CN109976879B - Cloud computing virtual machine placement method based on resource usage curve complementation - Google Patents
Cloud computing virtual machine placement method based on resource usage curve complementation Download PDFInfo
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Abstract
A cloud computing virtual machine placement method based on resource usage curve complementation is disclosed, and the main idea is as follows: selecting an overloaded physical host, selecting a virtual machine on the overloaded physical machine, predicting the resource use condition of t time points in the future for the physical host which can be migrated, searching a physical host to make the resource use condition of the physical host and the resource use condition of the virtual machine complementary, calculating the complementarity of the physical host and the virtual machine, then calculating the complementarity between all the virtual machines on the overloaded physical host and all the physical hosts which can be migrated according to the process, selecting a group of virtual machines and physical hosts with the maximum complementarity value, and migrating the virtual machine to the physical host. If the physical machine is still overloaded after the migration, removing the corresponding comprehensive complementarity from the comprehensive complementarity set and reselecting a group of virtual machines and the physical host for migration until the physical machine is no longer overloaded or the comprehensive complementarity set is empty.
Description
Technical Field
The invention belongs to the field of cloud computing and virtualization, and particularly relates to a cloud computing virtual machine placement method based on resource use curve complementation, which is mainly used for reducing the migration times of virtual machines and improving the utilization rate of CPU (central processing unit) resources of a physical machine so as to reduce the energy consumption of the whole data center.
Background
Cloud computing is a new technology for providing services to users on demand, and has revolutionary influence on the information technology industry. The development of cloud computing has led to thousands of data nodes. The virtualization technology is a key technology of cloud computing, and is a basis for realizing service provision on demand by cloud computing, wherein in a virtualization environment, a software operating environment is different from a traditional mode and operates on hardware, but operates in the virtualization environment, and in the virtualization environment, hardware resources can be allocated according to requirements. From the perspective of a data center, the virtualization technology realizes that one physical machine runs multiple virtual machines, thereby greatly reducing the cost of hardware.
One of the main features of virtualization technology is online migration: and moving a running virtual machine from the current physical machine to another physical machine. The online migration characteristic of the virtual machine has important significance for the management of the virtual machine, and the main significance is embodied in three aspects. The first is to facilitate load balancing of the physical machines. When the load of the physical machine is too high, part of the virtual machines are migrated to the physical machine with too low load, so that the balance between too high load and too low load of the virtual machines is realized; and secondly, energy consumption management is convenient for the data center. Migrating the virtual machine on the physical machine with the too low load to other virtual machines, and closing the current physical machine to achieve the aim of reducing energy consumption; and thirdly, the data center is convenient to maintain. When a problem occurs in the running physical machine, the virtual machine on the current physical machine can be migrated to another physical machine, so as to ensure the normal running of the virtual machine.
An indispensable link in the migration process of the virtual machine is placement of the virtual machine, two virtual machine placement strategies are available in the prior art, one is a virtual machine placement strategy based on the resource utilization rate of the physical host, the virtual machine placement strategy refers to that the load condition of the physical host after the virtual machine is placed is considered when the virtual machine is placed, and the algorithm aims to improve the utilization rate of the physical host. The other is a virtual machine placement algorithm for sensing the dependency relationship between virtual machines, which means that in the process of placing the virtual machines, the dependency relationship between the virtual machines (i.e. the communication traffic between the virtual machines) is considered, when two virtual machines are located on different physical hosts and the communication traffic between the two virtual machines is large, each communication needs to be accessed through a network, and the time required for completing one communication is relatively long; an effective solution to this problem is to place several interdependent (higher traffic) virtual machines on the same physical host. The algorithm is based on reducing the traffic throughout the data center.
For a large data center, physical machine overload sometimes occurs, but the overload is caused by that a plurality of loaded virtual machines collectively request host resources, and in order to effectively solve the problem and achieve the purpose of reducing energy consumption, the virtual machines loaded on the overloaded physical machine need to be migrated. However, most of the existing migration methods only perform migration according to whether the physical machine is overloaded or not, and do not perform specific analysis on the resource conditions of the virtual machine and the physical machine, which directly results in short effective time of migration and a large amount of waste of physical machine resources.
Disclosure of Invention
The invention provides a cloud computing virtual machine placement method based on resource use curve complementation, which is used for analyzing and predicting the resource use condition of a virtual machine and a physical machine within a period of time, selecting a reasonable placement strategy by combining the resource complementation degree, ensuring the load balance of a target physical machine within a period of time in the future and reducing the migration times while saving energy consumption.
A cloud computing virtual machine placement method based on resource usage curve complementation comprises the following steps:
step 1, obtaining a virtual machine set V ═ { V ═ on the overloaded physical machine 1 ,v 2 ,v 3 ,……v n P ═ P of the set of physical machines available for migration 1 ,p 2 ,p 3 ,……p k H, wherein each virtual machine and each physical machine contain z-type resources;
step 2, obtaining the virtual machine V in the overload virtual machine set V i Utilization matrix A of z-type resources at t time points i ；
Step 3, predicting the virtual machine v at t time points in the future by using prediction methods such as neural network and the like i Obtaining a predicted usage data matrix C from the usage data of the z-type resources i ；
Step 4, obtaining the physical machine P in the physical machine set P for migration j Utilization matrix B of z-type resources at t time points j ；
Step 5, predicting the physical machine p of t time points in the future by using prediction methods such as neural network and the like j The residual utilization rate of the z-type resources is obtained to obtain a matrix D of the residual utilization rate of the predicted resources j ；
Step 6, calculating the virtual machine v i Curvature r of upper z-th class resource utilization rate at t-th time point izt Thereby obtaining a virtual machine v i Up z type resource utilization c izt Curvature r at t time points in the future izt Matrix R of i ；
Step 7, calculating the physical computer p j Curvature q of upper z-th class resource utilization rate at t-th time point jzt Thereby obtaining a physical machine p j Up z type resource utilization b jzt Curvature q at t time points in the future jzt Of (2) matrix Q j ；
Step 8, calculating the virtual machine v i And physical machine p j The complementarity h of the z-th type resource between ijz Thereby obtaining a virtual machine v i And physical machine p j The complementarity of all z-type resources between them is combined to obtain a complementary set H ij ；
Step 9, calculating the virtual machine v i And physical machine p j Comprehensive degree of complementarity s between ij Thus, the comprehensive complementarity s between the virtual machine in the virtual machine set V and the physical machine in the physical machine set P is obtained ij A set S of (2);
step 10, calculating the comprehensive complementarity s ij S is a minimum value of min. To obtain the corresponding virtual machine v with the minimum complementary degree i And physical machine p j Will virtual machine v i Put to a physical machine p j And remove S from the set S min ；
Step 11, calculating the overloaded physical host p o Residual utilization e of upper z-type resources ozt If e is present jzt >0 and the set S is not empty, go back to step 10; otherwise, the migration is finished.
Further, in step 2, a virtual machine V in the overloaded virtual machine set V is obtained i Utilization matrix A of z-type resources at t time points i Wherein a is izt For virtual machines v i The utilization rate of the z-th type resource at the t-th time and a virtual machine resource utilization rate matrix A i Each row in (a) represents a virtual machine v i Utilization of the above class of resources at t points in time.
Further, in step 3, a prediction method such as a neural network is used to predict the virtual machine v at t time points in the future i Z-type resource usage data C i (ii) a Wherein c is izt For virtual machines v obtained by prediction i Utilization of the z-th class resource at the t-th time.
Further, in step 4, a physical machine P in the set of physical machines P available for migration is obtained j Utilization matrix B of z-type resources at t time points j Wherein b is jzt Defined as physical machines p j The utilization rate of the z-th type resource at the t-th time and a physical machine resource utilization rate matrix B j Each row in (a) represents a physical machine p j Utilization of the above class of resources at t points in time.
Further, in the step 5, the physical machine p of the future t time points is predicted by applying a prediction method such as a neural network and the like j Z-class resource residual utilization matrix D j Wherein d is jzt Is defined as the predicted physical machine p j Utilization of z-th resources at the t-th time, u z Representing an upper threshold for utilization of class z resources.
Further, in the step 6, the virtual machine v is calculated by formula (1) i Curvature r of upper z-th class resource utilization rate at t-th time point izt ；
Get virtual machine v i Upper z type resource utilization c izt Curvature r at t time points in the future izt Matrix R of i 。
Further, in the step 7, the physical machine p is calculated by the formula (2) j Curvature q of upper z-th class resource utilization rate at t-th time point jzt ；
Get the physical machine p j Up z type resource utilization b jzt Curvature q at t time points in the future jzt Of (2) matrix Q j 。
Further, in the step 8, the virtual machine v is calculated by formula (3) i And physical machine p j The complementarity h of the z-th type resource between ijz ；
Get virtual machine v i And physical machine p j The complementation degree combination of all z-type resources is a complementation set H ij 。
H ij ＝[h ij1 ,h ij2 ,…h ijz ]
Further, in the step 9, the virtual machine v is calculated by formula (4) i And physical machine p j Comprehensive degree of complementarity s between ij ，
Obtaining the comprehensive complementarity s between the virtual machine in the virtual machine set V and the physical machine in the physical machine set P ij Set S of (a).
S＝[s 11 ,s 12 …s 1k ,s 21 …s nk ]
Further, in the step 10, the comprehensive complementarity s is calculated by the formula (5) ij S is a minimum value of min .；
s min ＝min{S ij ，0<i<n,0<j<k} (5)
i. j is s min Subscript in set S, let virtual machine v i Put to a physical machine p j Removing S from the set S min ；
In said step 11, the overloaded physical host p is calculated o Residual utilization e of upper z-type resources ozt ；
e ozt ＝u zt -u z -c ozt (6)
u zt For the total utilization rate of the z-th class resource on the overloaded physical machine at the time t, if e exists jzt >0 and the set S is not empty, go back to step 10; otherwise, the migration is finished.
Compared with the current mainstream placement strategy, the invention has the main advantages that: the method analyzes and predicts the use conditions of various resources in a period of time of the virtual machine and the physical machine, can ensure the load balance of the target physical machine in a period of time in the future, and selects a reasonable placement strategy by combining the comprehensive complementary degree of the resources, so that the utilization rates of various resources between the virtual machine to be migrated and the target physical machine are complemented as much as possible, the energy consumption is saved, and the migration times are reduced.
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Fig. 1 is a flowchart of a cloud computing virtual machine placement method based on resource usage curve complementation according to the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
A cloud computing virtual machine placement method based on resource usage curve complementation is disclosed, referring to FIG. 1, and comprises the following steps:
step 1, obtaining a virtual machine set V ═ { V ═ on the overloaded physical machine 1 ,v 2 ,v 3 ,……v n P ═ P of the set of physical machines available for migration 1 ,p 2 ,p 3 ,……p k And f, wherein each virtual machine and each physical machine contain z-type resources.
Step 2, obtaining the virtual machine V in the overload virtual machine set V i Utilization matrix A of z-type resources at t time points i 。
In the step 2, the virtual machine V in the overload virtual machine set V is obtained i Utilization matrix A of z-type resources at t time points i Wherein a is izt For virtual machines v i The utilization rate of the z-th type resource at the t-th time and a virtual machine resource utilization rate matrix A i Each row in (a) represents a virtual machine v i Utilization of the above class of resources at t points in time.
Step 3, predicting the virtual machine v at t time points in the future by using prediction methods such as neural network and the like i Obtaining the predicted usage data matrix C i 。
In the step 3, the virtual machine v of t time points in the future is predicted by applying a prediction method such as a neural network and the like i Z-type resource usage data C i (ii) a Wherein c is izt For virtual machines v obtained by prediction i Utilization of the z-th class resource at the t-th time.
Step 4, obtaining the physical machine P in the physical machine set P for migration j Utilization matrix B of z-type resources at t time points j 。
In the step 4, the physical machine P in the physical machine set P available for migration is obtained j Utilization matrix B of z-type resources at t time points j Wherein b is jzt Is defined as a physical machine p j The utilization rate of the z-th type resource at the t-th time and a physical machine resource utilization rate matrix B j Each row in (a) represents a physical machine p j Utilization of the above class of resources at t points in time.
Step 5, predicting the physical machine p of t time points in the future by using prediction methods such as neural network and the like j The residual utilization rate of the z-type resources is obtained to obtain a matrix D of the residual utilization rate of the predicted resources j 。
In the step 5, the physical machine p of the future t time points is predicted by applying a prediction method such as a neural network and the like j Z-class resource residual utilization matrix D j Wherein d is jzt Is defined as the predicted physical machine p j Utilization of z-th resources at the t-th time, u z Representing an upper threshold for utilization of class z resources.
Step 6, calculating the virtual machine v i Curvature r of upper z-th class resource utilization rate at t-th time point izt Thereby obtaining a virtual machine v i Up z type resource utilization c izt Curvature r at t time points in the future izt Matrix R of i 。
In the step 6, the virtual machine v is calculated by the formula (1) i Curvature r of upper z-th class resource utilization rate at t-th time point izt ；
Get virtual machine v i Up z type resource utilization c izt Curvature r at t time points in the future izt Matrix R of i 。
Step 7, calculating the physical computer p j Curvature q of upper z-th resource utilization at t-th time point jzt Thereby obtaining a physical machine p j Up z type resource utilization b jzt Curvature q at t time points in the future jzt Of (2) matrix Q j 。
In the step 7, the physical computer p is calculated by the formula (2) j Curvature q of upper z-th class resource utilization rate at t-th time point jzt ；
Get the physical machine p j Up z type resource utilization b jzt Curvature q at t time points in the future jzt Of (2) matrix Q j 。
Step 8, calculating the virtual machine v i And physical machine p j The complementarity h of the z-th type resource between ijz Thereby obtaining a virtual machine v i And physical machine p j The complementarity of all z-type resources between them is combined to obtain a complementary set H ij 。
In the step 8, the virtual machine v is calculated by the formula (3) i And physical machine p j The complementarity h of the z-th type resource between ijz ；
Get virtual machine v i And physical machine p j The complementation degree combination of all z-type resources is a complementation set H ij 。
H ij ＝[h ij1 ,h ij2 ,…h ijz ]
Step 9, calculating the virtual machine v i And physical machine p j Comprehensive degree of complementarity s between ij Thus, the comprehensive complementarity s between the virtual machine in the virtual machine set V and the physical machine in the physical machine set P is obtained ij Set S of (a).
In the step 9, the virtual machine v is calculated by the formula (4) i And physical machine p j Comprehensive degree of complementarity s between ij ，
Obtaining the comprehensive complementarity s between the virtual machine in the virtual machine set V and the physical machine in the physical machine set P ij Set S of (a).
S＝[s 11 ,s 12 …s 1k ,s 21 …s nk ]
Step 10, calculating the comprehensive complementarity s ij S is a minimum value of min The corresponding virtual machine v with the minimum complementarity is obtained i And physical machine p j Will virtual machine v i Put to a physical machine p j And remove S from the set S min 。
In the step 10, the comprehensive complementarity s is calculated by the formula (5) ij S is a minimum value of min. ；
s min ＝min{S ij ，0<i<n,0<j<k} (5)
i. j is s min Subscript in set S, let virtual machine v i Put to a physical machine p j Removing S from the set S min 。
Step 11, calculating the overloaded physical host p o Residual utilization e of upper z-type resources ozt If e is present jzt >0 andif the set S is not empty, returning to the step 10; otherwise, the migration is finished.
In said step 11, the overloaded physical host p is calculated o Residual utilization e of upper z-type resources ozt ；
e ozt ＝u zt -u z -c ozt (6)
u zt For the total utilization rate of z-th class resources on the overloaded physical machine at the time t, if e exists jzt >0 and the set S is not empty, go back to step 10; otherwise, the migration is finished.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the present disclosure should be included in the scope of the present invention as set forth in the appended claims.
Claims (2)
1. A cloud computing virtual machine placement method based on resource usage curve complementation is characterized in that: the method comprises the following steps:
step 1, obtaining a virtual machine set V ═ { V ═ on the overloaded physical machine 1 ,v 2 ,v 3 ,……v n P ═ P of the set of physical machines available for migration 1 ,p 2 ,p 3 ,……p k H, wherein each virtual machine and each physical machine contain z-type resources;
step 2, obtaining the virtual machine V in the overload virtual machine set V i Utilization matrix A of z-type resources at t time points i ；
In the step 2, the virtual machine V in the overload virtual machine set V is obtained i Utilization matrix A of z-type resources at t time points i Wherein a is izt For virtual machines v i The utilization rate of the z-th type resource at the t-th time and a virtual machine resource utilization rate matrix A i Each row in (a) represents a virtual machine v i The utilization rate of the above resources at t time points;
step 3, predicting the virtual machine v of t time points in the future by using a neural network i Obtaining a predicted usage data matrix C from the usage data of the z-type resources i ；
In the step 3, the neural network is used for predicting the virtual machine v at t time points in the future i Z-type resource usage data C i (ii) a Wherein c is izt For virtual machines v obtained by prediction i The utilization rate of the z-th type resource at the t-th time;
step 4, obtaining the physical machine P in the physical machine set P for migration j Utilization matrix B of z-type resources at t time points j ；
In the step 4, the physical machine P in the physical machine set P available for migration is obtained j Utilization matrix B of z-type resources at t time points j Wherein b is jzt Is defined as a physical machine p j The utilization rate of the z-th type resource at the t-th time and a physical machine resource utilization rate matrix B j Each row in (a) represents a physical machine p j The utilization rate of the above resources at t time points;
step 5, predicting the physical machine p of t time points in the future by using a neural network j The residual utilization rate of the z-type resources is obtained to obtain a matrix D of the residual utilization rate of the predicted resources j ；
In the step 5, the neural network is used for predicting the physical machine p at t time points in the future j Z-class resource residual utilization matrix D j Wherein d is jzt Is defined as the predicted physical machine p j Utilization of z-th resources at the t-th time, u z Representing utilization of class z resourcesAn upper threshold;
step 6, calculating the virtual machine v i Curvature r of upper z-th class resource utilization rate at t-th time point izt Thereby obtaining a virtual machine v i Up z type resource utilization c izt Curvature r at t time points in the future izt Matrix R of i ；
In the step 6, the virtual machine v is calculated by the formula (1) i Curvature r of upper z-th class resource utilization rate at t-th time point izt ；
Get virtual machine v i Upper z type resource utilization c izt Curvature r at t time points in the future izt Matrix R of i ；
Step 7, calculating the physical computer p j Curvature q of upper z-th class resource utilization rate at t-th time point jzt Thereby obtaining a physical machine p j Up z type resource utilization b jzt Curvature q at t time points in the future jzt Of (2) matrix Q j ；
In the step 7, the physical computer p is calculated by the formula (2) j Curvature q of upper z-th class resource utilization rate at t-th time point jzt ；
Get the physical machine p j Up z type resource utilization d jzt Curvature q at t time points in the future jzt Of (2) matrix Q j ；
Step 8, calculating the virtual machine v i And physical machine p j The complementarity h of the z-th type resource between ijz Thereby obtaining a virtual machine v i And physical machine p j The complementarity of all z-type resources between them is combined to obtain a complementary set H ij ；
In the step 8, the virtual machine v is calculated by the formula (3) i And physical machine p j The complementarity h of the z-th type resource between ijz ；
Get virtual machine v i And physical machine p j The complementation degree combination of all z-type resources is a complementation set H ij ；
H ij ＝[h ij1 ，h ij2 ，...h ijz ]
Step 9, calculating the virtual machine v i And physical machine p j Comprehensive degree of complementarity s between ij Thus, the comprehensive complementarity s between the virtual machine in the virtual machine set V and the physical machine in the physical machine set P is obtained ij A set S of (2);
in the step 9, the virtual machine v is calculated by the formula (4) i And physical machine p j Comprehensive degree of complementarity s between ij ，
Obtaining the comprehensive complementarity s between the virtual machine in the virtual machine set V and the physical machine in the physical machine set P ij A set S of (2);
s＝[s 11 ，s 12 …s 1k ，s 21 …s nk ]
step 10, calculating the comprehensive complementarity s ij S is a minimum value of min To obtain the corresponding virtual machine v with the minimum complementary degree i And physical machine p j Will virtual machine v i Put to a physical machine p j And remove S from the set S min ；
Step 11, calculating the overloaded physical host p o Residual utilization e of upper z-type resources ozt If e is present jzt >0 and the set S is not empty, go back to step 10; otherwise, the migration ends.
2. The cloud computing virtual machine placement method based on resource usage curve complementation according to claim 1, wherein: in the step 10, the comprehensive complementarity s is calculated by the formula (5) ij S is a minimum value of min ；
s min ＝min{S ij ，0<i<n,0<j<k} (5)
i. j is s min Subscript in set S, let virtual machine v i Put to a physical machine p j Removing S from the set S min ；
In said step 11, the overloaded physical host p is calculated o Residual utilization e of upper z-type resources ozt ；
e ozt ＝u zt -u z -c ozt (6)
u zt For the total utilization rate of z-th class resources on the overloaded physical machine at the time t, if e exists jzt >0 and the set S is not empty, go back to step 10; otherwise, the migration is finished.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104008018A (en) * | 2014-05-22 | 2014-08-27 | 南京邮电大学 | Virtual machine online migration method in cloud computing environment |
| US8990805B2 (en) * | 2011-12-01 | 2015-03-24 | Tunghai University | Method of dynamic resource allocation for a virtual machine cluster |
| CN106125888A (en) * | 2016-06-23 | 2016-11-16 | 河海大学常州校区 | The efficient power-economizing method of the utilization of resources based on virtual machine (vm) migration in cloud data center |
-
2019
- 2019-03-29 CN CN201910249791.4A patent/CN109976879B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8990805B2 (en) * | 2011-12-01 | 2015-03-24 | Tunghai University | Method of dynamic resource allocation for a virtual machine cluster |
| CN104008018A (en) * | 2014-05-22 | 2014-08-27 | 南京邮电大学 | Virtual machine online migration method in cloud computing environment |
| CN106125888A (en) * | 2016-06-23 | 2016-11-16 | 河海大学常州校区 | The efficient power-economizing method of the utilization of resources based on virtual machine (vm) migration in cloud data center |
Non-Patent Citations (1)
| Title |
|---|
| 云计算环境下网络感知的虚拟机迁移研究;陈驹洲;《硕士电子期刊》;20180215;全文 * |
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| CN109976879A (en) | 2019-07-05 |
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