CN109150987A - The two-layer container cluster elasticity expansion method of Intrusion Detection based on host layer and container floor - Google Patents
The two-layer container cluster elasticity expansion method of Intrusion Detection based on host layer and container floor Download PDFInfo
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- CN109150987A CN109150987A CN201810846155.5A CN201810846155A CN109150987A CN 109150987 A CN109150987 A CN 109150987A CN 201810846155 A CN201810846155 A CN 201810846155A CN 109150987 A CN109150987 A CN 109150987A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
- H04L41/5041—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
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Abstract
The invention discloses the two-layer container cluster elasticity expansion methods of a kind of Intrusion Detection based on host layer and container floor, are related to Resource Calculation technical field.This method propose a telescopic scheduling schemes of cloud upper container cluster resource, when carrying out United Dispatching to container, if it find that when cluster resource deficiency, the number that expanding node is calculated based on resource load situation of this method can be passed through, again by calling the API of cloud service provider to carry out the creation of node to which the automatic elastic for reaching host layer is flexible, control in conjunction with RC itself to Pod number, to realize the unified elastic telescopic in host layer and container floor, be conducive to integrated scheduling and the management of container cluster.
Description
Technical field
The present invention relates to Resource Calculation technical field more particularly to the two-layer containers of a kind of Intrusion Detection based on host layer and container floor
Cluster elasticity expansion method.
Background technique
All resources of cloud computing era are all resilientiy stretchables, such as using Cloud Server as the computing resource of representative.Tradition
IT application framework also comply with and made change, the serviceization of application had both realized the decoupling between large scale system difference component,
Also allow each service convenient for management and stand-alone development, O&M and deployment.It is represented when Docker is as currently a popular container operation
Technology can be packaged the runtime environment of software and be mirrored into, and carry out unified storage to mirror image by mirror image warehouse.It applies
Mirror image can be directly pulled when deployment, and quick start is carried out on node.Many modules can be divided into after large scale system decoupling,
Kubernetes is that the container orchestration technology of representative can allow the Docker organization of unity of multimode and abstract, is ultimately formed multiple
Service software organization's framework of intercommunication.And for the multiple containers group (claiming Pod in Kubernetes) of each rear end,
The controller that Kubernetes proposes a Replication Control is used to control the number of same configuration Pod, and energy
It is enough to be stretched by configuring replicates attribute to the number of Pod.Each container can define memory in configuration file
With the minimum and maximum of the resource bids such as CPU, then container cluster can be potentially encountered the inadequate situation of cluster resource.Mesh
Before, need to go to increase for container cluster manually node in this case, and a quantization is equipped with to the number for increasing node
Index.It is unfavorable for integrated scheduling and the management of container cluster.
Summary of the invention
The purpose of the present invention is to provide the two-layer container cluster elasticity dilatation sides of a kind of Intrusion Detection based on host layer and container floor
Method, to solve foregoing problems existing in the prior art.
To achieve the goals above, The technical solution adopted by the invention is as follows:
A kind of two-layer container cluster elasticity expansion method of Intrusion Detection based on host layer and container floor, includes the following steps:
S1 builds Kubernetes cluster；
S2 disposes autoScaling process and Master-Server process on the host node of Kubernetes cluster,
And two processes are respectively started；
RC object in S3, one typical Kubernetes of creation and deployment, for being controlled to Pod copy number
System；
S4 is carried out for the corresponding ARC object of RC Object Creation disposed, and to Master-Server process
Post is submitted, and Master-Server process is by ARC object encapsulation at JSON format persistent storage into backstage ETCD；
Whether monitored inside ETCD after S5, autoScaling process initiation has ARC Object Creation, if monitored,
A thread is created to safeguard this ARC object, is realized to a certain unified elastic dilatation serviced in host layer and container floor.
Preferably, in S5, a newly-built thread safeguards this ARC object, realize to a certain service host layer with
The unified elastic dilatation of container floor, specifically: it obtains cluster resource and occupies situation, calculate the ratio of resources occupation and resource bid
Weight, judges whether to need elastic expanding resource, if it is desired, then judge whether cluster aggregate resource fills after expanding by expected value
Foot calculates Pod number of expanding resource if sufficient, and modifies the replicas parameter of associated RC object directly to realize
The expansion of container level；If cluster aggregate resource is insufficient after expanding, the number for expanding cloud host is calculated, and create corresponding number
The cloud host of amount realizes the automatic elastic dilatation of host level, and host level is expanded into Pod number for calculating expansion after function again,
Carry out the expansion of container level.
Preferably, the cluster resource occupies situation, obtains: disposing as follows on Kubernetes cluster
Heapster, Heapster pass through the API for accessing kubelet on each node, then pass through kubelet and call cAdvisor's
API acquires the performance datas of all containers on each node, obtains the cluster resource and occupies situation.
Preferably, influxdb database is disposed on Kubernetes cluster, Heapster is by collected each section
The performance data of all containers is put into influxdb database on point.
Preferably, the resource bid situation obtains as follows: the configuration file Pod definition part of RC passes through
Spec.containers [] .resources defines entire Pod to the limiting value and application value of CPU and memory,
AutoScaling process to kube-apiserver by requesting Pod list in cluster to obtain CPU and the memory Shen of all Pod
It please be worth.
Preferably, S2 specifically, on the host node of Kubernetes cluster deployment comprising autoScaling process and
The jar packet of Master-Server process, and two processes are respectively started.
The beneficial effects of the present invention are: the two-layer sets of containers of Intrusion Detection based on host layer provided in an embodiment of the present invention and container floor
The elastic expansion method of group, proposes the telescopic scheduling scheme of cloud upper container cluster resource, is carrying out unified tune to container
When spending, if it find that when cluster resource deficiency expanding node can be calculated based on resource load situation by this method
Number, then the automatic elastic for creating to reach host layer by calling the API of cloud service provider to carry out node are flexible, in conjunction with
Control of the RC to Pod number itself is conducive to container cluster to realize the unified elastic telescopic in host layer and container floor
Integrated scheduling and management.
Detailed description of the invention
Fig. 1 is that the two-layer container cluster elasticity expansion method process of Intrusion Detection based on host layer and container floor provided by the invention is shown
It is intended to；
Fig. 2 is the flow diagram for safeguarding the unified elastic dilatation of ARC object implementatio8 host layer and container floor.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing, to the present invention into
Row is further described.It should be appreciated that the specific embodiments described herein are only used to explain the present invention, it is not used to
Limit the present invention.
As shown in Figure 1, the embodiment of the invention provides the two-layer container cluster bullets of a kind of Intrusion Detection based on host layer and container floor
Property expansion method, includes the following steps:
S1 builds Kubernetes cluster；
S2 disposes autoScaling process and Master-Server process on the host node of Kubernetes cluster,
And two processes are respectively started；
RC object in S3, one typical Kubernetes of creation and deployment, for being controlled to Pod copy number
System；
S4 is carried out for the corresponding ARC object of RC Object Creation disposed, and to Master-Server process
Post is submitted, and Master-Server process is by ARC object encapsulation at JSON format persistent storage into backstage ETCD；
Whether monitored inside ETCD after S5, autoScaling process initiation has ARC Object Creation, if monitored,
A thread is created to safeguard this ARC object, is realized to a certain unified elastic dilatation serviced in host layer and container floor.
In the above method, ARC (Auto Replication Control) is one to the Kubernetes RC carried
Encapsulation, can be associated by targetRef with target RC.CheckPeriod defines the period of inspection, defaults 30s.This
Its outer RC that target is associated with by spec, while defining in metrics as realization host layer and container floor appliance for releasing single
Property flexible computing resource classification and while judging all Pod actual motions that whether it need resource to expand occupy resource and pre-
Apply for the accounting of resource.CPU and memory two kinds of resources are supported at present.
Client can have the hair of the Master-Server on the Kubernetes container cluster of RC object of the same name to some
The instruction of creation ARC is sent, Master-Server here is a typical HTTP server, provides interface, is responsible for receiving outer
Portion's client creates the request of ARC object, while being written in ETCD storage according to request construction ARC object.Master-
Server realizes that Jersey-client class libraries realizes the access to ETCD API based on Jersey Open Framework.
ARC can be periodically checked after being created by autoScaling process, and one side is associated with specific management copy
RC, on the one hand define resource type, can all be written in ETCD storage, supply as the attribute of ARC this object
AutoScaling process is inquired.AutoScaling is an independent JAVA process, is located at Kubernetes container cluster
Master node on, be responsible for maintenance ARC object, to carry out the unified elastic dilatation of node and container to a certain service.
Wherein, S1, can be with the following method in implementation process: by taking CentOS7 operating system as an example, being pacified using yum
Dress mode installs the service such as Kubernetes-Master, Kubernetes-Slave, ETCD, Flannel respectively, is then collecting
Successively start on the respective node of group.Kubernetes-Master and Kubernetes-Slave service all contains conduct
Kubernetes memory host node and the respective process needed for node.
As shown in Fig. 2, in the embodiment of the present invention, it is described to create a thread to safeguard this ARC object, realization pair in S5
The a certain unified elastic dilatation serviced in host layer and container floor, specifically: it obtains cluster resource and occupies situation, calculate resource
Occupy the specific gravity with resource bid, judge whether to need elastic expanding resource, if it is desired, then judges to collect after expanding by expected value
Whether group's aggregate resource is sufficient, and Pod number of expanding resource is calculated if sufficient, and directly modifies associated RC object
Replicas parameter realizes the expansion of container level；If cluster aggregate resource is insufficient after expanding, calculates and expand cloud host
Number, and create the cloud host of corresponding number, realize the automatic elastic dilatation of host level, host level is expanded into after function again
The Pod number expanded is calculated, the expansion of container level is carried out.
In the specific implementation process, Real-Time Cluster resources occupation situation and resource bid situation are obtained first, to calculate
Resources occupation and the specific gravity of resource bid decide whether elastic expanding resource out.If desired, being calculated again by expected value
Whether cluster aggregate resource is sufficient after expansion, and Pod number of expanding resource is calculated if sufficient, then directly associated by modification
The replicas parameter of RC object realizes the expansion of container level.When the total inadequate resource of cluster, it is necessary first to calculate and expand
The number of cloud host is filled, the order of creation Cloud Server is then first issued to publicly-owned cloud service provider, poll judges that Cloud Server is created
It when building up function, then is issued to publicly-owned cloud service provider and the order of host to cluster is added, thus the appliance for releasing single to realize host level
Property dilatation.Host level is expanded into Pod number for calculating expansion after function again, realizes the expansion of container level.
Wherein, the cluster resource occupies situation, obtains: disposing as follows on Kubernetes cluster
Heapster, Heapster pass through the API for accessing kubelet on each node, then pass through kubelet and call cAdvisor's
API acquires the performance datas of all containers on each node, obtains the cluster resource and occupies situation.
Influxdb database is disposed on Kubernetes cluster, Heapster will own on collected each node
The performance data of container is put into influxdb database.
For the above method, detailed description are as follows:
Cluster is by many nodes at the total resources of cluster can be understood as the summation of each node resource.Resource, which can be divided into, answers
With application resource and apply manual resource.For manual computing resource, needs a monitoring and collect each section
The scheme of point resource service condition is realized to the receipts of resource service condition each on each node using Heapster service in the present invention
Collection.
Kubernetes built-in monitoring process of an entitled cAdvisor in the kubelet process of each node,
CAdvisor can obtain the performance indicator of respective node in real time and run the performance indicator of container on node, including CPU makes
With information such as situation, memory service condition, network throughput and file system service conditions, and REST API is provided for visitor
It calls at family end.And the computing resource of actually occupying of cluster always is the practical sum for occupying computing resource of each node, then needing one
Collection process collects the monitoring information of each node cAsvisor process, thus calculate cluster it is total actually occupy resource.It is right
In this problem, the present invention is solved using Heapster.Heapster passes through the API for accessing kubelet on each Node, then leads to
It crosses kubelet and calls the API of cAdvisor to acquire the performance data of all containers on the node.The data being collected into can be put
Enter in the time series database such as influxDB etc.
In the embodiment of the present invention, the resource bid situation can obtain as follows: the configuration file Pod of RC
Definition part defines limiting value and Shen of the entire Pod to CPU and memory by spec.containers [] .resources
It please be worth, autoScaling process to kube-apiserver by requesting in cluster Pod list to obtain the CPU of all Pod and interior
Deposit application value.
In a preferred embodiment of the invention, S2 is specifically as follows, on the host node top of Kubernetes cluster
Administration includes the jar packet of autoScaling process and Master-Server process, and two processes are respectively started.
By using above-mentioned technical proposal disclosed by the invention, obtained following beneficial effect: the embodiment of the present invention is mentioned
The Intrusion Detection based on host layer of confession and the two-layer container cluster elasticity expansion method of container floor, propose a cloud upper container cluster resource
Telescopic scheduling scheme, when carrying out United Dispatching to container, if it find that this method can be passed through when cluster resource deficiency
The number that expanding node is calculated based on resource load situation, then by call cloud service provider API carry out node creation
To which the automatic elastic for reaching host layer is flexible, the control in conjunction with RC itself to Pod number, to realize in host layer and appearance
The unified elastic telescopic of device layer is conducive to integrated scheduling and the management of container cluster.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
Depending on protection scope of the present invention.
Claims (6)
1. the two-layer container cluster elasticity expansion method of a kind of Intrusion Detection based on host layer and container floor, which is characterized in that including as follows
Step:
S1 builds Kubernetes cluster；
S2 disposes autoScaling process and Master-Server process on the host node of Kubernetes cluster, and divides
It Qi Dong not two processes；
RC object in S3, one typical Kubernetes of creation and deployment, for controlling Pod copy number；
S4 for the corresponding ARC object of RC Object Creation disposed, and carries out Post to Master-Server process and mentions
It hands over, Master-Server process is by ARC object encapsulation at JSON format persistent storage into backstage ETCD；
Whether have ARC Object Creation, if monitored, create if being monitored after S5, autoScaling process initiation inside ETCD
One thread safeguards this ARC object, realize to a certain service host layer and container floor unified elastic dilatation.
2. the two-layer container cluster elasticity expansion method of Intrusion Detection based on host layer according to claim 1 and container floor, special
Sign is, in S5, a newly-built thread safeguards this ARC object, realizes to a certain service in host layer and container floor
Unified elasticity dilatation, specifically: it obtains cluster resource and occupies situation, calculate the specific gravity of resources occupation and resource bid, judge
Whether elastic expanding resource is needed, if it is desired, whether cluster aggregate resource is sufficient after then judgement is expanded by expected value, if filled
Pod number that be sufficient then calculating expanding resource, and modify the replicas parameter of associated RC object directly to realize container level
Expansion；If cluster aggregate resource is insufficient after expanding, the number for expanding cloud host is calculated, and create the cloud master of corresponding number
Machine realizes the automatic elastic dilatation of host level, and host level is expanded into Pod number for calculating expansion after function again, carries out container
The expansion of level.
3. the two-layer container cluster elasticity expansion method of Intrusion Detection based on host layer according to claim 2 and container floor, special
Sign is that the cluster resource occupies situation, obtains as follows: disposing Heapster on Kubernetes cluster,
Heapster passes through the API for accessing kubelet on each node, then calls the API of cAdvisor by kubelet to acquire often
The performance data of all containers on a node obtains the cluster resource and occupies situation.
4. the two-layer container cluster elasticity expansion method of Intrusion Detection based on host layer according to claim 3 and container floor, special
Sign is, influxdb database is disposed on Kubernetes cluster, and Heapster will own on collected each node
The performance data of container is put into influxdb database.
5. the two-layer container cluster elasticity expansion method of Intrusion Detection based on host layer according to claim 2 and container floor, special
Sign is that the resource bid situation obtains as follows: the configuration file Pod definition part of RC passes through
Spec.containers [] .resources defines entire Pod to the limiting value and application value of CPU and memory,
AutoScaling process to kube-apiserver by requesting Pod list in cluster to obtain CPU and the memory Shen of all Pod
It please be worth.
6. the two-layer container cluster elasticity expansion method of Intrusion Detection based on host layer according to claim 1 and container floor, special
Sign is that S2 is specifically, disposing on the host node of Kubernetes cluster includes autoScaling process and Master-
The jar packet of Server process, and two processes are respectively started.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110531987A (en) * | 2019-07-30 | 2019-12-03 | 平安科技（深圳）有限公司 | Management method, device and computer readable storage medium based on Kubernetes cluster |
| CN110784347A (en) * | 2019-10-18 | 2020-02-11 | 北京浪潮数据技术有限公司 | Node management method, system, equipment and storage medium for container cluster |
| CN111464355A (en) * | 2020-03-31 | 2020-07-28 | 北京金山云网络技术有限公司 | Method and device for controlling expansion capacity of Kubernetes container cluster and network equipment |
| CN111522636A (en) * | 2020-04-03 | 2020-08-11 | 安超云软件有限公司 | Application container adjusting method, application container adjusting system, computer readable medium and terminal device |
| CN111752701A (en) * | 2019-03-29 | 2020-10-09 | 北京数安鑫云信息技术有限公司 | System cluster and resource scheduling method thereof |
| CN112291288A (en) * | 2019-07-24 | 2021-01-29 | 北京金山云网络技术有限公司 | Container cluster expansion method and device, electronic equipment and readable storage medium |
| CN112532722A (en) * | 2020-11-27 | 2021-03-19 | 中国—东盟信息港股份有限公司 | Kubernetes cloud native cluster node-based graceful shutdown method |
| CN112600942A (en) * | 2021-02-18 | 2021-04-02 | 杭州网银互联科技股份有限公司 | Method and system for improving route calculation efficiency in sd-wan |
| CN112882794A (en) * | 2021-02-25 | 2021-06-01 | 重庆紫光华山智安科技有限公司 | pod capacity expansion method, device, node and storage medium |
| WO2021143702A1 (en) * | 2020-01-14 | 2021-07-22 | 阿里巴巴集团控股有限公司 | Traffic distribution method, device and system, and storage medium |
| CN114327846A (en) * | 2020-09-30 | 2022-04-12 | 腾讯科技（深圳）有限公司 | Cluster capacity expansion method and device, electronic equipment and computer readable storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170346683A1 (en) * | 2016-05-24 | 2017-11-30 | Futurewei Technologies, Inc. | Automated Generation of Deployment Workflows for Cloud Platforms Based on Logical Stacks |
| CN107426034A (en) * | 2017-08-18 | 2017-12-01 | 国网山东省电力公司信息通信公司 | A kind of extensive container scheduling system and method based on cloud platform |
| CN107704310A (en) * | 2017-09-27 | 2018-02-16 | 郑州云海信息技术有限公司 | A kind of method, apparatus and equipment for realizing container cluster management |
-
2018
- 2018-07-27 CN CN201810846155.5A patent/CN109150987B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170346683A1 (en) * | 2016-05-24 | 2017-11-30 | Futurewei Technologies, Inc. | Automated Generation of Deployment Workflows for Cloud Platforms Based on Logical Stacks |
| CN107426034A (en) * | 2017-08-18 | 2017-12-01 | 国网山东省电力公司信息通信公司 | A kind of extensive container scheduling system and method based on cloud platform |
| CN107704310A (en) * | 2017-09-27 | 2018-02-16 | 郑州云海信息技术有限公司 | A kind of method, apparatus and equipment for realizing container cluster management |
Non-Patent Citations (1)
| Title |
|---|
| 杜威科: "基于Kubernetes的大数据流式计算Spark平台设计与实现", 《中国优秀硕士学位论文全文库数据库（电子期刊）》 * |
Cited By (15)
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|---|---|---|---|---|
| CN111752701A (en) * | 2019-03-29 | 2020-10-09 | 北京数安鑫云信息技术有限公司 | System cluster and resource scheduling method thereof |
| CN111752701B (en) * | 2019-03-29 | 2024-01-26 | 北京数安鑫云信息技术有限公司 | System cluster and resource scheduling method thereof |
| CN112291288A (en) * | 2019-07-24 | 2021-01-29 | 北京金山云网络技术有限公司 | Container cluster expansion method and device, electronic equipment and readable storage medium |
| CN112291288B (en) * | 2019-07-24 | 2022-10-04 | 北京金山云网络技术有限公司 | Container cluster expansion method and device, electronic equipment and readable storage medium |
| CN110531987A (en) * | 2019-07-30 | 2019-12-03 | 平安科技（深圳）有限公司 | Management method, device and computer readable storage medium based on Kubernetes cluster |
| CN110784347A (en) * | 2019-10-18 | 2020-02-11 | 北京浪潮数据技术有限公司 | Node management method, system, equipment and storage medium for container cluster |
| WO2021143702A1 (en) * | 2020-01-14 | 2021-07-22 | 阿里巴巴集团控股有限公司 | Traffic distribution method, device and system, and storage medium |
| CN111464355A (en) * | 2020-03-31 | 2020-07-28 | 北京金山云网络技术有限公司 | Method and device for controlling expansion capacity of Kubernetes container cluster and network equipment |
| CN111464355B (en) * | 2020-03-31 | 2022-11-15 | 北京金山云网络技术有限公司 | Method and device for controlling expansion and contraction capacity of Kubernets container cluster and network equipment |
| CN111522636A (en) * | 2020-04-03 | 2020-08-11 | 安超云软件有限公司 | Application container adjusting method, application container adjusting system, computer readable medium and terminal device |
| CN111522636B (en) * | 2020-04-03 | 2023-03-14 | 安超云软件有限公司 | Application container adjusting method, application container adjusting system, computer readable medium and terminal device |
| CN114327846A (en) * | 2020-09-30 | 2022-04-12 | 腾讯科技（深圳）有限公司 | Cluster capacity expansion method and device, electronic equipment and computer readable storage medium |
| CN112532722A (en) * | 2020-11-27 | 2021-03-19 | 中国—东盟信息港股份有限公司 | Kubernetes cloud native cluster node-based graceful shutdown method |
| CN112600942A (en) * | 2021-02-18 | 2021-04-02 | 杭州网银互联科技股份有限公司 | Method and system for improving route calculation efficiency in sd-wan |
| CN112882794A (en) * | 2021-02-25 | 2021-06-01 | 重庆紫光华山智安科技有限公司 | pod capacity expansion method, device, node and storage medium |
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