CN109961204B - Service quality analysis method and system under micro-service architecture - Google Patents
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Abstract
The invention provides a method and a system for analyzing service quality under a micro-service architecture, wherein the method comprises the following steps: acquiring all micro-service calling record data in real time, and analyzing the calling record data to obtain the topological relation between the service and all micro-services; acquiring occurrence probability indexes of each micro service in the service based on the topological relation, and acquiring the weight of each micro service in the service based on the probability indexes; and calculating the performance index of the micro-service, and obtaining the real-time health index of the service based on the weight of the micro-service in the service and the performance index of the micro-service. According to the influence dependence degree of each micro service on the business link and the performance index of each micro service, the high-efficiency and accurate real-time monitoring and analyzing capacity of the quality of the business process is realized, cluster aggregation identification is realized through track slicing, vector conversion and a feature extraction algorithm and a secondary similarity algorithm, and therefore the large amount of labor is avoided being invested in the carding process.
Description
Technical Field
The present invention relates to the field of micro-service technologies, and in particular, to a method and a system for analyzing service quality under a micro-service architecture.
Background
Micro-services are an emerging software architecture, which is a major single application program and services are split into a plurality of supported micro-services, and with the continuous development of internet technology, micro-services are becoming mainstream design architectures adopted by internet applications due to the characteristics of high cohesion and low coupling and lightweight communication mechanisms. However, due to the fact that the components of the microservice architecture are numerous, the dependency relationship is complex, and the local capability center code is updated frequently, the fault location and the influence evaluation of the business system are very difficult. Especially, in some large-scale IT enterprises, after the realization capability of introducing the micro-service architecture is opened, the situation that the business process spans multiple systems and multiple developers, even business is crossed occurs, and how to evaluate the comprehensive evaluation of the business quality becomes a difficult point to be solved urgently in the industry.
There are two main schemes for monitoring the service quality of the system at present: the method comprises the steps that firstly, analysis of a calling chain is carried out based on a code embedding mode, and calling associated label information is output through embedding points of program codes from the front end to the rear end of a system; if an http request sent by a user is received, generating a call chain logic ID, and marking a call chain of the http request by using the call chain logic ID; meanwhile, the service execution process is utilized for summarizing, and the calling sequence and the nesting relation of the micro-service are marked by utilizing the micro-service calling mark; therefore, the technology can call the dependency relationship from front to back, but the realization of the technology needs to depend on the complete code transformation of the original system code from front to back, and the monitoring analysis level is limited to the method level in the service, and the analysis at the service level is lacked; the second scheme is that the service quality is evaluated based on a manually-carded service path, and scene information of the service used by the user who has signed a service level agreement contract is obtained; according to the scene information, combing and finding out a service topological path corresponding to the service used by the user; according to the service topological path, the service quality data of the service topological path is found, so that the process that a single user uses the service is monitored in the service providing process; therefore, the technology can realize comprehensive evaluation of the business process, but the analysis of the business path and the evaluation of the influence of each path node need to be completed by a sophisticated expert, and the combing cost and the model accuracy are difficult to measure in the complex environment of a large IT system.
Although the two schemes can solve part of problems, the two schemes have excessive limitations in actual operation, and the acquired information is relatively limited, so that the actual requirements are difficult to meet effectively. Although the first scheme can realize the link track analysis of each call record, the track log information embedded point transformation needs to be carried out on all codes of each micro-service center from the front end to the back end of the system, the transformation amount is large, and the embedded point granularity and depth of a research and development team formed by multiple people are difficult to realize a uniform standard; moreover, a single operation track with a large amount of fine granularity is easy to cause a data storm to submerge the view angle of operation and maintenance monitoring, and the occurrence link of service abnormity cannot be quickly defined; in the second scheme, the accuracy of monitoring the service quality can be effectively improved by comprehensively evaluating the core KPI indexes according to the service paths, but each service path node except a specific developer cannot be automatically found more effectively, and corresponding experts are often required to manually comb one by one. Under the situation of agile development iteration which is gradually rising in the current industry, it is difficult to keep up with the update frequency of the system. Therefore, the existing technical schemes have serious defects, not only are the operation process complicated and cannot be popularized and implemented, but also the information acquisition is limited, and the requirement of actual business service capability analysis cannot be met.
Disclosure of Invention
The invention provides a method and a system for analyzing the service quality under a micro-service architecture, which overcome the problems or at least partially solve the problems, and solves the problems that the service quality analysis in the prior art is complicated in operation process, cannot be popularized and implemented, has limitations in information acquisition and cannot meet the actual service capability analysis requirement.
According to an aspect of the present invention, there is provided a method for analyzing quality of service, including:
acquiring all micro-service calling record data in real time, and analyzing the calling record data to obtain the topological relation between the service and all micro-services;
acquiring occurrence probability indexes of each micro service in the service based on the topological relation, and acquiring the weight of each micro service in the service based on the probability indexes;
and calculating the performance index of the micro-service, and obtaining the real-time health index of the service based on the weight of the micro-service in the service and the performance index of the micro-service.
Preferably, analyzing the call record data to obtain a topological relation between the service and each microservice, specifically including:
performing atomic data slicing processing on the operation track of the business based on each call record of each micro service to generate a data set of each call record;
and performing similarity analysis on all the data sets, setting the data sets with the similarity exceeding a set threshold as similar data sets, and classifying the micro-services corresponding to the similar data sets into a service call to obtain the topological relation between the service and each micro-service.
Preferably, the similarity analysis is performed on all data sets, and specifically includes:
performing vector conversion on the data set to obtain a data set vector, and extracting a feature vector of the data set vector and a feature weight vector of the data set vector;
calculating the similarity of the feature vectors of every two data set vectors, attributing the two data set vectors with the similarity exceeding a set threshold value to a unified cluster, and taking the unified cluster as a service set.
Preferably, the vector conversion is performed on the data set to obtain a data set vector, and the feature vector and the feature weight vector of the data set vector are extracted, specifically including:
mapping the data set to an n-dimensional vector space, and performing vector transformation to obtain a corresponding data set vector:
C(di)＝[ci,1,ci,2,...,ci,n]
where n is the total number of full microservices, diFor the ith data set, ci,jIs d atiJ-th service in each data setThe number of calls of the port;
calculating by reverse frequency to obtain a data set diAnd a feature weight w of each data interfaceij：
W(di)＝[wi1,wi2,...,win]
Wherein j is an element of [1, n ]]N is the total number of vectors of all data sets, { j: vj∈dkIs a packet containing microservices vjThe number of data sets.
Preferably, the calculating the feature vector similarity of each two data set vectors specifically includes:
performing Simhash dimension reduction on all data set vectors, performing first-level similarity screening according to Hamming distance, performing second-level similarity judgment according to a similarity function, and judging the data set vectors with the similarity exceeding a set threshold value as similar vectors.
Preferably, the step of attributing the two data set vectors with the similarity exceeding the set threshold to the unified cluster specifically includes:
performing similar calculation on traversal of a data set vector d and other vectors, and searching k similar vectors;
if k similar vectors are not found, d is an isolated or extended node vector, and the vector d is classified into a set
If k similar vectors are found successfully, d is a vector close to the interior of the cluster group, d is classified into a set B, and k vectors are classified into a set B';
selecting a new vector from B ', searching k similar vectors again, if the k similar vectors are found, putting a new node into the set B, classifying the k vectors into the set B ', and removing the existing vectors in B and B '; and if the set B' is empty, the set B is a micro-service set of one service.
Preferably, the obtaining of the real-time health index of the service based on the weight of the micro service called by the service and the performance index of the micro service includes:
by mapping the micro-service asset information and the set B with a service label, the occurrence probability of the vector service node is used as the weight p of the service node in the serviceB(i) And obtaining the performance index t of the ith micro-service node in the serviceB(i) Calculating the real-time health degree index of the service as follows:
a quality of service analysis system comprising:
the record collector is used for collecting the call record of each micro service to obtain the call record data of all the micro services;
the micro-service topology analyzer is used for analyzing and mining data based on the calling record data of the micro-service to obtain the topological relation between the service and each micro-service;
the service quality evaluator is used for acquiring the occurrence probability index of each micro-service in the service, obtaining the weight of the micro-service in the service based on the probability index and calculating the performance index of the micro-service; and obtaining a real-time health degree index of the service based on the weight of the micro service called by the service and the performance index of the micro service.
A quality of service analysis device comprising:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, which when invoked by the processor is capable of performing a quality of service analysis method as described above.
A computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform a quality of service analysis method as described above.
The invention provides a method and a system for analyzing the quality of service under a micro-service architecture, which realize the high-efficiency and accurate real-time monitoring and analyzing capability of the quality of a service process by depending on the influence dependence degree of each micro-service on a service link and the performance index of each micro-service, and have obvious innovation breakthrough compared with the traditional local monitoring based on a traffic index and a single micro-service; the cluster aggregation identification is realized through track slicing, vector conversion and a feature extraction algorithm and a secondary similarity algorithm, so that the carding process is avoided from being invested in a large amount of manpower, and the method has obvious advantages in analysis efficiency, accuracy and knowledge management.
Drawings
Fig. 1 is a schematic flow chart of a method for analyzing quality of service according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a service quality analysis system according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The deployment dynamics and logic complexity of internet applications are continuously increased, and the traditional software architecture is difficult to adapt to the rapid change of user requirements. A Microservice (MS) is a variant of a Service-Oriented Architecture (SOA), and a complex software system is split into Service components with single functions and capable of being independently developed and deployed, and information interaction is performed through a lightweight communication mechanism. Because these microservices are built to specific business capabilities, with minimal centralized management, they can be written in different programming languages and use different data storage technologies. Thus, these services are tiny, focusing only on a small task, controlling all components, including the UI, middleware, data access, and transactions.
After the application architecture is converted into the micro-service architecture from the single architecture, the link process of the complete service function is substantially converted into the micro-service link topological relation. And the analysis of the micro-service call topological relation is difficult to be implemented by a unified development framework to restrict in a cross-language and cross-developer environment.
Thus, as shown in fig. 1, a quality of service analysis method is shown, comprising:
acquiring all micro-service calling record data in real time, and analyzing the calling record data to obtain the topological relation between the service and all micro-services;
acquiring occurrence probability indexes of each micro service in the service based on the topological relation, and acquiring the weight of each micro service in the service based on the probability indexes;
and calculating the performance index of the micro-service, and obtaining the real-time health index of the service based on the weight of the micro-service in the service and the performance index of the micro-service.
In this embodiment, analyzing the call record data to obtain a topological relationship between the service and each microservice specifically includes:
performing atomic data slicing processing on the operation track of the business based on each call record of each micro service to generate a data set of each call record;
and performing similarity analysis on all the data sets, setting the data sets with the similarity exceeding a set threshold as similar data sets, and classifying the micro-services corresponding to the similar data sets into a service call to obtain the topological relation between the service and each micro-service.
The internal logic call chain of a single micro service can be realized by performing log embedding in a development framework of a corresponding developer, however, the realization of the complete business link analysis of cross-development languages, cross-development frameworks and cross-developers is the core difficulty of realizing the link analysis of business functions under the micro service architecture. Therefore, the method of the embodiment realizes the microservice topology analysis by invoking the analysis mining of the recorded data in a loose coupling mode.
Each micro-service call outputs corresponding attribute information, such as a calling party IP, a micro-service name, a system ID, a job number ID, a user ID and the like, so that slicing processing is performed on a certain agent, a certain job number or a service operation track of a certain user through a combination strategy, and an atomic Data Set (DataSet) is generated. For example, the business desk can slice the atomic data of each dimension of the business staff and the user through the user ID information. The number of all track slice datasets is defined as N.
In this embodiment, the similarity analysis is performed on all data sets, and specifically includes:
performing vector conversion on the data set to obtain a data set vector, and extracting a feature vector of the data set vector and a feature weight vector of the data set vector;
calculating the similarity of the feature vectors of every two data set vectors, attributing the two data set vectors with the similarity exceeding a set threshold value to a unified cluster, and taking the unified cluster as a service set.
In this embodiment, since the data set of the previous slice data is track information with certain timing information, in order to promote the subsequent data calculation processing, the data set is mapped to an n-dimensional vector space, where n is the total number of full-scale micro services, and then the ith data set is defined as di，ci,jIs diThe number of calls of the jth interface in the data set is as follows, and then after vector conversion, there will be vectors:
C(di)＝[ci,1,ci,2,...,ci,n]
feature extraction, calculating by inverse frequency in order to evaluate the feature weight of each service interface, defining W (d) as a data set diN-dimensional feature vector of, and wijFor the ith data set diThe feature weight of the j-th dimension of (1) is then:
W(di)＝[wi1,wi2,...,win]
wherein j is an element of [1, n ]]N isWith the total number of data set vectors, { j: vj∈dkIs a packet containing microservices vjThe number of data sets.
In this embodiment, calculating the feature vector similarity of each two data set vectors specifically includes:
performing Simhash dimension reduction on all data set vectors, performing first-level similarity screening according to Hamming distance, performing second-level similarity judgment according to a similarity function, and judging the data set vectors with the similarity exceeding a set threshold value as similar vectors.
Specifically, in this embodiment, in order to calculate the similarity between two eigenvectors of each data set, the similarity between the two eigenvectors can be calculated by cosine similarity, and the formula is as follows:
however, when the vector space is too large, the cosine similarity calculation efficiency is too low, so in the scheme, the first-level similarity screening is realized through simhash dimension reduction processing, and then accurate calculation is realized according to a similar function.
simhash(W(di))＝Ai
The Simhash function is implemented by converting each dimension into a binary code with a fixed length of s, and then combining the binary code with s bits and the characteristic weight w of the corresponding dimensionijMultiplication (encoding 0 instead of-1) is performed to generate s eigenvalues, and the n-dimensional eigenvalues are all processed accordingly, resulting in s × n data in total. Finally, according to the position of s-bit code, accumulating the characteristic numbers of n same positions, the positive number is 1, the negative number is 0, namely the fingerprint Ai。
When the feature similarity of two feature vectors is calculated, the first-level judgment can be realized through the hamming distance:
Hamming(Ai,Aj)＝t
when T < T, then judge this diAnd djThe two vectors are similar, T is a set similarity threshold value, and then a similar function is passedA quadratic similarity calculation is performed to determine that the closer to 1, the more similar the two vectors are.
Neighbor clustering, wherein the similarity between vectors can be judged through similarity calculation, when the similarity reaches a certain threshold value, the two vectors are judged to be a neighbor attribution unified cluster, and the main calculation process is as follows:
performing similar calculation on traversal of a data set vector d and other vectors, and searching k similar vectors;
if k similar vectors are not found, d is an isolated or extended node vector, and the vector d is classified into a set
If k similar vectors are found successfully, d is a vector close to the interior of the cluster group, d is classified into a set B, and k vectors are classified into a set B';
selecting a new vector from B ', searching k similar vectors again, if the k similar vectors are found, putting a new node into the set B, classifying the k vectors into the set B ', and removing the existing vectors in B and B '; and if the set B' is empty, the set B is a micro-service set of one service.
In this embodiment, the obtaining of the real-time health index of the service based on the weight of the microservice called by the service and the performance index of the microservice specifically includes:
path identification, namely mapping the service label of the micro-service asset information and the set B, and taking the occurrence probability of the vector service node as the weight p of the service node in the serviceB(i) And obtaining the performance index t of the ith micro-service node in the serviceB(i) Calculating the real-time health degree index of the service as follows:
as shown in fig. 2, this embodiment further provides a service quality analysis system, including:
a Microservice API Gateway (MAG), a part of Microservice MSs in the Microservice system architecture provide Microservice capability to the outside through a unified Microservice Gateway, and another part of Microservice MSs are only called between the insides of back-end service centers.
The Record Collector (RC) is used for collecting the call records of each micro service, has strong compatibility, can be used for independently reporting the application through a log4x framework, and can also form a uniform format through collecting the call logs in real time, wherein the uniform format comprises a called system ID, a job number ID, a serial number, a user ID, a micro service ID, an IP address, a call result, a parameter entering function, a parameter leaving function, a timestamp, time consumption and the like.
The Message Queue (MQ) mainly caches call record messages from all the microservice MSs, and realizes Queue ordering and distribution consumption processing of backend data.
An index Aggregator (MA) mainly implements real-time atomic aggregation (for example, by minutes) of indexes through a storm/spark and other real-time stream processing framework, and aggregation dimensions include a system ID, a micro-service ID, a call result, a success rate, time consumption, failure error distribution and the like.
In this embodiment, the flow of the MTA processing is as the above-mentioned service quality analysis method, and the method for the MTA to implement the automatic construction of the service link process includes slicing a trajectory, converting a vector, and implementing cluster aggregation identification through a feature extraction algorithm and a secondary similarity algorithm.
Service Directory (SD): the service asset information of all the micro services MS is mainly managed, including names, atomic function descriptions, versions, developers, release dates, change records, and the like of the micro services.
The Service Quality Evaluator (SQE) is used for acquiring the occurrence probability index of each micro Service in the Service, obtaining the weight of the micro Service in the Service based on the probability index and calculating the performance index of the micro Service; and obtaining a real-time health degree index of the service based on the weight of the micro service called by the service and the performance index of the micro service.
The embodiment further provides a device for analyzing service quality under the micro-service architecture, which includes: a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus;
wherein,
the processor, the memory and the communication interface complete mutual communication through the bus;
the communication interface is used for information transmission between the test equipment and the communication equipment of the display device;
the processor is configured to call program instructions in the memory to perform the quality of service analysis method provided by the above-mentioned method embodiments, for example, including:
acquiring all micro-service calling record data in real time, and analyzing the calling record data to obtain the topological relation between the service and all micro-services;
acquiring occurrence probability indexes of each micro service in the service based on the topological relation, and acquiring the weight of each micro service in the service based on the probability indexes;
and calculating the performance index of the micro-service, and obtaining the real-time health index of the service based on the weight of the micro-service in the service and the performance index of the micro-service.
The present embodiment discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, which when executed by a computer, enable the computer to perform a quality of service analysis method provided by the above-mentioned method embodiments, for example, comprising:
acquiring all micro-service calling record data in real time, and analyzing the calling record data to obtain the topological relation between the service and all micro-services;
acquiring occurrence probability indexes of each micro service in the service based on the topological relation, and acquiring the weight of each micro service in the service based on the probability indexes;
and calculating the performance index of the micro-service, and obtaining the real-time health index of the service based on the weight of the micro-service in the service and the performance index of the micro-service.
The present embodiment provides a non-transitory computer-readable storage medium, which stores computer instructions, which cause the computer to execute the quality of service analysis method provided by the foregoing method embodiments, for example, including:
acquiring all micro-service calling record data in real time, and analyzing the calling record data to obtain the topological relation between the service and all micro-services;
acquiring occurrence probability indexes of each micro service in the service based on the topological relation, and acquiring the weight of each micro service in the service based on the probability indexes;
and calculating the performance index of the micro-service, and obtaining the real-time health index of the service based on the weight of the micro-service in the service and the performance index of the micro-service.
In summary, the present invention provides a method and a system for analyzing service quality under a micro-service architecture, which achieve efficient and accurate real-time monitoring and analysis capability of quality in a service process according to an influence dependency degree of each micro-service on a service link and a performance index of each micro-service, and have an obvious innovative breakthrough compared to the traditional local monitoring based on a traffic index and a single micro-service; the cluster aggregation identification is realized through track slicing, vector conversion and a feature extraction algorithm and a secondary similarity algorithm, so that the carding process is avoided from being invested in a large amount of manpower, and the method has obvious advantages in analysis efficiency, accuracy and knowledge management.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above-described embodiments of the test equipment and the like of the display device are merely illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, the method of the present invention is only a preferred embodiment and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A method for quality of service analysis, comprising:
acquiring all micro-service calling record data in real time, and analyzing the calling record data to obtain the topological relation between the service and all micro-services;
acquiring occurrence probability indexes of each micro service in the service based on the topological relation, and acquiring the weight of each micro service in the service based on the probability indexes;
calculating the performance index of the micro-service, and obtaining the real-time health index of the service based on the weight of the micro-service in the service and the performance index of the micro-service;
analyzing the call record data to obtain a topological relation between the service and each microservice, specifically comprising:
performing atomic data slicing processing on the operation track of the business based on each call record of each micro service to generate a data set of each call record;
performing similarity analysis on all data sets, setting the data sets with the similarity exceeding a set threshold as similar data sets, and classifying the micro services corresponding to the similar data sets into one service to obtain a topological relation between the service and the micro services;
and performing similarity analysis on all data sets, specifically comprising:
performing vector conversion on the data set to obtain a data set vector, and extracting a feature vector of the data set vector and a feature weight vector of the data set vector;
calculating the similarity of the feature vectors of every two data set vectors, attributing the two data set vectors with the similarity exceeding a set threshold value to a unified cluster, and taking the unified cluster as a service set;
the step of attributing the two data set vectors with the similarity exceeding the set threshold to the unified cluster specifically comprises the following steps:
performing similar calculation on traversal of a data set vector d and other vectors, and searching k similar vectors;
if k similar vectors are not found, d is an isolated or extended node vector, and the vector d is classified into a set
If k similar vectors are found successfully, d is a vector close to the interior of the cluster group, d is classified into a set B, and k vectors are classified into a set B';
selecting a new vector from B ', searching k similar vectors again, if the k similar vectors are found, putting a new node into the set B, classifying the k vectors into the set B ', and removing the existing vectors in B and B '; until the set B' is empty, the set B is a micro-service set of one service;
obtaining a real-time health degree index of the service based on the weight of the micro service called by the service and the performance index of the micro service, and specifically comprising the following steps:
by mapping the micro-service asset information and the set B with a service label, the occurrence probability of the vector service node is used as the weight p of the service node in the serviceB(i) And obtaining the performance index t of the ith micro-service node in the serviceB(i) Calculating the real-time health degree index of the service as follows:
2. the method of analyzing service quality according to claim 1, wherein the data set is vector-converted to obtain a data set vector, and the extracting a feature vector and a feature weight vector of the data set vector specifically includes:
mapping the data set to an n-dimensional vector space, and performing vector transformation to obtain a corresponding data set vector:
C(di)＝[ci,1,ci,2,...,ci,n]
where n is the total number of full microservices, diFor the ith data set, ci,jIs d atiThe number of times of calling of the jth service interface in each data set;
calculating by reverse frequency to obtain a data set diAnd a feature weight w of each data interfaceij：
W(di)＝[wi1,wi2,...,win]
Wherein j is an element of [1, n ]]N is the total number of vectors of all data sets, { j: vj∈dkIs a packet containing microservices vjThe number of data sets.
3. The method of claim 1, wherein calculating the similarity of the feature vectors of every two data set vectors specifically comprises:
performing Simhash dimension reduction on all data set vectors, performing first-level similarity screening according to Hamming distance, performing second-level similarity judgment according to a similarity function, and judging the data set vectors with the similarity exceeding a set threshold value as similar vectors.
4. A quality of service analysis system, comprising:
the record collector is used for collecting the call record of each micro service to obtain the call record data of all the micro services;
the micro-service topology analyzer is used for analyzing and mining data based on the calling record data of the micro-service to obtain the topological relation between the service and each micro-service;
the service quality evaluator is used for acquiring the occurrence probability index of each micro-service in the service, obtaining the weight of the micro-service in the service based on the probability index and calculating the performance index of the micro-service; obtaining a real-time health degree index of the service based on the weight of the micro service called by the service and the performance index of the micro service;
data analysis and mining are carried out on calling record data based on the micro-services to obtain the topological relation between the service and each micro-service, and the method specifically comprises the following steps:
performing atomic data slicing processing on the operation track of the business based on each call record of each micro service to generate a data set of each call record;
performing similarity analysis on all data sets, setting the data sets with the similarity exceeding a set threshold as similar data sets, and classifying the micro services corresponding to the similar data sets into one service to obtain a topological relation between the service and the micro services;
and performing similarity analysis on all data sets, specifically comprising:
performing vector conversion on the data set to obtain a data set vector, and extracting a feature vector of the data set vector and a feature weight vector of the data set vector;
calculating the similarity of the feature vectors of every two data set vectors, attributing the two data set vectors with the similarity exceeding a set threshold value to a unified cluster, and taking the unified cluster as a service set;
the step of attributing the two data set vectors with the similarity exceeding the set threshold to the unified cluster specifically comprises the following steps:
performing similar calculation on traversal of a data set vector d and other vectors, and searching k similar vectors;
if k similar vectors are not found, d is an isolated or extended node vector, and the vector d is classified into a set
If k similar vectors are found successfully, d is a vector close to the interior of the cluster group, d is classified into a set B, and k vectors are classified into a set B';
selecting a new vector from B ', searching k similar vectors again, if the k similar vectors are found, putting a new node into the set B, classifying the k vectors into the set B ', and removing the existing vectors in B and B '; until the set B' is empty, the set B is a micro-service set of one service;
obtaining a real-time health degree index of the service based on the weight of the micro service called by the service and the performance index of the micro service, and specifically comprising the following steps:
by mapping the micro-service asset information and the set B with a service label, the occurrence probability of the vector service node is used as the weight p of the service node in the serviceB(i) And obtaining the performance index t of the ith micro-service node in the serviceB(i) Calculating the real-time health degree index of the service as follows:
5. a quality of service analysis device, comprising:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 3.
6. A computer program product, characterized in that the computer program product comprises a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to carry out the method according to any one of claims 1 to 3.
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Families Citing this family (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN113592034B (en) * | 2021-08-23 | 2022-07-22 | 江苏鼎跃腾信息技术有限公司 | Content push method and AI (Artificial Intelligence) management and control system based on big data visualization mining processing |
| CN113869989B (en) * | 2021-12-01 | 2022-05-06 | 阿里云计算有限公司 | Information processing method and device |
| CN114390109B (en) * | 2021-12-13 | 2024-02-20 | 中国银联股份有限公司 | Service processing method, micro-service gateway and data center system |
| CN114240466B (en) * | 2021-12-23 | 2023-04-18 | 中科星通(廊坊)信息技术有限公司 | Remote sensing product authenticity checking method based on micro-service architecture |
| CN114615521B (en) * | 2022-03-10 | 2024-02-23 | 网易（杭州）网络有限公司 | Video processing method and device, computer readable storage medium and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008040803A (en) * | 2006-08-07 | 2008-02-21 | Mitsubishi Electric Corp | Business monitoring system, business monitoring method, and program |
| US8886549B2 (en) * | 2005-12-30 | 2014-11-11 | Oracle International Corporation | Incremental, real-time computation of aggregate expressions |
| CN106850832A (en) * | 2017-03-01 | 2017-06-13 | 郑州云海信息技术有限公司 | A kind of cloud management system based on micro services framework |
| CN107102847A (en) * | 2016-02-23 | 2017-08-29 | 中国水电工程顾问集团有限公司 | Software development methodology, apparatus and system based on micro services |
| CN107491858A (en) * | 2017-07-05 | 2017-12-19 | 北京科东电力控制系统有限责任公司 | A kind of electric power transaction platform based on micro services application architecture |
-
2017
- 2017-12-26 CN CN201711433929.3A patent/CN109961204B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8886549B2 (en) * | 2005-12-30 | 2014-11-11 | Oracle International Corporation | Incremental, real-time computation of aggregate expressions |
| JP2008040803A (en) * | 2006-08-07 | 2008-02-21 | Mitsubishi Electric Corp | Business monitoring system, business monitoring method, and program |
| CN107102847A (en) * | 2016-02-23 | 2017-08-29 | 中国水电工程顾问集团有限公司 | Software development methodology, apparatus and system based on micro services |
| CN106850832A (en) * | 2017-03-01 | 2017-06-13 | 郑州云海信息技术有限公司 | A kind of cloud management system based on micro services framework |
| CN107491858A (en) * | 2017-07-05 | 2017-12-19 | 北京科东电力控制系统有限责任公司 | A kind of electric power transaction platform based on micro services application architecture |
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