A hybrid cloud elastic scaling method, apparatus, device, and storage medium

Abstract

The present application discloses a hybrid cloud elastic scaling method, device, equipment, and storage medium, which relates to the field of cloud computing technology, including: monitoring the business nodes in the elastic scaling group to obtain performance data indicators of the business nodes; comparing the performance data indicators with a preset first threshold to obtain a corresponding comparison result; judging whether to trigger the scaling task based on the comparison result, and if it is judged that the scaling task is triggered, using a virtual machine to add a business node to complete the elastic scaling operation of the hybrid cloud. It can be seen that the present application realizes intelligent monitoring capabilities, and the monitoring system can monitor the performance data of the business nodes in the elastic scaling system in real time, and then choose whether to trigger the scaling task based on the performance and health status of the monitored instances in the scaling group, and after the scaling task is triggered, using a virtual machine to add a business node to complete the elastic scaling operation of the hybrid cloud, realizing monitoring access and unified elastic scheduling management of resources.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing technology, and in particular to a hybrid cloud elastic scaling method, device, equipment, and storage medium. Background Art

[0002] With the development of cloud computing, the business and workload demands of external service industries represented by e-commerce, games, and live broadcasting, especially those enterprises operating 2C businesses, are largely affected by external user traffic. Usually, the traffic of such businesses is basically stable, and the service needs can be well met by relying on private clouds or daily configured servers. However, during special times, such as the "Double 11" promotion, the launch of new games, and the National Day live broadcasting activities, a large number of external users will pour in, resulting in high concurrency that private clouds based on daily business scale are difficult to cope with, resulting in serious queues, reduced user experience, and lost users and business opportunities. From a cost perspective, due to the huge difference between business peak and trough traffic, it is too costly to deploy local servers at full capacity. Therefore, more and more users are beginning to use hybrid cloud load scheduling design to achieve a balance between cost and business needs.

[0003] Currently, public cloud vendors such as Alibaba and Tencent all provide public cloud elastic scaling services, and private clouds also have similar service capabilities. However, both public and private clouds are not flexible enough when faced with such elastic expansion scenarios. Taking public clouds as an example, the prices of public cloud service instances are generally expensive. For customers, it is sufficient to deploy daily services in private computer rooms. If they migrate to public clouds, they will have to pay additional public cloud service fees, which is costly. If they are only deployed in a private cloud environment, when enterprises cannot quickly add hardware devices to meet expansion needs, the purpose of peak load regulation cannot be achieved.

[0004] In summary, how to help enterprises quickly build flexible elastic scaling systems to reduce costs and increase efficiency and ensure service quality during peak visit periods is a technical problem that needs to be solved in this field. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a hybrid cloud elastic scaling method, device, equipment, and storage medium, which can help enterprises quickly build a flexible elastic scaling system to reduce costs and increase efficiency, and ensure service quality during peak access periods. The specific solution is as follows:

[0006] In a first aspect, the present application discloses a hybrid cloud elastic scaling method, comprising:

[0007] Monitor the service nodes in the elastic scaling group to obtain performance data indicators of the service nodes;

[0008] Comparing the performance data indicator with a preset first threshold to obtain a corresponding comparison result;

[0009] Based on the comparison result, it is determined whether a scaling task is triggered. If it is determined that the scaling task is triggered, a service node is added by using a virtual machine to complete the elastic scaling operation of the hybrid cloud.

[0010] Optionally, before determining whether to trigger a scaling task based on the comparison result, the method further includes:

[0011] The service node is used to pre-create a scaling task.

[0012] Optionally, the determining whether to trigger a scaling task based on the comparison result includes:

[0013] If the comparison result is that the node performance data indicator is greater than the preset first threshold, a target instance is created and deployed according to the scaling task and the preset scaling rule.

[0014] Optionally, the creating and deploying a target instance according to the scaling task and preset scaling rules includes:

[0015] Create and deploy target instances according to the pre-configured scaling tasks, instance quantity, instance type, and instance configuration information.

[0016] Optionally, the determining whether to trigger a scaling task based on the comparison result includes:

[0017] If the comparison result is that the node performance data indicator is less than the preset second threshold, the deployment instance is reduced according to the pre-configured preset scaling rule.

[0018] Optionally, the determining whether to trigger a scaling task based on the comparison result includes:

[0019] If the comparison result is that the node performance data indicator is between the preset first threshold and the preset second threshold, the scaling task is not triggered.

[0020] Optionally, the adding of service nodes by using virtual machines to complete the elastic scaling operation of the hybrid cloud includes:

[0021] Create a virtual machine and download the target script to execute the current elastic scaling activity;

[0022] Add the virtual machine that has downloaded the target script to the elastic scaling group to serve as a new business node to handle new business and complete the elastic scaling operation of the hybrid cloud.

[0023] In a second aspect, the present application discloses a hybrid cloud elastic scaling device, including:

[0024] A monitoring module is used to monitor the service nodes in the elastic scaling group to obtain performance data indicators of the service nodes;

[0025] A comparison module, used to compare the performance data indicator with a preset first threshold value to obtain a corresponding comparison result;

[0026] The scaling module is used to determine whether to trigger a scaling task based on the comparison result. If it is determined that the scaling task is triggered, a business node is added by using a virtual machine to complete the elastic scaling operation of the hybrid cloud.

[0027] In a third aspect, the present application discloses an electronic device, comprising:

[0028] Memory, used to store computer programs;

[0029] The processor is used to execute the computer program to implement the steps of the hybrid cloud elastic shrinking method disclosed above.

[0030] In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the hybrid cloud elastic shrinking method disclosed above are implemented.

[0031] It can be seen that the present application discloses a hybrid cloud elastic scaling method, including: monitoring the business nodes in the elastic scaling group to obtain the performance data indicators of the business nodes; comparing the performance data indicators with the preset first threshold to obtain the corresponding comparison result; judging whether to trigger the scaling task based on the comparison result, if it is judged that the scaling task is triggered, using a virtual machine to add a business node to complete the elastic scaling operation of the hybrid cloud. It can be seen that the present application realizes intelligent monitoring capabilities, and the monitoring system can monitor the performance data of the business nodes in the elastic scaling system in real time, and then choose whether to trigger the scaling task based on the performance and health status of the monitored instances in the scaling group, and after the scaling task is triggered, using a virtual machine to add a business node to complete the elastic scaling operation of the hybrid cloud, realizing monitoring access and unified elastic scheduling management of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention 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, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0033] Figure 1 A flow chart of a hybrid cloud elastic scaling method disclosed in this application;

[0034] Figure 2 A flow chart of a specific hybrid cloud elastic scaling method disclosed in this application;

[0035] Figure 3 This is another specific hybrid cloud elastic scaling method flow chart disclosed in this application;

[0036] Figure 4 A flow chart of an intelligent configuration management method disclosed in this application;

[0037] Figure 5 The present application discloses a hybrid cloud elastic scaling overall architecture;

[0038] Figure 6 This is a schematic diagram of the structure of a hybrid cloud elastic scaling device disclosed in this application;

[0039] Figure 7 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0041] With the development of cloud computing, the business and workload demands of external service industries represented by e-commerce, games, and live broadcasting, especially those enterprises operating 2C businesses, are largely affected by external user traffic. Usually, the traffic of such businesses is basically stable, and the service needs can be well met by relying on private clouds or daily configured servers. However, during special times, such as the "Double 11" promotion, the launch of new games, and the National Day live broadcasting activities, a large number of external users will pour in, resulting in high concurrency that private clouds based on daily business scale are difficult to cope with, resulting in serious queues, reduced user experience, and lost users and business opportunities. From a cost perspective, due to the huge difference between business peak and trough traffic, it is too costly to deploy local servers at full capacity. Therefore, more and more users are beginning to use hybrid cloud load scheduling design to achieve a balance between cost and business needs.

[0042] Currently, public cloud vendors such as Alibaba and Tencent all provide public cloud elastic scaling services, and private clouds also have similar service capabilities. However, both public and private clouds are not flexible enough when faced with such elastic expansion scenarios. Taking public clouds as an example, the prices of public cloud service instances are generally expensive. For customers, it is sufficient to deploy daily services in private computer rooms. If they migrate to public clouds, they will have to pay additional public cloud service fees, which is costly. If they are only deployed in a private cloud environment, when enterprises cannot quickly add hardware devices to meet expansion needs, the purpose of peak load regulation cannot be achieved.

[0043] To this end, the present application discloses a hybrid cloud elastic scaling solution that can help enterprises quickly build a flexible elastic scaling system to meet the needs of cost reduction and efficiency improvement and ensure service quality during peak access periods.

[0044] Reference Figure 1 As shown, an embodiment of the present invention discloses a hybrid cloud elastic scaling method, including:

[0045] Step S11: monitor the service nodes in the elastic scaling group to obtain performance data indicators of the service nodes.

[0046] In this embodiment, by using intelligent monitoring capabilities, the monitoring system can monitor the performance data of the business nodes in the elastic scaling group in real time, which may include but is not limited to: monitoring and recording the CPU load of the business node; obtaining performance data indicators of the business node, wherein there may be multiple business nodes in the elastic scaling group for processing received business requests in parallel, and in the business node, instances are used to process corresponding business requests. However, due to limited resources of business nodes, business nodes and corresponding instances that can handle normal business volumes are generally configured. However, due to the occurrence of some emergencies, high-concurrency business requests are suddenly added within a certain period of time. At this time, relying on the processing capabilities of existing business nodes to process business requests is bound to lead to crashes. Therefore, in order to avoid the above situation and to provide customers with a good experience, it is necessary to use intelligent monitoring capabilities to monitor the performance data in the business nodes in real time to avoid problems.

[0047] Step S12: Compare the performance data indicator with a preset first threshold to obtain a corresponding comparison result.

[0048] In this embodiment, the acquired performance data indicator is compared with a preset first threshold to obtain a result that the current performance data indicator is greater than the preset first threshold, less than the preset first threshold, or equal to the preset first threshold.

[0049] Step S13: determining whether a scaling task is triggered based on the comparison result; if the scaling task is triggered, using a virtual machine to increase a service node to complete the elastic scaling operation of the hybrid cloud.

[0050] In this embodiment, whether to trigger a scaling task is determined based on the above comparison result. The scaling task is pre-configured and may include: a scheduled task, a monitoring task, a trigger task, a custom task, etc. Whether to trigger a scaling task is determined based on the comparison result. The specific implementation method is as follows:

[0051] In a specific implementation, if the comparison result is that the node performance data index is greater than the preset first threshold, the target instance is created and deployed according to the scaling task and the preset scaling rule. It can be understood that when the virtual machine performance data index of the current business node reaches the preset first threshold, it means that the workload of the virtual machine of the current business node has been saturated, and other nodes are required to share the business needs of the current business node. Therefore, based on the pre-configured scaling task and the preset scaling rule, a new target instance is created and deployed on the public cloud or private cloud. Specifically, the target instance can be created and deployed according to the pre-configured scaling task, the number of instances, the instance type, and the instance configuration information. It can be seen that the content of the preset scaling rule includes information such as the number of instances, the type of instance, and the configuration of the instance. For example: if the cpu load of the virtual machine of the business node is set to 85%, it exceeds the business demand processing capacity of the current business node, that is, the preset first threshold is set to 85%. Therefore, when the cpu load of the current virtual machine of the business node is monitored to be 89%, the target instance is immediately created and deployed according to the scaling task and the preset scaling rule, and the number of created target instances is expanded.

[0052] In another specific implementation, if the comparison result is that the node performance data index is less than the preset second threshold, the deployment instance is reduced according to the pre-configured preset scaling rule. It can be understood that when the virtual machine performance data index of the current business node is less than the preset second threshold, it means that the virtual machine workload of the current business node is very small, not only does it not require other nodes to share the business needs of the current business node, but it is also possible to reduce the business instances in the current business node to reduce resource waste, for example: setting the CPU load of the business node virtual machine to less than 25%, that is, the current business demand is far lower than the business demand processing capacity of the current business node, then there is no need to create additional new business nodes in the public cloud or private cloud and reduce the number of instance creation, which is used to reduce the waste of resources caused by too many business nodes, that is, setting the preset second threshold to 25%, then the number of target instances created is reduced based on the preset scaling rule scaling task.

[0053] In another specific implementation, if the comparison result is that the node performance data index is between the preset first threshold and the preset second threshold, the scaling task is not triggered. It can be understood that when the CPU load of the virtual machine of the business node is within the normal range, it means that the business demand of the current business node is in a state of equilibrium with the processing capacity of the business node. At this time, the processing capacity of the business node is greatly utilized, the resource waste of the business node is reduced, and the processing of the business demand is not affected. Therefore, there is no need to trigger the scaling task, and there is no need to increase or decrease the target instance. For example, when the virtual machine performance data index of the business node is 60%, between the preset second threshold of 25% and the preset second threshold of 85%, it belongs to the normal working state of the business node. In order to reduce the waste of node resources, it is not necessary to create a target instance on the public cloud or private cloud. It should be noted that the relationship between the preset second threshold and the preset first threshold in this application is that the preset second threshold is less than the preset first threshold. It can be seen that the present application realizes the elastic scaling of the business node by monitoring the performance data indicator information of the business node in the elastic scaling group and controlling the increase or decrease of the created target instance.

[0054] It can be seen that the present application discloses a hybrid cloud elastic scaling method, including: monitoring the business nodes in the elastic scaling group to obtain the performance data indicators of the business nodes; comparing the performance data indicators with the preset first threshold to obtain the corresponding comparison result; judging whether to trigger the scaling task based on the comparison result, if it is judged that the scaling task is triggered, using a virtual machine to add a business node to complete the elastic scaling operation of the hybrid cloud. It can be seen that the present application realizes intelligent monitoring capabilities, and the monitoring system can monitor the performance data of the business nodes in the elastic scaling system in real time, and then choose whether to trigger the scaling task based on the performance and health status of the monitored instances in the scaling group, and after the scaling task is triggered, using a virtual machine to add a business node to complete the elastic scaling operation of the hybrid cloud, realizing monitoring access and unified elastic scheduling management of resources.

[0055] Reference Figure 2 As shown, the embodiment of the present invention discloses a specific hybrid cloud elastic shrinking method. Compared with the previous embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically:

[0056] Step S21: monitor the service nodes in the elastic scaling group to obtain performance data indicators of the service nodes.

[0057] Step S22: Compare the performance data indicator with a preset first threshold to obtain a corresponding comparison result.

[0058] For more detailed processing procedures in step S21 and step S22, please refer to the aforementioned disclosed embodiments, which will not be described in detail here.

[0059] Step S23: Pre-create a scaling task using the service node.

[0060] In this embodiment, there are business bearing nodes in the elastic scaling group, and scaling trigger tasks are created. The tasks include scheduled tasks, monitoring tasks, trigger tasks, and custom tasks. The monitoring tasks will regularly monitor the performance data information of the load nodes. When the trigger threshold is detected, the scaling tasks will be executed, and the scaling activities will be performed according to the scaling specifications configured by the user. The scaling activities will trigger the creation of virtual machines or containers. The created virtual machines will be automatically configured with business information and added to the scaling group to carry business traffic.

[0061] Step S24: determining whether the scaling task is triggered based on the comparison result; if the scaling task is triggered, using a virtual machine to increase a service node to complete the elastic scaling operation of the hybrid cloud.

[0062] In this embodiment, it is determined whether to trigger the scaling task based on the comparison result. If it is determined that the scaling task is triggered, a business node is added using a virtual machine. It can be understood that if the scaling task is triggered based on the comparison result, additional business nodes need to be added. In the process of adding business nodes, according to the intelligent configuration management capability, since many business systems are generally deployed in clusters, the clustered deployment mode can effectively reduce single-point bottlenecks and improve business access capabilities. However, after the elastic scaling system automatically creates a node, it is necessary to configure the node to access the business cluster. The configuration may include adding IP addresses, adding database nodes, dynamic routing configuration, etc. In this embodiment, flexible integration capabilities are also provided. Users can build-in expansion scripts. When a node is added, the node information will be automatically configured to join the cluster, reducing the complexity of manual configuration.

[0063] It can be seen that monitoring is a relatively core part of the entire system. The monitoring module deploys telegraf scripts inside each virtual machine and collects corresponding indicators according to business characteristics, including the CPU and memory usage of the virtual machine, including network traffic statistics, and hard disk access statistics, and pushes them to the influxdb time series database. The monitoring module compares the threshold information configured in advance to determine whether the threshold is triggered. If the threshold is triggered, the monitoring module will notify the scaling group to perform the corresponding scaling activities. It supports intelligent monitoring. Compared with ordinary monitoring, in addition to the basic collection function, this intelligent monitoring also adds data processing and analysis capabilities. It can analyze the collected data and provide data analysis services based on specific scenarios, which is more intelligent.

[0064] Reference Figure 3As shown, the embodiment of the present invention discloses a specific hybrid cloud elastic shrinking method. Compared with the previous embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically:

[0065] Step S31: monitor the service nodes in the elastic scaling group to obtain performance data indicators of the service nodes.

[0066] Step S32: Compare the performance data indicator with a preset first threshold to obtain a corresponding comparison result.

[0067] For more detailed processing procedures in step S31 and step S32, please refer to the aforementioned disclosed embodiments, which will not be described in detail here.

[0068] Step S33: Based on the comparison result, determine whether the scaling task is triggered. If it is determined that the scaling task is triggered, create a virtual machine and download the target script for executing the current elastic scaling activity; add the virtual machine that has downloaded the target script to the elastic scaling group to serve as a new business node to handle the new business, thereby completing the elastic scaling operation of the hybrid cloud.

[0069] In this embodiment, refer to Figure 4 As shown, the execution of the scaling activity will first pull the virtual machine template or container image through the image module, and then automatically start the virtual machine creation process. After the virtual machine is successfully created, the configuration script download action is automatically executed. The script needs to be configured in advance and written according to the corresponding business. Then the script starts to execute. The execution content may include business startup, configuration information delivery, etc. After the business configuration is completed, the scaling group is notified to join the scaling group and automatically start carrying business traffic.

[0070] This embodiment provides flexible network link scheduling capabilities, that is, network access capabilities. In terms of business traffic distribution, it is necessary to dispatch traffic to different entrances when the business increases. This process is mainly completed by the cooperation of intelligent DNS resolution and SLB (load balancing). Among them, intelligent DNS distributes the access requests of application services to the public cloud or private cloud according to information such as traffic and region; SLB is responsible for balancing the load between multiple instances, and flexibly increases the back-end instances in conjunction with elastic scaling tasks to cope with business peaks. It also provides strong scalability. For containerized deployed businesses, you can also choose to deploy the extended nodes in containers to improve the efficiency and responsiveness of elastic scaling.

[0071] Reference Figure 5As shown, an embodiment of the present invention also discloses a specific overall architecture of hybrid cloud scaling adjustment. The intelligent elastic scaling architecture based on hybrid cloud computing can be specifically divided into four parts: intelligent monitoring, intelligent computing services, IOT access capabilities, and security audit capabilities. First, the performance indicators of the business bearing nodes in the elastic scaling group are monitored, and the monitored conditions are compared with the preset thresholds. After the creation of the scaling task is selected based on the comparison results, the scheduled tasks, monitoring tasks, trigger tasks, and custom tasks in the scaling task group are created. After the above-mentioned scaling tasks are created, the scaling scheduling module is used to perform the following steps: 1. Obtain scaling rules; 2. Create scaling activities; When the scaling nodes are configured based on the scaling rules, and then the scaling activities are created, the target instances are created, and then the virtual machines / containers are created. In the process of creating virtual machines or containers, the virtual machine template or container image is first pulled through the image module, and then the virtual machine creation process is automatically started. After the virtual machine is successfully created, the download configuration script action is automatically executed. The script needs to be configured in advance and the script needs to be written according to the corresponding business. Then the script starts to execute. The execution content may include business startup, configuration information delivery, etc. After the business configuration is completed, the newly generated business node is notified to the scaling group, added to the scaling group and automatically starts to carry business traffic. In addition, this application also provides intelligent computing services, which can provide flexible elastic scaling services. Users can configure whether to prioritize public clouds or private clouds. When elastic scheduling is triggered, the appropriate resource pool will be selected to create virtual machines based on the priority. After the virtual machine is created, the business attributes will be automatically configured and added to the scaling group to improve the elastic scaling efficiency and corresponding capabilities of the application. For example, when the business needs to expand multiple applications in a very short time, containerized application deployment can be used to achieve low-cost, second-level capacity expansion in a hybrid cloud environment; when there are significant differences in the access pressure faced by different processes in business applications, for example: when the concurrency requirements for access and ordering are quite different, the application can be split and transformed, divided into different microservices, etc., so that they can be deployed on both the public cloud and the private cloud, respectively, while achieving flexibility and stability. In addition, the security audit function can record all elastic scaling operations and save the records to facilitate later security audits.

[0072] It can be seen that the present application provides flexible network link scheduling to reasonably schedule the surging business traffic to different entrances for access when there is a surge in business flow, to cooperate with elastic scaling tasks, to flexibly adjust the back-end instances, and to cope with the surge in business. It also provides strong scalability. For containerized deployed businesses, the expanded business nodes will be selected for container deployment, thereby improving the efficiency and responsiveness of elastic scaling, and realizing capabilities such as automatic traffic switching and security auditing through intelligent hybrid cloud elastic scaling methods.

[0073] Reference Figure 6 As shown, the embodiment of the present invention also discloses a hybrid cloud elastic scaling device, including:

[0074] The monitoring module 11 is used to monitor the service nodes in the elastic scaling group to obtain performance data indicators of the service nodes;

[0075] A comparison module 12, used to compare the performance data indicator with a preset first threshold value to obtain a corresponding comparison result;

[0076] The scaling module 13 is used to determine whether to trigger a scaling task based on the comparison result. If it is determined that the scaling task is triggered, a business node is added by using a virtual machine to complete the elastic scaling operation of the hybrid cloud.

[0077] The scaling module 13 can be used to determine whether to trigger a scaling task based on the above comparison result. The scaling task is pre-configured and can include: a scheduled task, a monitoring task, a trigger task, a custom task, etc. Whether the scaling task is triggered is determined based on the comparison result. The specific implementation method is as follows: In a specific implementation method, if the comparison result is that the node performance data index is greater than the preset first threshold, the target instance is created and deployed according to the scaling task and the preset scaling rule. It can be understood that when the virtual machine performance data index of the current business node reaches the preset first threshold, it means that the virtual machine workload of the current business node is saturated, and other nodes are required to share the business needs of the current business node. Therefore, based on the pre-configured scaling task and the preset scaling rule, a new target instance is created and deployed on the public cloud or private cloud. Specifically, the target instance can be created and deployed according to the pre-configured scaling task, the number of instances, the type of instance, and the instance configuration information. It can be seen that the content of the preset scaling rule includes information such as the number of instances, the type of instance, and the configuration of the instance. For example: if the CPU load of the virtual machine of the service node reaches 85%, it means that the service demand processing capacity of the current service node is exceeded, that is, the preset first threshold is set to 85%. Therefore, when the CPU load of the current virtual machine of the service node is monitored to be 89%, the target instance is immediately created and deployed according to the scaling task and the preset scaling rule, and the number of created target instances is increased. In another specific implementation, if the comparison result is that the node performance data indicator is less than the preset second threshold, the deployment instance is reduced according to the pre-configured preset scaling rule. It is understandable that when the virtual machine performance data index of the current business node is less than the preset second threshold, it means that the workload of the virtual machine of the current business node is very small, and not only does it not require other nodes to share the business needs of the current business node, but it is also possible to reduce the business instances in the current business node to reduce resource waste. For example, the CPU load of the business node virtual machine is set to be lower than 25%, that is, the current business demand is far lower than the business demand processing capacity of the current business node, then there is no need to create additional new business nodes in the public cloud or private cloud and reduce the number of instance creation, which is used to reduce the waste of resources caused by too many business nodes, that is, the preset second threshold is set to 25%, then the number of target instances created is reduced based on the preset scaling rule scaling task. In another specific implementation, if the comparison result is that the node performance data index is between the preset first threshold and the preset second threshold, the scaling task is not triggered.It is understandable that when the CPU load of the virtual machine of the business node is within the normal range, it means that the business demand of the current business node is in a state of equilibrium with the processing capacity of the business node. At this time, the processing capacity of the business node is greatly utilized, the resource waste of the business node is reduced, and the processing of business needs is not affected. Therefore, there is no need to trigger the scaling task, and there is no need to increase or decrease the target instance. For example, when the performance data index of the virtual machine of the business node is 60%, between the preset second threshold of 25% and the preset second threshold of 85%, it belongs to the normal working state of the business node. In order to reduce the waste of node resources, it is not necessary to create a target instance on the public cloud or private cloud. It should be noted that the relationship between the preset second threshold and the preset first threshold in this application is that the preset second threshold is less than the preset first threshold. It can be seen that this application realizes the elastic scaling of the business node by monitoring the performance data indicator information of the business node in the elastic scaling group and controlling the increase or decrease of the created target instance.

[0078] It can be seen that the present application discloses a hybrid cloud elastic scaling method, including: monitoring the business nodes in the elastic scaling group to obtain the performance data indicators of the business nodes; comparing the performance data indicators with the preset first threshold to obtain the corresponding comparison result; judging whether to trigger the scaling task based on the comparison result, if it is judged that the scaling task is triggered, using a virtual machine to add a business node to complete the elastic scaling operation of the hybrid cloud. It can be seen that the present application realizes intelligent monitoring capabilities, and the monitoring system can monitor the performance data of the business nodes in the elastic scaling system in real time, and then choose whether to trigger the scaling task based on the performance and health status of the monitored instances in the scaling group, and after the scaling task is triggered, using a virtual machine to add a business node to complete the elastic scaling operation of the hybrid cloud, realizing monitoring access and unified elastic scheduling management of resources.

[0079] In some specific embodiments, the telescopic module 13 may specifically include:

[0080] The task creation unit is used to create a scaling task in advance using the service node.

[0081] In some specific embodiments, the telescopic module 13 may specifically include:

[0082] The instance deployment submodule is used to create and deploy a target instance according to the scaling task and the preset scaling rule if the comparison result is that the node performance data indicator is greater than the preset first threshold.

[0083] In some specific embodiments, the instance deployment submodule may specifically include:

[0084] The instance deployment unit is used to create and deploy target instances according to the pre-configured scaling tasks, instance quantity, instance type, and instance configuration information.

[0085] In some specific embodiments, the telescopic module 13 may specifically include:

[0086] The instance reduction unit is configured to reduce the deployment instance according to a pre-configured preset scaling rule if the comparison result is that the node performance data indicator is less than a preset second threshold.

[0087] In some specific embodiments, the telescopic module 13 may specifically include:

[0088] The task triggering unit is configured to not trigger the scaling task if the comparison result is that the node performance data indicator is between a preset first threshold and a preset second threshold.

[0089] In some specific embodiments, the telescopic module 13 may specifically include:

[0090] The scaling operation unit is used to create a virtual machine and download the target script for executing the current elastic scaling activity; the virtual machine that has downloaded the target script is added to the elastic scaling group to serve as a new business node to handle the new business, thereby completing the elastic scaling operation of the hybrid cloud.

[0091] Furthermore, the present application also discloses an electronic device. Figure 7 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of use of the present application.

[0092] Figure 7 A schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the hybrid cloud elastic scaling method disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0093] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0094] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0095] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0096] Among them, the operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20 to realize the operation and processing of the massive data 223 in the memory 22 by the processor 21, which can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program that can be used to complete the hybrid cloud elastic scaling method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks. In addition to data transmitted from an external device received by the electronic device, the data 223 can also include data collected by its own input and output interface 25.

[0097] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the hybrid cloud elastic scaling method disclosed above is implemented. For the specific steps of the method, reference can be made to the corresponding contents disclosed in the above embodiments, and no further description will be given here.

[0098] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0099] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. The steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be directly implemented with a software module executed by a hardware or processor, or a combination of the two. The software module can be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0100] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0101] The above is a detailed introduction to a hybrid cloud elastic scaling method, device, equipment, and storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A hybrid cloud elastic scaling method, characterized in that: include: Monitor the service nodes in the elastic scaling group to obtain performance data indicators of the service nodes; Comparing the performance data indicator with a preset first threshold to obtain a corresponding comparison result; Based on the comparison result, it is determined whether a scaling task is triggered. If it is determined that the scaling task is triggered, a service node is added by using a virtual machine to complete the elastic scaling operation of the hybrid cloud; The method of using a virtual machine to add a service node to complete the elastic scaling operation of the hybrid cloud includes: When executing a scaling activity, the image module will first pull the virtual machine template or container image, and then automatically start the process of creating a virtual machine. After the virtual machine is successfully created, it will download the target script for executing the current elastic scaling activity; the virtual machine that has downloaded the target script will be added to the elastic scaling group, and the script will start to execute the business startup and the configuration information will be sent. After the business configuration is completed, the scaling group will be notified to join the scaling group and automatically start to carry business traffic, so as to process the new business as a new business node, and complete the elastic scaling operation of the hybrid cloud.

2. The hybrid cloud elastic scaling method according to claim 1, characterized in that: Before determining whether to trigger a scaling task based on the comparison result, the method further includes: The service node is used to pre-create a scaling task.

3. The hybrid cloud elastic scaling method according to claim 2, characterized in that: The determining whether to trigger a scaling task based on the comparison result includes: If the comparison result is that the node performance data indicator is greater than the preset first threshold, a target instance is created and deployed according to the scaling task and the preset scaling rule.

4. The hybrid cloud elastic scaling method according to claim 3, characterized in that: The creating and deploying the target instance according to the scaling task and the preset scaling rule includes: Create and deploy target instances according to the pre-configured scaling tasks, instance quantity, instance type, and instance configuration information.

5. The hybrid cloud elastic scaling method according to claim 1, characterized in that: The determining whether to trigger a scaling task based on the comparison result includes: If the comparison result is that the node performance data indicator is less than the preset second threshold, the deployment instance is reduced according to the pre-configured preset scaling rule.

6. The hybrid cloud elastic scaling method according to claim 1, characterized in that: The determining whether to trigger a scaling task based on the comparison result includes: If the comparison result is that the node performance data indicator is between the preset first threshold and the preset second threshold, the scaling task is not triggered.

7. A hybrid cloud elastic scaling device, characterized in that: include: A monitoring module is used to monitor the service nodes in the elastic scaling group to obtain performance data indicators of the service nodes; A comparison module, used to compare the performance data indicator with a preset first threshold value to obtain a corresponding comparison result; A scaling module, configured to determine whether to trigger a scaling task based on the comparison result, and if it is determined that the scaling task is triggered, to increase a service node by using a virtual machine to complete an elastic scaling operation of the hybrid cloud; Wherein, the telescopic module is also used for: When executing a scaling activity, the image module will first pull the VM template or container image, and then automatically start the VM creation process. After the VM is successfully created, the target script for executing the current elastic scaling activity will be downloaded. The virtual machine that has downloaded the target script is added to the elastic scaling group. The script starts to execute the business startup and the configuration information is delivered. After the business configuration is completed, the scaling group is notified to join the scaling group and automatically start carrying business traffic, so as to process the new business as a new business node, and complete the elastic scaling operation of the hybrid cloud.

8. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor is used to execute the computer program to implement the steps of the hybrid cloud elastic shrinking method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: Used to store computer programs; wherein, when the computer program is executed by a processor, the steps of the hybrid cloud elastic shrinking method as described in any one of claims 1 to 6 are implemented.

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