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 the field of virtualization and cloud computing, and particularly relates to a cloud computing resource scheduling method based on minimum relative load imbalance.
Background
Cloud computing has emerged in response to the social trend of enterprises and individual consumers that demand ever-increasing processing power from data centers, and is currently one of the most hot issues in IT research. The cloud computing platform adopts a virtual cluster constructed by a virtualization technology, can dynamically organize heterogeneous computing resources, can isolate a specific hardware architecture and a diversified software system platform, can flexibly construct computing environments meeting different application requirements, and improves the use efficiency of the computing resources. Under the cloud computing environment, the application system is not limited to the system performance of the application system, strong computing capacity, massive data resources and various applications can be obtained from cloud resources, and diversified and high-level service requirements of various users can be met by utilizing more ways. The user can obtain more perfect service experience and can enjoy various services of the application system in time, efficiently and without barriers.
As the most key and most core technical motive power in the IaaS layer of cloud computing, the virtualization technology can abstract underlying architectures such as physical resources, so that the difference and compatibility of equipment are transparent to upper-layer applications, and the cloud is allowed to uniformly manage the underlying widely-different resources. Just because of the maturity and wide application of virtualization technology, computing, storage, applications and services in cloud computing become resources, and these resources can be dynamically expanded and configured, so that cloud computing can be logically presented in a single integral form finally. In a cloud environment, a virtual machine is used as a computing resource, and a user generally requires that the operation of the virtual machine has stability, and does not want to frequently migrate the virtual machine.
In a cloud computing application platform, resources are widely distributed and diversified, and a key problem is to process the allocation of the resources so that a user can really use the resources in a cloud environment like using water and electricity. Meanwhile, the real-time dynamic state of user requirements is difficult to be accurately predicted, and the problems of system performance, cost and the like are also considered, so that the efficient cloud computing data center allocation scheduling strategy algorithm becomes a research hotspot. In a cloud computing system, when a user requests to create a new virtual machine, or the virtual machine needs to be migrated, cloud computing resource scheduling is required to keep the whole system running efficiently and reliably. An existing cloud computing resource Scheduling method, for example, an invention patent application of "large-scale virtual machine fast migration decision method facing cloud Data center" (application No. 201310186581.8 published: 2013-08-14) in the paper "ADynamic And Integrated Load Scheduling Algorithm for cloud Data Centers" (IEEE International Conference on cloud computing And integration System,311-315, Wenhong Tian, yong zhao, yuan hang, min Xu, Chen jin, 2011-09-15), mainly considers the System performance And Load balance, but does not consider the problems such as whether the capacity of a host meets the requirement And the influence on the resource utilization rate.
Disclosure of Invention
The invention aims to provide a cloud computing resource scheduling method based on minimum relative load imbalance, and solves the problems that the prior art cannot obtain higher resource utilization rate and cannot maintain lower level of load imbalance on the premise of ensuring higher system performance.
The technical scheme adopted by the invention is a cloud computing resource scheduling method based on minimum relative load imbalance, a load balancer is used for evaluating the load level of each physical machine in a cluster, whether an overload phenomenon occurs on the physical machine is judged, if the physical machine is overloaded, the relative load imbalance of all normally operated physical machines relative to the physical machine overloaded with the load is calculated, the physical machine with the minimum relative load imbalance is selected as an alternative physical host, whether the computing resource capacity of the alternative host meets the resource requirement of a virtual machine needing to be migrated is judged, and if the resource capacity meets, the physical machine is taken as the alternative physical machine to migrate the virtual machine; if the resource capacity is not satisfied, the physical machine is excluded, and the alternative physical host is reselected for migration of the virtual machine.
The specific steps for evaluating the load level of the physical machine are as follows:
step 1, a monitor tracks load information in each physical machine by taking a certain time interval as a unit;
step 2, the load information memory takes the same time interval as the monitor as a unit to record all the load information tracked in the step 1;
step 3, the load information memory feeds back the load information recorded in the step 2 in a certain time interval to the load balancer, and the load balancer calculates the average value of the resource load:
wherein,iis the resource load average, UiIs the average utilization of resources in the physical machine, TiThe total amount of resources in the physical machine;
and 4, calculating the load value of the physical machine according to the value obtained in the step 3:
γi＝E+i，
wherein, the load value of the physical machine is E, and E is a relatively small constant;
and 5, comparing the value obtained in the step 4 with an alarm value set in the system, and if the obtained value exceeds the load alarm value, judging: the physical machine is overloaded;
the relative load imbalance degree refers to the ratio of the resource load value of the normally running physical machine to the load value of the overloaded physical machine at a certain time, and is calculated according to a formula:
wherein, BrFor the degree of imbalance of the relative loads of the overloaded physical machine, UriFor average utilization of resources of normally operating physical machines, UmiFor average utilization of overloaded physical machine resources, TriFor the resource capacity of a normally operating physical machine, TmiTo overload the resource capacity of a physical machine, aiRepresenting the weight factor of the computing resource i.
The load information packetAverage utilization rate U of resources including CPU, memory, storage space, network bandwidth and the likeiAnd the total amount TiAnd i represents a cloud computing resource dimension.
The invention has the beneficial effects that:
1. the utilization rate of system resources is high: comparing the load information of the normally running physical machine with the load information of the overloaded physical machine to obtain relative unbalance, selecting the physical machine with the minimum relative unbalance, calculating resource requirements, and performing dynamic migration on the virtual machine after the physical machine meets the conditions;
2. and (3) system load balancing: and the virtual machine scheduling is guided according to the load level of each computing node to balance the system load of the nodes, so that the stability of the system is improved.
Drawings
FIG. 1 is a cloud computing system model schematic;
fig. 2 is a flowchart of a cloud computing resource scheduling method based on minimum relative load imbalance according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a schematic diagram of a cloud computing system model according to the present invention: a client sends a request through a computer terminal, the cloud system stores the request in a central task cache queue in a task cache, and virtual machines are sequentially established in a physical machine according to the request; the monitor tracks the load information of the physical machine in real time, and stores the result into a load information memory, the load information memory feeds the received load information of the physical machine back to a load balancer, the load balancer evaluates the load level of the physical machine and judges whether the physical machine is overloaded or not, if the physical machine is overloaded, the relative load imbalance degree of all normally operated physical machines relative to the physical machine overloaded by the load is calculated, the physical machine with the minimum relative load imbalance degree value is selected as an alternative physical host, whether the computing resource capacity of the alternative host meets the resource requirement of the virtual machine to be migrated or not is judged, and if the resource capacity meets, the physical machine is taken as the alternative physical machine to migrate the virtual machine; if the resource capacity is not satisfied, the physical machine is excluded, and the alternative physical host is reselected for migration of the virtual machine.
The cloud computing resource scheduling method based on the minimum relative load imbalance in the embodiment is implemented specifically according to the following steps, which are shown in fig. 2:
step 1, the cloud system receives client request information, sequentially stores the client request information and establishes a corresponding virtual machine in a physical machine;
step 2, the monitor tracks the load information of each physical machine by taking T as a unit of 20s, and the load information memory records the tracked load information by taking the same time period T as the monitor;
step 3, at the time of S, the load information memory feeds back all the physical machine load information received at the time to the load balancer;
step 4, obtaining the load average value of the resource by the load balancer by using the physical information received in the step 3:
wherein,ias a resourceLoad mean value, UiIs the average utilization of resources in the physical machine, TiThe total amount of resources in the physical machine;
and 5, calculating the load value of the physical machine according to the value obtained in the step 4:
γi＝E+i
wherein, γiE is a relatively small constant, and is the load value of the physical machine;
and 6, comparing the value obtained in the step 5 with an alarm value set in the system, and if the obtained value exceeds the load alarm value, the load judger judges that: when the load of the physical machine is overloaded at the S moment, the virtual machine running on the physical machine needs to be migrated;
and 7, feeding back the load information of each normally-operated physical host and the load information of the overload physical machine at the S-1 moment by the load information memory, and calculating the imbalance degree of the overload physical machine relative to the load:
wherein, BrFor the degree of imbalance of the relative loads of the overloaded physical machine, UriIs the average utilization rate, U, of the resources of the physical machine which normally operates at S-1 momentmiIs S-1Average utilization of constantly overloaded physical machine resources, TriResource capacity, T, for a normally operating physical machine at S-1 timemiResource capacity of physical machine overloaded at S-1 time, aiA weight factor representing a computational resource i;
step 8, the task scheduler selects a physical machine corresponding to the minimum relative unbalance as a standby host of virtual migration in combination with the decision sent by the load balancer;
step 9, performing resource constraint check on the alternative host, acquiring a task request distributed to the physical machine at the S-1 moment from the task buffer, and judging whether the task request can cause the alternative host to generate load overload: if the resource requirement of the virtual machine to be migrated is an I-dimensional vector, and each dimension represents the requirement on a certain computing resource, then: h ═ H (H)1,h2...hi...hI) I1, 2,3.. I; acquiring the load information of the alternative host at the S moment from the load information memory, and still writing the load information into a vector form:＝(n1,n2...ni...nI) I1, 2,3.. I; comparison h in sequenceiAnd niIf the condition h is satisfiedi≤niI is 1,2,3 … I; dynamically migrating the virtual machine in the overloaded physical machine to the alternative host; if h isi＞niIf I is 1,2,3 … I, then excluding the candidate machine, and reselecting the candidate host until selecting the candidate host meeting the requirement;
and 10, migrating the virtual machine to the alternative host.
Claims (4)
1. The cloud computing resource scheduling method based on the minimum relative load imbalance degree is characterized in that a load balancer is used for evaluating the load level of each physical machine in a cluster, judging whether an overload phenomenon occurs on the physical machine or not, if the physical machine is overloaded, calculating the relative load imbalance degree of all normally-operated physical machines relative to the physical machine with the overloaded load, selecting the physical machine with the minimum relative load imbalance degree as an alternative physical host, judging whether the computing resource capacity of the alternative host meets the resource requirement of a virtual machine needing to be migrated or not, and if the resource capacity meets the requirement, taking the physical machine as the alternative physical machine to migrate the virtual machine; and if the resource capacity is not satisfied, excluding the physical machine from reselecting the alternative physical host.
2. The method for scheduling cloud computing resources based on minimum relative load imbalance according to claim 1, wherein the specific steps of evaluating the load level of the physical machine are as follows:
step 1, a monitor tracks load information in each physical machine by taking a certain time interval as a unit;
step 2, the load information memory takes the same time interval as the monitor as a unit to record all the load information tracked in the step 1;
step 3, the load information memory feeds back the load information recorded in the step 2 in a certain time interval to the load balancer, and the load balancer calculates the average value of the resource load:
wherein,iis the resource load average, UiIs the average utilization of resources in the physical machine, TiThe total amount of resources in the physical machine;
and 4, calculating the load value of the physical machine according to the value obtained in the step 3:
γi＝E+i，
wherein, γiE is a relatively small constant, and is the load value of the physical machine;
and 5, comparing the value obtained in the step 4 with an alarm value set in the system, and if the obtained value exceeds the load alarm value, judging: the physical machine is overloaded.
3. The method for scheduling cloud computing resources based on minimum relative load imbalance according to claim 1, wherein the relative load imbalance is a ratio of a resource load value of a normally operating physical machine to a load value of an overloaded physical machine at a certain time, and is calculated according to a formula:
wherein, BrFor the degree of imbalance of the relative loads of the overloaded physical machine, UriFor average utilization of resources of normally operating physical machines, UmiFor average utilization of overloaded physical machine resources, TriFor the resource capacity of a normally operating physical machine, TmiTo overload the resource capacity of a physical machine, aiRepresenting the weight factor of the computing resource i.
4. The method as claimed in claim 2, wherein the load information includes an average utilization rate U of resources such as CPU, memory, storage space, and network bandwidthiAnd the total amount TiAnd i represents a cloud computing resource dimension.
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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 |
| 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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| 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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| 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 |
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| 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 |
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| 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 |
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