CN110633152A - Method and device for realizing horizontal scaling of service cluster - Google Patents
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Abstract
The application discloses a method for realizing horizontal expansion of a service cluster, which comprises the following steps: the cloud platform compares the monitoring index of each service cluster in the plurality of service clusters with a corresponding preset threshold value; and when the monitoring index exceeds a preset threshold value, distributing computing nodes between the service cluster and the standby resource pool controlled by the cloud platform so as to enable the monitoring index not to exceed the preset threshold value. The corresponding device for realizing horizontal scaling of the service cluster is also disclosed. The method provided by the application can reasonably set the resource utilization rate of the computing nodes in the cluster, enables the unused computing nodes to be shut down or standby so as to save energy and reduce emission, and fulfills the aims of green, environment-friendly and efficient operation of cloud computing.
Description
Technical Field
The present invention relates to server technologies, and in particular, to a method and an apparatus for implementing horizontal scaling of a service cluster.
Background
With the development of information technology, cloud computing has gradually become a development hotspot in the industry, and the cloud computing technology is also gradually applied to multiple fields of education, science, culture, public security, government, sanitation, high-performance computing, electronic commerce, internet of things and the like, and accordingly, the usage amount and the activity of a cloud computing service platform (referred to as a "cloud platform") are increasing day by day. With the development of cloud computing, cloud computing clusters are increasingly large and complex, system complexity and operation and maintenance difficulty are exponentially increased, operation and maintenance difficulty is extremely high, in an actual container production environment, due to the fact that actual business of a user has periodicity, when business volume is in a wave crest, a large amount of computing resources need to be invested to provide container service and efficient business service, when the business volume is in a wave trough, the situation of waste of a large amount of computing resources occurs, and the periods of the wave crests and the wave troughs of different businesses are different. Some service peaks appear in the daytime, some service peaks appear at night, in a large-scale data center, different service requirements are isolated and cannot be divided into uniform clusters, the non-sharing of computing resources of the clusters causes great waste of certain cluster resources in a certain time period, in order to meet the requirements of the service peaks, the computing resources of a single cluster are redundant, so that too many computing resources are used, but the service peaks are not long in duration, and the utilization rate of part of the computing resources is too low, so that the aims of green environmental protection and high running efficiency of cloud computing are violated.
Disclosure of Invention
In order to solve the technical problem, the invention provides a method and a device for realizing horizontal scaling of a service cluster, which can reasonably set the resource utilization rate of computing nodes in the cluster and enable unused computing nodes to be shut down or standby so as to save energy and reduce emission.
In order to achieve the object of the present invention, an embodiment of the present invention provides a method for implementing horizontal scaling of a service cluster, where the method includes:
the cloud platform compares the monitoring index of each service cluster in the plurality of service clusters with a corresponding preset threshold value;
and when the monitoring index exceeds a preset threshold value, distributing computing nodes between the service cluster and the standby resource pool controlled by the cloud platform so as to enable the monitoring index not to exceed the preset threshold value.
In an optional embodiment, when the monitoring index exceeds a preset threshold, allocating a computing node between the service cluster and the standby resource pool until the monitoring index does not exceed the preset threshold includes:
when the monitoring index of a first cluster in the plurality of service clusters exceeds an upper limit monitoring threshold value, asynchronously calling a first number of computing nodes in the standby resource pool to operate;
when the first number of computing nodes respond, a portion of the application instances loaded by the computing nodes of the first cluster when the monitoring index of the first cluster reaches the upper monitoring threshold are migrated to the first number of computing nodes to execute the migrated application instances by the first number of computing nodes such that the monitoring index of the first cluster is below the upper monitoring threshold.
In an optional embodiment, when the monitoring index exceeds a preset threshold, allocating a computing node between the service cluster and the standby resource pool until the monitoring index does not exceed the preset threshold includes:
when the monitoring index of a second cluster in the plurality of service clusters is lower than a lower limit monitoring threshold value, migrating application instances executed on a second number of computing nodes in the second cluster to other computing nodes in the second cluster;
after migrating the application instances executing on the second number of compute nodes to other compute nodes in the second cluster, running, by the compute node receiving the migrated application instances, the migrated application instances such that the monitoring index of the second cluster is above a lower monitoring threshold;
setting a second number of compute nodes to stand by or shut down in the standby resource pool.
In an optional embodiment, the step of setting the second number of computing nodes to be in standby or shutdown in the standby resource pool comprises:
the cloud platform calls an interface used for powering on and powering off the computing nodes in the standby resource pool so as to enable the computing nodes in the standby resource pool to be powered off or to be in standby.
In an alternative embodiment, the compute node is a bare metal server, or is a virtualized resource.
In an alternative embodiment, the monitoring indicators include CPU utilization and memory utilization.
In order to achieve the above object, an embodiment of the present invention provides an apparatus for implementing horizontal scaling of a service cluster, where the apparatus includes a memory and a processor;
the memory is to store computer readable instructions;
the processor is configured to execute the computer-readable instructions to perform operations comprising:
the cloud platform compares the monitoring index of each service cluster in the plurality of service clusters with a corresponding preset threshold value;
and when the monitoring index exceeds a preset threshold value, distributing computing nodes between the service cluster and the standby resource pool controlled by the cloud platform so as to enable the monitoring index not to exceed the preset threshold value.
In an optional embodiment, when the monitoring index exceeds a preset threshold, allocating a computing node between the service cluster and the standby resource pool so that the monitoring index does not exceed the preset threshold includes:
when the monitoring index of a first cluster in the plurality of service clusters exceeds an upper limit monitoring threshold value, asynchronously calling a first number of computing nodes in the standby resource pool to operate;
when the first number of computing nodes respond, a portion of the application instances loaded by the computing nodes of the first cluster when the monitoring index of the first cluster reaches the upper monitoring threshold are migrated to the first number of computing nodes to execute the migrated application instances by the first number of computing nodes such that the monitoring index of the first cluster is below the upper monitoring threshold.
In an optional embodiment, when the monitoring index exceeds a preset threshold, allocating a computing node between the service cluster and the standby resource pool, so that the operation until the monitoring index does not exceed the preset threshold includes:
when the monitoring index of a second cluster in the plurality of service clusters is lower than a lower limit monitoring threshold value, migrating application instances executed on a second number of computing nodes in the second cluster to other computing nodes in the second cluster;
after migrating the application instances executing on the second number of compute nodes to other compute nodes in the second cluster, running, by the compute node receiving the migrated application instances, the migrated application instances such that the monitoring index of the second cluster is above a lower monitoring threshold;
setting a second number of compute nodes to stand by or shut down in the standby resource pool.
In an alternative embodiment, the operation of setting the second number of compute nodes to stand by or shut down in the standby resource pool includes:
the cloud platform calls an interface used for powering on and powering off the computing nodes in the standby resource pool so as to enable the computing nodes in the standby resource pool to be powered off or to be in standby.
According to the scheme provided by the embodiment of the invention, the distribution of the application examples operated by the computing nodes in the cluster among the computing nodes can be adjusted by monitoring the monitoring indexes of the cluster, the computing nodes are reasonably applied to operate the application examples, so that the computing capacity of the computing nodes in the cluster can be matched with the load condition of the cluster, the resource utilization rate of the computing nodes is in a reasonable state, and under the condition, the computing nodes which are not loaded with the application examples are shut down or stand by in the standby resource pool, so that the energy is saved, the consumption is reduced, and the aims of green and environment-friendly cloud computing and high-efficiency operation are fulfilled.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a flowchart of a method for implementing horizontal scaling of a service cluster according to an embodiment of the present invention;
fig. 2 is a flowchart of step S103 provided in an alternative embodiment of the present invention;
fig. 3 is a block diagram of an apparatus for implementing horizontal scaling of a service cluster according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
The steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.
With the development of information technology, cloud computing has gradually become a development hotspot in the industry, and the cloud computing technology is also gradually applied to multiple fields of education, science, culture, public security, government, sanitation, high-performance computing, electronic commerce, internet of things and the like, and accordingly, the usage amount and the activity of a cloud computing service platform (referred to as a "cloud platform") are increasing day by day. With the development of cloud computing, cloud computing clusters are increasingly large and complex, system complexity and operation and maintenance difficulty are exponentially increased, operation and maintenance difficulty is extremely high, in an actual container production environment, due to the fact that actual business of a user has periodicity, when business volume is in a wave crest, a large amount of computing resources need to be invested to provide container service and efficient business service, when the business volume is in a wave trough, the situation of waste of a large amount of computing resources occurs, and the periods of the wave crests and the wave troughs of different businesses are different. Some service peaks appear in the daytime, some service peaks appear at night, in a large-scale data center, different service requirements are isolated and cannot be divided into uniform clusters, the non-sharing of computing resources of the clusters causes great waste of certain cluster resources in a certain time period, in order to meet the requirements of the service peaks, the computing resources of a single cluster are redundant, so that too many computing resources are used, but the service peaks are not long in duration, and the utilization rate of part of the computing resources is too low, so that the aims of green environmental protection and high running efficiency of cloud computing are violated.
In order to solve the above technical problem, an embodiment of the present invention provides a method for implementing horizontal scaling of a service cluster, as shown in fig. 1, the method includes step S101 and step S103.
Step S101, the cloud platform compares the monitoring index of each service cluster in the plurality of service clusters with a corresponding preset threshold value.
Step S103, when the monitoring index exceeds a preset threshold value, distributing computing nodes between the service cluster and the standby resource pool controlled by the cloud platform so as to enable the monitoring index not to exceed the preset threshold value.
The cloud platform takes the service cluster as a monitoring unit, compares the monitoring index of the service cluster with a preset threshold, and adjusts the computing nodes in the service cluster and the application instances operated by the computing nodes if the monitoring index exceeds the upper threshold or exceeds the lower threshold, so that the monitoring index of the service cluster does not exceed the preset threshold. The computing nodes in the service cluster are started to operate, and the power consumption is high; and the computing nodes in the standby resource pool are in standby or shutdown, so that the power consumption is low. According to the comparison between the monitoring index and the threshold value, a proper amount of computing nodes with proper computing capacity can be reasonably arranged to run the application examples, so that the computing tasks of the service cluster can be efficiently completed, and unnecessary resource waste caused by running of redundant computing nodes can be reduced. In addition, compared with the scheme of only migrating the application examples among the computing nodes, the scheme provided by the embodiment of the invention has higher regulation efficiency because the control is carried out by taking the computing nodes running the application examples as a unit; and by adjusting the actual hardware facility for running the application instance, namely whether the computing node runs or not, the computing node which does not need to run the application instance after adjustment can be shut down or standby in the standby resource pool, so that the energy consumption of the computing node can be saved.
In an alternative embodiment, step S103 comprises:
when the monitoring index of a first cluster in the plurality of service clusters exceeds an upper limit monitoring threshold value, asynchronously calling a first number of computing nodes in the standby resource pool to operate; here, "running" is a state contrary to "shutdown" or "standby".
When the first number of computing nodes respond, a portion of the application instances loaded by the computing nodes of the first cluster when the monitoring index of the first cluster reaches the upper monitoring threshold are migrated to the first number of computing nodes to execute the migrated application instances by the first number of computing nodes such that the monitoring index of the first cluster is below the upper monitoring threshold.
The steps can be executed in a loop iteration mode until the monitoring index of the cluster is reduced to a reasonable range and the resource utilization rate of the computing nodes of the cluster is in the reasonable range.
In an alternative embodiment, as shown in fig. 2, step S103 includes steps S1031 to S1035.
Step S1031, when the monitoring index of the second cluster among the plurality of service clusters is lower than the lower limit monitoring threshold, migrating the application instances executed on the second number of computing nodes in the second cluster to other computing nodes in the second cluster.
Step S1033, after migrating the application instances executed on the second number of computing nodes to other computing nodes in the second cluster, running the migrated application instances by the computing nodes receiving the migrated application instances, so that the monitoring index of the second cluster is higher than the lower limit monitoring threshold.
Step S1035 sets a second number of compute nodes to stand by in the standby resource pool or shut down.
Step S1033 and step S0135 may be iteratively executed in a loop until the monitoring index of the cluster rises to a reasonable range, and the resource utilization rate of the computing node of the cluster is in the reasonable range.
In an alternative embodiment, step S1035 includes:
the cloud platform calls an interface used for powering on and powering off the computing nodes in the standby resource pool so as to enable the computing nodes in the standby resource pool to be powered off or to be in standby.
In an alternative embodiment, the compute node is a bare metal server, or is a virtualized resource. For example, the virtualized resources may be organized in an OpenStack, VMware, ICS manner.
In an alternative embodiment, the monitoring indicators include CPU utilization and memory utilization.
The CPU utilization and the memory utilization referred to herein are relative to the entire kubernets cluster, that is, the CPU utilization and the memory utilization of the entire kubernets cluster are monitored by the Prometheus cloud platform.
According to the scheme provided by the embodiment of the invention, the distribution of the application examples operated by the computing nodes in the cluster among the computing nodes can be adjusted by monitoring the monitoring indexes of the cluster, the computing nodes are reasonably applied to operate the application examples, so that the computing capacity of the computing nodes in the cluster can be matched with the load condition of the cluster, the resource utilization rate of the computing nodes is in a reasonable state, and under the condition, the computing nodes which are not loaded with the application examples are shut down or stand by in the standby resource pool, so that the energy is saved, the consumption is reduced, and the aims of green and environment-friendly cloud computing and high-efficiency operation are fulfilled.
In order to solve the above technical problem, an embodiment of the present invention provides an apparatus for implementing horizontal scaling of a service cluster, as shown in fig. 3, including a memory 10 and a processor 20.
the processor 20 is configured to execute the computer-readable instructions to perform the following operations:
the cloud platform compares the monitoring index of each service cluster in the plurality of service clusters with a corresponding preset threshold value;
and when the monitoring index exceeds a preset threshold value, distributing computing nodes between the service cluster and the standby resource pool controlled by the cloud platform so as to enable the monitoring index not to exceed the preset threshold value.
The cloud platform takes the service cluster as a monitoring unit, compares the monitoring index of the service cluster with a preset threshold, and adjusts the computing nodes in the service cluster and the application instances operated by the computing nodes if the monitoring index exceeds the upper threshold or exceeds the lower threshold, so that the monitoring index of the service cluster does not exceed the preset threshold. The computing nodes in the service cluster are started to operate, and the power consumption is high; and the computing nodes in the standby resource pool are in standby or shutdown, so that the power consumption is low. According to the comparison between the monitoring index and the threshold value, a proper amount of computing nodes with proper computing capacity can be reasonably arranged to run the application examples, so that the computing tasks of the service cluster can be efficiently completed, and unnecessary resource waste caused by running of redundant computing nodes can be reduced. In addition, compared with the scheme of only migrating the application examples among the computing nodes, the scheme provided by the embodiment of the invention has higher regulation efficiency because the control is carried out by taking the computing nodes running the application examples as a unit; and by adjusting the actual hardware facility for running the application instance, namely whether the computing node runs or not, the computing node which does not need to run the application instance after adjustment can be shut down or standby in the standby resource pool, so that the energy consumption of the computing node can be saved.
In an optional embodiment, when the monitoring index exceeds a preset threshold, allocating a computing node between the service cluster and the standby resource pool so that the monitoring index does not exceed the preset threshold includes:
when the monitoring index of a first cluster in the plurality of service clusters exceeds an upper limit monitoring threshold value, asynchronously calling a first number of computing nodes in the standby resource pool to operate;
when the first number of computing nodes respond, a portion of the application instances loaded by the computing nodes of the first cluster when the monitoring index of the first cluster reaches the upper monitoring threshold are migrated to the first number of computing nodes to execute the migrated application instances by the first number of computing nodes such that the monitoring index of the first cluster is below the upper monitoring threshold.
In an optional embodiment, when the monitoring index exceeds a preset threshold, allocating a computing node between the service cluster and the standby resource pool so that the monitoring index does not exceed the preset threshold includes:
when the monitoring index of a second cluster in the plurality of service clusters is lower than a lower limit monitoring threshold value, migrating application instances executed on a second number of computing nodes in the second cluster to other computing nodes in the second cluster;
after migrating the application instances executing on the second number of compute nodes to other compute nodes in the second cluster, running, by the compute node receiving the migrated application instances, the migrated application instances such that the monitoring index of the second cluster is above a lower monitoring threshold;
setting a second number of compute nodes to stand by or shut down in the standby resource pool.
In an alternative embodiment, the operation of setting the second number of compute nodes to stand by or shut down in the standby resource pool includes:
the cloud platform calls an interface used for powering on and powering off the computing nodes in the standby resource pool so as to enable the computing nodes in the standby resource pool to be powered off or to be in standby.
According to the scheme provided by the embodiment of the invention, the distribution of the application examples operated by the computing nodes in the cluster among the computing nodes can be adjusted by monitoring the monitoring indexes of the cluster, the computing nodes are reasonably applied to operate the application examples, so that the computing capacity of the computing nodes in the cluster can be matched with the load condition of the cluster, the resource utilization rate of the computing nodes is in a reasonable state, and under the condition, the computing nodes which are not loaded with the application examples are shut down or stand by in the standby resource pool, so that the energy is saved, the consumption is reduced, and the aims of green and environment-friendly cloud computing and high-efficiency operation are fulfilled.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for implementing horizontal scaling of service clusters, comprising:
the cloud platform compares the monitoring index of each service cluster in the plurality of service clusters with a corresponding preset threshold value;
and when the monitoring index exceeds a preset threshold value, distributing computing nodes between the service cluster and a standby resource pool controlled by the cloud platform so as to enable the monitoring index not to exceed the preset threshold value.
2. The method according to claim 1, wherein the step of allocating a computing node between the service cluster and a standby resource pool when the monitoring index exceeds a preset threshold so that the monitoring index does not exceed the preset threshold comprises:
when the monitoring index of a first cluster in the plurality of service clusters exceeds an upper limit monitoring threshold value, asynchronously calling a first number of computing nodes in the standby resource pool to operate;
when the first number of compute nodes respond, migrating a portion of the application instances loaded by the compute nodes of the first cluster when the monitoring metrics of the first cluster reach an upper monitoring threshold to the first number of compute nodes to execute the migrated application instances by the first number of compute nodes such that the monitoring metrics of the first cluster are below the upper monitoring threshold.
3. The method according to claim 1, wherein the step of allocating a computing node between the service cluster and a standby resource pool when the monitoring index exceeds a preset threshold so that the monitoring index does not exceed the preset threshold comprises:
when the monitoring index of a second cluster in the plurality of service clusters is lower than a lower limit monitoring threshold value, migrating application instances executed on a second number of computing nodes in the second cluster to other computing nodes in the second cluster;
after migrating the application instances executing on the second number of compute nodes to other compute nodes in the second cluster, executing, by the compute node receiving the migrated application instances, the migrated application instances such that the monitoring index of the second cluster is above the lower monitoring threshold;
setting the second number of compute nodes to stand by or shut down in a standby resource pool.
4. The method of claim 3, wherein the step of setting the second number of compute nodes to stand by or shut down in a standby resource pool comprises:
and the cloud platform calls an interface for switching on and off the computing nodes in the standby resource pool so as to enable the computing nodes in the standby resource pool to be switched off or to be in standby.
5. The method of claim 1, wherein the compute node is a bare metal server or a virtualized resource.
6. The method of claim 1, wherein the monitoring metrics include CPU usage and memory usage.
7. An apparatus for implementing horizontal scaling of a service cluster, comprising a memory and a processor;
the memory is to store computer readable instructions;
the processor is configured to execute the computer-readable instructions to perform operations comprising:
the cloud platform compares the monitoring index of each service cluster in the plurality of service clusters with a corresponding preset threshold value;
and when the monitoring index exceeds a preset threshold value, distributing computing nodes between the service cluster and a standby resource pool controlled by the cloud platform so as to enable the monitoring index not to exceed the preset threshold value.
8. The apparatus of claim 7, wherein the operation of allocating a computing node between the service cluster and a backup resource pool when the monitoring index exceeds a preset threshold so that the monitoring index does not exceed the preset threshold comprises:
when the monitoring index of a first cluster in the plurality of service clusters exceeds an upper limit monitoring threshold value, asynchronously calling a first number of computing nodes in the standby resource pool to operate;
when the first number of compute nodes respond, migrating a portion of the application instances loaded by the compute nodes of the first cluster when the monitoring metrics of the first cluster reach an upper monitoring threshold to the first number of compute nodes to execute the migrated application instances by the first number of compute nodes such that the monitoring metrics of the first cluster are below the upper monitoring threshold.
9. The apparatus of claim 7, wherein the operation of allocating a computing node between the service cluster and a backup resource pool when the monitoring index exceeds a preset threshold so that the monitoring index does not exceed the preset threshold comprises:
when the monitoring index of a second cluster in the plurality of service clusters is lower than a lower limit monitoring threshold value, migrating application instances executed on a second number of computing nodes in the second cluster to other computing nodes in the second cluster;
after migrating the application instances executing on the second number of compute nodes to other compute nodes in the second cluster, executing, by the compute node receiving the migrated application instances, the migrated application instances such that the monitoring index of the second cluster is above the lower monitoring threshold;
setting the second number of compute nodes to stand by or shut down in a standby resource pool.
10. The apparatus of claim 7, wherein the operation of setting the second number of compute nodes to standby or shutdown in a standby resource pool comprises:
and the cloud platform calls an interface for switching on and off the computing nodes in the standby resource pool so as to enable the computing nodes in the standby resource pool to be switched off or to be in standby.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111274033A (en) * | 2020-01-19 | 2020-06-12 | 北京达佳互联信息技术有限公司 | Resource deployment method, device, server and storage medium |
| CN111506390A (en) * | 2020-03-31 | 2020-08-07 | 新浪网技术(中国)有限公司 | Video transcoding scheduling method and system based on containerization deployment |
| CN111562968A (en) * | 2020-05-13 | 2020-08-21 | 苏州浪潮智能科技有限公司 | Method, device, equipment and medium for realizing management of ICS (Internet connection sharing) to Kata container |
| CN113923216A (en) * | 2021-09-29 | 2022-01-11 | 阿里巴巴（中国）有限公司 | Distributed cluster current limiting system and method and distributed cluster nodes |
| CN114629821A (en) * | 2020-12-10 | 2022-06-14 | 新智云数据服务有限公司 | Internet of things usage data generation method, device, equipment and medium |
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2019
- 2019-09-20 CN CN201910894349.7A patent/CN110633152A/en not_active Withdrawn
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111274033A (en) * | 2020-01-19 | 2020-06-12 | 北京达佳互联信息技术有限公司 | Resource deployment method, device, server and storage medium |
| CN111274033B (en) * | 2020-01-19 | 2024-02-13 | 北京达佳互联信息技术有限公司 | Resource deployment method, device, server and storage medium |
| CN111506390A (en) * | 2020-03-31 | 2020-08-07 | 新浪网技术(中国)有限公司 | Video transcoding scheduling method and system based on containerization deployment |
| CN111506390B (en) * | 2020-03-31 | 2024-01-19 | 新浪技术（中国）有限公司 | Video transcoding scheduling method and system based on containerized deployment |
| CN111562968A (en) * | 2020-05-13 | 2020-08-21 | 苏州浪潮智能科技有限公司 | Method, device, equipment and medium for realizing management of ICS (Internet connection sharing) to Kata container |
| CN111562968B (en) * | 2020-05-13 | 2022-05-24 | 苏州浪潮智能科技有限公司 | Method, device, equipment and medium for realizing management of ICS (Internet connection sharing) to Kata container |
| CN114629821A (en) * | 2020-12-10 | 2022-06-14 | 新智云数据服务有限公司 | Internet of things usage data generation method, device, equipment and medium |
| CN114629821B (en) * | 2020-12-10 | 2023-11-10 | 新智云数据服务有限公司 | Internet of things consumption data generation method, device, equipment and medium |
| CN113923216A (en) * | 2021-09-29 | 2022-01-11 | 阿里巴巴（中国）有限公司 | Distributed cluster current limiting system and method and distributed cluster nodes |
| CN113923216B (en) * | 2021-09-29 | 2023-12-15 | 阿里巴巴（中国）有限公司 | Distributed cluster current limiting system and method and distributed cluster node |
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