CN115914224A - Intelligent application service management system and method based on micro-service data architecture - Google Patents
Intelligent application service management system and method based on micro-service data architecture Download PDFInfo
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Abstract
The invention discloses an intelligent application service management system and method based on a micro-service data architecture, which relate to the field of application service management, wherein the system is used for executing an intelligent application service management method based on the micro-service data architecture, and the method comprises the following steps: acquiring a plurality of cloud service nodes of a cloud platform end; acquiring a plurality of services of a target application; obtaining a plurality of occupied memory information; obtaining a plurality of running memory information and a plurality of communication distance information; obtaining a plurality of demand degree information; carrying out random distribution management on a plurality of services according to a plurality of occupied memory information, a plurality of cloud service nodes and a local node to obtain a plurality of management schemes; and optimizing the management schemes according to the communication distance information and the demand degree information to obtain an optimal management scheme, and managing the target application. The technical problem that management accuracy of application services is not enough and management effects of the application services are not good in the prior art is solved.
Description
Technical Field
The invention relates to the field of application service management, in particular to an intelligent application service management system and method based on a micro-service data architecture.
Background
With the continuous development of the information technology, the number, scale and complexity of application services are continuously increased, the interaction between the application services is more and more, and a new challenge is provided for the management of the application services. The traditional application service management mode cannot adapt to the requirement of modern application service management due to logic coupling. Due to the adoption of the micro-service data architecture, a series of problems faced by the current application service management are effectively solved. The complex application service can be divided into services through the micro-service architecture, the complex application service is divided into a plurality of small and simple basic application services, and a coordination mechanism of lightweight communication is adopted among the plurality of small and simple basic application services, so that the centralized management of the complex application service is gradually realized. The management method for optimizing the application service is researched and designed by combining the micro-service data architecture with the application service management, and has important practical significance.
In the prior art, the technical problem that the management effect of the application service is poor due to the fact that the management accuracy of the application service is not enough exists.
Disclosure of Invention
The application provides an intelligent application service management system and method based on a micro-service data architecture. The technical problem that management accuracy of application services is not enough in the prior art, and therefore management effects of the application services are not good is solved.
In view of the foregoing, the present application provides an intelligent application service management system and method based on micro service data architecture.
In a first aspect, the present application provides an intelligent application service management system based on a microservice data architecture, where the system includes: the cloud platform comprises a cloud service node acquisition module, a cloud service node selection module and a cloud service node selection module, wherein the cloud service node acquisition module is used for acquiring a plurality of cloud service nodes of a cloud platform end; the service acquisition module is used for acquiring a plurality of services of a target application, wherein the target application is an application for providing service services for a plurality of target clients, and the plurality of services are based on a micro-service architecture and provide service services for the plurality of target clients at the plurality of cloud service nodes and the local node; the memory occupation acquisition module is used for acquiring the memory occupation of the plurality of services during operation to obtain a plurality of memory occupation information; the information acquisition module is used for acquiring the operating memories of the cloud service nodes and the communication distances between the cloud service nodes and the target clients to obtain a plurality of operating memory information and a plurality of communication distance information; the demand degree calculation module is used for calculating the demand degrees of the services to obtain a plurality of demand degree information; a management scheme obtaining module, configured to perform random allocation management on the multiple services according to the multiple pieces of occupied memory information, the multiple cloud service nodes, and the local node, so as to obtain multiple management schemes; and the management module is used for optimizing the management schemes according to the communication distance information and the demand degree information to obtain an optimal management scheme and manage the target application.
In a second aspect, the present application further provides an intelligent application service management method based on a micro service data architecture, where the method is applied to an intelligent application service management system based on a micro service data architecture, and the method includes: acquiring a plurality of cloud service nodes of a cloud platform end; acquiring a plurality of businesses of a target application, wherein the target application is an application for providing business services for a plurality of target clients, and the businesses are based on a micro-service architecture and provide business services for the target clients at a plurality of cloud service nodes and local nodes; acquiring occupied internal memories of the plurality of services during operation to obtain a plurality of occupied internal memory information; acquiring the operating memories of the cloud service nodes and the communication distances between the cloud service nodes and the target clients to obtain a plurality of operating memory information and a plurality of communication distance information; calculating the demand degrees of the plurality of services to obtain a plurality of demand degree information; according to the plurality of occupied memory information, the plurality of cloud service nodes and the local node, carrying out random distribution management on the plurality of services to obtain a plurality of management schemes; and optimizing the management schemes according to the communication distance information and the demand degree information to obtain an optimal management scheme, and managing the target application.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
acquiring a plurality of cloud service nodes through a cloud platform end; acquiring a plurality of occupied memory information by acquiring the occupied memory of a plurality of services of a target application during operation; the method comprises the steps of collecting operating memories of a plurality of cloud service nodes and communication distances between the operating memories and a plurality of target clients to obtain a plurality of operating memory information and a plurality of communication distance information; calculating the demand degrees of a plurality of services to obtain a plurality of demand degree information; according to the plurality of occupied memory information, the plurality of cloud service nodes and the local node, carrying out random distribution management on the plurality of services to obtain a plurality of management schemes; and optimizing the management schemes according to the communication distance information and the demand degree information to obtain an optimal management scheme, and managing the target application. The accuracy of application service management is improved, and the quality of application service management is improved; meanwhile, the application service is intelligently, efficiently and reliably managed, and the technical effects of service satisfaction and service experience of the application service are improved.
Drawings
Fig. 1 is a schematic flowchart of an intelligent application service management method based on a micro service data architecture according to the present application;
fig. 2 is a schematic flow chart illustrating a process of acquiring multiple services of a target application in an intelligent application service management method based on a micro-service data architecture according to the present application;
fig. 3 is a schematic flow chart illustrating a process of acquiring multiple requirement degree information in an intelligent application service management method based on a micro-service data architecture according to the present application;
fig. 4 is a schematic structural diagram of an intelligent application service management system based on a microservice data architecture according to the present application.
Description of reference numerals: the system comprises a cloud service node acquisition module 11, a service acquisition module 12, an occupied memory acquisition module 13, an information acquisition module 14, a demand calculation module 15, a management scheme acquisition module 16 and a management module 17.
Detailed Description
The application provides an intelligent application service management system and method based on a micro-service data architecture. The technical problem that management accuracy of application services is not enough in the prior art, and therefore management effects of the application services are not good is solved. The accuracy of application service management is improved, and the quality of application service management is improved; meanwhile, the application service is intelligently, efficiently and reliably managed, and the technical effects of service satisfaction and service experience of the application service are improved.
Example one
Referring to fig. 1, the present application provides an intelligent application service management method based on a micro service data architecture, wherein the method is applied to an intelligent application service management system based on a micro service data architecture, and the method specifically includes the following steps:
step S100: acquiring a plurality of cloud service nodes of a cloud platform end;
specifically, cloud server analysis is performed on a cloud platform end to obtain a plurality of cloud service nodes. The cloud platform end is a platform end which provides cloud computing service in the prior art. The plurality of cloud service nodes comprise a plurality of cloud servers at the cloud platform end. The cloud platform management method and the cloud platform management system achieve the technical effects of determining a plurality of cloud service nodes of the cloud platform end and laying a foundation for follow-up intelligent application service management.
Step S200: acquiring a plurality of businesses of a target application, wherein the target application is an application for providing business services for a plurality of target customers, and the businesses are based on a micro-service architecture and provide the business services for the target customers at a plurality of cloud service nodes and a local node;
further, as shown in fig. 2, step S200 of the present application further includes:
step S210: acquiring registration addresses of the target application in the cloud service nodes to acquire a plurality of registration addresses;
step S220: obtaining a plurality of cloud services according to the plurality of registration addresses;
step S230: acquiring a plurality of local services of the target application arranged on a local node;
step S240: and obtaining the plurality of services according to the plurality of cloud services and the plurality of local services.
Specifically, registration addresses of the target application in the cloud service nodes are collected, the registration addresses are obtained, cloud service collection is carried out on the cloud platform end according to the registration addresses, and a plurality of cloud services are obtained. Further, based on the target application, a local node is determined, then a plurality of local services set in the local node by the target application are determined, and a plurality of services of the target application are obtained by combining the plurality of cloud services. Wherein the target application comprises an application providing business services to a plurality of target customers using the intelligent application business management system based on the micro-service data architecture. The plurality of target clients includes a plurality of users using the target application. The plurality of registration addresses comprise a plurality of registration addresses corresponding to a plurality of target customers in a plurality of cloud service nodes. The plurality of cloud services comprise a plurality of cloud services provided by the cloud platform end to a plurality of registration addresses. The local node comprises a server of a target application. The plurality of local services comprise that the target application is arranged on the local node and provides a plurality of service services for a plurality of target clients. The plurality of services of the target application comprise a plurality of cloud services and a plurality of local services. The plurality of businesses are based on the micro-service architecture, and business services are provided for a plurality of target customers at a plurality of cloud service nodes and local nodes. The micro-service architecture is a new technology for creating and deploying business services around the business field, and the micro-service architecture can be used for independently developing and managing the business services, so that more efficient and convenient business services are provided for a plurality of target customers. The technical effects of determining a plurality of services of the target application by analyzing the service of the target application, and improving the comprehensiveness and the adaptability of service management of the target application are achieved.
Step S300: acquiring occupied internal memories of the plurality of services during operation to obtain a plurality of occupied internal memory information;
step S400: acquiring the operating memories of the cloud service nodes and the communication distances between the cloud service nodes and the target clients to obtain a plurality of operating memory information and a plurality of communication distance information;
specifically, based on a plurality of services, the method performs operation occupied memory acquisition to obtain a plurality of occupied memory information. And then, based on the cloud service nodes, acquiring the operation memory and the communication distance to obtain a plurality of operation memory information and a plurality of communication distance information. The plurality of services comprise a plurality of cloud services and a plurality of local services. The plurality of occupied memory information comprises a plurality of cloud service occupied memory information and a plurality of local service occupied memory information. The memory occupation information of the plurality of cloud services comprises a plurality of memory occupation information of a plurality of corresponding cloud service nodes when the plurality of cloud services run. The memory occupied by the local services comprises a plurality of memory occupied information of corresponding local nodes when the local services run. The running memory information comprises a plurality of running memories corresponding to the cloud service nodes. The plurality of communication distance information includes a plurality of communication distances between a plurality of cloud service nodes and a plurality of target customers. The technical effects of acquiring the operation memory occupation information of a plurality of services, acquiring the operation memory and communication distance information of a plurality of cloud service nodes, acquiring the operation memory information and communication distance information, and providing reliable data support for the subsequent service management of target applications are achieved.
Step S500: calculating the demand degrees of the plurality of services to obtain a plurality of demand degree information;
further, as shown in fig. 3, step S500 of the present application further includes:
step S510: acquiring a preset time period, acquiring the times of the plurality of services called by the plurality of target users in a plurality of preset time periods before, and acquiring a plurality of calling time information sets;
step S520: according to a preset calculation rule, calculating initial demand degrees of the services by adopting the plurality of calling frequency information sets to obtain a plurality of initial demand degree information;
further, step S520 in the present application further includes:
step S521: calculating the demand degrees of the plurality of services in the current preset period according to the plurality of calling time information sets to obtain a plurality of initial demand degree information, and calculating according to the following formula:
wherein, G i The demand degree of the jth service in the ith preset time period,
and alpha is weight, and is the calling frequency information of the jth service in the ith preset time period.
Specifically, based on a preset time period, the times of calling multiple services by multiple target users in the previous multiple preset time periods are collected, and multiple calling time information sets are obtained. And further, taking the plurality of calling time information sets as input information, inputting a preset calculation rule, calculating the plurality of calling time information sets according to the preset calculation rule, and outputting a plurality of pieces of initial demand degree information. In the preset calculation rule, G i Outputting a plurality of pieces of initial demand degree information, including the demand degree of the jth service in the ith preset time period;
the method comprises the steps that a plurality of input call frequency information sets comprise call frequency information of a jth service in an ith preset time period; α is a weight determined for the adaptation setting. Wherein the preset time period can be determined in a self-adaptive setting mode. The plurality of calling number information sets comprise a plurality of calling numbers of calling the plurality of services by a plurality of target users in a plurality of preset time periods. The initial demand degree information comprises a plurality of calling times through a preset calculation ruleAnd calculating the information set to obtain a plurality of initial demand degrees corresponding to a plurality of services.
Illustratively, the preset time period includes 1 week. The previous preset time periods include the previous week and the previous week of the previous week. The information sets of the calling times comprise the calling times a of the service A in the last week 1 (ii) a And the calling times of the service A in the last week of the last week is a 2 . α is 0.4. Calculating a plurality of calling time information sets according to a preset calculation rule, wherein the obtained initial demand information comprises the initial demand information a for the service A in the last week 1 (ii) a And the initial demand information for the service A in the last week of the last week is 0.4 × a 2 +0.6×a 1 。
The technical effect that the multiple calling frequency information sets are calculated according to the preset calculation rule to obtain the accurate multiple initial demand information is achieved, and therefore the accuracy of demand calculation on multiple services is improved.
Step S530: calculating a plurality of total calling times information of the plurality of services according to the plurality of calling times information sets;
step S540: according to the size of the information of the total calling times, carrying out weight distribution to obtain a first weight distribution result;
step S550: and performing weighted calculation on the plurality of initial demand degree information by adopting the first weight distribution result to obtain the plurality of demand degree information.
Specifically, based on a plurality of services, a plurality of call frequency information sets are respectively counted to obtain a plurality of total call frequency information, and weight distribution is performed according to the size of the plurality of total call frequency information to obtain a first weight distribution result. Further, weighting calculation is carried out on the obtained plurality of pieces of initial demand degree information according to the first weight distribution result, and a plurality of pieces of demand degree information are obtained. The information of the total calling times comprises the total calling times corresponding to each service in a plurality of services in a plurality of information sets of the calling times. The first weight distribution result comprises a plurality of total calling timesAnd the weight value corresponding to each total calling frequency information in the number information. The larger the total calling frequency information is, the larger the corresponding weight value is. Illustratively, the plurality of pieces of total call count information include total call count information b, total call count information c, and total call count information d. Then, the obtained first weight distribution result includes a weight value corresponding to the total call frequency information b
Weight value corresponding to total calling time information c>
Weight value corresponding to total calling time information d>
The plurality of demand degree information comprise a plurality of weighting calculation results obtained by performing weighting calculation on the obtained plurality of initial demand degree information according to the first weighting distribution result. The technical effects that a first weight distribution result is determined through a plurality of pieces of total calling frequency information, a plurality of pieces of initial demand information are subjected to weighted calculation according to the first weight distribution result, a plurality of pieces of accurate and reasonable demand information are obtained, and accordingly accuracy of service management on target application is improved are achieved.
Step S600: according to the plurality of occupied memory information, the plurality of cloud service nodes and the local node, carrying out random distribution management on the plurality of services to obtain a plurality of management schemes;
specifically, based on a plurality of occupied memory information, a plurality of businesses are randomly distributed to a plurality of cloud service nodes and local nodes. A plurality of management schemes is obtained. The management schemes comprise a plurality of random distribution modes of a plurality of businesses distributed to a plurality of cloud service nodes and local nodes. And, the plurality of management schemes satisfy a plurality of occupied memory information. The technical effects that based on a plurality of occupied memory information, a plurality of services are randomly distributed and managed to a plurality of cloud service nodes and local nodes, a plurality of management schemes are obtained, and the optimization and compaction foundation is carried out on the plurality of management schemes subsequently are achieved.
Step S700: and optimizing the management schemes according to the communication distance information and the demand degree information to obtain an optimal management scheme, and managing the target application.
Further, step S700 of the present application further includes:
step S710: randomly selecting a management scheme from the plurality of management schemes as a first management scheme;
step S720: calculating and obtaining a first management score of the first management scheme according to the communication distance information and the demand degree information;
further, step S720 of the present application further includes:
step S721: acquiring communication distance information between the nodes distributed by the services and the target clients in the first management scheme to acquire a plurality of first communication distance information sets;
step S722: according to the magnitude of the multiple demand degree information, carrying out weight distribution to obtain a second weight distribution result;
step S723: respectively carrying out weighted calculation and summation on the plurality of communication distance information in the plurality of first communication distance information sets by adopting the second weight distribution result to obtain total communication distance information;
step S724: and obtaining the first management score according to the total communication distance information.
Specifically, a first management scheme is obtained by performing random selection based on a plurality of management schemes. And further, in the first management scheme, the communication distance information among the plurality of cloud service nodes, the local nodes and the plurality of target clients is matched and collected, and a plurality of first communication distance information sets are obtained. Further, a second weight distribution result is obtained by carrying out weight distribution on the sizes of the plurality of demand degree information, the plurality of first communication distance information sets are weighted, calculated and summed respectively according to the second weight distribution result, total communication distance information is obtained, and then a first management score is determined. The larger the total communication distance information is, the smaller the corresponding first management score is. The first management scheme may be any one of a plurality of management schemes. The plurality of first communication distance information sets include communication distance information between a plurality of cloud service nodes and a plurality of target customers within a first management scheme, and communication distance information between a local node and the plurality of target customers within the first management scheme. The second weight distribution result comprises a weight value corresponding to each demand degree information in the plurality of demand degree information. The larger the demand degree information is, the larger the weight value corresponding to the demand degree information is in the obtained second weight distribution result. And the total communication distance information comprises a calculation result obtained by respectively carrying out weighted calculation summation on a plurality of first communication distance information sets according to a second weight distribution result. The technical effects that in a plurality of management schemes, random selection is carried out, the first management scheme is obtained, the accurate and reliable first management score is obtained through calculation, and the accuracy of the subsequently obtained optimal management scheme is improved are achieved.
Step S730: randomly adjusting the first management scheme by adopting a plurality of preset adjustment modes to construct a first neighborhood of the first management scheme, wherein the first neighborhood comprises a plurality of adjustment management schemes, and the adjustment management schemes are included in the management schemes;
step S740: calculating a plurality of adjustment management scores of the plurality of adjustment management schemes according to the plurality of communication distance information and the plurality of demand degree information;
step S750: obtaining a maximum value of the plurality of adjustment management scores as a second adjustment management score, and obtaining a corresponding second management scheme;
further, step S750 of the present application further includes:
step S751: adding a preset adjustment mode of the second management scheme obtained by adjustment into a taboo space, wherein the taboo space comprises a taboo iteration number;
step S752: and after the iteration optimization reaches the taboo iteration times, deleting the preset adjustment mode of the second management scheme obtained by adjustment from the taboo space.
Step S760: continuing to construct a second neighborhood of the second management scheme for iterative optimization;
step S770: and stopping optimizing until the iterative optimizing reaches a preset number, and outputting the final management scheme to obtain the optimal management scheme.
Specifically, the first management scheme is randomly adjusted based on a plurality of preset adjustment modes to obtain a first neighborhood. Further, based on the plurality of communication distance information and the plurality of demand degree information, a score calculation is performed on the plurality of adjustment management plans to obtain a plurality of adjustment management scores, and a maximum value screening is performed on the plurality of adjustment management scores, and a maximum value of the plurality of adjustment management scores is set as a second adjustment management score. And matching the second adjustment management score with a plurality of adjustment management schemes to obtain a second management scheme. And then, adding a preset adjusting mode corresponding to the second management scheme in the plurality of preset adjusting modes to the taboo space. The preset adjusting mode in the tabu space does not participate in the subsequent iteration optimization, namely, the neighborhood is constructed without using the preset adjusting mode in the tabu space when the subsequent iteration optimization is carried out. Further, a second neighborhood is constructed based on a second management scheme, iterative optimization is continued based on the obtained second neighborhood, when the iterative optimization times reach preset times, optimization is stopped, the final management scheme is output as an optimal management scheme, and the target application is managed according to the optimal management scheme. In addition, during iterative optimization, whether the iterative optimization times reach the taboo iterative times is judged, and when the iterative optimization times reach the taboo iterative times, the preset adjusting mode for adjusting the second management scheme is deleted from the taboo space.
The preset adjustment modes comprise a plurality of adjustment distribution modes which are preset and randomly distribute a plurality of services to a plurality of cloud service nodes and local nodes. The first neighborhood comprises a plurality of adjustment management schemes obtained by randomly adjusting the first management scheme according to a plurality of preset adjustment modes. And, the plurality of adjustment management schemes are included in a plurality of management schemes. The adjustment management scores are obtained in the same manner as the first management score, and for the sake of brevity of the description, the details are not repeated herein. The second adjustment management score is a maximum value of a plurality of adjustment management scores. The second management plan includes a regulation management plan corresponding to the second regulation management score among the plurality of regulation management plans. The taboo space comprises preset taboo iteration times, and in optimization iteration of the taboo iteration times, a neighborhood cannot be constructed by using a preset adjusting mode in the taboo space, so that the optimization is prevented from falling into local optimization. The second neighborhood is obtained in the same manner as the first neighborhood, and for the sake of brevity of the description, further description is omitted here. The preset times comprise a preset iteration optimizing time threshold value. The optimal management scheme comprises a corresponding management scheme when iterative optimization reaches a preset number of times. The technical effects of obtaining the accurate and high-adaptability optimal management scheme and improving the management quality of the application service by performing the preset times of iterative optimization on a plurality of management schemes are achieved.
In summary, the intelligent application service management method based on the micro service data architecture provided by the present application has the following technical effects:
1. acquiring a plurality of cloud service nodes through a cloud platform end; acquiring a plurality of occupied memory information by acquiring the occupied memory of a plurality of services of a target application during operation; the method comprises the steps of collecting operating memories of a plurality of cloud service nodes and communication distances between the operating memories and a plurality of target clients to obtain a plurality of operating memory information and a plurality of communication distance information; calculating the demand degrees of a plurality of services to obtain a plurality of demand degree information; according to the plurality of occupied memory information, the plurality of cloud service nodes and the local node, carrying out random distribution management on the plurality of services to obtain a plurality of management schemes; and optimizing the plurality of management schemes according to the plurality of communication distance information and the plurality of demand degree information to obtain an optimal management scheme, and managing the target application. The accuracy of application service management is improved, and the quality of application service management is improved; meanwhile, the application service is intelligently, efficiently and reliably managed, and the technical effects of service satisfaction and service experience of the application service are improved.
2. By analyzing the target application service, a plurality of services of the target application are determined, so that the comprehensiveness and the adaptability of service management on the target application are improved.
3. And determining a first weight distribution result according to the information of the total calling times, and performing weighted calculation on the initial demand information according to the first weight distribution result to obtain accurate and reasonable demand information, so that the accuracy of performing service management on the target application is improved.
Example two
Based on the method for managing the intelligent application service based on the micro service data architecture in the foregoing embodiment, the present invention also provides an intelligent application service management system based on the micro service data architecture, referring to fig. 4, where the system includes:
the cloud service node acquisition module 11 is used for acquiring a plurality of cloud service nodes of a cloud platform end;
a service obtaining module 12, where the service obtaining module 12 is configured to obtain multiple services of a target application, where the target application is an application providing service to multiple target clients, and the multiple services are based on a micro service architecture and provide service to the multiple target clients at the multiple cloud service nodes and the local node;
an occupied memory acquisition module 13, where the occupied memory acquisition module 13 is configured to acquire an occupied memory of the multiple services during operation, and obtain multiple pieces of occupied memory information;
the information acquisition module 14 is configured to acquire the operating memories of the cloud service nodes and the communication distances between the cloud service nodes and the target clients, and acquire a plurality of operating memory information and a plurality of communication distance information;
the demand degree calculation module 15 is configured to calculate demand degrees of the multiple services, and obtain multiple demand degree information;
a management scheme obtaining module 16, where the management scheme obtaining module 16 is configured to perform random allocation management on the multiple services according to the multiple pieces of occupied memory information, the multiple cloud service nodes, and the local node, so as to obtain multiple management schemes;
and the management module 17 is configured to optimize the multiple management schemes according to the multiple communication distance information and the multiple demand information, obtain an optimal management scheme, and manage the target application.
Further, the system further comprises:
a registration address obtaining module, configured to obtain registration addresses of the target application in the cloud service nodes, and obtain multiple registration addresses;
the cloud service acquisition module is used for acquiring a plurality of cloud services according to the plurality of registration addresses;
a local service acquisition module, configured to acquire a plurality of local services of the target application set in a local node;
the plurality of service determination modules are used for obtaining the plurality of services according to the plurality of cloud services and the plurality of local services.
Further, the system further comprises:
the calling number information acquisition module is used for acquiring a preset time period, acquiring the number of times of calling of the plurality of services by the plurality of target users in a plurality of preset time periods before, and acquiring a plurality of calling number information sets;
the initial demand information acquisition module is used for calculating the initial demand of the plurality of services by adopting the plurality of calling time information sets according to a preset calculation rule to obtain a plurality of pieces of initial demand information;
a total calling time information determining module, configured to calculate, according to the plurality of calling time information sets, a plurality of total calling time information of the plurality of services;
the weight distribution module is used for carrying out weight distribution according to the information of the total calling times to obtain a first weight distribution result;
and the demand degree information determining module is used for performing weighted calculation on the plurality of initial demand degree information by adopting the first weight distribution result to obtain the plurality of demand degree information.
Further, the system further comprises:
the initial demand degree calculation module is used for calculating the demand degrees of the services in the current preset period according to the calling time information sets to obtain a plurality of initial demand degree information, and the calculation is carried out according to the following formula:
wherein G is i The demand degree of the jth service in the ith preset time period,
and alpha is weight, and is the calling frequency information of the jth service in the ith preset time period.
Further, the system further comprises:
a first management scheme determination module configured to randomly select a management scheme among the plurality of management schemes as a first management scheme;
a first management score calculation module, configured to calculate a first management score of the first management scheme according to the plurality of communication distance information and the plurality of demand degree information;
a first neighborhood building module, configured to randomly adjust the first management scheme by using a plurality of preset adjustment modes to build a first neighborhood of the first management scheme, where the first neighborhood includes a plurality of adjustment management schemes, and the adjustment management schemes are included in the management schemes;
a plurality of adjustment management score obtaining modules configured to obtain a plurality of adjustment management scores for the plurality of adjustment management solutions by calculation according to the plurality of communication distance information and the plurality of demand degree information;
a second management plan obtaining module, configured to obtain a maximum value of the multiple adjustment management scores as a second adjustment management score, and obtain a corresponding second management plan;
the iterative optimization module is used for continuously constructing a second neighborhood of the second management scheme to carry out iterative optimization;
and the optimal management scheme obtaining module is used for stopping optimizing until the iterative optimizing reaches a preset number of times, and outputting the final management scheme to obtain the optimal management scheme.
Further, the system further comprises:
a first communication distance information obtaining module, configured to obtain communication distance information between the nodes allocated to the multiple services and the multiple target clients in the first management scheme, and obtain multiple first communication distance information sets;
a second weight distribution result obtaining module, configured to perform weight distribution according to the magnitude of the multiple pieces of demand degree information to obtain a second weight distribution result;
a total communication distance information obtaining module, configured to perform weighted calculation and summation on the multiple pieces of communication distance information in the multiple first communication distance information sets respectively by using the second weight distribution result, so as to obtain total communication distance information;
a first management score obtaining module, configured to obtain the first management score according to the total communication distance information.
Further, the system further comprises:
a first execution module, configured to add a preset adjustment mode for adjusting to obtain the second management scheme into a tabu space, where the tabu space includes a tabu iteration number;
and the second execution module is used for deleting the preset adjustment mode of the second management scheme obtained by adjustment from the taboo space after the iteration optimization reaches the taboo iteration number.
The application provides an intelligent application service management system based on a micro-service data architecture, which is used for executing an intelligent application service management method based on the micro-service data architecture, and the method comprises the following steps: acquiring a plurality of cloud service nodes through a cloud platform end; acquiring a plurality of occupied memory information by acquiring the occupied memory of a plurality of services of a target application during operation; the method comprises the steps of collecting operating memories of a plurality of cloud service nodes and communication distances between the operating memories and a plurality of target clients to obtain a plurality of operating memory information and a plurality of communication distance information; calculating the demand degrees of a plurality of services to obtain a plurality of demand degree information; according to the plurality of occupied memory information, the plurality of cloud service nodes and the local node, carrying out random distribution management on the plurality of services to obtain a plurality of management schemes; and optimizing the plurality of management schemes according to the plurality of communication distance information and the plurality of demand degree information to obtain an optimal management scheme, and managing the target application. The technical problem that management accuracy of application services is not enough in the prior art, and therefore management effects of the application services are not good is solved. The accuracy of application service management is improved, and the quality of application service management is improved; meanwhile, the application service is intelligently, efficiently and reliably managed, and the technical effects of service satisfaction and service experience of the application service are improved.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The specification and drawings are merely illustrative of the present application, and it is intended that the present invention cover modifications and variations of this invention provided they come within the scope of the invention and their equivalents.
Claims (8)
1. An intelligent application service management system based on micro service data architecture, the system comprising:
the cloud service node acquisition module is used for acquiring a plurality of cloud service nodes of a cloud platform end;
the service acquisition module is used for acquiring a plurality of services of a target application, wherein the target application is an application for providing service services for a plurality of target clients, and the plurality of services are based on a micro-service architecture and provide service services for the plurality of target clients at the plurality of cloud service nodes and the local node;
the memory occupation acquisition module is used for acquiring the memory occupation of the plurality of services during operation to obtain a plurality of memory occupation information;
the information acquisition module is used for acquiring the operating memories of the cloud service nodes and the communication distances between the cloud service nodes and the target clients to obtain a plurality of operating memory information and a plurality of communication distance information;
the demand degree calculation module is used for calculating the demand degrees of the plurality of services to obtain a plurality of demand degree information;
a management scheme obtaining module, configured to perform random allocation management on the multiple services according to the multiple pieces of occupied memory information, the multiple cloud service nodes, and the local node, so as to obtain multiple management schemes;
and the management module is used for optimizing the management schemes according to the communication distance information and the demand degree information to obtain an optimal management scheme and manage the target application.
2. The system of claim 1, comprising:
a registration address obtaining module, configured to obtain registration addresses of the target application in the multiple cloud service nodes, and obtain multiple registration addresses;
the cloud service acquisition module is used for acquiring a plurality of cloud services according to the plurality of registration addresses;
a local service acquisition module, configured to acquire a plurality of local services of the target application set in a local node;
the plurality of service determination modules are used for obtaining the plurality of services according to the plurality of cloud services and the plurality of local services.
3. The system of claim 1, comprising:
the calling number information acquisition module is used for acquiring a preset time period, acquiring the number of times of calling of the plurality of services by the plurality of target users in a plurality of preset time periods before, and acquiring a plurality of calling number information sets;
the initial demand information acquisition module is used for calculating the initial demand of the plurality of services by adopting the plurality of calling time information sets according to a preset calculation rule to obtain a plurality of initial demand information;
a total calling time information determining module, configured to calculate, according to the plurality of calling time information sets, a plurality of total calling time information of the plurality of services;
the weight distribution module is used for carrying out weight distribution according to the information of the total calling times to obtain a first weight distribution result;
and the demand degree information determining module is used for performing weighted calculation on the plurality of initial demand degree information by adopting the first weight distribution result to obtain the plurality of demand degree information.
4. The system of claim 3, comprising:
the initial demand degree calculation module is used for calculating the demand degrees of the services in the current preset period according to the calling time information sets to obtain a plurality of initial demand degree information, and the initial demand degree information is calculated according to the following formula:
wherein G is i The demand degree of the jth service in the ith preset time period,
and alpha is weight, and is the calling frequency information of the jth service in the ith preset time period.
5. The system of claim 1, comprising:
a first management scheme determining module configured to randomly select a management scheme among the plurality of management schemes as a first management scheme;
a first management score calculating module, configured to calculate a first management score of the first management scheme according to the plurality of communication distance information and the plurality of demand degree information;
a first neighborhood building module, configured to randomly adjust the first management scheme by using a plurality of preset adjustment modes to build a first neighborhood of the first management scheme, where the first neighborhood includes a plurality of adjustment management schemes, and the adjustment management schemes are included in the management schemes;
a plurality of adjustment management score obtaining modules configured to obtain a plurality of adjustment management scores for the plurality of adjustment management solutions by calculation according to the plurality of communication distance information and the plurality of demand degree information;
a second management plan obtaining module, configured to obtain a maximum value of the multiple adjustment management scores as a second adjustment management score, and obtain a corresponding second management plan;
an iterative optimization module, configured to continue building a second neighborhood of the second management solution for iterative optimization;
and the optimal management scheme obtaining module is used for stopping optimizing until the iterative optimizing reaches a preset number of times, and outputting the final management scheme to obtain the optimal management scheme.
6. The system of claim 5, comprising:
a first communication distance information obtaining module, configured to obtain communication distance information between the nodes allocated by the multiple services and the multiple target clients in the first management scheme, so as to obtain multiple first communication distance information sets;
a second weight distribution result obtaining module, configured to perform weight distribution according to the magnitude of the multiple pieces of demand degree information to obtain a second weight distribution result;
a total communication distance information obtaining module, configured to perform weighted calculation and summation on the plurality of communication distance information in the plurality of first communication distance information sets respectively by using the second weight distribution result, so as to obtain total communication distance information;
a first management score obtaining module, configured to obtain the first management score according to the total communication distance information.
7. The system of claim 5, further comprising:
a first execution module, configured to add a preset adjustment mode for adjusting to obtain the second management scheme into a tabu space, where the tabu space includes a tabu iteration number;
and the second execution module is used for deleting the preset adjustment mode of the second management scheme obtained by adjustment from the taboo space after the iteration optimization reaches the taboo iteration number.
8. An intelligent application service management method based on a micro service data architecture is applied to an intelligent application service management system based on the micro service data architecture, and the method comprises the following steps:
acquiring a plurality of cloud service nodes of a cloud platform end;
acquiring a plurality of businesses of a target application, wherein the target application is an application for providing business services for a plurality of target clients, and the businesses are based on a micro-service architecture and provide business services for the target clients at a plurality of cloud service nodes and local nodes;
acquiring occupied internal memories of the plurality of services during operation to obtain a plurality of occupied internal memory information;
acquiring the operating memories of the cloud service nodes and the communication distances between the cloud service nodes and the target clients to obtain a plurality of operating memory information and a plurality of communication distance information;
calculating the demand degrees of the plurality of services to obtain a plurality of demand degree information;
according to the plurality of occupied memory information, the plurality of cloud service nodes and the local node, carrying out random distribution management on the plurality of services to obtain a plurality of management schemes;
and optimizing the management schemes according to the communication distance information and the demand degree information to obtain an optimal management scheme, and managing the target application.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116471821A (en) * | 2023-06-19 | 2023-07-21 | 广州豪特节能环保科技股份有限公司 | Method, system, equipment and medium for dynamic control energy conservation of data center |
| CN116642262A (en) * | 2023-07-20 | 2023-08-25 | 博纳环境设备(太仓)有限公司 | Intelligent management method and system for heat recovery of industrial air conditioner |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116471821A (en) * | 2023-06-19 | 2023-07-21 | 广州豪特节能环保科技股份有限公司 | Method, system, equipment and medium for dynamic control energy conservation of data center |
| CN116471821B (en) * | 2023-06-19 | 2023-09-15 | 广州豪特节能环保科技股份有限公司 | Method, system, equipment and medium for dynamic control energy conservation of data center |
| CN116642262A (en) * | 2023-07-20 | 2023-08-25 | 博纳环境设备(太仓)有限公司 | Intelligent management method and system for heat recovery of industrial air conditioner |
| CN116642262B (en) * | 2023-07-20 | 2023-10-17 | 博纳环境设备(太仓)有限公司 | Intelligent management method and system for heat recovery of industrial air conditioner |
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