CN108287749A - A kind of data center's total management system cloud resource dispatching method - Google Patents
A kind of data center's total management system cloud resource dispatching method Download PDFInfo
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- CN108287749A CN108287749A CN201810024580.6A CN201810024580A CN108287749A CN 108287749 A CN108287749 A CN 108287749A CN 201810024580 A CN201810024580 A CN 201810024580A CN 108287749 A CN108287749 A CN 108287749A
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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
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
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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
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
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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
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/4557—Distribution of virtual machine instances; Migration and load balancing
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Abstract
The present invention relates to computer realms, more particularly to a kind of data center's total management system cloud resource dispatches system and method, the present invention is to data center's total management system cloud resource dispatching platform management and the further investigation of development technique and analysis, it devises and is based on data center's total management system cloud resource dispatching platform, on the basis of Open Stack platform basic managements operate, cloud resource scheduling feature module is designed and developed, pass through dependence test, data center's total management system cloud resource dispatching platform is demonstrated in resource management, there is certain advance and application value in terms of resource migration function.
Description
Technical field
The present invention relates to computer realm, more particularly to a kind of data center's total management system cloud resource scheduling system and
Method.
Background technology
Modular data center (Module Data Center, MDC) is the New Generation of IDC portion based on cloud computing
Administration's form uses modularized design to cope with the trend of the servers such as cloud computing, virtualization, centralization, high densification development
Theory reduces coupling of the infrastructure to building environment to the greatest extent.Be integrated with power supply and distribution, refrigeration, cabinet, air-flow containment,
The subsystems such as comprehensive wiring, power ＆ environment supervision, improve the whole efficiency of operation of data center, realize rapid deployment, resilient expansion and
Green energy conservation
With the rapid development of big data information industry, the development of data center also enters a new stage.Management
System is the important component configured inside data center.Traditional management system mainly based on power ＆ environment supervision, has more
Kind of data-interface, can access UPS, power distribution cabinet, precision air conditioner, gate inhibition, Temperature Humidity Sensor, smoke detector, temperature detector,
A variety of monitored object such as leakage sensor, turning roof window and web camera.
Currently, with the fast development of cloud computing, big data and internet, basic turn has occurred in information-based infrastructure
Become, the demand of monitoring management from some individual system requirements be converted into integral platform, the unified platform, unified management system
It is required that.Every application server is no longer individual computing module, but will be calculated, deposited by platforms such as cloud computing, big datas
Storage resource is united, and forms in large scale, unified monitoring and management resource pools across data center's range, it is therefore desirable to energy
It is enough to monitor extensive, distributed, the virtual resource of cross-region and physical resource unified monitoring system.
Data center's infrastructure is the core of cloud computing framework, it is supplied to user to including CPU, memory, storage, net
The use of the computing resources such as network is effectively reduced the cost and complexity of IT O＆Ms.Cloud computing framework has distributed, inter-network
Feature more than network, resource category brings unprecedented challenge, compared to traditional services for resource management aspect thereupon
Device aggregated structure, in addition to the management to physical resources such as Web server, application servers, it is also necessary to CPU, memory, storage,
The unified management of the virtual resources such as network, virtual machine.
Cloud computing has following feature for user
(1) on demand from service (on-demand self-service)：Cloud computing can be that user quickly provides service, money
Source, and user can use, dispose, return cloud resource according to self-demand, and service cloud supplier can be according to user's need
Ask scheduling, all kinds of resources of recycling；
(2) resource pool (resource pooling)：IaaS layers in cloud computing form after safeguarding all resource virtualizings
Resource pool by multi-tenant form according to demand by all kinds of resource allocations to user be that another layer on virtualization layer is abstract；
(3) network accesses (network access)：All services are provided by Internet in cloud computing, are used
Family is without being concerned about that resource deployment form, position can facilitate resource needed for acquisition by network；
(4) quickly elastic (rapid elasticity)：The service supplier of cloud computing can be quick according to user demand
It flexibly disposes, discharge, Resource recovery and service, according to demand dynamically scheduling resource, for a user, resource is unlimited
, any demand can be met at any time；
(5) measurable service (measured Service)：It is exactly on demand on-demand charging, cloud meter from the inevitable requirement of service
Calculating service provider can be according to the on-demand charging of service condition of user resources and service, these are all abstract resources certainly,
Such as bandwidth, storage, CPU.
The type that service is provided according to cloud computing, is divided into three layers (IaaS, PaaS, SaaS), IaaS by the framework of cloud computing
(Infrastructure as a Service) layer, infrastructure service.
IaaS layers of core technology is virtualization, it shields the physical resource of bottom, is on the basis of virtualization technology
PaaS layers of offer virtual machine service.This layer can provide highly reliable, high-performance by using the cluster of a large amount of cheap computers
Large-scale calculations ability.This layer is the basis of entire cloud computing system, and all top services are all based on IaaS layers of void
For quasi-ization come what is carried out, this layer is also the part for being most difficult to realize in cloud computing system.
PaaS (Platform as a Service) layer, platform service.PaaS layers of core technology are parallel computations, it
Software Development Platform and tool can be provided to the user, such as Java, python .NET, in the application deployment of this layer exploitation
To IaaS layers, user is submitted to SaaS layers of programming mode.
SaaS (Software as a Service) layer, software service.SaaS layers provide the user with the soft of rental form
Part service, user do not need to buy software and the application program being deployed on IaaS layers can be used.User is without being concerned about relevant net
Network, operating system, storage etc., you can obtain software systems.
The key technology that (Virtualization) technology of virtualization is IaaS layers, while being entire cloud computing system core
Component part, be the basis of cloud computing system.For virtualization, it can be understood as resource is abstracted.By virtualization technology,
Difference between Resource Properties can be shielded, used with unified interface, safeguard it is virtual after resource.Its essence is exactly a kind of isolation
Software and hardware middleware Technology shields underlying differences, for upper layer provides unified interface service.
Server virtualization is to use server virtualization as multiple, therefore, server virtualization technology
It is IaaS layers of basis.More virtual machines can be usually run on a physical host, it is virtual by virtualizing more obtained
There is independence between machine, it is completely isolated with other virtual machines, ensure the safety of virtual machine and reliable, server virtualization is main
It is to be abstracted for resources such as CPU, Memory, Disk, equipment and the I/O of physical server.Wherein, CPU is virtualized object
Reason CPU is abstracted as several virtual cpus, and more virtual cpus can improve the utilization rate of physical cpu；Internal memory virtualization is by physics
Memory is abstracted into multiple virtual memorys and is shared for more virtual machines, and every virtual machine is independent of one another, possesses the memory headroom of isolation；
Equipment virtualizes with I/O physical host real equipment being abstracted as multiple virtual units, asks and sets for more virtual machine I/O
Standby access.
In the environment of server virtualization, if server failure occurs or system maintenance needs server to stop
How some period of time in service ensures the work of virtual machineThe migrating technology of virtual machine perfectly solves the above problem.At certain
In the case of platform server fail, in time by the virtual machine (vm) migration run thereon to other servers, system is improved
Fault-tolerant ability；When system server is unable to meet demand, need to update new server when, by virtual machine (vm) migration to new
On server, the load balance ability of system can be improved while promoting the experience of user, reduce the difficulty of system upgrade.Cause
This, virtual machine migration technology is the key link in virtualization technology.
There are three the leading indicators for weighing virtual machine (vm) migration：Transit time, downtime, the influence to application program.Its
In, transit time is referred to since migration, terminates the time used to migration.Downtime refers to the process in migration
In, the All hosts of participation non-serviceable time simultaneously.Influence to application program refers to being migrated to be run on object
The influence degree that application program is migrated by this.During migration, user experience can be poor, therefore transit time and stops
The length of machine time is to weigh the important indicator of a Migration tools performance.
Although being said in technical aspect, cloud computing is increasingly mature, and cloud computing mode brings advantage to the user, to enterprise's band
Come income, but being continuously increased with cloud infrastructure, causes data center excessive due to the utilization of resources is unreasonable etc.
The problems such as energy consumption, excessively high CO2 emissions, also highlights.Excessive energy consumption means the drop of cloud supplier profit
It is low, and environmental problem caused by carbon dioxide also should not be underestimated.
Therefore, one of the main problem that nowadays cloud computing faces at present is how to realize the efficient profit of resource in cloud computing
With effectively save with the energy.Wherein very crucial technology is how to carry out the scheduling of resource, the case where demand increases
When dynamic dispatching, ensure that the quality of service, while the dynamic dispatching in the case where demand is reduced close idle physical machine,
Save energy consumption.
The rapid development of cloud computing has promoted the dispatching algorithm of cloud computing resources to obtain very big breakthrough.But most of strategies
It is provided to the utilization rate for improving cloud data center resource, the energy expenditure for reducing data center, the stability for maintaining system.Point
Analyse the existing scheduling strategies of cloud platform Open Stack of increasing income, it was found that deficiency therein, and it is directed to scheduling virtual machine, it is proposed that
A kind of dynamic scheduling strategy improves the resource utilization of cloud platform and reaches energy-efficient purpose.
It is dispersed with many calculate nodes in the cloud environment built using Open Stack, in resource pool, is asked when there is user
When seeking establishment example, it will face and virtual machine instance is created into the problem in that calculate node.In Open Stack
In, nova-scheduler (scheduler) is responsible for that suitable node is selected to create virtual machine instance in resource pool.
Currently, realizing Chance Scheduler (random schedule device) in Nova, Filter Scheduler (adjust by filtering
Spend device), Caching Scheduler (cache scheduler).Different schedulers cannot coexist, and need the configuration file in Nova
In specify scheduler_driver options, acquiescence is Filter Scheduler.
In order to adapt to the working environment of cluster, dynamic resource dispatching strategy is introduced in Open Stack, supports two kinds
Virtual machine migration policies：Cold migration and thermophoresis.
Cold migration is static migrating, is referred in the state that virtual machine shuts down, by virtual machine from a calculate node letter
Single is transported on another node, is suitable for user's situation not high to availability requirement.
Thermophoresis, that is, dynamic migration, refers to that the service on guarantee virtual machine is available, and virtual machine is counted from one
Operator node moves on other node, and the downtime in transition process is very of short duration, the interruption of the imperceptible service of user.
Although introducing virtual machine migration policies in Open Stack, strategy ability only under artificial trigger condition
It can use.That is, the administrator of Open Stack cloud environments wants the fortune of each calculate node in moment focused data center
Market condition manually triggers the migration work of virtual machine according to working experience.Obvious this scheduling mode can not meet system
Demand.Moreover, which platform virtual machine is selected to move out, which moves to where, can be determined according to the judgement of administrator,
The influence on system operation of system entirety is not also understood.Therefore, it is necessary to a kind of perfect dynamic dispatching mechanism to determine automatic identification tune
The opportunity of degree, the strategy of scheduling, ensure the normal service of system.
Invention content
The present invention is achieved through the following technical solutions, and the present invention proposes a kind of data center's total management system cloud
Resource scheduling system, specifically includes monitoring and controlling forecast module, scheduler module, transferring module, scheduling controller, monitoring and controlling forecast module,
Scheduler module and transferring module are coordinated by scheduling controller, work together with Open Stack components, complete system jointly
Dynamic dispatching.
Preferably, monitoring and controlling forecast module includes periodical monitoring data collector (Data Collector) and fallout predictor
(Predictor), the periodic data collection device is mainly used for the resource to physical host in Open Stack cluster environment
Service condition does periodic collection, and fallout predictor carries out prediction to resource service condition, and then selects proper moment triggering
The migration of virtual machine.
Preferably, the scheduler module is by two module compositions：Target virtual machine selector (VMSelector), scheduling clothes
Module of being engaged in (nova-scheduler).
The present invention also provides a kind of dispatching methods for data center's total management system cloud resource, including walk as follows
Suddenly：
(1) dynamic Scheduler services start, and Scheduler Controller start periodically to Data
Collector asks the data of resource service condition；
(2) Data Collector receive request, the history number that physical host resource uses in respond request acquisition system
According to, and data are returned；
(3) Scheduler Controller obtain historical data, analyze and predict to Predictor request datas；
(4) Predictor receives Scheduler Controller requests, analyzes data and returns to prediction result；
(5) Scheduler Controller obtain prediction result, if you do not need to virtual machine (vm) migration is carried out, in this period
Traffic control terminate, Scheduler Controller return to initial conditions, wait for the dynamic dispatching in next period；
If necessary to carry out virtual machine (vm) migration, the information of overload or underload physical host is sent to VMSelector,
One on request selecting physical host or all virtual machines are as target virtual machine；
(6) VMSelector receives request, the resource service condition of virtual machine on physical host and host is analyzed, according to object
That manages host exceeds threshold type, carries out the selection of target virtual machine, and the target virtual machine VM of decision is returned to
Scheduler Controller；
(7) Scheduler Controller obtain target virtual machine VM set, are successively sent to each VM information
Migration Trigger, request start to dispatch；
(8) Migration Trigger receive the order of Scheduler Controller triggering migrations, call virtual machine
Migrate interface；
(9) by nova-scheduler filters and link of weighing, destination host is selected, calls nova-compute moulds
Block completes a virtual machine (vm) migration action, and returns result to Scheduler Controller；
If the analysis result obtained in (4) contains more physical hosts, repeats (5) and arrive (9) link, Zhi Daosuo
The completion of the physical host migration action of some need scheduling.
It can be seen that invention defines data center's total management system cloud resource dispatching method, a cloud is defined
Scheduling of resource frame defines scheduling controller function module, defines cloud resource system monitoring forecast function module；It defines
Data center's cloud resource scheduling feature module defines data center's total management system shift function module, realizes one kind
The method of data center's integrated management cloud resource scheduling.
Description of the drawings
Data center's total management system cloud resource that Fig. 1 one embodiment of the invention provides dispatches system block diagram
Specific implementation mode
The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.Following embodiment is only used for clearer
Ground illustrates technical scheme of the present invention, and not intended to limit the protection scope of the present invention.
A kind of frame of data center's total management system cloud resource dispatching method is as shown in Fig. 1.
The specific implementation process of frame is as follows：First, definition is described as follows,
CPU Usage：Cpu busy percentage；
Mem Usage：The utilization rate of memory；
Band Usage：The utilization rate of bandwidth.
Data center's total management system cloud resource needs the resource paid close attention to mainly to have cpu busy percentage, memory usage, band
Wide utilization rate, electric quantity consumption.Electric quantity consumption depends primarily on the service condition of CPU, and cpu busy percentage is higher, operating frequency meeting
Go up, electric quantity consumption is also more；Conversely, cpu busy percentage is lower, operating frequency can decline, and electric quantity consumption is also fewer.Therefore,
Electric quantity consumption can sum up in the point that on cpu busy percentage.It is cpu busy percentage, memory usage and bandwidth finally to need the resource monitored
Utilization rate.
Data center's total management system cloud resource dispatching method is to the threshold value above and below the resource setting of above monitoring.
When physical host the CPU Usage upper limits be 80%, lower limit 15%.That is, certain physical host in the cluster
When CPUUsage is higher than 80%, the cpu load of physical host is excessively high, and a part of virtual machine is selected from the physical host
The case where moving out, reducing physical host CPU Usage, prevent from violating SLA occurs；Conversely, certain physical host in the cluster
CPU Usage are less than 15%, and the cpu load of physical host is too low, it is necessary to all virtual machines in the physical host are moved out, and
Host will be allowed to enter sleep pattern, to reduce energy consumption.
Since all virtual machines on physical host share physical resource, workload is dynamic, may be led
The CPU Usage instantaneous peak values of physical host or instantaneous low ebb are caused, is possible to exceed preset threshold value, triggering in this way
Unnecessary virtual machine (vm) migration.Whether touched it is thus impossible to be simply used as with primary monitoring value.
It is as follows for the design scheme of data center's total management system cloud resource dispatching method:
Dynamic dispatching (dynamic Scheduler) service of system also works in the layer, and dynamic Scheduler make
For the submodule of Nova, by with the existing service interactions of Open Stack, achieve the purpose that dynamic dispatching.In order to realize dynamic
Scheduling, dynamic Scheduler contain three modules again：Monitoring and controlling forecast module, scheduler module, transferring module.These three
Module is coordinated by scheduling controller (Scheduler Controller), works together with Open Stack components, jointly
The dynamic dispatching of completion system.
Since the dynamic dispatching of system is the analysis result based on historical data, system allows for periodically obtaining
The data that physical host resource uses,
Therefore, monitoring and controlling forecast module is integrated in system, which is divided into as two little modules：Periodical monitoring data is received
Storage (Data Collector) and fallout predictor (Predictor).
Periodic data collection device is mainly used for the resource service condition to physical host in Open Stack cluster environment
Do periodic collection.These historical datas are obtained, fallout predictor can carry out prediction to resource service condition, into
And select the migration of proper moment triggering virtual machine.
The major function of scheduler module is exactly to dispatch the selection of target virtual machine in link, destination host.Therefore, mould is dispatched
Block is also that there are two module compositions：Target virtual machine selector (VMSelector), dispatch service (nova-scheduler).
For transferring module, exactly the migration strategy of scheduler module decision is executed, the module is by migration trigger
(Migration Trigger) starts the migration work of virtual machine.
Three modules are mutual indepedent, and dynamic migration, Scheduler are completed under the coordination of scheduling controller
Controller controls the life cycle of entire dynamic dispatching, and detailed process is as follows：
(1) dynamic Scheduler services start, and Scheduler Controller start periodically to Data
Collector asks the data of resource service condition；
(2) Data Collector receive request, the history number that physical host resource uses in respond request acquisition system
According to, and data are returned；
(3) Scheduler Controller obtain historical data, analyze and predict to Predictor request datas；
(4) Predictor receives Scheduler Controller requests, analyzes data and returns to prediction result；
(5) Scheduler Controller obtain prediction result, if you do not need to virtual machine (vm) migration is carried out, in this period
Traffic control terminate, Scheduler Controller return to initial conditions, wait for the dynamic dispatching in next period；
If necessary to carry out virtual machine (vm) migration, the information of overload or underload physical host is sent to VMSelector,
One on request selecting physical host or all virtual machines are as target virtual machine；
(6) VMSelector receives request, the resource service condition of virtual machine on physical host and host is analyzed, according to object
That manages host exceeds threshold type, carries out the selection of target virtual machine, and the target virtual machine VM of decision is returned to
Scheduler Controller；
(7) Scheduler Controller obtain target virtual machine VM set, are successively sent to each VM information
Migration Trigger, request start to dispatch；
(8) Migration Trigger receive the order of Scheduler Controller triggering migrations, call virtual machine
Migrate interface；
(9) by nova-scheduler filters and link of weighing, destination host is selected, calls nova-compute moulds
Block completes a virtual machine (vm) migration action, and returns result to Scheduler Controller.
If the analysis result obtained in (4) contains more physical hosts, repeats (5) and arrive (9) link, Zhi Daosuo
The completion of the physical host migration action of some need scheduling.So far, dynamic dispatching work is completed.
The present invention is the further investigation to data center's total management system management of cloud resource dispatching platform and development technique
And analysis, it devises based on data center's total management system cloud resource dispatching platform.In Open-Stack platform basic managements
On the basis of operation, cloud resource scheduling feature module has been designed and developed.By dependence test, data center's integrated management is demonstrated
System cloud scheduling of resource platform has certain advance and application value in terms of resource management, resource migration function.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement etc., should all be included in the protection scope of the present invention made by within refreshing and principle.
Claims (4)
1. a kind of scheduling system for data center's total management system cloud resource specifically includes monitoring and controlling forecast module, scheduling
Module, transferring module, scheduling controller, monitoring and controlling forecast module, scheduler module and transferring module are coordinated by scheduling controller,
It works with together with Open Stack components, the common dynamic dispatching for completing system.
2. system according to claim 1, it is characterised in that：Monitoring and controlling forecast module includes periodical monitoring data collector
(Data Collector) and fallout predictor (Predictor), the periodic data collection device are mainly used for Open Stack
The resource service condition of physical host does periodic collection in cluster environment, fallout predictor analyzes resource service condition,
Prediction, and then select the migration of proper moment triggering virtual machine.
3. system according to claim 1, it is characterised in that：The scheduler module is by two module compositions：Destination virtual
Machine selector (VMSelector), dispatch service module (nova-scheduler).
4. a kind of dispatching method for data center's total management system cloud resource includes the following steps：
(1) dynamic Scheduler services start, and Scheduler Controller start periodically to Data
Collector asks the data of resource service condition；
(2) Data Collector receive request, the history number that physical host resource uses in respond request acquisition system
According to, and data are returned；
(3) Scheduler Controller obtain historical data, analyze and predict to Predictor request datas；
(4) Predictor receives Scheduler Controller requests, analyzes data and returns to prediction result；
(5) Scheduler Controller obtain prediction result, if you do not need to carrying out virtual machine (vm) migration, the tune in this period
Degree work terminates, and Scheduler Controller return to initial conditions, wait for the dynamic dispatching in next period；
If necessary to carry out virtual machine (vm) migration, the information of overload or underload physical host is sent to VMSelector, is asked
Select one on physical host or all virtual machines as target virtual machine；
(6) VMSelector receives request, the resource service condition of virtual machine on physical host and host is analyzed, according to physics master
Machine exceeds threshold type, carries out the selection of target virtual machine, and the target virtual machine VM of decision is returned to Scheduler
Controller；
(7) Scheduler Controller obtain target virtual machine VM set, are successively sent to each VM information
Migration Trigger, request start to dispatch；
(8) Migration Trigger receive the order of Scheduler Controller triggering migrations, call virtual machine (vm) migration
Interface；
(9) by nova-scheduler filters and link of weighing, destination host is selected, calls nova-compute modules complete
It is acted at a virtual machine (vm) migration, and returns result to Scheduler Controller；
If the analysis result obtained in (4) contains more physical hosts, repeats (5) and arrive (9) link, until all
The completion for needing the physical host migration dispatched to act.
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| CN109800051A (en) * | 2018-12-13 | 2019-05-24 | 中国电子科技集团公司电子科学研究院 | Unified resource dispatching method and system based on OpenStack |
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