CN109144734A - A kind of container resource quota distribution method and device - Google Patents
A kind of container resource quota distribution method and device Download PDFInfo
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- CN109144734A CN109144734A CN201811060406.3A CN201811060406A CN109144734A CN 109144734 A CN109144734 A CN 109144734A CN 201811060406 A CN201811060406 A CN 201811060406A CN 109144734 A CN109144734 A CN 109144734A
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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
- G06F9/5077—Logical partitioning of resources; Management or configuration of virtualized resources
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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]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
- G06F2209/50—Indexing scheme relating to G06F9/50
- G06F2209/508—Monitor
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Abstract
The invention discloses a kind of container resource quota distribution method and devices.It is related to computing cluster technology, solves the problems, such as container computational resource allocation.This method comprises: monitoring computer cluster and container, collection obtains history data；According to the history data, the resource quota allocation plan of each container is generated；Start container application or task according to the resource quota allocation plan.Technical solution provided by the invention is suitable for container operation, realizes the container resource distribution based on real resource service condition.
Description
Technical field
The present invention relates to computing cluster technology, espespecially a kind of container resource quota distribution method and device.
Background technique
Container is the OS-Level virtual of lightweight, can be run in the process of a resource isolation application and its
Rely on item.Component necessary to operation application program will all be packaged into a mirror image and can be multiplexed.When executing mirror image, mirror image meeting
It operates in an isolation environment, and the memory, CPU, GPU and disk of host will not be shared, this guarantees containers
Interior process is unable to any process outside monitoring of containers.Resource original allocation when container starting operation, which often determines in container, appoints
The operational efficiency of business, reasonable resource quota distribution can either ensure that the operation of the stability and high efficiency of container can adequately utilize meter again
Resource is calculated to complete required task.
Summary of the invention
In order to solve the above-mentioned technical problems, the present invention provides a kind of container resource distribution distribution in accordance with the law and device, solves
The problem of container computational resource allocation.
In order to reach the object of the invention, the present invention provides a kind of container resource quota distribution methods, comprising:
Computer cluster and container are monitored, collection obtains history data；
According to the history data, the resource quota allocation plan of each container is generated；
Start container application or task according to the resource quota allocation plan.
Preferably, the history data includes CPU, GPU, memory, disk utilization rate and operation total duration, the prison
Computer cluster and container are controlled, collecting the step of obtaining history data includes:
Collect the operating status real-time logs file for the container being currently running；
The operating status real-time logs file is sorted out according to different container application types；
Extract CPU, GPU, memory, disk utilization rate and the operation total duration after sorting out in operating status real-time logs file
Information generates history data, forms database.
Preferably, according to the history data, the step of generating the resource quota allocation plan of each container, includes:
According to the history data, the resource service condition of different containers types application and/or container task is analyzed；
Using analysis as a result, generating the resource quota allocation plan of each container of optimization.
Preferably, the configuration information comprising following any or any multinomial resource in the resource quota allocation plan:
CPU, GPU, memory.
The present invention also provides a kind of container resource quota distributors, comprising:
Monitoring module, for monitoring computer cluster and container, collection obtains history data；
Deep learning module, for generating the resource quota allocation plan of each container according to the history data；
Execution module is configured, for starting container application or task according to the resource quota allocation plan.
Preferably, the history data includes CPU, GPU, memory, disk utilization rate and operation total duration, the prison
Controlling module includes:
Log collection unit, for collecting the operating status real-time logs file for the container being currently running；
Unit is sorted out in log, for carrying out according to different container application types to the operating status real-time logs file
Sort out；
Data pre-processing unit, for extracting the CPU, GPU after sorted out in operating status real-time logs file, memory, magnetic
Disk utilization rate and operation total duration information, generate history data, form database.
Preferably, the deep learning module includes:
Analytical unit, for according to the history data, analyzing different containers types application and/or container task
Resource service condition；
Schemes generation unit, for using analysis as a result, generating the resource quota allocation plan of each container of optimization.
The present invention provides a kind of container resource quota distribution method and devices, monitor computer cluster and container, collect
History data is obtained, according to the history data, the resource quota allocation plan of each container is generated, then according to institute
State the starting container application of resource quota allocation plan or task.The container resource based on real resource service condition is realized to match
It sets, solves the problems, such as container computational resource allocation.
Other features and advantages of the present invention will be illustrated in the following description, also, partly becomes from specification
It obtains it is clear that understand through the implementation of the invention.The objectives and other advantages of the invention can be by specification, right
Specifically noted structure is achieved and obtained in claim and attached drawing.
Detailed description of the invention
Attached drawing is used to provide to further understand technical solution of the present invention, and constitutes part of specification, with this
The embodiment of application technical solution for explaining the present invention together, does not constitute the limitation to technical solution of the present invention.
Fig. 1 is a kind of flow diagram for container resource quota distribution method that one embodiment of the invention provides；
Fig. 2 is the idiographic flow schematic diagram of step 101 in Fig. 1；
Fig. 3 is the idiographic flow schematic diagram of step 102 in Fig. 1；
Fig. 4 is a kind of realization principle schematic diagram for container resource quota distribution method that one embodiment of the invention provides；
Fig. 5 is a kind of structural schematic diagram for container resource quota distributor that one embodiment of the invention provides；
Fig. 6 is the structural schematic diagram of monitoring module 501 in Fig. 5；
Fig. 7 is the structural schematic diagram of deep learning module 502 in Fig. 5.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing to the present invention
Embodiment be described in detail.It should be noted that in the absence of conflict, in the embodiment and embodiment in the application
Feature can mutual any combination.
Step shown in the flowchart of the accompanying drawings can be in a computer system such as a set of computer executable instructions
It executes.Also, although logical order is shown in flow charts, and it in some cases, can be to be different from herein suitable
Sequence executes shown or described step.
When executing mirror image, it can be operated in an isolation environment, and will not share the memory of host, CPU, GPU
And disk, any process being unable to this guarantees process in container outside monitoring of containers.At the beginning of resource when container starting operation
Begin to distribute the operational efficiency for often determining task in container, reasonable resource quota distribution can either ensure that stablizing for container is high
The operation of effect can adequately complete required task using computing resource again.
In order to solve the problems, such as container computational resource allocation, the embodiment provides a kind of container resource quotas point
Method of completing the square and device analyze Historical Monitoring data using depth learning technology, then generate for different vessels application/
The solution of the resource quota reasonable distribution of task.With reference to the accompanying drawing, the embodiment of the present invention is described in detail.
One embodiment of the invention provides a kind of window resource quota distribution method, completes container resource using this method
The process of configuration is as shown in Figure 1, comprising:
Step 101, monitoring computer cluster and container, collection obtain history data.
In the embodiment of the present invention, when the history data includes CPU, GPU, memory, disk utilization rate and total operation
It is long.
Third parties' open source software such as cAdvisor can be used, to task in the container, computing cluster, container being currently running
State is monitored in real time, and to CPU, GPU, memory, the operating status of container has real-time logs file to export and keep records of.Number
According to pretreatment include first to a variety of different container application types sort out, then in history monitoring data CPU, GPU,
Memory, disk utilization rate and operation total duration carry out inquiry and record forms database, and the same container of same class is applied or appointed
Log of the business acquisitions more as far as possible under different time, different situations is with universality.
This step is specifically as shown in Figure 2, comprising:
The operating status real-time logs file for the container that step 1011, collection are currently running.
Step 1012 sorts out the operating status real-time logs file according to different container application types.
Step 1013, extract the CPU, GPU after sorted out in operating status real-time logs file, memory, disk utilization rate and
Total duration information is run, history data is generated, forms database.
Step 102, according to the history data, generate the resource quota allocation plan of each container.
Configuration information comprising following any or any multinomial resource in the resource quota allocation plan: CPU, GPU, interior
It deposits.
In this step, pretreated data are trained and are analyzed using deep learning, proposes that the resource of optimization is matched
Volume allocation plan.By CPU, GPU, memory and the disk utilization to container application or task and computing cluster and run total
The monitoring information analysis different containers types of duration are applied and the resource service condition of task, after then obtaining optimization according to training
Container resource quota scheme, help starting container application or when task has automatically configured CPU, GPU, the memory etc. of container
Computing resource.
This step is specifically as shown in Figure 3, comprising:
Step 1021, according to the history data, analyze the resource of different containers types application and/or container task
Service condition；
Step 1022, using analysis as a result, generate optimization each container resource quota allocation plan.
The resource quota allocation plan of generation can be stored to solution database.
Step 103 starts container application or task according to the resource quota allocation plan.
In this step, when needing to start container application or task, access solution database obtains resource and matches first
Then volume allocation plan carries out scheduling of resource according to the resource quota allocation plan, for container application or task distribution starting
Run resource.
One embodiment of the invention additionally provides a kind of container resource quota distribution method, by container, computing cluster
And task status in container, the monitoring information historical data applied in container carry out deep learning analysis, carry out comprehensive assessment,
Then container resource quota distribution method with strong points is formulated for the application of different types of container, container task, in this way may be used
To improve the utilization rate of slack resources, the resource quota allocation plan provided according to deep learning is to container application or task point
Resource is run with starting, can either ensure that the operation of the stability and high efficiency of container can be completed adequately using computing resource again in this way
Required task.
The embodiment of the present invention mainly includes that deep learning carries out analysis data and proposes prioritization scheme, by pretreated appearance
Device state, computing cluster resource are applied in container or the historical data of the state monitoring information of task.
Realization principle is as shown in figure 4, specific implementation process is as follows:
1) resource of container is used by the monitoring tools of the container of third party's open source, computing cluster, container task, meter
Calculate cluster with being monitored in real time and recorded with slack resources, the same container application of same class or task are more as far as possible
The log under different time, different situations is acquired with universality.
2) container that monitors in real time, task in container, computing cluster resource utilisation information carry out unloading, it is unified to sort out note
It records to a database and is stored.
3) pretreatment of history monitoring information data can be to the container application or task being already recorded in database
The data of CPU, GPU, memory, the operating status of container are analyzed and are handled, and pretreatment includes first to a variety of different appearances
Device application type is sorted out, and the key message in database is extracted, as CPU, GPU, memory, the disk in history monitoring data use
Row is inquired and recorded to rate and operation total duration into database, then sort out and the formation of category can be calculated by deep learning
Data mode used in method.
4) deep learning carries out analysis data and proposes prioritization scheme: by container application or task and computing cluster
CPU, GPU, memory and disk utilization and run the monitoring information analysis different containers types application and task of total duration
Then resource service condition obtains the container resource quota scheme after optimization according to training, and saves into can be accessed accordingly
Database, starting container application or when task, the accessible database of task dispatch has simultaneously automatically configured container
The computing resources such as CPU, GPU, memory.
One embodiment of the invention additionally provides a kind of container resource quota distributor, and structure is as shown in figure 5, packet
It includes:
Monitoring module 501, for monitoring computer cluster and container, collection obtains history data；
Deep learning module 502, for generating the resource quota configuration side of each container according to the history data
Case；
Execution module 503 is configured, for starting container application or task according to the resource quota allocation plan.
Preferably, the history data includes CPU, GPU, memory, disk utilization rate and operation total duration, the prison
The structure for controlling module 501 is as shown in Figure 6, comprising:
Log collection unit 5011, for collecting the operating status real-time logs file for the container being currently running；
Log sort out unit 5012, for according to different container application types to the operating status real-time logs file
Sorted out；
Data pre-processing unit 5013, for extracting the CPU, GPU after sorted out in operating status real-time logs file, interior
It deposits, disk utilization rate and operation total duration information, generation history data form database.
Preferably, the deep learning module 502 structure as shown in fig. 7, comprises:
Analytical unit 5021, for analyzing different containers types application and/or container being appointed according to the history data
The resource service condition of business；
Schemes generation unit 5022, for using analysis as a result, generating the resource quota allocation plan of each container of optimization.
The embodiment provides a kind of container resource quota distribution method and devices, monitor computer cluster and appearance
Device, collection obtain history data, according to the history data, generate the resource quota allocation plan of each container, so
Container application or task are started according to the resource quota allocation plan afterwards.Realize the container based on real resource service condition
Resource distribution solves the problems, such as container computational resource allocation.
By to the monitoring information historical data applied in task status in container, computing cluster or container, container into
The analysis of row deep learning, carries out comprehensive assessment, is then directed to for the task in the application of different types of container, container is specified
The strong container resource quota distribution method of property, can be improved the utilization rate of slack resources, the money provided according to deep learning in this way
Allocation of quota scheme in source is applied to container or task distribution starting operation resource, can either ensure the stability and high efficiency of container in this way
Operation can adequately complete required task using computing resource again.
It will appreciated by the skilled person that whole or certain steps, system, dress in method disclosed hereinabove
Functional module/unit in setting may be implemented as software, firmware, hardware and its combination appropriate.In hardware embodiment,
Division between the functional module/unit referred in the above description not necessarily corresponds to the division of physical assemblies；For example, one
Physical assemblies can have multiple functions or a function or step and can be executed by several physical assemblies cooperations.Certain groups
Part or all components may be implemented as by processor, such as the software that digital signal processor or microprocessor execute, or by
It is embodied as hardware, or is implemented as integrated circuit, such as specific integrated circuit.Such software can be distributed in computer-readable
On medium, computer-readable medium may include computer storage medium (or non-transitory medium) and communication media (or temporarily
Property medium).As known to a person of ordinary skill in the art, term computer storage medium is included in for storing information (such as
Computer readable instructions, data structure, program module or other data) any method or technique in the volatibility implemented and non-
Volatibility, removable and nonremovable medium.Computer storage medium include but is not limited to RAM, ROM, EEPROM, flash memory or its
His memory technology, CD-ROM, digital versatile disc (DVD) or other optical disc storages, magnetic holder, tape, disk storage or other
Magnetic memory apparatus or any other medium that can be used for storing desired information and can be accessed by a computer.This
Outside, known to a person of ordinary skill in the art to be, communication media generally comprises computer readable instructions, data structure, program mould
Other data in the modulated data signal of block or such as carrier wave or other transmission mechanisms etc, and may include any information
Delivery media.
Claims (7)
1. a kind of container resource quota distribution method characterized by comprising
Computer cluster and container are monitored, collection obtains history data；
According to the history data, the resource quota allocation plan of each container is generated；
Start container application or task according to the resource quota allocation plan.
2. container resource quota distribution method according to claim 1, which is characterized in that the history data includes
CPU, GPU, memory, disk utilization rate and operation total duration, the monitoring computer cluster and container, collection obtain history run
The step of data includes:
Collect the operating status real-time logs file for the container being currently running；
The operating status real-time logs file is sorted out according to different container application types；
Extract CPU, GPU, memory, disk utilization rate and the operation total duration letter after sorting out in operating status real-time logs file
Breath generates history data, forms database.
3. container resource quota distribution method according to claim 2, which is characterized in that according to the history run number
Include: according to, the step of generating the resource quota allocation plan of each container
According to the history data, the resource service condition of different containers types application and/or container task is analyzed；
Using analysis as a result, generating the resource quota allocation plan of each container of optimization.
4. container resource quota distribution method according to claim 3, which is characterized in that the resource quota allocation plan
In the configuration information comprising following any or any multinomial resource:
CPU, GPU, memory.
5. a kind of container resource quota distributor characterized by comprising
Monitoring module, for monitoring computer cluster and container, collection obtains history data；
Deep learning module, for generating the resource quota allocation plan of each container according to the history data；
Execution module is configured, for starting container application or task according to the resource quota allocation plan.
6. container resource quota distributor according to claim 5, which is characterized in that the history data includes
CPU, GPU, memory, disk utilization rate and operation total duration, the monitoring module include:
Log collection unit, for collecting the operating status real-time logs file for the container being currently running；
Unit is sorted out in log, for returning according to different container application types to the operating status real-time logs file
Class；
Data pre-processing unit makes for extracting the CPU, GPU after sorted out in operating status real-time logs file, memory, disk
With rate and operation total duration information, history data is generated, forms database.
7. container resource quota distributor according to claim 6, which is characterized in that the deep learning module packet
It includes:
Analytical unit, for analyzing the resource of different containers types application and/or container task according to the history data
Service condition；
Schemes generation unit, for using analysis as a result, generating the resource quota allocation plan of each container of optimization.
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| CN110413391A (en) * | 2019-07-24 | 2019-11-05 | 上海交通大学 | Deep learning task service method for ensuring quality and system based on container cluster |
| CN110413408A (en) * | 2019-06-29 | 2019-11-05 | 苏州浪潮智能科技有限公司 | A kind of video memory control method, equipment and the storage medium of deep learning frame |
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| CN111124782A (en) * | 2019-12-18 | 2020-05-08 | 无线生活（杭州）信息科技有限公司 | Method and device for intelligently configuring computer cluster |
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| CN110413408A (en) * | 2019-06-29 | 2019-11-05 | 苏州浪潮智能科技有限公司 | A kind of video memory control method, equipment and the storage medium of deep learning frame |
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