Middleware cloud service management and control system and method

Abstract

The invention discloses a middleware cloud service management and control system and method, and relates to the technical field of data processing, in which a parameter acquisition module is used for acquiring a scene parameter and a target middleware cloud service input by a user; the cloud service management and control module is used for determining a target telescopic scene according to the scene parameters and performing real-time operation monitoring on the target middleware cloud service; and the cloud service management and control module is also used for controlling the target telescopic scene to carry out resource management and control on the target middleware cloud service according to the operation monitoring result. According to the method and the device, all configurations of elastic scaling of the cloud service resources are encapsulated into the scaling scene in advance, so that after a user correspondingly inputs the scene parameters and the target middleware cloud service resources, the corresponding target scaling scene can be immediately determined according to the scene parameters; and cloud service automatic scaling adjustment is carried out based on the target scaling scene and the real-time operation monitoring result of the target middleware cloud service, so that the use cost of a user is remarkably reduced, and the operation and maintenance efficiency of the cloud service is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a middleware cloud service management and control system and method. Background Art

[0002] With the popularization of cloud computing technology, many cloud service providers have launched middleware cloud service elastic scaling solutions or services to help users automatically adjust cloud service computing resources according to business needs.

[0003] However, these traditional methods have the following shortcomings in implementation:

[0004] Traditional methods are often very complex when configuring the automatic scaling rules of middleware cloud services, requiring users to have advanced technical knowledge and an in-depth understanding of the underlying resource deployment architecture of middleware cloud services. This is costly and may even be difficult to operate for small businesses or teams with limited technical resources, resulting in low O&M efficiency.

[0005] Therefore, how to reduce the complexity of elastic scaling control of existing middleware cloud services and improve the efficiency of cloud service operation and maintenance has become an urgent problem to be solved. Summary of the invention

[0006] The main purpose of this application is to provide a middleware cloud service management and control system and method, aiming to solve the technical problem of how to reduce the complexity of elastic scaling management and control of existing middleware cloud services and improve the efficiency of cloud service operation and maintenance.

[0007] To achieve the above objectives, the present application proposes a middleware cloud service management and control system, which includes:

[0008] The parameter acquisition module is used to obtain the scene parameters and target middleware cloud services input by the user;

[0009] A cloud service management and control module, used to determine a target scaling scenario according to the scenario parameters, and to monitor the operation of the target middleware cloud service in real time;

[0010] The cloud service management and control module is further used to control the target scaling scenario to perform resource management and control on the target middleware cloud service according to the operation monitoring result.

[0011] In one embodiment, the cloud service management and control module includes: a scaling trigger submodule and a scaling control submodule;

[0012] The scaling trigger submodule is used to determine the target scaling scenario and scaling policy information according to the scenario parameters, and to perform real-time operation monitoring of the target middleware cloud service according to the scaling policy information;

[0013] The scaling trigger submodule is further configured to send scaling trigger information to the scaling control submodule when it is determined according to the operation monitoring result that the target middleware cloud service meets a preset trigger condition;

[0014] The scaling control submodule is configured to perform resource management and control on the target middleware cloud service according to the target scaling scenario when the scaling trigger information is received.

[0015] In one embodiment, the scaling trigger submodule is further configured to perform time verification on the target middleware cloud service when it is determined according to the operation monitoring result that the target middleware cloud service meets a preset trigger condition;

[0016] The scaling trigger submodule is further configured to send the scaling trigger information to the cloud service adjustment module when verification is passed.

[0017] In one embodiment, the telescoping trigger submodule includes an indicator platform;

[0018] The indicator platform is used to monitor the target operation indicator corresponding to the target middleware cloud service in real time according to the target monitoring dimension when the current service trigger type is indicator trigger;

[0019] The indicator platform is further used to determine that the target middleware cloud service meets the preset trigger condition when it is monitored that the operating indicator matches the scenario parameter.

[0020] In one embodiment, the telescopic trigger submodule further includes a timing trigger unit;

[0021] The timing trigger unit is used to monitor the running time of the target middleware cloud service in real time when the current service trigger type is a timing trigger;

[0022] The timing trigger unit is further configured to determine that the target middleware cloud service meets the preset trigger condition when monitoring that the running time of the target middleware cloud service reaches a preset timing period.

[0023] In one embodiment, the scaling control submodule includes a scaling configuration unit and a task scheduling unit;

[0024] The scaling configuration unit is configured to create a service adjustment request according to the current resource change operation corresponding to the target scaling scenario, and send the service adjustment request to the task scheduling unit;

[0025] The task scheduling unit is used to generate a target deployment task according to the service adjustment request, and adjust resources of the target middleware cloud service through the target deployment task.

[0026] In one embodiment, the task scheduling unit is further used to determine a target deployment template according to the service adjustment request;

[0027] The task scheduling unit is further used to generate the target deployment task based on the target deployment template.

[0028] In one embodiment, the system further includes: a cloud service docking module;

[0029] The cloud service docking module is used to obtain cloud service metadata information corresponding to the target middleware cloud service;

[0030] The task scheduling unit is further used to parse the service adjustment request to obtain service request resources;

[0031] The task scheduling unit is further used to perform a qualification review on the service request resource according to the cloud service metadata information, and generate the target deployment task when the review is passed.

[0032] In one embodiment, the cloud service docking module is further used to obtain the specification group configuration information corresponding to the target middleware cloud service;

[0033] The task scheduling unit is further used to adjust resources of the target middleware cloud service according to the specification group configuration information and the target deployment task.

[0034] In addition, to achieve the above purpose, the present application also proposes a middleware cloud service management and control method based on the above middleware cloud service management and control system, the method comprising:

[0035] The parameter acquisition module acquires the scenario parameters and target middleware cloud service input by the user;

[0036] The cloud service management and control module determines the target scaling scenario according to the scenario parameters, and performs real-time operation monitoring on the target middleware cloud service;

[0037] The cloud service management and control module controls the target scaling scenario to perform resource management and control on the target middleware cloud service according to the operation monitoring result.

[0038] The present application provides a middleware cloud service management and control system and method, the system comprising: a parameter acquisition module, used to obtain scenario parameters and a target middleware cloud service input by a user; a cloud service management and control module, used to determine a target scaling scenario according to the scenario parameters, and to perform real-time operation monitoring of the target middleware cloud service; the cloud service management and control module is also used to control the target scaling scenario according to the operation monitoring results to perform resource management on the target middleware cloud service.

[0039] The present application provides an automatic elastic scaling framework for cloud service resources. The present application encapsulates all configurations in the middleware cloud service automatic scaling process into a scaling scenario that is easy for users to understand and configure, so that when users need to achieve elastic scaling requirements, they only need to input the scenario parameters corresponding to the scaling scenario and the target middleware cloud service resources that need to be automatically scaled based on the parameter acquisition module. Then, the cloud service management and control module provided by the present application can determine the corresponding target scaling scenario according to the scenario parameters, and monitor the target middleware cloud service in real time, and then automatically scale the target middleware cloud service based on the target scaling scenario and the real-time operation monitoring results of the target middleware cloud service. Therefore, in the present application, if users need to achieve elastic scaling requirements, they only need to simply associate the scaling scenario with the cloud service resources applicable to the scenario, thereby significantly reducing the user's usage cost, and solving the problems of easy errors, low efficiency, and untimely response caused by manual monitoring and adjustment of the service instance scale in traditional cloud service management, and significantly improving the cloud service operation and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0042] Figure 1 This is a first structural block diagram of the first embodiment of the middleware cloud service management and control system of the present invention;

[0043] Figure 2 This is a second structural block diagram of the first embodiment of the middleware cloud service management and control system of the present invention;

[0044] Figure 3 This is a first structural block diagram of the second embodiment of the middleware cloud service management and control system of the present invention;

[0045] Figure 4 A second structural block diagram of the second embodiment of the middleware cloud service management and control system of the present invention;

[0046] Figure 5 This is a functional module interaction diagram of the second embodiment of the middleware cloud service management and control system of the present invention;

[0047] Figure 6 This is a flow chart of an embodiment of the middleware cloud service management and control method of the present application.

[0048] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0049] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0050] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0051] The main solution of this application is to propose an integrated middleware cloud service management and control system, which includes: a parameter acquisition module, which is used to obtain scenario parameters and target middleware cloud services input by users; a cloud service management and control module, which is used to determine the target scaling scenario according to the scenario parameters and perform real-time operation monitoring of the target middleware cloud service; the cloud service management and control module is also used to control the target scaling scenario according to the operation monitoring results to perform resource management on the target middleware cloud service.

[0052] Currently, configuring the automatic scaling rules of middleware cloud services is often very complex, requiring users to have advanced technical knowledge and an in-depth understanding of the underlying resource deployment architecture of middleware cloud services. This is costly and may even be difficult to operate for small businesses or teams with limited technical resources, resulting in low operation and maintenance efficiency.

[0053] In order to solve the above problems, the present application provides an automatic elastic scaling framework for cloud service resources. The present application encapsulates all configurations in the middleware cloud service automatic scaling process into a scaling scenario that is easy for users to understand and configure, so that when users need to achieve elastic scaling requirements, they only need to input the scenario parameters corresponding to the scaling scenario and the target middleware cloud service resources that need to be automatically scaled based on the parameter acquisition module. Then, the cloud service management and control module provided by the present application can determine the corresponding target scaling scenario according to the scenario parameters, and monitor the target middleware cloud service in real time, and then automatically scale the target middleware cloud service based on the target scaling scenario and the real-time operation monitoring results of the target middleware cloud service. Therefore, in the present application, if users need to achieve elastic scaling requirements, they only need to simply associate the scaling scenario with the cloud service resources applicable to the scenario, thereby significantly reducing the user's usage cost, and solving the problems of easy errors, low efficiency, and untimely response caused by manual monitoring and adjustment of the service instance scale in traditional cloud service management, and significantly improving the cloud service operation and maintenance efficiency.

[0054] The embodiment of the present invention provides a middleware cloud service management and control system, referring to Figure 1 , Figure 1 This is a first structural block diagram of the first embodiment of the middleware cloud service management and control system of the present invention. Figure 1As shown, in this embodiment, the middleware cloud service management and control system includes:

[0055] The parameter acquisition module is used to obtain the scene parameters and target middleware cloud services input by the user;

[0056] A cloud service management and control module, used to determine a target scaling scenario according to the scenario parameters, and to monitor the operation of the target middleware cloud service in real time;

[0057] The cloud service management and control module is further used to control the target scaling scenario to perform resource management and control on the target middleware cloud service according to the operation monitoring result.

[0058] It should be understood that in this embodiment, the above-mentioned middleware cloud service management and control system can be divided into the tenant side and the management side, and the two sides are configured separately without interfering with each other. The core of this application to reduce the complexity of users configuring the automatic elastic scaling of the middleware cloud service is to encapsulate the automatic elastic scaling related configurations of the middleware cloud service into "scaling scenarios" through the management side for the tenant side to configure and use, and the tenant side only needs to decide which elastic scaling scenarios to apply to which resources. Specifically, in this embodiment, the above-mentioned parameter acquisition module corresponds to the system configuration on the tenant side, and the above-mentioned cloud service management and control module corresponds to the system configuration on the management side.

[0059] It is understandable that in this embodiment, the parameter acquisition module can configure and display several scaling groups to the user. Each scaling group contains several scaling rules and encapsulates a specific scaling scenario and the target middleware cloud service resource composition applicable to the scenario. Therefore, the tenant can configure the scaling group to input the scenario parameters and the target middleware cloud service to achieve automatic elastic scaling of the target middleware cloud service.

[0060] In addition, in this embodiment, the parameter acquisition module can also support users to view the list of all managed resources under the scaling group, and support viewing the historical ARM deployment tasks, planned ARM deployment tasks and indicator trigger events of all scaling rules, so that tenants can clearly know the specific changes that will occur to resources after the scaling rules take effect. At the same time, the parameter acquisition module can also provide a quick configuration entry from the resource perspective, that is, after selecting a middleware cloud service resource, you can directly apply its applicable scaling rules or scaling scenarios to it. The configuration time can be shortened to seconds, further reducing the user's usage cost.

[0061] It should be noted that, in this embodiment, the above-mentioned scenario parameters may include the scenario identifier corresponding to the target scaling scenario, so that the cloud service control module can directly determine the corresponding target scaling scenario based on the scenario parameters input by the user, and the target scaling scenario encapsulates all configurations related to the corresponding automatic elastic scaling, including scaling trigger conditions and resource change operations to be performed. Therefore, in this embodiment, after determining the target middleware cloud service, the cloud service control module can monitor the real-time operation of the target middleware cloud service, and directly perform automatic elastic scaling resource control on the target middleware cloud service according to the operation monitoring results and the target scaling scenario.

[0062] Therefore, this embodiment can automatically elastically scale the target middleware cloud service according to the real-time operation monitoring results of the target middleware cloud service through the encapsulated target scaling scenario. Since the encapsulated target scaling scenario allows users to not care about the internal logical details of resource elastic scaling, it is easy for users to understand and use it directly, thereby reducing user usage costs; in addition, since the target scaling scenario is encapsulated, this embodiment can also avoid the problems of easy errors, low efficiency, and untimely response caused by manual monitoring and adjustment of service instance scale in traditional cloud service management, and significantly improve the efficiency of cloud service operation and maintenance.

[0063] Further, as an implementable embodiment, refer to Figure 2 , Figure 2 This is a second structural block diagram of the first embodiment of the middleware cloud service management and control system of the present invention. In this embodiment, the cloud service management and control module includes: a scaling trigger submodule and a scaling control submodule;

[0064] The scaling trigger submodule is used to determine the target scaling scenario and scaling policy information according to the scenario parameters, and to perform real-time operation monitoring of the target middleware cloud service according to the scaling policy information;

[0065] The scaling trigger submodule is further configured to send scaling trigger information to the scaling control submodule when it is determined according to the operation monitoring result that the target middleware cloud service meets a preset trigger condition;

[0066] The scaling control submodule is configured to perform resource management and control on the target middleware cloud service according to the target scaling scenario when the scaling trigger information is received.

[0067] It should be understood that the above-mentioned scaling policy information can be the scaling type corresponding to the target middleware cloud service, such as indicator triggering or timer triggering. That is, in this embodiment, an automatic elastic scaling policy based on indicator triggering and a timer automatic elastic scaling policy can be provided, and the scaling trigger submodule can perform different types of real-time operation monitoring on the target middleware cloud service based on the scaling policy information.

[0068] As an implementable method, in this embodiment, the telescoping trigger submodule includes an indicator platform;

[0069] The indicator platform is used to monitor the target operation indicator corresponding to the target middleware cloud service in real time according to the target monitoring dimension when the current service trigger type is indicator trigger;

[0070] The indicator platform is further used to determine that the target middleware cloud service meets the preset trigger condition when it is monitored that the operating indicator matches the scenario parameter.

[0071] It is easy to understand that in this embodiment, the middleware cloud service resources included in the elastic scaling service management will automatically report their own indicator data to the indicator platform at regular intervals (the default is to report once per minute). However, taking the Redis middleware cloud service as an example, this type of resource covers hundreds of operating indicators uploaded to the indicator platform, but not all indicators require the attention of the cloud service management and control module. In response to this problem, this embodiment can support users to independently configure the indicators that the target middleware cloud service automatically elastically scales based on the parameter acquisition module, that is, the above-mentioned target operating indicators, and only monitor the target operating indicators configured by the user, thereby reducing the complexity of indicator monitoring. The indicator platform will perform real-time calculations on the target operating indicators reported by the target middleware cloud service according to the indicator thresholds pre-configured by the service provider administrator in the target scaling scenario, and determine the operation monitoring results based on the real-time calculation results.

[0072] In addition, the existing indicator monitoring can generally only collect and aggregate indicators based on the instance as the dimension, while the present application supports cloud service administrators to configure the dimension of indicator aggregation calculation by themselves. Taking the Redis instance as an example, a Redis instance is often composed of multiple virtual machine nodes. When an exception occurs, only a certain node may be abnormal, and the indicators of the remaining nodes may be in a normal state. If you look at the overall indicator data of Redis, the indicators it outputs may be both normal and abnormal. If the program only monitors the duration of the abnormal indicator of the instance, misjudgment may occur. However, the present application can support cloud service custom configuration indicator aggregation dimensions, support indicator threshold configuration based on the instance ID of Redis and / or the ID of the virtual machine under Redis as the dimension, and can set more accurate instance status monitoring. Therefore, in this embodiment, the indicator platform can perform real-time operation monitoring of the target operation indicator corresponding to the target middleware cloud service according to the pre-custom configured indicator aggregation dimension, that is, the above-mentioned target monitoring dimension, when the current service trigger type is an indicator trigger.

[0073] It is easy to understand that when the indicator platform determines that the real-time operating parameters of the target middleware cloud service reach the trigger threshold set corresponding to the scenario parameters input by the user based on the real-time calculation results, it indicates that the indicator platform monitors that the operating indicators match the scenario parameters. The indicator platform can determine that the target middleware cloud service meets the preset trigger conditions and send scaling trigger information to the scaling control submodule to perform elastic scaling operations on the target middleware cloud service corresponding to the target scaling scenario.

[0074] As an implementable method, in this embodiment, the telescopic trigger submodule further includes a timing trigger unit;

[0075] The timing trigger unit is used to monitor the running time of the target middleware cloud service in real time when the current service trigger type is a timing trigger;

[0076] The timing trigger unit is further configured to determine that the target middleware cloud service meets the preset trigger condition when monitoring that the running time of the target middleware cloud service reaches a preset timing period.

[0077] It is understandable that in addition to the automatic elastic scaling method triggered by the above indicators, the present embodiment can also perform automatic elastic scaling triggered by timed triggering on the target middleware cloud service. Specifically, when the scenario of the timed automatic elastic expansion and contraction strategy is adopted, the present embodiment can support the user to configure the timed trigger frequency in the parameter acquisition module, including: single, daily cycle, weekly cycle and monthly cycle, so as to determine the preset timing period based on the timed trigger frequency. The timed trigger unit can start real-time monitoring of the running time of the target middleware cloud service when the current service trigger type is a timed trigger, and when it is monitored that the running time of the target middleware cloud service reaches the preset timing period, it determines that the target middleware cloud service has reached the preset trigger condition, and sends the scaling trigger information to the scaling control submodule to perform elastic scaling operations on the target middleware cloud service corresponding to the target scaling scenario.

[0078] As an implementable method, in this embodiment, the scaling trigger submodule is further used to perform time verification on the target middleware cloud service when it is determined according to the operation monitoring result that the target middleware cloud service reaches a preset trigger condition;

[0079] The scaling trigger submodule is further configured to send the scaling trigger information to the cloud service adjustment module when verification is passed.

[0080] It should be understood that in this embodiment, scaling time information such as indicator duration and executable time period can also be configured, and time verification is performed based on the scaling time information when determining that the target middleware cloud service has reached the preset trigger condition. Specifically, the scaling trigger submodule can determine that elastic scaling can be triggered only when the duration of the target middleware cloud service reaching the preset trigger condition is up to standard and within the executable time period, so as to avoid triggering during business peak hours or sensitive false triggering. Furthermore, in this embodiment, the above-mentioned scaling time information can also include a trigger cooling time to suppress frequent scaling operations.

[0081] In addition, this embodiment can also support approval configuration for each scenario before executing automatic scaling. The scenario and the approval process are in a many-to-many relationship, which has good flexibility.

[0082] In this implementation, all configurations in the middleware cloud service automatic scaling process are pre-encapsulated into scaling scenarios that are easy for users to understand and configure, thereby simplifying the configuration process and significantly reducing the technical requirements of users, so that small enterprises and teams with limited technical resources can also use it easily.

[0083] At the same time, this embodiment can perform rapid middleware cloud service elastic scaling based on modular target scaling scenarios, solve the problems of easy errors, low efficiency, and untimely response caused by manual monitoring and adjustment of service instance scale in traditional cloud service management, and significantly improve the cloud service operation and maintenance efficiency.

[0084] The present embodiment discloses a middleware cloud service management and control system, which includes a parameter acquisition module for acquiring scenario parameters and target middleware cloud services input by users; a cloud service management and control module for determining a target scaling scenario according to the scenario parameters and performing real-time operation monitoring on the target middleware cloud service; and the cloud service management and control module for controlling the target scaling scenario to perform resource management on the target middleware cloud service according to the operation monitoring result. Compared with the traditional middleware cloud service elastic scaling solution, the present embodiment simplifies the configuration process by pre-encapsulating all configurations in the middleware cloud service automatic scaling process into a scaling scenario that is easy for users to understand and configure, thereby significantly reducing the user's technical requirements, so that small enterprises and teams with limited technical resources can also use it easily; at the same time, the present embodiment can perform rapid middleware cloud service elastic scaling based on modular target scaling scenarios, solve the problems of easy errors, low efficiency, and untimely response caused by manual monitoring and adjustment of service instance scale in traditional cloud service management, and significantly improve the cloud service operation and maintenance efficiency.

[0085] Reference Figure 3 , Figure 3 This is a first structural block diagram of the second embodiment of the middleware cloud service management and control system of the present invention. Figure 1The illustrated embodiment provides a second embodiment of the middleware cloud service management and control system of the present invention.

[0086] In this embodiment, the scaling control submodule includes a scaling configuration unit and a task scheduling unit;

[0087] The scaling configuration unit is configured to create a service adjustment request according to the current resource change operation corresponding to the target scaling scenario, and send the service adjustment request to the task scheduling unit;

[0088] The task scheduling unit is used to generate a target deployment task according to the service adjustment request, and adjust resources of the target middleware cloud service through the target deployment task.

[0089] It should be understood that, in this embodiment, the scaling configuration unit may be pre-configured with several scaling scenarios, and the scaling trigger information sent by the scaling trigger submodule may include a scenario identifier corresponding to the target scaling scenario. Therefore, the scaling configuration unit may determine the target scaling scenario according to the scaling trigger information, and create a service adjustment request based on the specific resource scaling operation corresponding to the target scaling scenario, that is, the above-mentioned current resource change operation.

[0090] It is easy to understand that in this embodiment, the above-mentioned task scheduling unit may include an ARM (ACS Resource Manager) deployment task scheduling engine. ARM provides a set of general resource operation definition frameworks that can be used to create, update and delete resources in platform subscriptions. In this embodiment, the above-mentioned scaling configuration unit can be a caller of the unified ARMAPI provided by the task scheduling unit, so that the task scheduling unit can generate a corresponding ARM deployment task based on the service adjustment request, that is, the above-mentioned target deployment task. The target deployment task can be handed over to the ARM scheduling engine in the task scheduling module for processing, and then based on the target deployment task, a specific resource change operation is initiated for the target middleware cloud service, so as to realize the resource change operation of different cloud services triggered by the elastic scaling function corresponding to each scaling scenario. It is understandable that in this embodiment, ARM not only standardizes the definition of the specific operations of each cloud service on the cloud service management and control module, but also supports the orchestration and scheduling of multiple deployment tasks, further improving the operation and maintenance efficiency.

[0091] It should be noted that, based on the above description, this embodiment can register the resource change operation corresponding to each scaling scenario as an ARM type elastic scaling operation in the scaling configuration unit based on the existing resource change scheme of the cloud service. The scaling operation supports extension and can be expanded to HTTP synchronous interface call, HTTP asynchronous interface call, message push, etc.

[0092] In addition, when a resource is scaled, the input parameters required for the above resource changes can be determined and generated based on the current information, indicator data, etc. of the instance. Specifically, static configuration and dynamic configuration are supported for the input parameters in the scenario configuration. Among them, static configuration supports the cloud service administrator to configure the numerical values ​​and strings, that is, to set fixed trigger thresholds and scaling operations; dynamic configuration supports the administrator to configure parameter placeholders and write expressions, that is, to set the actual trigger thresholds and scaling operations according to the actual situation of the target middleware cloud service, that is, when a scaling activity occurs, the program can use the context data of the current resource state as the input parameter basis for data replacement and calculation. For example, when setting the target scenario of expanding the number of Redis instance connections, this embodiment supports expanding the number of connections to twice the number of current instance connections. At this time, the specific value of the number of connections needs to be determined based on the written expression and the current number of instance connections.

[0093] As an implementable method, in this embodiment, the task scheduling unit is further used to determine a target deployment template according to the service adjustment request;

[0094] The task scheduling unit is further used to generate the target deployment task based on the target deployment template.

[0095] It should be noted that in this embodiment, in order to avoid the ARM deployment task message being too large and reduce the operation and maintenance efficiency, the concept of ARM template is also introduced to simplify the input parameter configuration of the deployment task. Specifically, in this embodiment, the resource change operation corresponding to each scaling scenario can correspond to an ARM template, and the task scheduling unit only needs to fill in different parameters for the same or the same type of resource change operations for different resources to achieve corresponding scheduling. Therefore, in this embodiment, when receiving a service adjustment request, the task scheduling unit can call the ARM template corresponding to the service feature request, that is, the above-mentioned target deployment template, and obtain the adjustment input parameters corresponding to the target scaling scenario from the service adjustment request, so as to easily and quickly generate the corresponding target deployment task based on the adjustment input parameters and the target deployment template.

[0096] For example, if the current resource change operation is "When the number of Redis instance connections exceeds 95%, expand the number of connections to 4096", the target deployment template can be: "When the number of Redis instance connections exceeds A%, expand the number of connections to B". At this time, the task scheduling unit can directly adjust the parameters 95 and 4096 passed by the service, assign A to 95 and B to 4096, thereby quickly generating the target deployment task.

[0097] It is important to understand that the elastic scaling services currently provided by cloud service providers are usually applicable to IaaS cloud resources such as virtual machines. There is a lack of automatic elastic scaling solutions that support both IaaS and PaaS middleware cloud service resources. Most elastic scaling capabilities that support PaaS middleware cloud services are specially developed for the services, which increases the complexity of docking and cannot be extended to universal scenarios.

[0098] Therefore, in order to solve the above problems, as an implementable method, refer to Figure 4 , Figure 4 It is a second structural block diagram of the second embodiment of the middleware cloud service management and control system of the present invention. In this embodiment, the system further includes: a cloud service docking module;

[0099] The cloud service docking module is used to obtain cloud service metadata information corresponding to the target middleware cloud service;

[0100] The task scheduling unit is further used to parse the service adjustment request to obtain service request resources;

[0101] The task scheduling unit is further used to perform a qualification review on the service request resource according to the cloud service metadata information, and generate the target deployment task when the review is passed.

[0102] It should be understood that in response to the cloud service adaptation problem, this embodiment can provide a unified interface for different cloud service providers based on the above-mentioned cloud service docking module to manage various cloud service resources into the cloud service management and control system, so that each cloud service provider can reuse the unified specification management, quota management, indicator management, resource deployment task scheduling engine and other basic platform functions provided by the system, thereby realizing a unified automatic elastic scaling service without the need for special development, making cloud service management more efficient and economical.

[0103] In addition, in this embodiment, the cloud service docking module can not only realize the unified docking of various cloud services, but the cloud service docking module can also obtain cloud service metadata information such as cloud service specifications, quotas, applicable regions, etc. corresponding to each middleware cloud service. Therefore, on the one hand, this embodiment can realize metadata management services for all resource types managed by the system based on the cloud service docking module, including specification (SKU) management, region management, and quota management.

[0104] On the other hand, in this embodiment, after receiving the service adjustment request, the task scheduling unit can parse the request message, translate the various function methods in the request, parse the related resource dependencies, and obtain the service request resources. Then the task scheduling unit can obtain the cloud service metadata corresponding to the target middleware cloud service from the cloud service docking module to perform qualification review such as authentication, quota check, and compliance check on the service request resources. Finally, after the review is passed, the target deployment tasks corresponding to the service resource request are forwarded to the corresponding cloud services for actual resource change processing.

[0105] As an implementable method, in this embodiment, the cloud service docking module is further used to obtain the specification group configuration information corresponding to the target middleware cloud service;

[0106] The task scheduling unit is further used to adjust resources of the target middleware cloud service according to the specification group configuration information and the target deployment task.

[0107] It should be understood that in this embodiment, under special circumstances, the existing resource configuration of the target middleware cloud service, such as the remaining amount of CPU and memory, cannot meet the scenario parameters input by the user. At this time, the task scheduling unit can generate a new scaling group for resource management and control, and it is necessary to expand or reduce the resources of the target middleware cloud service. The above-mentioned specification group configuration information is the specification upgrade path for resource expansion and the specification reduction path for resource reduction corresponding to the target middleware cloud service. Therefore, the task scheduling unit can adjust the resources of the target middleware cloud service according to the specification group configuration information and the target deployment task.

[0108] In the specific implementation, taking the indicator triggering scenario "when the number of Redis instance connections exceeds 95%, the number of connections is expanded to 4096" as an example, the pre-configuration of the target scaling scenario in this embodiment is explained:

[0109] The new indicator is the percentage of Redis instance connections; the new operation is to expand the number of instance connections; in some scenarios, it is also necessary to add or reduce scaling groups, clarify the path of increasing or decreasing the specification group, and plan the configuration information of the specification group; finally, a new scenario is added: "When the number of Redis instance connections exceeds 95%, the number of connections is expanded to 4096", which defines the above configuration, specific instance percentage and the specific number of connections to be expanded in an indicator-triggered scaling scenario. Tenants can choose which middleware cloud services under which range to apply the above indicator-triggered scenario "When the number of Redis instance connections exceeds 95%, the number of connections is expanded to 4096". At this time, when the middleware cloud service management and control system detects that the number of Redis instance connections corresponding to the corresponding middleware cloud service that meets the indicator trigger scenario exceeds 95%, it will automatically create an elastic scaling task to expand the number of connections to 4096 for the corresponding middleware cloud service.

[0110] In summary, if Figure 5 As shown, Figure 5 This is a functional module interaction diagram of the second embodiment of the middleware cloud service management and control system of the present invention. In this embodiment, the overall functions of the middleware cloud service management and control system can be mainly divided into five parts according to the modules:

[0111] 1) The parameter acquisition module on the tenant side encapsulates Figure 5 The following table shows several specific scaling scenarios and target middleware cloud service resources applicable to the scenarios. Users only need to configure scaling groups to achieve automatic elastic scaling of middleware cloud services. Scaling groups contain several scaling rules, each of which is pre-configured by the management side for selection by users on the tenant side.

[0112] 2) The scaling trigger submodule located on the management side may include an indicator platform and a timing trigger unit (i.e. Figure 5 In this embodiment, the middleware cloud service resources included in the elastic scaling service management will automatically report their own indicator data to the indicator platform at regular intervals (the default is to report once per minute). The indicator platform will perform real-time calculations on the reported data based on the indicator thresholds configured by the service provider administrator in the scaling scenario, and determine whether the target middleware cloud service meets the indicator satisfaction conditions based on the calculation results, triggering the elastic scaling scenario; and the timing trigger unit can monitor the running time of the middleware cloud service to reach the preset timing period (or Figure 5 The elastic scaling is triggered when the timing trigger frequency in

[0113] 3) The scaling configuration unit on the management side: This embodiment proposes the core configuration of the system, corresponding to the configuration Figure 5 Each scaling scenario encapsulates all management-side configurations related to automatic elastic scaling, including trigger conditions, elastic scaling operations to be performed, and specific parameter construction logic for resource change operations (i.e. Figure 5 The encapsulated scaling scenarios not only do not require tenants to care about the internal details, but are also easy to understand and can be used directly.

[0114] 4) Task scheduling unit on the management side: Figure 5 The ARM deployment task scheduling engine in the scaling configuration unit and the resource change operations of different cloud services triggered by the elastic scaling function in the scaling configuration unit can all generate ARM deployment tasks with different parameters based on the ARM template, and hand them over to the ARM scheduling engine in the task scheduling unit for scheduling and execution according to priority. ARM not only standardizes the definition of specific operations of each cloud service on the cloud management platform, but also supports the orchestration and scheduling of multiple deployment tasks involved in the resource changes of middleware cloud services. At the same time, in order to avoid the ARM deployment task message being too large, the concept of ARM template is introduced to simplify the input parameter configuration;

[0115] 5) Cloud service docking module on the management side: mainly manages the specifications, quotas, and applicable regions of different cloud services.

[0116] In this implementation, the resource scheduling strategy is further optimized by deploying task templates, which improves resource utilization efficiency and effectively reduces operating costs. At the same time, this embodiment provides an easy-to-use and economical solution for cloud service management docking. This embodiment manages cloud services of different architectures of different cloud service providers into the system in a unified manner, thereby reusing the basic capabilities (such as resource authentication, quotas, specification management, etc.) and expansion capabilities (such as the target scaling scenario of this patent) provided by the system. As a result, cloud service providers do not need to develop and can quickly access, which not only improves service quality but also reduces operating costs, effectively solves the complexity and uncertainty of resource management in cloud environments, and further improves the efficiency of cloud service operation and maintenance.

[0117] This embodiment discloses a scaling control submodule including a scaling configuration unit and a task scheduling unit; the scaling configuration unit is used to create a service adjustment request according to the current resource change operation corresponding to the target scaling scenario, and send the service adjustment request to the task scheduling unit; the task scheduling unit is used to generate a target deployment task according to the service adjustment request, and adjust the resources of the target middleware cloud service through the target deployment task. The task scheduling unit is also used to determine the target deployment template according to the service adjustment request; the task scheduling unit is also used to generate a target deployment task based on the target deployment template. This embodiment further optimizes the resource scheduling strategy by deploying the task template, improves the resource utilization efficiency, and effectively reduces the operating cost.

[0118] In addition, in this embodiment, the system also includes: a cloud service docking module; a cloud service docking module for obtaining cloud service metadata information corresponding to the target middleware cloud service; a task scheduling unit for parsing the service adjustment request message to obtain the service request resource; the task scheduling unit for performing qualification review on the service request resource according to the cloud service metadata information, and generating a target deployment task when the review is passed. The cloud service docking module is also used to obtain the specification group configuration information corresponding to the target middleware cloud service; the task scheduling unit is also used to adjust the resources of the target middleware cloud service according to the specification group configuration information and the target deployment task. This embodiment provides an easy-to-use and economical solution for cloud service management docking. This embodiment manages cloud services of different architectures of different cloud service providers in a unified way to the system, thereby reusing the basic capabilities and expansion capabilities provided by the system. As a result, cloud service providers can quickly access without development, which not only improves the service quality, but also reduces the operating cost, effectively solves the complexity and uncertainty of resource management in the cloud environment, and further improves the cloud service operation and maintenance efficiency.

[0119] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0120] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0121] It should be noted that the execution subject of this embodiment can be a middleware cloud service management and control system, or a computing service device with data processing, network communication, trajectory drawing and program running functions, such as a tablet computer, a personal computer, etc., or a middleware cloud service management and control device capable of realizing the above functions, etc., and this embodiment does not specifically limit this. The following takes the middleware cloud service management and control system as an example of the execution subject to illustrate this embodiment and the following embodiments.

[0122] The embodiment of the present invention provides a middleware cloud service management and control method based on the middleware cloud service management and control system. Figure 4 , Figure 4 This is a schematic diagram of the first process of an embodiment of the middleware cloud service management and control method of the present application.

[0123] In this embodiment, the middleware cloud service management and control method includes steps S10 to S30:

[0124] Step S10, the parameter acquisition module acquires the scenario parameters and target middleware cloud service input by the user;

[0125] Step S20, the cloud service management and control module determines the target scaling scenario according to the scenario parameters, and performs real-time operation monitoring on the target middleware cloud service;

[0126] Step S30: the cloud service management and control module controls the target scaling scenario to perform resource management and control on the target middleware cloud service according to the operation monitoring result.

[0127] In a feasible implementation, in this embodiment, the cloud service management and control module includes: a scaling trigger submodule and a scaling control submodule; step S20 may include steps A1 to A2:

[0128] Step A1, the scaling trigger submodule determines a target scaling scenario and scaling policy information according to the scenario parameters, and performs real-time operation monitoring of the target middleware cloud service according to the scaling policy information;

[0129] Step A2, when the scaling trigger submodule determines that the target middleware cloud service meets a preset trigger condition according to the operation monitoring result, the scaling trigger submodule sends scaling trigger information to the scaling control submodule;

[0130] Accordingly, step S30 may include:

[0131] Step A3: When the scaling control submodule receives the scaling trigger information, it performs resource management and control on the target middleware cloud service according to the target scaling scenario.

[0132] In a feasible implementation manner, step A2 includes steps A21 to A22:

[0133] Step A21, when the scaling trigger submodule determines that the target middleware cloud service meets a preset trigger condition according to the operation monitoring result, the scaling trigger submodule performs a time check on the target middleware cloud service;

[0134] Step A22: When the scaling trigger submodule passes the verification, the scaling trigger submodule sends the scaling trigger information to the cloud service adjustment module.

[0135] In a feasible implementation manner, in this embodiment, the telescoping trigger submodule includes an indicator platform; before step A2, steps B1 to B2 may be included:

[0136] Step B1, when the current service trigger type is indicator trigger, the indicator platform monitors the target operation indicator corresponding to the target middleware cloud service in real time according to the target monitoring dimension;

[0137] Step B1: When the indicator platform detects that the operating indicator matches the scenario parameter, it determines that the target middleware cloud service meets the preset trigger condition.

[0138] In another feasible implementation manner, in this embodiment, the telescopic trigger submodule further includes a timing trigger unit; and steps B3 to B4 may be included before step A2:

[0139] Step B3, when the current service trigger type is a timing trigger, the timing trigger unit monitors the running time of the target middleware cloud service in real time;

[0140] Step B4: When the timing trigger unit monitors that the running time of the target middleware cloud service reaches a preset timing period, it determines that the target middleware cloud service meets the preset trigger condition.

[0141] In a feasible implementation, in this embodiment, the scaling control submodule includes a scaling configuration unit and a task scheduling unit; step A3 may include steps C1 to C2:

[0142] Step C1, the scaling configuration unit is used to create a service adjustment request according to the current resource change operation corresponding to the target scaling scenario, and send the service adjustment request to the task scheduling unit;

[0143] Step C2, the task scheduling unit is used to generate a target deployment task according to the service adjustment request, and adjust resources of the target middleware cloud service through the target deployment task.

[0144] In a feasible implementation manner, in this embodiment, step C2 may include steps C21 to C22:

[0145] Step C21, the task scheduling unit is further used to determine a target deployment template according to the service adjustment request;

[0146] Step C22: the task scheduling unit is further used to generate the target deployment task based on the target deployment template.

[0147] In a feasible implementation, in this embodiment, the system further includes: a cloud service docking module; in this embodiment, step C2 may also include steps D1 to D3:

[0148] Step D1, the cloud service docking module obtains cloud service metadata information corresponding to the target middleware cloud service;

[0149] Step D2, the task scheduling unit performs message parsing on the service adjustment request to obtain service request resources;

[0150] Step D3, the task scheduling unit performs a qualification review on the service request resource according to the cloud service metadata information, and generates the target deployment task when the review is passed.

[0151] In a feasible implementation, in this embodiment, the system further includes: a cloud service docking module; in this embodiment, step C2 may further include steps D4 to D5:

[0152] Step D4, the cloud service docking module obtains the specification group configuration information corresponding to the target middleware cloud service;

[0153] Step D5: the task scheduling unit adjusts resources of the target middleware cloud service according to the specification group configuration information and the target deployment task.

[0154] The middleware cloud service control method provided in this application is a system operation method proposed based on the middleware cloud service control system in the above embodiment, which can also solve the technical problem of improving the safety of CNC machining. Compared with the prior art, the beneficial effects of the middleware cloud service control method provided in this application are the same as the beneficial effects of the middleware cloud service control system provided in the above embodiment, and other technical features in the middleware cloud service control method are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0155] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0156] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A middleware cloud service management and control system, characterized in that: The system comprises: The parameter acquisition module is used to obtain the scene parameters and target middleware cloud services input by the user; A cloud service management and control module, used to determine a target scaling scenario according to the scenario parameters, and to monitor the operation of the target middleware cloud service in real time; The cloud service management and control module is further used to control the target scaling scenario to perform resource management and control on the target middleware cloud service according to the operation monitoring result.

2. The middleware cloud service management and control system according to claim 1, characterized in that: The cloud service management and control module includes: a scaling trigger submodule and a scaling control submodule; The scaling trigger submodule is used to determine the target scaling scenario and scaling policy information according to the scenario parameters, and to perform real-time operation monitoring of the target middleware cloud service according to the scaling policy information; The scaling trigger submodule is further configured to send scaling trigger information to the scaling control submodule when it is determined according to the operation monitoring result that the target middleware cloud service meets a preset trigger condition; The scaling control submodule is configured to perform resource management and control on the target middleware cloud service according to the target scaling scenario when the scaling trigger information is received.

3. The middleware cloud service management and control system according to claim 2, characterized in that: The scaling trigger submodule is further configured to perform time verification on the target middleware cloud service when it is determined according to the operation monitoring result that the target middleware cloud service meets a preset trigger condition; The scaling trigger submodule is further configured to send the scaling trigger information to the cloud service adjustment module when verification is passed.

4. The middleware cloud service management and control system according to claim 2, characterized in that: The telescoping trigger submodule includes an indicator platform; The indicator platform is used to monitor the target operation indicator corresponding to the target middleware cloud service in real time according to the target monitoring dimension when the current service trigger type is indicator trigger; The indicator platform is further used to determine that the target middleware cloud service meets the preset trigger condition when it is monitored that the operating indicator matches the scenario parameter.

5. The middleware cloud service management and control system according to claim 2, characterized in that: The telescopic trigger submodule also includes a timing trigger unit; The timing trigger unit is used to monitor the running time of the target middleware cloud service in real time when the current service trigger type is a timing trigger; The timing trigger unit is further configured to determine that the target middleware cloud service meets the preset trigger condition when monitoring that the running time of the target middleware cloud service reaches a preset timing period.

6. The middleware cloud service management and control system according to claim 2, characterized in that: The scaling control submodule includes a scaling configuration unit and a task scheduling unit; The scaling configuration unit is configured to create a service adjustment request according to the current resource change operation corresponding to the target scaling scenario, and send the service adjustment request to the task scheduling unit; The task scheduling unit is used to generate a target deployment task according to the service adjustment request, and adjust resources of the target middleware cloud service through the target deployment task.

7. The middleware cloud service management and control system according to claim 6, characterized in that: The task scheduling unit is further used to determine a target deployment template according to the service adjustment request; The task scheduling unit is further used to generate the target deployment task based on the target deployment template.

8. The middleware cloud service management and control system according to claim 7, characterized in that: The system also includes: a cloud service docking module; The cloud service docking module is used to obtain cloud service metadata information corresponding to the target middleware cloud service; The task scheduling unit is further used to parse the service adjustment request to obtain service request resources; The task scheduling unit is further used to perform a qualification review on the service request resource according to the cloud service metadata information, and generate the target deployment task when the review is passed.

9. The middleware cloud service management and control system according to claim 7, characterized in that: The cloud service docking module is further used to obtain the specification group configuration information corresponding to the target middleware cloud service; The task scheduling unit is further used to adjust resources of the target middleware cloud service according to the specification group configuration information and the target deployment task.

10. A middleware cloud service management and control method based on the middleware cloud service management and control system according to any one of claims 1 to 9, characterized in that: The method comprises: The parameter acquisition module acquires the scenario parameters and target middleware cloud service input by the user; The cloud service management and control module determines the target scaling scenario according to the scenario parameters, and performs real-time operation monitoring on the target middleware cloud service; The cloud service management and control module controls the target scaling scenario to perform resource management and control on the target middleware cloud service according to the operation monitoring result.

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