CN108469989A - A kind of reaction type based on clustering performance scalable appearance method and system automatically - Google Patents
A kind of reaction type based on clustering performance scalable appearance method and system automatically Download PDFInfo
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- 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]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
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- 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
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- 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]
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Abstract
The present invention provides a kind of reaction type based on clustering performance scalable appearance method and system automatically, belong to field of communication technology, which includes：Each node and container parameters are obtained, arrange the performance indicator for obtaining entire cluster, and performance indicator is stored, storage format is timestamp+json formats；The performance indicator being collected into is given a mark by judgment models, finally decides whether scalable appearance, and provides dilatation scheme；According to the scalable scalable appearance configuration template of appearance schemes generation, and issue scalable appearance order；Obtain the scalable appearance implementing result that cluster returns；The processing system includes：Performance collection subsystem and the judgement of scalable appearance and executive subsystem.This method and system can perceive special container self performance, while can perceive container place host and cluster overall performance, and be automatically performed the scalable appearance of container, solve the problems, such as that existing expansion capacity reduction method specific aim is poor, response speed is slow.
Description
Technical field
The invention belongs to fields of communication technology, and in particular to a kind of reaction type based on clustering performance expands capacity reduction method automatically
And system.
Background technology
In actual production system, we are frequently encountered the scene that some service needs dilatation, it is also possible to encounter because
The scene for needing to reduce Service Instance quantity is reduced for resource anxiety or workload.And such case is with container technique
Based on micro services scene in it is especially prominent.The reason is that micro services be the big application system of granularity is divided into it is different
Service module, such as：By web application be divided into five big service modules (1, load balancing module nginx 2, web server
Module 3, back-end data library module 4, cache module 5, session keep module), each module is (right by the identical several containers of function
Should be in the pod of kubernetes) composition load balancing cluster externally provides service, with the development of business, in each module
Container needs laterally scalable appearance.Current stage, there are two types of scalable capacitance types on kubernetes container cloud clusters：
1. (testing the speed by external test tools) according to system response time, scalable appearance is carried out to container manually；
2. using HPA (Horizontal Pod Autoscaler) function of kubernetes itself, container is pre-set
Dilatation threshold value (CPU, memory etc.) triggers dilatation.
For method 1, by external test tools measuring system response speed and on this basis scalable appearance manually, have with
Lower disadvantage：First, the Whole Response speed of system can only be held roughly, the pressure and utilization rate of each service module can not be perceived,
Be difficult to pointedly carry out dilatation processing, second is that manual expansion method can not quick response business extension demand.
For method 2, using HPA (Horizontal Pod Autoscaler) function of kubernetes itself, in advance
Container dilatation threshold value (CPU, memory etc.) triggering dilatation is arranged can only needle although can meet System Expansion demand to a certain degree
Threshold value setting (can only be directed to CPU, memory usage at present) is carried out to a small amount of index of special container, can not also perceive host master
The loading condition of machine and entire cluster carries out targetedly dilatation.
There are specific aims in conclusion existing kubernetes containers cloud cluster expands capacity reduction method poor, response speed is slowly
Problem.
Invention content
In order to overcome above-mentioned the shortcomings of the prior art, the present invention provides a kind of reaction types based on clustering performance certainly
Move scalable appearance method and system.
To achieve the goals above, the present invention provides the following technical solutions：
A kind of reaction type based on clustering performance expands capacity reduction method automatically, including：
Each node and container parameters are obtained, arrange the performance indicator for obtaining entire cluster, and the performance indicator is carried out
Storage, storage format are timestamp+json formats；
The performance indicator being collected into is given a mark by judgment models, finally decides whether scalable appearance, and provide
Dilatation scheme；According to the scalable scalable appearance configuration template of appearance schemes generation, and issue scalable appearance order；Obtain the scalable of cluster return
Hold implementing result.
Preferably, the method for each node of the acquisition and container parameters is：Collection kit data dock kubernetes sections
The kubelet components of point obtain each node and container parameters by the northbound interface of kubelet components.
Preferably, the performance indicator includes joint behavior index and node upper container parameter, the joint behavior index
Including cpu busy percentage, memory usage, network interface bandwidth availability ratio, file system utilization rate and disk I/O rate, on the node
Container parameters include cpu busy percentage, memory usage, network interface bandwidth availability ratio, file system utilization rate and disk I/O rate.
Preferably, the method for issuing scalable appearance order is：By the restful interfaces of kubernetes clusters with
Deployment modes issue scalable appearance order.
Another object of the present invention is to provide a kind of reaction type based on clustering performance, scalable appearance system, performance are received automatically
Subsystem and the judgement of scalable appearance and executive subsystem；
The performance collection subsystem obtains each node and container parameters, arranges the performance indicator for obtaining entire cluster, and
The performance indicator of the entire cluster is stored, storage format is timestamp+json formats；
The scalable appearance judges and executive subsystem, is decided whether according to the performance indicator for the entire cluster being collected into
Scalable appearance, and provide dilatation scheme；According to the scalable scalable appearance configuration template of appearance schemes generation, and issue scalable appearance order；It obtains
The scalable appearance implementing result that cluster returns.
Preferably, the performance collection subsystem is by performance collection probe, the performance indicator manifold on node
It is formed with performance indicator storage module three parts；
The performance collection probe, for collecting each node and container parameters；
The performance indicator manifold, the performance for arranging, collecting entire cluster according to each node and container parameters
Index；
The performance indicator storage module, for formatting the performance indicator for storing entire cluster, storage format is the time
Stamp+json formats；
The scalable appearance judges and executive subsystem is made of scalable appearance judgment module and scalable appearance execution module two parts；
The scalable appearance judgment module, the performance indicator for obtaining entire cluster, by judgment models to collected
Performance indicator is given a mark, and making scalable appearance according to marking situation judges；
The scalable appearance execution module for generating scalable appearance configuration template and issuing scalable appearance order, while obtaining collection
The scalable appearance executive condition that group returns.
Preferably, the kubelet components of the performance collection probe data docking kubernetes nodes, pass through
The northbound interface of kubelet components obtains each node and container parameters.
Preferably, the scalable appearance execution module by the restful interfaces of kubernetes clusters with deployment
Mode issues scalable appearance order.
Preferably, the performance indicator includes joint behavior index and node upper container parameter, the joint behavior index
Including cpu busy percentage, memory usage, network interface bandwidth availability ratio, file system utilization rate and disk I/O rate, on the node
Container parameters include cpu busy percentage, memory usage, network interface bandwidth availability ratio, file system utilization rate and disk I/O rate.
Automatically scalable appearance method and system, this method include reaction type provided by the invention based on clustering performance：It obtains
Each node and container parameters arrange the performance indicator for obtaining entire cluster, and performance indicator are stored, when storage format is
Between stamp+json formats；The performance indicator being collected into is given a mark by judgment models, finally decides whether scalable appearance, and give
Go out dilatation scheme；According to the scalable scalable appearance configuration template of appearance schemes generation, and issue scalable appearance order；Obtain the expansion that cluster returns
Capacity reducing implementing result；This method can not only be collected into performance indicator (cluster, section each inside kubernetes containers cloud cluster
Point, container), and the scalable appearance of container, quick response business demand can be automatically performed based on performance indicator integrally scores；
The system can perceive the entire clusters of kubernetes performance indicator (covering cluster, node, container, and cover CPU, memory,
File system utilization rate, network interface card bandwidth, disk I/O rate), and scalable appearance is made by marking mechanism based on this and is judged simultaneously
Formulation and implementation scheme, host and cluster overall performance where capable of perceiving container, more comprehensively, accurately perception service is scalable
Appearance demand, and accurately realize the scalable appearance of container；It is automatically finished the scalable appearance of container by feedback mechanism, is more quickly responded
The scalable appearance demand of business；Solve the problems, such as that existing expansion capacity reduction method specific aim is poor, response speed is slow.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Obtain other attached drawings according to these attached drawings.
Fig. 1 is a kind of automatic scalable appearance method flow signal of reaction type based on clustering performance provided in an embodiment of the present invention
Figure；
Fig. 2 is a kind of structure of reaction type based on clustering performance provided in an embodiment of the present invention scalable appearance system automatically
Block diagram；
Fig. 3 is that the automatic scalable appearance method flow of a kind of reaction type based on clustering performance that the embodiment of the present invention 1 provides shows
It is intended to.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
An embodiment of the present invention provides a kind of reaction type based on clustering performance scalable appearance method and system automatically, this method
With system mainly for kubernetes container clouds, Kubernetes is the container cluster management system that Google increases income, and is carried
For functions such as application deployment, maintenance, extension mechanisms, answering for across machine operation containerization can be easily managed using Kubernetes
With.Therefore, the needs of more multi-platform can be met by carrying out effectively scalable appearance to kubernetes container clouds.
Fig. 1 is a kind of automatic scalable appearance method flow signal of reaction type based on clustering performance provided in an embodiment of the present invention
Figure, this approach includes the following steps：
Step 101：It obtains each node and container parameters, arranges the performance indicator for obtaining entire cluster, and by performance indicator
It is stored, storage format is timestamp+json formats, and it can be the date and time information of electronic document to use the storage format
Safeguard protection is provided；
Step 102：The performance indicator being collected into is given a mark by judgment models, finally decides whether scalable appearance, and
Provide dilatation scheme；According to the scalable scalable appearance configuration template of appearance schemes generation, and issue scalable appearance order；Obtain what cluster returned
Scalable appearance implementing result.
In a step 101, the method for obtaining each node and container parameters is：Collection kit data dock kubernetes sections
The kubelet components of point obtain each node and container parameters by the northbound interface of kubelet components.Due to kubernetes
It is a distributed type colony management system, a worker will be run on each node (node), life is carried out to container
The management in period, this worker program is exactly kubelet.The major function of kubelet is exactly timing from somewhere obtaining
The expectation state of pod/container (runs how what container, the copy amount of operation, network or storage match on node
Set etc.), and corresponding container platform interface is called to reach this state.Under cluster state, kubelet not only can be from
Information is read on master, can also be from the pod information of acquisition node elsewhere, therefore kubelet is selected in the present embodiment
Component obtains each node and container parameters, and the data of acquisition are more comprehensive.
It should be noted that performance indicator includes joint behavior index and node upper container parameter, node in the present embodiment
Performance indicator includes cpu busy percentage, memory usage, network interface bandwidth availability ratio, file system utilization rate and disk I/O rate, section
Point upper container parameter includes cpu busy percentage, memory usage, network interface bandwidth availability ratio, file system utilization rate and disk I/O speed
Rate.The loading condition that can be good at perceiving host and entire cluster by above-mentioned performance indicator, to carry out specific aim
Dilatation.
In a step 102, issuing the scalable method for holding order is：By the restful interfaces of kubernetes clusters with
Deployment modes issue scalable appearance order.
Based on same inventive concept, an embodiment of the present invention provides a kind of reaction type based on clustering performance scalable appearances automatically
System, since the system solves the principle and a kind of automatic scalable appearance system, method phase of reaction type based on clustering performance of technical problem
Seemingly, therefore the implementation of the system may refer to the implementation of method, and overlaps will not be repeated.
Fig. 2 is a kind of structural frames of the reaction type based on clustering performance provided in an embodiment of the present invention scalable appearance system automatically
Figure, as shown in Fig. 2, the system includes mainly performance collection subsystem 1 and scalable appearance judges and executive subsystem 2；
Performance collection subsystem 1 obtains each node and container parameters, arranges the performance indicator for obtaining entire cluster, and will
The performance indicator of entire cluster is stored, and storage format is timestamp+json formats；
Scalable appearance judges and executive subsystem 2, decides whether scalable appearance according to the performance indicator for the entire cluster being collected into,
And provide dilatation scheme；According to the scalable scalable appearance configuration template of appearance schemes generation, and issue scalable appearance order；Cluster is obtained to return
Scalable appearance implementing result.
Further, performance collection subsystem 1 is by performance collection probe 11, the performance indicator manifold on node
12 and 13 three parts of performance indicator storage module composition；
Performance collection probe 11, for collecting each node and container parameters；
Performance indicator manifold 12, the performance for arranging, collecting entire cluster according to each node and container parameters refer to
Mark；
Performance indicator storage module 13, for formatting the performance indicator for storing entire cluster, storage format for timestamp+
Json formats use the storage format that can provide safeguard protection for the date and time information of electronic document；
Scalable appearance judges and executive subsystem 2 is by scalable appearance judgment module 21 and scalable 22 two parts group of appearance execution module
At；
Scalable appearance judgment module 21, the performance indicator for obtaining entire cluster, by judgment models to collected property
Energy index marking is made scalable appearance according to marking situation and is judged；
Scalable appearance execution module 22 for generating scalable appearance configuration template and issuing scalable appearance order, while obtaining cluster
The scalable appearance executive condition returned.
In the present embodiment, 11 data of performance collection probe dock the kubelet components of kubernetes nodes, pass through
The northbound interface of kubelet components obtains each node and container parameters.Herein, performance collection probe 11 is to be obtained in step 101
Take each node and the collection kit of container parameters.Scalable appearance restful interface of the execution module 22 by kubernetes clusters
Scalable appearance order is issued in a manner of deployment.
Further, performance indicator includes joint behavior index and node upper container parameter, and joint behavior index includes CPU
Utilization rate, memory usage, network interface bandwidth availability ratio, file system utilization rate and disk I/O rate, node upper container parameter packet
Include cpu busy percentage, memory usage, network interface bandwidth availability ratio, file system utilization rate and disk I/O rate.Pass through above-mentioned performance
Index can be good at perceiving the loading condition of host and entire cluster, to carry out targetedly dilatation.
Based on above-mentioned processing system, a kind of reaction type based on clustering performance that the embodiment of the present invention 1 provides is automatically scalable
Hold method flow schematic diagram, as shown in figure 3, this method mainly includes the following steps that：
Step 201：Performance collection probe 11 periodically obtains each node and container parameters on each node for installation；
In the present embodiment, 11 data of performance collection probe dock the kubelet components of kubernetes nodes, pass through
The northbound interface of kubelet components obtains each node and container parameters.Specifically, performance indicator includes joint behavior index and section
Point upper container parameter, joint behavior index include cpu busy percentage, memory usage, network interface bandwidth availability ratio, file system use
Rate and disk I/O rate, node upper container parameter include cpu busy percentage, memory usage, network interface bandwidth availability ratio, file system
Utilization rate and disk I/O rate.
Step 202：Performance collection probe 11 periodically sends each node and container parameters by udp protocol and is converged to performance indicator
Storage 12；
Step 203：Performance indicator manifold 12 summarizes each node and container parameters, and arrangement show that the performance of entire cluster refers to
Mark；
Step 204：Performance indicator manifold 12 stores clustering performance index into performance indicator storage module 13, and is arranged
The timestamp of json formats；
Scalable appearance judges and executes
Step 205：Scalable appearance judgment module 21 gives a mark to the clustering performance index being collected into, and finally decides whether to expand
Capacity reducing, and provide scalable appearance scheme；
Step 206：Scalable appearance execution module 22 passes through according to the scalable scalable appearance configuration template of appearance schemes generation
The restful interfaces of kubernetes clusters issue scalable appearance order in a manner of deployment；
Step 207：Scalable execution module 22 of holding obtains the scalable appearance implementing result that kubernetes clusters return.
The automatic scalable appearance method and system of reaction type provided in this embodiment based on clustering performance have below beneficial to effect
Fruit：
(1) the automatic scalable appearance method and system this system of the reaction type provided in this embodiment based on clustering performance can not only
It is collected into performance indicator (cluster, node, container) each inside kubernetes containers cloud cluster, and can be with performance indicator entirety
Based on scoring, it is automatically performed the scalable appearance of container, quick response business demand；
(2) system can perceive the performance indicators of the entire clusters of kubernetes (covering cluster, node, container, and cover
Cover CPU, memory, file system utilization rate, network interface card bandwidth, disk I/O rate), and expansion is made by marking mechanism based on this
Capacity reducing judges and scheme of formulating and implementing, and host and cluster overall performance, more comprehensively, accurately feel where capable of perceiving container
Know the scalable appearance demand of business, and accurately realizes the scalable appearance of container.
(3) the scalable appearance of container is automatically finished by feedback mechanism, more quickly responds the scalable appearance demand of business (as expanded
Hold on mysql containers to the minimum host of a load, and quantity is 10 times originally after dilatation).
It should be understood by those skilled in the art that, the embodiment of the present invention can be provided as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the present invention
Apply the form of example.Moreover, the present invention can be used in one or more wherein include computer usable program code computer
The computer program production implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of product.
The present invention be with reference to according to the method for the embodiment of the present invention, the flow of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that can be realized by computer program instructions every first-class in flowchart and/or the block diagram
The combination of flow and/or box in journey and/or box and flowchart and/or the block diagram.These computer programs can be provided
Instruct the processor of all-purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine so that the instruction executed by computer or the processor of other programmable data processing devices is generated for real
The device for the function of being specified in present one flow of flow chart or one box of multiple flows and/or block diagram or multiple boxes.
These computer program instructions, which may also be stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that instruction generation stored in the computer readable memory includes referring to
Enable the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one box of block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device so that count
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, in computer or
The instruction executed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic
Property concept, then additional changes and modifications may be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as
It selects embodiment and falls into all change and modification of the scope of the invention.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
God and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (9)
1. a kind of reaction type based on clustering performance expands capacity reduction method automatically, which is characterized in that including：
Each node and container parameters are obtained, arrange the performance indicator for obtaining entire cluster, and the performance indicator is stored,
Storage format is timestamp+json formats；
The performance indicator being collected into is given a mark by judgment models, finally decides whether scalable appearance, and provide dilatation
Scheme；According to the scalable scalable appearance configuration template of appearance schemes generation, and issue scalable appearance order；The scalable appearance that cluster returns is obtained to hold
Row result.
2. the reaction type according to claim 1 based on clustering performance expands capacity reduction method automatically, which is characterized in that described to obtain
The method for taking each node and container parameters is：Collection kit data dock the kubelet components of kubernetes nodes, pass through
The northbound interface of kubelet components obtains each node and container parameters.
3. the reaction type according to claim 1 based on clustering performance expands capacity reduction method automatically, which is characterized in that the property
Energy index includes joint behavior index and node upper container parameter, and the joint behavior index includes cpu busy percentage, memory utilization
Rate, network interface bandwidth availability ratio, file system utilization rate and disk I/O rate, the node upper container parameter include cpu busy percentage,
Memory usage, network interface bandwidth availability ratio, file system utilization rate and disk I/O rate.
4. the reaction type according to claim 2 based on clustering performance expands capacity reduction method automatically, which is characterized in that under described
Send out scalable hold order method be：It is issued in a manner of deployment by the restful interfaces of kubernetes clusters scalable
Hold order.
5. a kind of reaction type based on clustering performance scalable appearance system automatically, which is characterized in that performance collection subsystem and scalable
Hold judgement and executive subsystem；
The performance collection subsystem, obtains each node and container parameters, arranges the performance indicator for obtaining entire cluster, and by institute
The performance indicator for stating entire cluster is stored, and storage format is timestamp+json formats；
The scalable appearance judges and executive subsystem, is decided whether according to the performance indicator for the entire cluster being collected into scalable
Hold, and provides dilatation scheme；According to the scalable scalable appearance configuration template of appearance schemes generation, and issue scalable appearance order；Obtain cluster
The scalable appearance implementing result returned.
6. the reaction type according to claim 5 based on clustering performance scalable appearance system automatically, which is characterized in that the property
Energy collection subsystem is by performance collection probe, performance indicator manifold and the performance indicator storage module three on node
It is grouped as；
The performance collection probe, for collecting each node and container parameters；
The performance indicator manifold, the performance indicator for arranging, collecting entire cluster according to each node and container parameters；
The performance indicator storage module, for formatting the performance indicator for storing entire cluster, storage format for timestamp+
Json formats；
The scalable appearance judges and executive subsystem is made of scalable appearance judgment module and scalable appearance execution module two parts；
The scalable appearance judgment module, the performance indicator for obtaining entire cluster, by judgment models to collected performance
Index is given a mark, and making scalable appearance according to marking situation judges；
The scalable appearance execution module for generating scalable appearance configuration template and issuing scalable appearance order, while obtaining cluster and returning
The scalable appearance executive condition returned.
7. the reaction type according to claim 6 based on clustering performance scalable appearance system automatically, which is characterized in that the property
The kubelet components that probe data docking kubernetes nodes can be collected are obtained each by the northbound interface of kubelet components
Node and container parameters.
8. the reaction type according to claim 6 based on clustering performance scalable appearance system automatically, which is characterized in that the expansion
Capacity reducing execution module is issued scalable appearance by the restful interfaces of kubernetes clusters in a manner of deployment and ordered.
9. the reaction type according to claim 5 based on clustering performance scalable appearance system automatically, which is characterized in that the property
Energy index includes joint behavior index and node upper container parameter, and the joint behavior index includes cpu busy percentage, memory utilization
Rate, network interface bandwidth availability ratio, file system utilization rate and disk I/O rate, the node upper container parameter include cpu busy percentage,
Memory usage, network interface bandwidth availability ratio, file system utilization rate and disk I/O rate.
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