instance can be determined by automatically constructing and aggregating the index data corresponding to the parallel dimension of the cluster instance. Dimension features corresponding to semantic dimensions may be determined based on semantics. Alternatively, multiple target state assessment models may be trained based on at least one dimensional feature. And finally, according to the evaluation requirement of the cloud native platform, a proper target state evaluation model is selected to evaluate the state of the cloud native platform.

The cloud native platform state can be evaluated from any data dimension by determining the dimension characteristic corresponding to each data dimension and then evaluating the cloud native platform state according to the evaluation requirement of the cloud native platform, so that the cloud native platform state can be evaluated only based on the data dimension designated by the user, the abnormal condition of the cloud native platform can be conveniently and timely processed, and the stability and the usability of the cloud native platform are improved.

Example IV

Fig. 4 is a schematic structural diagram of a cloud native state evaluation device according to a fourth embodiment of the present invention, where the present embodiment is applicable to a situation of evaluating a cloud native state, and the cloud native state evaluation device may be implemented in a hardware and/or software form and may be configured in an electronic apparatus, such as a computer.

As shown in fig. 4, the cloud native state evaluation apparatus disclosed in the present embodiment includes:

the multidimensional index system establishing module 41 is configured to establish a multidimensional index system corresponding to the cloud native platform according to the evaluation requirement corresponding to the cloud native platform; the multi-dimensional index system comprises at least one of an application data dependent dimension, an infrastructure operation dependent dimension and a cluster instance parallel dimension;

the association relationship establishing module 42 is configured to obtain a plurality of index data corresponding to the cloud native platform, and establish an association relationship between the plurality of index data and the multidimensional index system;

The evaluation result determining module 43 is configured to determine dimension characteristics corresponding to each data dimension in the multidimensional index system according to the association relationship, and determine a target state evaluation result corresponding to the cloud native platform according to each dimension characteristic.

According to the technical scheme, the multidimensional index system establishment module, the association relation establishment module and the evaluation result determination module are matched with each other, so that the cloud native state can be comprehensively and accurately evaluated, the abnormal condition of the cloud native platform can be conveniently and timely processed, and the stability and usability of the cloud native platform are improved.

Optionally, the association relationship establishing module 42 includes:

a prefix determining unit, configured to obtain a prefix of each data name according to the data name of each index data;

and the index data mapping unit is used for mapping each index data to a multi-dimensional index system according to the prefix of each data name to obtain the association relation between a plurality of index data and the multi-dimensional index system.

Optionally, the evaluation result determining module 43 includes:

the service level index determining unit is used for determining SLI corresponding to each data dimension in the multidimensional index system according to the association relation;

the dimension characteristic determining unit is used for determining dimension characteristics corresponding to each data dimension in the multidimensional index system according to the SLI corresponding to each data dimension;

The dependency tree construction unit is used for constructing dependency trees respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension according to the association relation;

the semantic information acquisition unit is used for acquiring the root node semantic information in each dependency tree according to SLI (client side) corresponding to the application data dependency dimension and the infrastructure operation dependency dimension respectively;

the dependency dimension feature determining unit is used for determining dimension features respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension according to the root node semantic information in each dependency tree;

the semantic information conversion unit is used for converting the root node semantic information into semantic vectors if the root node semantic information in the dependency tree is text information;

the semantic feature aggregation unit is used for extracting target semantic features included in each semantic vector, and carrying out aggregation processing on the target semantic features respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension to obtain dimension features respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension;

the adding result determining unit is used for adding the index data corresponding to different clusters respectively according to the association relation to obtain a plurality of index data adding results corresponding to each cluster;

The cluster dimension feature determining unit is used for obtaining all index data addition results corresponding to all clusters, and taking the average value of all index data addition results as the dimension feature corresponding to the parallel dimension of the cluster instance.

The cloud native state evaluation device provided by the embodiment of the application can execute the cloud native state evaluation method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method. Reference is made to the description of any method embodiment of the application for details not described in this embodiment.

Example five

Fig. 5 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the application. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device 10 may also represent various forms of mobile equipment, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., helmets, eyeglasses, watches, etc.), and other similar computing equipment. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.

As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the cloud native state evaluation method.

In some embodiments, the cloud native state evaluation method may be implemented as a computer program, which is tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the cloud native state evaluation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the cloud native state evaluation method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

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

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A method for evaluating cloud primordial state, the method comprising:

establishing a multidimensional index system corresponding to a cloud native platform according to an evaluation requirement corresponding to the cloud native platform;

acquiring a plurality of index data corresponding to the cloud native platform, and establishing an association relationship between the plurality of index data and the multidimensional index system;

And determining dimension characteristics corresponding to each data dimension in the multi-dimensional index system according to the association relation, and determining a target state evaluation result corresponding to the cloud native platform according to each dimension characteristic.

2. The method of claim 1, wherein establishing an association between the plurality of metric data and the multi-dimensional metric system comprises:

3. The method according to claim 1, wherein if the data dimension is an application data dependent dimension or an infrastructure operation dependent dimension, determining, according to the association relationship, a dimension feature corresponding to each data dimension in the multidimensional index system includes:

determining service level indexes corresponding to each data dimension in the multi-dimensional index system according to the association relation;

and determining dimension characteristics corresponding to each data dimension in the multi-dimensional index system according to the service level index corresponding to each data dimension.

4. A method according to claim 3, wherein determining the dimension characteristics corresponding to each data dimension in the multi-dimensional index system according to the service level index corresponding to each data dimension comprises:

acquiring root node semantic information in each dependency tree according to service level indexes respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension;

5. The method of claim 4, wherein determining dimension characteristics for each of the application data dependency dimensions and the infrastructure run dependency dimensions based on root node semantic information in each of the dependency trees comprises:

extracting target semantic features contained in each semantic vector, and carrying out aggregation processing on the target semantic features respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension to obtain dimension features respectively corresponding to the application data dependency dimension and the infrastructure operation dependency dimension.

6. The method according to claim 1, wherein if the data dimension is a cluster instance parallel dimension, the determining, according to the association relationship, a dimension feature corresponding to each data dimension in the multidimensional index system includes:

7. A cloud raw state evaluation apparatus, the apparatus comprising:

the system comprises a multi-dimensional index system establishing module, a cloud native platform and a cloud native platform, wherein the multi-dimensional index system establishing module is used for establishing a multi-dimensional index system corresponding to the cloud native platform according to the evaluation requirement corresponding to the cloud native platform; the multi-dimensional index system comprises at least one of an application data dependent dimension, an infrastructure operation dependent dimension and a cluster instance parallel dimension;

And the evaluation result determining module is used for determining dimension characteristics corresponding to each data dimension in the multi-dimensional index system according to the association relation and determining a target state evaluation result corresponding to the cloud native platform according to each dimension characteristic.

8. An electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the cloud native state evaluation method of any of claims 1-6.

9. A computer readable storage medium storing computer instructions for causing a processor to implement the cloud native state evaluation method of any one of claims 1-6 when executed.

10. A computer program product, characterized in that it comprises a computer program which, when executed by a processor, implements the cloud native state evaluation method according to any one of claims 1-6.