CN104375897A - Cloud computing resource scheduling method based on minimum relative load imbalance degree - Google Patents
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Abstract
The invention discloses a cloud computing resource scheduling method based on the minimum relative load imbalance degree. According to the method, a load balancer is used for assessing the load levels of all physical machines in a cluster, and whether overloading happens to the physical machines is judged; if one physical machine is overloaded, the relative load imbalance degree of each normally-running physical machine to the overloaded physical machine is calculated; the physical machine with the minimum relative load imbalance degree is used as an alternative physical host, and whether the computing resource capacity of the alternative host meets the resource demand of a virtual machine requiring migration is judged; if the resource capacity meets the demand, the physical machine with the minimum relative load imbalance degree is used as the alternative physical host, and migration of the virtual machine is carried out; if the resource capacity does not meet the demand, the physical machine with the minimum relative load imbalance degree is excluded, and the alternative physical host is selected again. By means of the cloud computing resource scheduling method, the utilization rate of system resources can be effectively increased; meanwhile, system load balance is guaranteed, and system stability is improved.
Description
Technical field
The invention belongs to virtual and field of cloud calculation, be specifically related to the method for a kind of cloud computing resources based on the unbalanced degree of minimum relative load scheduling.
Background technology
The social trend that the requirement of cloud computing reply enterprises and individuals consumer to data center processing ability improves constantly occurs, current oneself becomes one of problem of IT research hottest point.The Virtual Cluster that cloud computing platform adopts Intel Virtualization Technology to build, dynamically can organize the computational resource of isomery, and concrete hardware architecture and diversified soft ware platform can be isolated, it can build the computing environment meeting different application demand flexibly, improves the service efficiency of computational resource.Under cloud computing environment, application system will no longer be confined to the system performance of self, powerful computing power, the data resource of magnanimity and diversified application can be obtained from cloud resource, more approach can be utilized to meet various user variation, high-level demand for services.User also will obtain more perfect service experience, and can timely, the various services of efficient, clog-free enjoyment application system.
As most critical, most crucial technology motive power in cloud computing IaaS layer, the bottom architecture such as physical resource can be carried out abstract by Intel Virtualization Technology, make the difference of equipment with compatible transparent to upper layer application, thus allow cloud to carry out unified management to the resource that bottom varies.Just because of maturation and the widespread use of Intel Virtualization Technology, the calculating in cloud computing, storage, application and service all become resource, and these resources can by dynamic expansion and configuration, and cloud computing finally could logically present with single holistic form.In cloud environment, virtual machine is as a computational resource, and user requires that its operation has stability usually, does not wish in the phenomenon occurring virtual machine frequent migration.
In cloud computing application platform, its resource distribution is extensive and of a great variety, and user be made as the electricity consumption of use water really can to use resource in cloud environment, and the distribution handling resource well is key issue.Meanwhile, being dynamically difficult to by Accurate Prediction in real time of user's request, also will consider the problem such as system performance and cost, therefore, efficient cloud computation data center allocation schedule policing algorithm becomes study hotspot.In cloud computing system, when the virtual machine that user asks establishment one new, or when virtual machine needs to move, all need to carry out cloud computing resources scheduling, efficiently, reliably run to keep whole system.Existing cloud computing resource scheduling method, as paper " ADynamic And Integrated Load-Balancing Scheduling Algorithm ForCloud Data Centers " (IEEE International Conference on CloudComputing and Intelligence System, 311-315, Wenhong Tian, YongZhao, Yuanliang Zhong, Minxian Xu, Chen Jing, 2011-09-15) Chinese invention patent application " a kind of large-scale virtual machine fast transferring decision-making technique of facing cloud data center " (application number: 201310186581.8 publication date: 2013-08-14), these methods mainly consider system performance and load balance, and do not consider whether host capacity reaches requirement and on problems such as the impacts of resource utilization.
Summary of the invention
The object of this invention is to provide a kind of cloud computing resource scheduling method based on the unbalanced degree of minimum relative load, solve prior art under the prerequisite ensureing higher system performance, higher resource utilization cannot be obtained and the problem of load imbalance degree of reduced levels can not be maintained.
The technical solution adopted in the present invention is the cloud computing resource scheduling method based on the unbalanced degree of minimum relative load, with load balancer, the load level of each physical machine in cluster is assessed, judge whether physical machine occurs transship phenomenon, if overload, calculate the relative load unbalanced degree of all normal operation physical machine relative to this load overload physical machine, the physical machine selecting the unbalanced angle value of wherein relative load minimum is as alternative physical host, and judge alternative host calculating resource capacity whether satisfy the demand migration resources of virtual machine demand, if resource capacity meets, then using this physical machine as alternative physical machine, carry out the migration of virtual machine, resource capacity does not meet, and gets rid of this physical machine, reselects out alternative physical host, carries out the migration of virtual machine.
The load level of physical machine carries out assessment concrete steps and is:
Step 1, watch-dog, in units of a certain period, follow the trail of the load information in each physical machine;
Step 2, load information storer, in units of the period identical with watch-dog, carry out record to all load informations tracked in step 1;
Step 3, load information storer by step 2 the load information recorded in interval sometime feed back to load balancer, load balancer calculates resource load mean value:
Wherein, δ
ifor resource load mean value, U
ifor the average utilization of resource in physical machine, T
ifor total resources in physical machine;
Step 4, according to step 3 income value, calculate the load value of this physical machine:
γ
i＝E+δ
i，
Wherein, be the load value of physical machine, E is relatively little constant;
Step 5, the alarming value arranged in step 4 income value and system to be contrasted, if income value exceedes load alarming value, namely judge: this physical machine load is transshipped;
The unbalanced degree of described relative load refers to that certain moment normally runs the ratio of physical machine resource load value and overload physical machine load value, according to formulae discovery:
Wherein, B
rfor the unbalanced degree of overload physical machine relative load, U
rifor normally running the average utilization of physical machine resource, U
mifor the average utilization of overload physical machine resource, T
rifor normally running the resource capacity of physical machine, T
mifor the resource capacity of overload physical machine, a
irepresent the weight factor of computational resource i.
Described load information comprises the average utilization U of the resources such as CPU, internal memory, storage space and the network bandwidth
iwith total amount T
i, i represents cloud computing resources dimension.
The invention has the beneficial effects as follows:
1, the utilization factor of system resource is high: run physical machine load information based on normal and transship physical machine load information and compare and obtain relative unbalanced degree, choose and there is minimum relatively unbalanced degree physical machine, calculate through resource requirement, after eligible, carry out the dynamic migration of virtual machine;
2, system load balancing: instruct scheduling virtual machine with the system load of balance node according to the load level of each computing node, improve the stability of system.
Accompanying drawing explanation
Fig. 1 is cloud computing system model schematic;
Fig. 2 is the cloud computing resource scheduling method process flow diagram that the present invention is based on the unbalanced degree of minimum relative load.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described in detail.
Fig. 1 is cloud computing system model schematic of the present invention: client sends request by computer terminal, and cloud system is left in the central task buffer queue being arranged in task buffer device, and in physical machine, sets up virtual machine successively according to request, watch-dog carries out real-time tracing to the load information of physical machine, and result is stored in load information storer, the physical machine load information received is fed back to load balancer by load information storer, load balancer is assessed physical machine load level, and adjudicate physical machine whether occur transship phenomenon, if overload, calculate the relative load unbalanced degree of all normal operation physical machine relative to this load overload physical machine, the physical machine selecting the unbalanced angle value of wherein relative load minimum is as alternative physical host, and judge alternative host calculating resource capacity whether satisfy the demand migration resources of virtual machine demand, if resource capacity meets, then using this physical machine as alternative physical machine, carry out the migration of virtual machine, resource capacity does not meet, and gets rid of this physical machine, reselects out alternative physical host, carries out the migration of virtual machine.
The present embodiment is specifically implemented, see Fig. 2 according to following steps based on the cloud computing resource scheduling method of the unbalanced degree of minimum relative load:
Step 1, cloud system receive client's solicited message, it are deposited successively and in physical machine, set up corresponding virtual machine;
Step 2, watch-dog, in units of T=20s, follow the trail of the load information of every platform physical machine, and load information storer, in units of the period T identical with watch-dog, records the load information tracked;
Step 3, in the S moment, load information storer by this reception to all physical machine load informations feed back to load balancer;
The physical message received in step 3 is utilized, the load mean value of Gains resources in step 4, load equalizer:
Wherein, δ
ifor resource load mean value, U
ifor the average utilization of resource in physical machine, T
ifor total resources in physical machine;
Step 5, according to step 4 income value, calculate the load value of this physical machine:
γ
i＝E+δ
i
Wherein, γ
ifor the load value of physical machine, E is relatively little constant;
Step 6, the alarming value arranged in step 5 income value and system to be contrasted, if income value exceedes load alarming value, namely load decision device judges: in this physical machine load of S moment overload, need the virtual machine to operating in this physical machine to move;
Step 7, load information storer feed back S-1 moment each normal operation physical host load information and this overload physical machine load information, and calculate the unbalanced degree of this overload physical machine relative load:
Wherein, B
rfor the unbalanced degree of overload physical machine relative load, U
rifor the S-1 moment normally runs the average utilization of physical machine resource, U
mifor the S-1 moment transships the average utilization of physical machine resource, T
rifor the S-1 moment normally runs the resource capacity of physical machine, T
mifor the S-1 moment transships the resource capacity of physical machine, a
irepresent the weight factor of computational resource i;
The judgement that step 8, task dispatcher send in conjunction with load equalizer, chooses the alternative main frame of the physical machine corresponding to minimum relatively unbalanced degree as virtual migration;
Step 9, resource constraint inspection is carried out to alternative main frame, the task requests that the S-1 moment distributes to physical machine is obtained from task buffer device, judge whether this task requests can cause alternative main frame generation load to transship: if required migration virtual machine is an I dimensional vector to resource requirement, by the demand of each dimension representative to a certain item computational resource, then: H=(h
1, h
2... h
i... h
i), i=1,2,3...I; From load information storer, obtain the load information of S moment alternative main frame, still write as vector form:
=(n
1, n
2... n
i... n
i), i=1,2,3...I; Compare h successively
iand n
isize, if satisfy condition h
i≤ n
i, i=1,2,3 ... I; Then will to transship in physical machine dynamic migration of virtual machine to this alternative main frame; If h
i> n
i, i=1,2,3 ... I, then get rid of this alternative machine, re-start the selection of alternative main frame, until select the alternative main frame met the demands;
Step 10, by virtual machine (vm) migration in alternative main frame.
Claims (4)
1. based on the cloud computing resource scheduling method of the unbalanced degree of minimum relative load, it is characterized in that, with load balancer, the load level of each physical machine in cluster is assessed, judge whether physical machine occurs transship phenomenon, if overload, calculate the relative load unbalanced degree of all normal operation physical machine relative to this load overload physical machine, the physical machine selecting the unbalanced angle value of wherein relative load minimum is as alternative physical host, and judge alternative host calculating resource capacity whether satisfy the demand migration resources of virtual machine demand, if resource capacity meets, then using this physical machine as alternative physical machine, carry out the migration of virtual machine, resource capacity does not meet, and gets rid of this physical machine and reselects out alternative physical host.
2. as claimed in claim 1 based on the cloud computing resource scheduling method of the unbalanced degree of minimum relative load, it is characterized in that, the load level of described physical machine carries out assessment concrete steps and is:
Step 1, watch-dog, in units of a certain period, follow the trail of the load information in each physical machine;
Step 2, load information storer, in units of the period identical with watch-dog, carry out record to all load informations tracked in step 1;
Step 3, load information storer by step 2 the load information recorded in interval sometime feed back to load balancer, load balancer calculates resource load mean value:
Wherein, δ
ifor resource load mean value, U
ifor the average utilization of resource in physical machine, T
ifor total resources in physical machine;
Step 4, according to step 3 income value, calculate the load value of this physical machine:
γ
i＝E+δ
i，
Wherein, γ
ifor the load value of physical machine, E is relatively little constant;
Step 5, the alarming value arranged in step 4 income value and system to be contrasted, if income value exceedes load alarming value, namely judge: this physical machine load is transshipped.
3. as claimed in claim 1 based on the cloud computing resource scheduling method of the unbalanced degree of minimum relative load, it is characterized in that, the unbalanced degree of described relative load refers to that certain moment normally runs the ratio of physical machine resource load value and overload physical machine load value, according to formulae discovery:
Wherein, B
rfor the unbalanced degree of overload physical machine relative load, U
rifor normally running the average utilization of physical machine resource, U
mifor the average utilization of overload physical machine resource, T
rifor normally running the resource capacity of physical machine, T
mifor the resource capacity of overload physical machine, a
irepresent the weight factor of computational resource i.
4. as claimed in claim 2 based on the cloud computing resource scheduling method of the unbalanced degree of minimum relative load, it is characterized in that, described load information comprises the average utilization U of the resources such as CPU, internal memory, storage space and the network bandwidth
iwith total amount T
i, i represents cloud computing resources dimension.
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| CN105718310A (en) * | 2016-01-13 | 2016-06-29 | 上海应用技术学院 | Virtual machine scheduling method for IO dense application under cloud platform |
| CN106133693A (en) * | 2015-02-28 | 2016-11-16 | 华为技术有限公司 | The moving method of virtual machine, device and equipment |
| CN106648829A (en) * | 2016-10-28 | 2017-05-10 | 广州市泰富信通科技有限公司 | Virtual machine transferring method for efficient utilization of cloud resource |
| CN106843998A (en) * | 2016-12-16 | 2017-06-13 | 郑州云海信息技术有限公司 | A kind of data center management method and device |
| CN107370783A (en) * | 2016-05-13 | 2017-11-21 | 北京金山云网络技术有限公司 | A kind of dispatching method and device of cloud computing cluster resource |
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| CN109840139A (en) * | 2017-11-29 | 2019-06-04 | 北京金山云网络技术有限公司 | Method, apparatus, electronic equipment and the storage medium of resource management |
| CN110198356A (en) * | 2019-06-10 | 2019-09-03 | 莫毓昌 | A kind of user's request scheduling mechanism based on mixed cloud |
| CN106656535B (en) * | 2015-10-29 | 2021-01-15 | 阿里巴巴集团控股有限公司 | Method and apparatus for resource scheduling |
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| CN106133693A (en) * | 2015-02-28 | 2016-11-16 | 华为技术有限公司 | The moving method of virtual machine, device and equipment |
| CN106133693B (en) * | 2015-02-28 | 2019-10-25 | 华为技术有限公司 | Moving method, device and the equipment of virtual machine |
| CN105141656B (en) * | 2015-07-20 | 2018-05-01 | 浙江工商大学 | A kind of implementation of load balancing of the internet lightweight application based on cloud platform |
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| CN106656535B (en) * | 2015-10-29 | 2021-01-15 | 阿里巴巴集团控股有限公司 | Method and apparatus for resource scheduling |
| CN105718310B (en) * | 2016-01-13 | 2018-09-18 | 上海应用技术学院 | The dispatching method of virtual machine that I/O intensive type is applied under a kind of cloud platform |
| CN105718310A (en) * | 2016-01-13 | 2016-06-29 | 上海应用技术学院 | Virtual machine scheduling method for IO dense application under cloud platform |
| CN107370783A (en) * | 2016-05-13 | 2017-11-21 | 北京金山云网络技术有限公司 | A kind of dispatching method and device of cloud computing cluster resource |
| CN106648829A (en) * | 2016-10-28 | 2017-05-10 | 广州市泰富信通科技有限公司 | Virtual machine transferring method for efficient utilization of cloud resource |
| CN106843998A (en) * | 2016-12-16 | 2017-06-13 | 郑州云海信息技术有限公司 | A kind of data center management method and device |
| CN109144658A (en) * | 2017-06-27 | 2019-01-04 | 阿里巴巴集团控股有限公司 | Load-balancing method, device and the electronic equipment of limited resources |
| CN109144658B (en) * | 2017-06-27 | 2022-07-15 | 阿里巴巴集团控股有限公司 | Load balancing method and device for limited resources and electronic equipment |
| CN107707612A (en) * | 2017-08-10 | 2018-02-16 | 北京奇艺世纪科技有限公司 | A kind of appraisal procedure and device of the resource utilization of load balancing cluster |
| CN109840139A (en) * | 2017-11-29 | 2019-06-04 | 北京金山云网络技术有限公司 | Method, apparatus, electronic equipment and the storage medium of resource management |
| CN110198356A (en) * | 2019-06-10 | 2019-09-03 | 莫毓昌 | A kind of user's request scheduling mechanism based on mixed cloud |
| CN112738193A (en) * | 2020-12-24 | 2021-04-30 | 山东鑫泰洋智能科技有限公司 | Load balancing method and device for cloud computing |
| CN112738193B (en) * | 2020-12-24 | 2022-08-19 | 青岛民航凯亚系统集成有限公司 | Load balancing method and device for cloud computing |
| CN115269120A (en) * | 2022-08-01 | 2022-11-01 | 江苏安超云软件有限公司 | NUMA node scheduling method, device, equipment and storage medium of virtual machine |
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