CN112506444A - Kubernetes cluster-based expansion and contraction capacity control method and device and electronic equipment - Google Patents
Kubernetes cluster-based expansion and contraction capacity control method and device and electronic equipment Download PDFInfo
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Abstract
The embodiment of the disclosure provides a Kubernetes cluster-based expansion and contraction capacity control method and device, electronic equipment and a storage medium, and belongs to the technical field of network communication. The scaling capacity control method based on the Kubernetes cluster comprises the following steps: acquiring a cluster establishing instruction; creating a target cluster according to the cluster creating instruction and preset required configuration information; deploying a monitoring module and a deployment capacity expansion module in the target cluster; the monitoring module is used for acquiring index data of a target cluster, and the capacity expansion and reduction module is an index-based capacity expansion and reduction module and is used for configuring a capacity expansion and reduction strategy; inquiring index data of the target cluster according to a preset index rule; and carrying out capacity expansion and reduction operation according to the index data and the capacity expansion and reduction strategy. The embodiment of the disclosure can dynamically adjust resource configuration according to actual conditions and reduce consumption of cluster resources.
Description
Technical Field
The present disclosure relates to the field of network communication technologies, and in particular, to a method and an apparatus for controlling scaling based on a Kubernetes cluster, and an electronic device.
Background
With the development of cloud computing and artificial intelligence, container technology is more and more widely used, containers need to be deployed in a large scale in a cloud computing environment, and a cluster management scheme corresponding to the containers is developed, wherein the Kubernets cluster provides functions of service registration, load balancing, service deployment and operation, online capacity expansion and capacity contraction, resource scheduling and the like for container application.
At present, because the whole resource information of the kubernets cluster needs to be monitored, the traditional capacity expansion method has lower performance and occupies more cluster resources.
Disclosure of Invention
The main purpose of the present disclosure is to provide a method and an apparatus for controlling scaling based on a Kubernetes cluster, an electronic device, and a storage medium, which can dynamically perform scaling operation and reduce consumption of cluster resources.
To achieve the above object, a first aspect of the present disclosure provides a kubernets cluster-based scaling capacity control method, including:
acquiring a cluster establishing instruction;
creating a target cluster according to the cluster creating instruction and preset required configuration information;
deploying a monitoring module and a deployment capacity expansion module in the target cluster; the monitoring module is used for acquiring index data of a target cluster, and the capacity expansion and reduction module is an index-based capacity expansion and reduction module and is used for configuring a capacity expansion and reduction strategy;
inquiring index data of the target cluster according to a preset index rule;
and carrying out capacity expansion and reduction operation according to the index data and the capacity expansion and reduction strategy.
Preferably, the capacity expansion and reduction operation includes a capacity reduction operation, the index data includes a total resource usage rate, and the performing the capacity expansion and reduction operation according to the index data and the capacity expansion and reduction policy includes:
comparing the total resource utilization rate with a preset lower limit value;
and if the total resource utilization rate is smaller than the preset lower limit value, carrying out the capacity reduction operation according to the capacity expansion strategy.
Preferably, the capacity expansion and reduction operation further includes a capacity expansion operation, and the performing the capacity expansion and reduction operation according to the index data and the capacity expansion and reduction policy includes:
and if the total resource utilization rate is greater than the preset lower limit value, carrying out capacity expansion operation according to the capacity expansion strategy.
Preferably, the index data includes a resource usage rate of each working node of the target cluster, and the performing the capacity expansion and reduction operation according to the index data and the capacity expansion and reduction policy further includes:
and calculating the total resource utilization rate according to the resource utilization rate of each working node.
Preferably, the method further comprises:
calculating the current resource allocation corresponding to the monitoring module according to the index data;
and adjusting the current resource configuration corresponding to the monitoring module according to the expansion and contraction capacity strategy.
Preferably, the adjusting the current resource configuration corresponding to the monitoring module according to the scaling strategy includes:
calculating a deviation value between the current resource configuration and the historical average resource configuration;
and if the deviation value is smaller than a preset difference value, adjusting the current resource configuration corresponding to the monitoring module according to the expansion-reduction capacity strategy.
Preferably, the method further comprises:
and updating the historical average resource allocation according to the adjusted current resource allocation.
To achieve the above object, a second aspect of the present disclosure provides a kubernets cluster-based scaling capacity control apparatus, including:
the creating instruction acquisition module is used for acquiring a cluster creating instruction;
the cluster creating module is used for creating a target cluster according to the cluster creating instruction and preset required configuration information;
the deployment module is used for deploying the monitoring module and the deployment capacity expansion module in the target cluster; the monitoring module is used for acquiring index data of a target cluster, and the capacity expansion and reduction module is an index-based capacity expansion and reduction module and is used for configuring a capacity expansion and reduction strategy;
the index query module is used for querying the index data of the target cluster according to a preset index rule;
and the capacity expansion and reduction operation module is used for carrying out capacity expansion and reduction operation according to the index data and the capacity expansion and reduction strategy.
To achieve the above object, a third aspect of the present disclosure provides an electronic device, including:
at least one memory;
at least one processor;
at least one program;
the program is stored in a memory and a processor executes the at least one program to implement the method of the present disclosure as described in the above first aspect.
To achieve the above object, a fourth aspect of the present disclosure proposes a storage medium that is a computer-readable storage medium storing computer-executable instructions for causing a computer to perform:
a method as described in the first aspect above.
According to the Kubernetes cluster-based capacity expansion and contraction control method and device, the electronic equipment and the storage medium, the cluster creation instruction is obtained, the target cluster is created according to the cluster creation instruction and the preset required configuration information, the monitoring module and the capacity expansion and contraction module are deployed in the target cluster, the monitoring module is used for collecting index data of the target cluster, the capacity expansion and contraction strategy is configured through the capacity expansion and contraction module, the index data of the target cluster is inquired according to the preset index rule, and the capacity expansion and contraction operation is carried out according to the index data and the capacity expansion and contraction strategy, so that resource configuration can be dynamically adjusted according to actual conditions.
Drawings
Fig. 1 is a flowchart of a kubernets cluster-based scalability control method according to an embodiment of the present disclosure.
Fig. 2 is a partial flow diagram of step 105 of fig. 1.
Fig. 3 is a functional block diagram of a kubernets cluster-based scaling capacity control device according to an embodiment of the present disclosure.
Fig. 4 is a schematic diagram of a hardware structure of an electronic device provided in an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the disclosure and are not intended to limit the disclosure.
It should be noted that although functional blocks are partitioned in a schematic diagram of an apparatus and a logical order is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the partitioning of blocks in the apparatus or the order in the flowchart. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing embodiments of the disclosure only and is not intended to be limiting of the disclosure.
First, several terms and techniques involved in the present disclosure are resolved:
kubernetes: kubernetes is widely applied to a container cluster management system; kubernets is an open source, is used for managing containerized applications on a plurality of hosts in a cloud platform, and is also a container arrangement engine; the Kubernetes cluster (also called container cluster), abbreviated as K8s cluster, is an abbreviation formed by replacing 8 characters "kubernete" with 8 characters. Kubernetes supports automated deployment, large-scale scalable, application containerization management. When an application is deployed in a production environment, multiple instances of the application are typically deployed to load balance application requests. In kubernets, a plurality of containers can be created, each container runs an application instance, and then management, discovery and access of the group of application instances are realized through a built-in load balancing strategy, and the details do not need operation and maintenance personnel to perform complicated manual configuration and processing. Kubernetes has a horizontal auto-expanding feature. Generally, monitoring of the Kubernetes cluster is mainly monitoring and elastic scaling of resources such as service-type Pod, and the monitoring data provided by a monitoring component is relied on. The monitoring component of the kubernets cluster is one of the core components, and can monitor various resources (such as pod, node, deployment, and the like) in the kubernets cluster, and the monitoring items include CPU, memory, traffic, health condition, disk usage, and the like. The monitoring data can be used as basic data of other components to provide decision support for the other components. Wherein Pod refers to application load in a Kubernetes cluster, and the Pod runs on a node. The Pod consists of one or more containers, such as Container containers created by the Docker Container engine, that share Container storage, network, and Container run configuration items. Containers in the Pod are scheduled simultaneously, with a common operating environment. In addition, when the capacity expansion processing is usually performed on the kubernets cluster, all resource load information in the kubernets cluster can be monitored, and then the capacity expansion operation is performed on the kubernets cluster according to the resource load information. However, monitoring the load information of the whole cluster resources has low performance and occupies more cluster resources.
Arranging containers: the method refers to the deployment, management, expansion and networking of the automatic container, and comprises the steps of carrying out automatic processing on deployment, management, elastic expansion and container network management, and managing the life cycle of the container by arranging; container orchestration can facilitate enterprises that need to deploy and manage hundreds of thousands of containers and hosts; the container arrangement can expand and contract the container application based on the CPU and the memory utilization rate.
Metric: the index is the monitored item monitored by Prometheus.
Metrics Server: the aggregator is used for collecting index information.
Prometheus: is a set of open source system monitoring and alarming framework; characteristics of Prometheus include: multidimensional data model (Key and Value Key Value pairs based on time series), flexible query and aggregation language PromQL, local storage and distributed storage, and collection of time series data through HTTP-based Pull model; the strategy for Prometheus to acquire data is Pull, i.e. to automatically grab (Pull) data without pushing (Push) data; the HTTP protocol is used by Prometous to fetch data, and the port, path and interval time of the target program are specified in the configuration file.
Exporter: in the Prometheus architecture, an exporter is a component responsible for collecting data and reporting information to a Prometheus Server; wherein, the node _ exporter is internally provided with basic monitoring for a host system; the node _ exporter is used for installing and monitoring the terminal.
Horizontal automatic scaling (HPA): is an automatic expansion, which is the horizontal automatic expansion of pod; the operation object of the HPA is a Pod corresponding to RC, RS or Deployment; automatically and horizontally expanding the capacity according to the load of the current system of the Pod, if the load of the system exceeds a preset value, starting to increase the number of the pods, and if the load of the system is lower than a certain value, automatically reducing the number of the pods. At present, the HPA of K8S can only measure the load of the system according to the use condition of resources such as CPU, and the like, and at present, the heapster is also relied on to collect the use condition of CPU.
CRD (custom Resource definition): kubernets provides many default resource types, such as Pod, Deployment, Service, Volume, etc.; and moreover, the Kubernets is added with the self-defined resource CRD for expanding the Kubernets API, and a new resource type can be added into the Kubernets API through the CRD without modifying Kubernets source codes or creating a self-defined API server, so that the expansion capability of the Kubernets is improved.
A controller: kubernets has built-in controllers (controllers) for controlling the specific states and behaviors of Pod, which are also called resource controllers; the resource controller of Kubernetes mainly includes five categories, which are: the system comprises a first Replication Controller (RC) and a second replication server (RC) which are used for ensuring that the number of copies of a container application is always kept at the number of copies defined by a user, namely if a container exits abnormally, a new Pod is automatically created to replace the container, and if the container which is abnormal excessively exists, the container is automatically recycled; second, Deployment, which is a capacity expansion and reduction controller, is used for realizing capacity expansion and capacity reduction; thirdly, DaemonSet, which ensures that a copy of Pod runs on all (or some) nodes; fourthly, StatefUlSet provides a unique identifier for the Pod, and the sequence of deployment and scale is ensured; and the HPA is used for automatically zooming the Pod level, clipping peaks and filling valleys, improving the overall resource utilization rate of the cluster and automatically adjusting the Pod number in the service.
Role-Based Access Control (RBAC): in RBAC, permissions are associated with roles, and users gain the permissions of the appropriate roles by becoming members of those roles.
With the development of cloud computing and artificial intelligence, container technology is more and more widely used, containers need to be deployed in a large scale in a cloud computing environment, and a cluster management scheme corresponding to the containers is developed, wherein the Kubernets cluster provides functions of service registration, load balancing, service deployment and operation, online capacity expansion and capacity contraction, resource scheduling and the like for container application.
At present, because the whole resource information of the kubernets cluster needs to be monitored, the traditional capacity expansion method has lower performance and occupies more cluster resources.
Based on this, the embodiment of the present disclosure provides a technical solution for controlling scaling based on a Kubernetes cluster, which consumes less resources of the cluster and dynamically adjusts resource configuration.
The embodiment of the present disclosure provides a method and an apparatus for controlling expansion and contraction capacity based on a kubernets cluster, an electronic device, and a read-storage medium, and specifically, the following embodiments are described to describe first a method for controlling expansion and contraction capacity based on a kubernets cluster in the embodiment of the present disclosure.
The capacity expansion and contraction control method based on the Kubernetes cluster, provided by the embodiment of the disclosure, can be applied to a terminal, a server side and software running in the terminal or the server side. In some embodiments, the terminal may be a smartphone, tablet, laptop, desktop computer, smart watch, or the like; the server side can be configured into an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and cloud servers for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (content delivery network) and big data and artificial intelligence platforms; the software may be an application or the like implementing a kubernets cluster-based scaling control method, but is not limited to the above form.
Fig. 1 is an optional flowchart of a kubernets cluster-based scalability control method provided in an embodiment of the present disclosure, where the method in fig. 1 includes steps 101 to 105.
102, creating a target cluster according to a cluster creating instruction and preset required configuration information;
103, deploying a monitoring module and a capacity expansion module in the target cluster; the monitoring module is used for acquiring index data of the target cluster, and the capacity expansion and reduction module is an index-based capacity expansion and reduction module and is used for configuring a capacity expansion and reduction strategy;
step 104: inquiring index data of the target cluster according to a preset index rule;
step 105: and carrying out capacity expansion and reduction operation according to the index data and the capacity expansion and reduction strategy.
In some embodiments, the cluster creation instructions are generated by the client according to the needs of the user. The target cluster is a Kubernetes cluster.
In some embodiments, the preset required configuration information includes at least one of: configuration information of Kubernetes application, configuration information of basic resources and configuration information of a cluster network;
the configuration information applied by Kubernetes at least comprises the following information: kubernets version information;
the configuration information of the base resource includes at least one of: whether the cluster is a high availability area, an available area, a host charging mode, a host mirror image, a key pair, a node specification and an external network;
the configuration information of the cluster network comprises at least one of the following: service network type, network plug-in, network segment address, subnet mask, gateway.
In some embodiments, the index data includes resource indexes and custom indexes, wherein the resource indexes include resource utilization rate, total resource utilization rate, CPU cumulative utilization rate, real-time utilization rate of the memory, average utilization rate of the memory, resource occupancy rate of the pod, disk occupancy rate of the container, and the like, the resource indexes are collected by Metrics Server, and the custom indexes are collected by prometheus.
In some embodiments, the monitoring module comprises Prometheus Export. In other embodiments, the monitoring module further comprises a Metrics Server. In the embodiment of the present disclosure, the monitoring module includes a metrocus Export and a Metrics Server, and in step 103, after the Metrics Server and the metrocus Export are deployed, the kubernets cluster-based expansion and contraction capacity control method further includes:
creating an API object and a database in a target cluster;
specifically, creating the API object includes: creating a CRD; through creating API objects, resources in Kubernets are expanded, a DeployScaler metadata definition file is created, related configuration of RBAC is configured, and an expansion capacity controller, such as a Deploy Scale, is started to perform process control.
Referring to fig. 2, in some embodiments, the capacity expansion and reduction operation includes a capacity reduction operation, the index data includes a total resource utilization rate, and the capacity expansion and reduction operation is performed according to the index data and a capacity expansion and reduction policy, including:
Carrying out capacity expansion and reduction operation according to the index data and the capacity expansion and reduction strategy, and further comprising the following steps:
And step 204, if the total resource utilization rate is between the preset lower limit value and the preset upper limit value, not executing the capacity expansion operation.
Further, the index data includes the resource utilization rate of each working node of the target cluster, and the capacity expansion and reduction operation is performed according to the index data and the capacity expansion and reduction strategy, and the method further includes:
and calculating the total resource utilization rate according to the resource utilization rate of each working node.
In some embodiments, the kubernets cluster-based scaling control method further comprises:
calculating the current resource allocation corresponding to the monitoring module according to the index data;
and adjusting the current resource configuration corresponding to the monitoring module according to the capacity expansion and reduction strategy.
Specifically, the current resource configuration may be represented as: the current usage of the CPU is 2 cores, and the current average usage of the memory is 2 GB.
Preferably, adjusting the current resource configuration corresponding to the monitoring module according to the scalability policy includes:
calculating a deviation value between the current resource configuration and the historical average resource configuration;
and if the deviation value is smaller than the preset difference value, adjusting the current resource configuration corresponding to the monitoring module according to the capacity expansion and reduction strategy.
Specifically, in some embodiments, the preset difference may be set to 2% -12%; in practical application, a preset difference value can be set according to actual needs, and the embodiment of the disclosure is not limited.
Further, the historical average resource allocation may be a resource allocation within a preset time, which may be set according to actual needs, for example, it may be in units of days, weeks, months, quarters, years, or the like.
Preferably, the kubernets cluster-based scaling capacity control method further includes:
and updating the historical average resource allocation according to the adjusted current resource allocation.
In the embodiment of the disclosure, kubernets has native container scheduler capability, for example, container arrangement is realized, and by using HPA characteristics, container application can be expanded and contracted based on CPU and memory utilization; and the service index item based on service customization is expanded and realized, and the high-efficiency and flexible rule configuration capability is provided in combination, so that the customizable container scheduling capability is provided for the flexible and changeable service form of the client. The technical scheme provided by the embodiment of the disclosure consumes less cluster resources and has high response speed.
In practical application, when configuring the index rule, provide visual configuration page for relevant personnel operate, based on the relevant index of institute, can set up according to actual need, for example can be according to the rate of utilization such as memory, disk, CPU, network, peak time period early and late, carry out corresponding scaling operation, scaling operation includes: the method includes the steps of performing capacity expansion and reduction operations, which may be displayed on a visual configuration page and operated by related personnel, and restarting an application, where the capacity expansion and reduction operations may be performed, specifically, the capacity expansion and reduction operations may include: expand 20 host computers, contract 50 host computers, expand 10%, contract 15%, or restart application, etc.
According to the capacity expansion and reduction control method based on the Kubernetes cluster, the cluster creation instruction is obtained, the target cluster is created according to the cluster creation instruction and the preset required configuration information, the monitoring module and the capacity expansion and reduction module are deployed in the target cluster, the monitoring module is used for collecting index data of the target cluster, the capacity expansion and reduction strategy is configured through the capacity expansion and reduction module, the index data of the target cluster is inquired according to the preset index rule, and the capacity expansion and reduction operation is carried out according to the index data and the capacity expansion and reduction strategy, so that the resource configuration can be dynamically adjusted according to the actual situation, and the consumption of cluster resources is reduced.
Referring to fig. 3, an embodiment of the present disclosure further provides a device for controlling scaling based on a kubernets cluster, which can implement the method for controlling scaling based on the kubernets cluster, where the device includes:
a creation instruction obtaining module 301, configured to obtain a cluster creation instruction;
a cluster creating module 302, configured to create a target cluster according to the cluster creating instruction and preset required configuration information;
a deployment module 303, configured to deploy a monitoring module and a deployment scale-up module in the target cluster; the monitoring module is used for acquiring index data of a target cluster, and the capacity expansion and reduction module is an index-based capacity expansion and reduction module and is used for configuring a capacity expansion and reduction strategy;
the index query module 304 is configured to query index data of the target cluster according to a preset index rule;
and a capacity expansion and reduction operation module 305 for performing capacity expansion and reduction operation according to the index data and the capacity expansion and reduction strategy.
An embodiment of the present disclosure further provides an electronic device, including:
at least one memory;
at least one processor;
at least one program;
the program is stored in the memory, and the processor executes the at least one program to realize the kubernets cluster-based scaling capacity control method in the embodiment of the disclosure. The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a Personal Digital Assistant (PDA for short), a vehicle-mounted computer and the like.
Referring to fig. 4, fig. 4 illustrates a hardware structure of an electronic device according to another embodiment, where the electronic device includes:
the processor 401 may be implemented by a general-purpose CPU (central processing unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits, and is configured to execute a relevant program to implement the technical solution provided by the embodiment of the present disclosure;
the memory 402 may be implemented in the form of a ROM (read only memory), a static memory device, a dynamic memory device, or a RAM (random access memory). The memory 402 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present disclosure is implemented by software or firmware, the relevant program codes are stored in the memory 402 and invoked by the processor 401 to execute the kubernets cluster-based scaling capacity control method according to the embodiments of the present disclosure;
an input/output interface 403 for implementing information input and output;
the communication interface 404 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g., USB, network cable, etc.) or in a wireless manner (e.g., mobile network, WIFI, bluetooth, etc.); and
a bus 405 that transfers information between the various components of the device (e.g., the processor 401, memory 402, input/output interface 403, and communication interface 404);
wherein the processor 401, the memory 402, the input/output interface 403 and the communication interface 404 are communicatively connected to each other within the device by a bus 405.
The embodiment of the disclosure also provides a computer-readable storage medium, where the computer-executable instructions are used for executing the kubernets cluster-based scaling capacity control method.
According to the capacity expansion and reduction control method based on the Kubernetes cluster, the capacity expansion and reduction control device based on the Kubernetes cluster, the electronic equipment and the computer readable storage medium, the cluster creation instruction is obtained, the target cluster is created according to the cluster creation instruction and the preset required configuration information, the monitoring module and the capacity expansion and reduction module are deployed in the target cluster, the index data of the target cluster are collected through the monitoring module, the capacity expansion and reduction strategy is configured through the capacity expansion and reduction module, the index data of the target cluster are inquired according to the preset index rule, and the capacity expansion and reduction operation is carried out according to the index data and the capacity expansion and reduction strategy, so that the resource configuration can be dynamically adjusted according to the actual situation, and the consumption of cluster resources is reduced.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The embodiments described in the embodiments of the present disclosure are for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation to the technical solutions provided in the embodiments of the present disclosure, and it is obvious to those skilled in the art that the technical solutions provided in the embodiments of the present disclosure are also applicable to similar technical problems with the evolution of technology and the emergence of new application scenarios.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the disclosure and in the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It is to be understood that in the present disclosure, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present disclosure, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present disclosure. And the aforementioned storage medium includes: various media capable of storing programs, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, and therefore do not limit the scope of the claims of the embodiments of the present disclosure. Any modifications, equivalents and improvements within the scope and spirit of the embodiments of the present disclosure should be considered within the scope of the claims of the embodiments of the present disclosure by those skilled in the art.
Claims (10)
1. A capacity expansion and contraction control method based on a Kubernetes cluster is characterized by comprising the following steps:
acquiring a cluster establishing instruction;
creating a target cluster according to the cluster creating instruction and preset required configuration information;
deploying a monitoring module and a deployment capacity expansion module in the target cluster; the monitoring module is used for acquiring index data of a target cluster, and the capacity expansion and reduction module is an index-based capacity expansion and reduction module and is used for configuring a capacity expansion and reduction strategy;
inquiring index data of the target cluster according to a preset index rule;
and carrying out capacity expansion and reduction operation according to the index data and the capacity expansion and reduction strategy.
2. The method of claim 1, wherein the capacity expansion operation comprises a capacity reduction operation, wherein the index data comprises a total resource usage rate, and wherein the performing the capacity expansion and reduction operation according to the index data and the capacity expansion policy comprises:
comparing the total resource utilization rate with a preset lower limit value;
and if the total resource utilization rate is smaller than the preset lower limit value, carrying out the capacity reduction operation according to the capacity expansion strategy.
3. The method of claim 2, wherein the index data includes resource usage of each working node of the target cluster, and wherein performing the scaling operation according to the index data and the scaling policy further includes:
and calculating the total resource utilization rate according to the resource utilization rate of each working node.
4. The method according to claim 2, wherein the capacity expansion operation includes a capacity expansion operation, and the performing the capacity expansion and reduction operation according to the index data and the capacity expansion and reduction policy includes:
and if the total resource utilization rate is greater than the preset lower limit value, carrying out capacity expansion operation according to the capacity expansion strategy.
5. The method of any one of claims 1 to 4, further comprising:
calculating the current resource allocation corresponding to the monitoring module according to the index data;
and adjusting the current resource configuration corresponding to the monitoring module according to the expansion and contraction capacity strategy.
6. The method according to claim 5, wherein the adjusting the current resource configuration corresponding to the monitoring module according to the scaling strategy comprises:
calculating a deviation value between the current resource configuration and the historical average resource configuration;
and if the deviation value is smaller than a preset difference value, adjusting the current resource configuration corresponding to the monitoring module according to the expansion-reduction capacity strategy.
7. The method of claim 6, further comprising:
and updating the historical average resource allocation according to the adjusted current resource allocation.
8. A capacity expansion and contraction control device based on a Kubernetes cluster is characterized by comprising:
the creating instruction acquisition module is used for acquiring a cluster creating instruction;
the cluster creating module is used for creating a target cluster according to the cluster creating instruction and preset required configuration information;
the deployment module is used for deploying the monitoring module and the deployment capacity expansion module in the target cluster; the monitoring module is used for acquiring index data of a target cluster, and the capacity expansion and reduction module is an index-based capacity expansion and reduction module and is used for configuring a capacity expansion and reduction strategy;
the index query module is used for querying the index data of the target cluster according to a preset index rule;
and the capacity expansion and reduction operation module is used for carrying out capacity expansion and reduction operation according to the index data and the capacity expansion and reduction strategy.
9. An electronic device, comprising:
at least one memory;
at least one processor;
at least one program;
the program is stored in the memory, the processor executing the at least one program to implement the method of any one of claims 1 to 7.
10. A storage medium that is a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform:
the method of any one of claims 1 to 7.
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