CN106020934A - Optimized deploying method based on virtual cluster online migration - Google Patents
Optimized deploying method based on virtual cluster online migration Download PDFInfo
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- CN106020934A CN106020934A CN201610346858.2A CN201610346858A CN106020934A CN 106020934 A CN106020934 A CN 106020934A CN 201610346858 A CN201610346858 A CN 201610346858A CN 106020934 A CN106020934 A CN 106020934A
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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
- G06F9/45504—Abstract machines for programme code execution, e.g. Java virtual machine [JVM], interpreters, emulators
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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
- G06F9/485—Task life-cycle, e.g. stopping, restarting, resuming execution
- G06F9/4856—Task life-cycle, e.g. stopping, restarting, resuming execution resumption being on a different machine, e.g. task migration, virtual machine migration
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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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- General Engineering & Computer Science (AREA)
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Abstract
The invention relates to an optimized deploying method based on virtual cluster online migration, and belongs to the cloud computing technical field; the method comprises the following steps: using a management module to connect with a management cluster node, wherein the management module is provided with an overall monitoring module, an algorithm forming module, a migration planning module and a migration driving module; using the overall monitoring module to receive the cluster node and virtual machine information data in the cloud computing environment, and sending the information to the algorithm forming module so as to predict future loads and build a long term load performance model; planning a deploying strategy for the virtual cluster, and sorting the virtual machines in the virtual cluster into different host nodes of the cluster, thus allowing the virtual machines to fully utilize the host resources in the cluster when the virtual cluster migration is finished. The novel method can solve the problems that the virtualization technology may easily cause resource fragments, and can solve the defects that the virtual machines cannot obtain the expected requesting resource quantity when the dynamic loading demands and the virtual machines are combined, thus reducing virtual machine performance loss in the cluster nodes, and improving resource utilization rate.
Description
Technical field
The present invention relates to field of cloud computer technology, a kind of Optimization deployment method migrated online based on Virtual Cluster.
Background technology
Cloud computing, utilizes system architecture technology that thousands of station servers are integrated, and provides the user resource distribution flexibly and task scheduling ability.In large-scale cloud computing environment, physical host is ten hundreds of, and the resource consumed is the hugest.Intel Virtualization Technology is one of key technology in cloud computing, and a physical computer can be become the virtual computer system of multiple stage by it.The bottom architecture such as physical resource are carried out abstract by Intel Virtualization Technology, make the difference between hardware device and compatibility transparent to upper layer application, thus realize the unified management of resources all kinds of to bottom.Intel Virtualization Technology provides fictitious host computer for cloud computing, fictitious host computer is the main object under cloud computing environment, resource is calculated by allotment, the different levels (hardware, software, data, network, storage etc.) of application system can be isolated, thus break data center, network, server, data, apply and store in physical equipment between division, realize unified management and dynamically use physical resource and virtual resource, improve motility and the elasticity of system structure.
Virtual machine monitoring software VMM (Virtual Machine Monitor) in Intel Virtualization Technology, or referred to as Hypervisor, it has access to that all hardware equipment on server.When startup of server and when calling Hypervisor, it can load the operating system on all virtual-machine client, gives the physical resources such as appropriate network, CPU, disk and the internal memory of each virtual machine distribution simultaneously.Hypervisor is responsible for coordinating the access of these hardware resources, the most also applies security protection between each virtual machine.
Intel Virtualization Technology makes the management of cloud computing environment become very flexible, can well tackle the various demands of user, but be easily caused generation resource fragmentation.On the one hand being due to the difference that stresses without application program, calculation type application needs a large amount of vcpu resources, data type application to need a large amount of memory spaces, communication type application that network bandwidth communication capacity is had the highest requirement.The demand different for meeting live load, distribute different configuration of virtual machine, this resource applies for distributing the effective ways being to make rational use of resources as desired, when application deployment run the Virtual Cluster of these application programs, if the most random selects physical host for it, distribution institute resource easily occurs on certain physical host, individual species resource wretched insufficiency so that it is he produces fragment at resource.On the other hand, when the end of job or in some period, when needing the resource that release Virtual Cluster takies, resource fragmentation also can be produced.Resource fragmentation reduces utilization rate and the availability of the resource of system, the high-performance calculation program that Virtual Cluster runs, the operation time is longer, bigger to the demand of various resources, if remaining resource is confirmed to be resource fragmentation after distribution, the state of poor efficiency will be maintained at for a long time, thus reduce the efficiency of whole system.
Additionally, the foundation of large-scale cloud data center will necessarily produce huge energy consumption, this is significantly greatly increased the operation cost of Liao Yun supplier great number.Intel Virtualization Technology can help cloud supplier to create multiple virtual machine instance on a physical host to improve the efficiency problem of data center, to improve resource utilization.Dynamic migration of virtual machine and merging simultaneously, virtual machine (vm) migration dynamically can be deployed on less node according to resource requirement, thus would sit idle for node and be converted to energy saver mode by cloud computing.But current application type is commonly designed the loading demand of dynamically change, this dynamic behaviour and virtual machine merging may cause virtual machine cannot obtain expection request stock number, thus brings performance loss.
Summary of the invention
The present invention is directed to demand and the weak point of current technology development, it is provided that a kind of Optimization deployment method migrated online based on Virtual Cluster.
A kind of Optimization deployment method migrated online based on Virtual Cluster of the present invention, the technical scheme solving the employing of above-mentioned technical problem is as follows: a kind of described Optimization deployment method migrated online based on Virtual Cluster, by a management module connection management clustered node, described management module is provided with global monitoring module, algorithm generation module, migrates planning module and migrate driving 4 modules of module, described clustered node arranges some virtual machines；In cloud computing environment, described global monitoring module is responsible for receiving clustered node and the information data of virtual machine, the data that described global monitoring module obtains are sent to algorithm generation module, predict future load by algorithm generation module and set up long-term load performance model, and formulating corresponding deployment and migration algorithm；According to described migration algorithm, described migration planning module is formulated virtual cluster deployment and to the allocation plan of cluster and is migrated planning；Described migration drives module to migrate according to migrating planning execution Virtual Cluster.
Preferably, in cloud computing environment, described global monitoring module is responsible for the resource of resources of virtual machine service condition, the application performance index of each virtual machine performance monitoring transmission, the virtual machine (vm) migration request controlled from clustered node and each clustered node that the periodic monitoring resource received on each virtual machine sends and is used and can use situation.
Preferably, described algorithm generation module mainly includes load estimation, load performance model and objective optimization three partial content；Described algorithm generation module receives the data that described global monitoring module sends, during each overall situation controls cycle period, by analogy method, described algorithm generation module analyzes the current of each application and historical data carries out load estimation, and sets up long-term load performance model；After drawing load performance model, it was predicted that the resource requirement of node, and whether decision node overloads or underloading；According to prediction and judged result, obtain global scope deploying virtual machine optimal solution by design object optimized algorithm, in combination with the migration request from node control each in cluster, formulate corresponding deployment and migration algorithm.
A kind of Optimization deployment method migrated online based on Virtual Cluster of the present invention compared with prior art has the beneficial effect that the present invention passes through management module connection management clustered node (physical host), in obtaining Virtual Cluster after the load performance model of virtual machine application, Virtual Cluster is carried out deployment strategy planning, virtual machine in Virtual Cluster is planned on the different host nodes of cluster, when Virtual Cluster has migrated, virtual machine can make full use of the resource of main frame in cluster；While making Intel Virtualization Technology can be good at tackling the various demand of user, solve the problem that Intel Virtualization Technology is easily caused resource fragmentation, overcome dynamic load requirements simultaneously and virtual machine merges the defect causing virtual machine cannot obtain expection request stock number, reduce the performance loss of virtual machine in clustered node, improve resource utilization.
Figure of description
Accompanying drawing 1 is the schematic diagram of the described Optimization deployment method migrated online based on Virtual Cluster.
Detailed description of the invention
For making the object, technical solutions and advantages of the present invention clearer, below in conjunction with specific embodiment, a kind of Optimization deployment method migrated online based on Virtual Cluster of the present invention is further described.
Embodiment:
It is optimized in the deployment of physical host for virtual machine, the power consumption of large-scale physical host is optimized, the present embodiment proposes a kind of Optimization deployment method migrated online based on Virtual Cluster, as shown in Figure 1, by management module connection management clustered node (physical host), described management module is provided with global monitoring module, algorithm generation module, migrates planning module and migrate driving 4 modules of module, described clustered node arranges some virtual machines；In cloud computing environment, described global monitoring module is responsible for receiving clustered node and the information data of virtual machine, the data that described global monitoring module obtains are sent to algorithm generation module, predict future load by algorithm generation module and set up long-term load performance model, and formulating corresponding deployment and migration algorithm；According to described migration algorithm, described migration planning module is formulated virtual cluster deployment and to the allocation plan of cluster and is migrated planning；Described migration drives module to migrate according to migrating planning execution Virtual Cluster.
The Optimization deployment method migrated online based on Virtual Cluster described in the present embodiment, in cloud computing environment, described global monitoring module is responsible for the resource of resources of virtual machine service condition, the application performance index of each virtual machine performance monitoring transmission, the virtual machine (vm) migration request controlled from clustered node and each clustered node that the periodic monitoring resource received on each virtual machine sends and is used and can use situation.
Described algorithm generation module mainly includes load estimation, load performance model and objective optimization three partial content；The data that described global monitoring module obtains are sent to algorithm generation module, during each overall situation controls cycle period, by analogy method, described algorithm generation module analyzes the current of each application and historical data carries out load estimation, and sets up long-term load performance model；After drawing load performance model, it is possible to the resource requirement of prediction node, and whether decision node overloads or underloading etc.；According to prediction and judged result, obtain global scope deploying virtual machine optimal solution by design object optimized algorithm, in combination with the migration request from node control each in cluster, formulate corresponding deployment and migration algorithm.
Migrate described in the Optimization deployment method migrated online based on Virtual Cluster described in the present embodiment in planning module, to run minimum clustered node as target；In obtaining Virtual Cluster after the load performance model of virtual machine application, migrate planning and Virtual Cluster will be carried out deployment strategy planning, virtual machine in Virtual Cluster is planned on the different host nodes of cluster, when Virtual Cluster has migrated, virtual machine can make full use of the resource of main frame in cluster.The probability that the virtual machine end time on same node enters dormancy or closed mode closer to, this node is the highest.While the invention enables Intel Virtualization Technology to can be good at tackling the various demand of user, solve the problem that Intel Virtualization Technology is easily caused resource fragmentation, overcome dynamic load requirements simultaneously and virtual machine merges the defect causing virtual machine cannot obtain expection request stock number, reduce the performance loss of virtual machine in clustered node.
Above-mentioned detailed description of the invention is only the concrete case of the present invention; the scope of patent protection of the present invention includes but not limited to above-mentioned detailed description of the invention; suitably change that it is done by any that meet claims of the present invention and any person of an ordinary skill in the technical field or replace, all should fall into the scope of patent protection of the present invention.
Claims (3)
1. the Optimization deployment method migrated online based on Virtual Cluster, it is characterized in that, by a management module connection management clustered node, described management module is provided with global monitoring module, algorithm generation module, migrates planning module and migrate driving 4 modules of module, described clustered node arranges some virtual machines；In cloud computing environment, described global monitoring module is responsible for receiving clustered node and the information data of virtual machine, the data that described global monitoring module obtains are sent to algorithm generation module, predict future load by algorithm generation module and set up long-term load performance model, and formulating corresponding deployment and migration algorithm；According to described migration algorithm, described migration planning module is formulated virtual cluster deployment and to the allocation plan of cluster and is migrated planning；Described migration drives module to migrate according to migrating planning execution Virtual Cluster.
A kind of Optimization deployment method migrated online based on Virtual Cluster, it is characterized in that, in cloud computing environment, described global monitoring module is responsible for the resource of resources of virtual machine service condition, the application performance index of each virtual machine performance monitoring transmission, the virtual machine (vm) migration request controlled from clustered node and each clustered node that the periodic monitoring resource received on each virtual machine sends and is used and can use situation.
A kind of Optimization deployment method migrated online based on Virtual Cluster, it is characterised in that described algorithm generation module mainly includes load estimation, load performance model and objective optimization three partial content；Described algorithm generation module receives the data that described global monitoring module sends, during each overall situation controls cycle period, by analogy method, described algorithm generation module analyzes the current of each application and historical data carries out load estimation, and sets up long-term load performance model；After drawing load performance model, it was predicted that the resource requirement of node, and whether decision node overloads or underloading；According to prediction and judged result, obtain global scope deploying virtual machine optimal solution by design object optimized algorithm, in combination with the migration request from node control each in cluster, formulate corresponding deployment and migration algorithm.
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106899660A (en) * | 2017-01-26 | 2017-06-27 | 华南理工大学 | Cloud data center energy-saving distribution implementation method based on trundle gray forecast model |
| CN108009016A (en) * | 2016-10-31 | 2018-05-08 | 华为技术有限公司 | A kind of balancing resource load control method and colony dispatching device |
| CN108153594A (en) * | 2017-12-25 | 2018-06-12 | 联想(北京)有限公司 | The resource fragmentation method for sorting and electronic equipment of a kind of artificial intelligence cloud platform |
| CN108519919A (en) * | 2018-03-19 | 2018-09-11 | 山东超越数控电子股份有限公司 | A method of realizing server resource dynamic dispatching under virtual cluster environment |
| CN109271257A (en) * | 2018-10-11 | 2019-01-25 | 郑州云海信息技术有限公司 | A kind of method and apparatus of virtual machine (vm) migration deployment |
| CN110249310A (en) * | 2017-02-03 | 2019-09-17 | 微软技术许可有限责任公司 | The resource management for virtual machine in cloud computing system |
| CN110597599A (en) * | 2019-09-16 | 2019-12-20 | 电子科技大学广东电子信息工程研究院 | Virtual machine migration method and system |
| CN110795204A (en) * | 2018-08-03 | 2020-02-14 | 西安电子科技大学 | Virtual machine deployment method and device |
| CN111159859A (en) * | 2019-12-16 | 2020-05-15 | 万般上品(常州)物联网系统有限公司 | Deployment method and system of cloud container cluster |
| CN111371583A (en) * | 2018-12-26 | 2020-07-03 | 中兴通讯股份有限公司 | Server capacity expansion method and device, server and storage medium |
| CN111556165A (en) * | 2019-08-01 | 2020-08-18 | 广州知弘科技有限公司 | Information processing method and system based on cloud computing |
| CN111858040A (en) * | 2020-07-07 | 2020-10-30 | 中国建设银行股份有限公司 | Resource scheduling method and device |
| CN112099918A (en) * | 2019-09-13 | 2020-12-18 | 谷歌有限责任公司 | Live migration of clusters in containerized environments |
| CN112328332A (en) * | 2021-01-05 | 2021-02-05 | 苏州博纳讯动软件有限公司 | Database configuration optimization method for cloud computing environment |
| CN112732408A (en) * | 2021-01-18 | 2021-04-30 | 浪潮云信息技术股份公司 | Method for computing node resource optimization |
| US11038926B2 (en) * | 2019-01-23 | 2021-06-15 | Vmware, Inc. | System and method for embedding infrastructure security services into management nodes |
| CN116719614A (en) * | 2023-08-11 | 2023-09-08 | 中国电信股份有限公司 | Virtual machine monitor selection method, device, computer equipment and storage medium |
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Cited By (25)
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| CN108009016A (en) * | 2016-10-31 | 2018-05-08 | 华为技术有限公司 | A kind of balancing resource load control method and colony dispatching device |
| CN108009016B (en) * | 2016-10-31 | 2021-10-22 | 华为技术有限公司 | Resource load balancing control method and cluster scheduler |
| CN106899660A (en) * | 2017-01-26 | 2017-06-27 | 华南理工大学 | Cloud data center energy-saving distribution implementation method based on trundle gray forecast model |
| WO2018137402A1 (en) * | 2017-01-26 | 2018-08-02 | 华南理工大学 | Cloud data centre energy-saving scheduling implementation method based on rolling grey prediction model |
| CN110249310A (en) * | 2017-02-03 | 2019-09-17 | 微软技术许可有限责任公司 | The resource management for virtual machine in cloud computing system |
| CN110249310B (en) * | 2017-02-03 | 2023-03-28 | 微软技术许可有限责任公司 | Resource management for virtual machines in cloud computing systems |
| CN108153594A (en) * | 2017-12-25 | 2018-06-12 | 联想(北京)有限公司 | The resource fragmentation method for sorting and electronic equipment of a kind of artificial intelligence cloud platform |
| CN108153594B (en) * | 2017-12-25 | 2022-01-18 | 联想(北京)有限公司 | Resource fragment sorting method of artificial intelligence cloud platform and electronic equipment |
| CN108519919A (en) * | 2018-03-19 | 2018-09-11 | 山东超越数控电子股份有限公司 | A method of realizing server resource dynamic dispatching under virtual cluster environment |
| CN110795204A (en) * | 2018-08-03 | 2020-02-14 | 西安电子科技大学 | Virtual machine deployment method and device |
| CN110795204B (en) * | 2018-08-03 | 2022-11-18 | 西安电子科技大学 | Virtual machine deployment method and device |
| CN109271257A (en) * | 2018-10-11 | 2019-01-25 | 郑州云海信息技术有限公司 | A kind of method and apparatus of virtual machine (vm) migration deployment |
| CN111371583B (en) * | 2018-12-26 | 2022-09-23 | 中兴通讯股份有限公司 | Server capacity expansion method and device, server and storage medium |
| CN111371583A (en) * | 2018-12-26 | 2020-07-03 | 中兴通讯股份有限公司 | Server capacity expansion method and device, server and storage medium |
| US11038926B2 (en) * | 2019-01-23 | 2021-06-15 | Vmware, Inc. | System and method for embedding infrastructure security services into management nodes |
| CN111556165A (en) * | 2019-08-01 | 2020-08-18 | 广州知弘科技有限公司 | Information processing method and system based on cloud computing |
| CN112099918A (en) * | 2019-09-13 | 2020-12-18 | 谷歌有限责任公司 | Live migration of clusters in containerized environments |
| CN110597599A (en) * | 2019-09-16 | 2019-12-20 | 电子科技大学广东电子信息工程研究院 | Virtual machine migration method and system |
| CN111159859A (en) * | 2019-12-16 | 2020-05-15 | 万般上品(常州)物联网系统有限公司 | Deployment method and system of cloud container cluster |
| CN111159859B (en) * | 2019-12-16 | 2024-02-06 | 万般上品(常州)物联网系统有限公司 | Cloud container cluster deployment method and system |
| CN111858040A (en) * | 2020-07-07 | 2020-10-30 | 中国建设银行股份有限公司 | Resource scheduling method and device |
| CN112328332A (en) * | 2021-01-05 | 2021-02-05 | 苏州博纳讯动软件有限公司 | Database configuration optimization method for cloud computing environment |
| CN112328332B (en) * | 2021-01-05 | 2021-04-13 | 苏州博纳讯动软件有限公司 | Database configuration optimization method for cloud computing environment |
| CN112732408A (en) * | 2021-01-18 | 2021-04-30 | 浪潮云信息技术股份公司 | Method for computing node resource optimization |
| CN116719614A (en) * | 2023-08-11 | 2023-09-08 | 中国电信股份有限公司 | Virtual machine monitor selection method, device, computer equipment and storage medium |
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Application publication date: 20161012 |