CN113806017A - Method, system, equipment and storage medium for initial deployment of virtual machine in cloud computing - Google Patents
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- 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
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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
- G06F2009/45562—Creating, deleting, cloning virtual machine instances
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- G—PHYSICS
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- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/4557—Distribution of virtual machine instances; Migration and load balancing
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention provides a method, a system, equipment and a storage medium for initial deployment of a virtual machine in cloud computing, wherein the method comprises the following steps: setting a weight vector according to the importance of a cpu, a memory, a network and storage on a physical host, and setting a real-time load vector of the cpu, the memory, the network and the storage according to the current load condition; calculating the comprehensive weighted load of the physical host according to the weight vector and the real-time load vector; calculating the dynamic weighted load of the physical host and the predicted weighted load of the virtual machine deployed on the physical host according to the comprehensive weighted load; and determining the initial deployment position of the virtual machine according to the dynamic weighted load and the predicted weighted load. The invention ensures the balanced utilization and the maximized utilization rate of the basic resources in the cloud data center, thereby reducing the number of physical servers and reducing the energy consumption, and on the other hand, reducing the competition of various basic resources and further reducing the influence on the performance of the virtual machine.
Description
Technical Field
The present invention relates to the field of cloud computing, and in particular, to a method, a system, a device, and a storage medium for initial deployment of a virtual machine in cloud computing.
Background
Cloud computing can provide computing resources allocated according to needs for enterprises, modern enterprise IT (Information Technology) infrastructures are gradually migrated from traditional infrastructures to clouds, and the cloud computing can fully utilize expensive hardware resources through virtualization technologies and can also isolate dependency relationships between hardware architectures and software systems, improve the security performance of the system and improve the utilization rate of the computing resources. The virtual server is easy to expand and establish, and can distribute required hardware infrastructure according to the requirements of customers, so that the aims of rapidly deploying customer services, reducing the time for the customer services to be on line and saving the cost of the customers are fulfilled.
With the development of informatization, the scale of a cloud data center is continuously increased, the number of physical hosts and virtual hosts in the cloud data center is increased along with the continuous increase of user requirements, the initial deployment of the virtual machines is used as an important component of resource management of a cloud platform, the initial deployment positions of the virtual machines can directly influence the energy consumption of the cloud data center, and different initial deployment positions can also have different influences on other virtual machines. The existing cloud platform initial placement strategy is basically to place at random or only detect whether a memory of a physical machine is placed to meet an opening condition, so that the utilization rate of resources of the physical machine is low, which increases energy consumption of a cloud data center and wastes resources, on the other hand, due to lack of consideration of other dimension resources, resource competition with other virtual machines deployed on the same physical host machine, such as network resources or computing resources, is possibly caused, and thus not only performance of the cloud platform is affected, but also performance of the other virtual machines is reduced.
Disclosure of Invention
In view of this, an object of the embodiments of the present invention is to provide a method, a system, a computer device, and a computer-readable storage medium for initial deployment of a virtual machine in cloud computing, where the use conditions of cpu, memory, network, and storage resources are considered comprehensively when initially deploying the virtual machine, and balanced utilization and maximized utilization rate of basic resources in a cloud data center are ensured, so that the number of physical servers can be reduced, energy consumption can be reduced, and on the other hand, competition of various basic resources can be reduced, thereby reducing the influence on performance of the virtual machine.
Based on the above object, an aspect of the embodiments of the present invention provides a method for initially deploying a virtual machine in cloud computing, including the following steps: setting a weight vector according to the importance of a cpu, a memory, a network and storage on a physical host, and setting a real-time load vector of the cpu, the memory, the network and the storage according to the current load condition; calculating the comprehensive weighted load of the physical host according to the weight vector and the real-time load vector; calculating the dynamic weighted load of the physical host and the predicted weighted load of the virtual machine deployed on the physical host according to the comprehensive weighted load; and determining the initial deployment position of the virtual machine according to the dynamic weighted load and the predicted weighted load.
In some embodiments, said determining the location of the initial deployment of the virtual machine based on the dynamic weighted load and the predicted weighted load comprises: selecting a physical machine with the sum of the dynamic weighted load and the predicted weighted load smaller than a threshold value as a candidate physical machine; sorting the alternative physical machines from small to large according to the difference value between the sum of the dynamic weighted load and the predicted weighted load and the threshold, and sequentially judging whether the remaining dimensional resources in the alternative physical machines are all larger than the resources applied by the virtual machines; and selecting a first physical machine with the resource of each remaining dimension larger than the resource applied by the virtual machine as the initial deployment position of the virtual machine.
In some embodiments, said setting the weight vector according to importance of cpu, memory, network and storage on the physical host comprises: and constructing a contrast matrix according to the importance degrees of the cpu, the memory, the network and the storage, carrying out geometric averaging on the vectors of each row of the contrast matrix, and normalizing the vectors of each row after the geometric averaging to obtain a weight vector.
In some embodiments, said calculating a dynamic weighted load of the physical host and a predicted weighted load of virtual machine deployments on the physical host from the integrated weighted load comprises: and calculating the average value of the comprehensive weighted load of the physical host and the comprehensive weighted load of all the virtual machines deployed on the physical host to obtain the dynamic weighted load of the physical host.
In another aspect of the embodiments of the present invention, a system for initial deployment of a virtual machine in cloud computing is provided, including: the setting module is configured to set a weight vector according to the importance of the cpu, the memory, the network and the storage on the physical host, and set a real-time load vector of the cpu, the memory, the network and the storage according to the current load condition; the first calculation module is configured to calculate the comprehensive weighted load of the physical host according to the weight vector and the real-time load vector; the second calculation module is configured to calculate a dynamic weighted load of the physical host and a predicted weighted load of the virtual machine deployed on the physical host according to the comprehensive weighted load; and the determining module is configured to determine the initial deployment position of the virtual machine according to the dynamic weighted load and the predicted weighted load.
In some embodiments, the determining module is configured to: selecting a physical machine with the sum of the dynamic weighted load and the predicted weighted load smaller than a threshold value as a candidate physical machine; sorting the alternative physical machines from small to large according to the difference value between the sum of the dynamic weighted load and the predicted weighted load and the threshold, and sequentially judging whether the remaining dimensional resources in the alternative physical machines are all larger than the resources applied by the virtual machines; and selecting a first physical machine with the resource of each remaining dimension larger than the resource applied by the virtual machine as the initial deployment position of the virtual machine.
In some embodiments, the setup module is configured to: and constructing a contrast matrix according to the importance degrees of the cpu, the memory, the network and the storage, carrying out geometric averaging on the vectors of each row of the contrast matrix, and normalizing the vectors of each row after the geometric averaging to obtain a weight vector.
In some embodiments, the second computing module is configured to: and calculating the average value of the comprehensive weighted load of the physical host and the comprehensive weighted load of all the virtual machines deployed on the physical host to obtain the dynamic weighted load of the physical host.
In another aspect of the embodiments of the present invention, there is also provided a computer device, including: at least one processor; and a memory storing computer instructions executable on the processor, the instructions when executed by the processor implementing the steps of the method as above.
In a further aspect of the embodiments of the present invention, a computer-readable storage medium is also provided, in which a computer program for implementing the above method steps is stored when the computer program is executed by a processor.
The invention has the following beneficial technical effects: the using conditions of the cpu, the memory, the network and the storage resources are comprehensively considered when the virtual machine is initially deployed, and the balanced utilization and the maximized utilization rate of the basic resources in the cloud data center are ensured, so that the number of physical servers can be reduced, the energy consumption is reduced, and on the other hand, the competition of various basic resources can be reduced, so that the influence on the performance of the virtual machine is reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained by using the drawings without creative efforts.
Fig. 1 is a schematic diagram of an embodiment of a method for initial deployment of a virtual machine in cloud computing according to the present invention;
fig. 2 is a schematic diagram of an embodiment of a system for initial deployment of a virtual machine in cloud computing according to the present invention;
fig. 3 is a schematic hardware structure diagram of an embodiment of a computer device initially deployed by a virtual machine in cloud computing according to the present invention;
fig. 4 is a schematic diagram of an embodiment of a computer storage medium for initial deployment of a virtual machine in cloud computing according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following embodiments of the present invention are described in further detail with reference to the accompanying drawings.
It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are used for distinguishing two entities with the same name but different names or different parameters, and it should be noted that "first" and "second" are merely for convenience of description and should not be construed as limitations of the embodiments of the present invention, and they are not described in any more detail in the following embodiments.
In a first aspect of the embodiments of the present invention, an embodiment of a method for initially deploying a virtual machine in cloud computing is provided. Fig. 1 is a schematic diagram illustrating an embodiment of a method for initial deployment of a virtual machine in cloud computing according to the present invention. As shown in fig. 1, the embodiment of the present invention includes the following steps:
s1, setting weight vectors according to the importance of a cpu, a memory, a network and storage on the physical host, and setting real-time load vectors of the cpu, the memory, the network and the storage according to the current load condition;
s2, calculating the comprehensive weighted load of the physical host according to the weight vector and the real-time load vector;
s3, calculating the dynamic weighted load of the physical host and the predicted weighted load of the virtual machine on the physical host according to the comprehensive weighted load; and
and S4, determining the initial deployment position of the virtual machine according to the dynamic weighted load and the predicted weighted load.
The embodiment of the invention fully considers the balanced utilization of all dimensional resources of the deployed physical machine when the virtual machine is initially placed, firstly sets the relative weight of CPU, memory, network and storage according to the current load condition on the physical host, then calculates the comprehensive load condition of the host according to the use resource condition of the deployed virtual machine on the host and the use condition of the host resource, and finally judges whether the virtual machine is suitable for being deployed on the physical machine by calculating the predicted load of the virtual machine deployed on the physical machine and combining the comprehensive load of the physical machine. According to the invention, the use conditions of the cpu, the memory, the network and the storage resources are comprehensively considered when the virtual machine is initially deployed, and the balanced utilization and the maximized utilization rate of the basic resources in the cloud data center are ensured, so that the number of physical servers can be reduced, the energy consumption is reduced, and on the other hand, the competition of various basic resources can be reduced, thereby reducing the influence on the performance of the virtual machine.
The cloud platform global monitor is a core module for calculating relevant load information of each node (physical node and virtual node), and is used for an environment for implementing the invention by building a virtualization system on an x86 server based on a linux system and a KVM.
And a local monitor is deployed on each physical node, and the local monitor collects the use information of the CPU, the memory, the network and the storage through related tools carried by linux. A service agent vmtools is installed inside the virtual machine, and the local monitor acquires relevant monitoring information of a virtual machine memory through the agent vmtools inside the virtual machine, wherein the monitoring information comprises monitoring information such as a CPU (central processing unit), the memory, a network and storage. The physical host where the cloud platform global monitor is located is communicated with each physical node in the cloud data center through a network, and the local monitor transmits the collected CPU, memory, network and stored use information to the cloud platform global monitor in real time through the network.
And setting a weight vector according to the importance of the cpu, the memory, the network and the storage on the physical host, and setting a real-time load vector of the cpu, the memory, the network and the storage according to the current load condition.
In some embodiments, said setting the weight vector according to importance of cpu, memory, network and storage on the physical host comprises: and constructing a contrast matrix according to the importance degrees of the cpu, the memory, the network and the storage, carrying out geometric averaging on the vectors of each row of the contrast matrix, and normalizing the vectors of each row after the geometric averaging to obtain a weight vector.
Calculating the weight of each dimension attribute by using an analytic hierarchy process according to the cpu, the memory, the bandwidth and the dynamic utilization rate of storage of the node, wherein the node can be a physical node or a virtual machine node, and the specific description is as follows:
calculating weight vector of node based on analytic hierarchy process, and constructing contrast matrix R with 5-quantile scale of relative importance of each dimension attribute as shown in Table 1AAs in formula (1.1)Shown in the figure.
TABLE 1
| Importance of | Is very important | Of little importance | Of equal importance | Slightly less important | Very minor |
| Evaluation value | 5 | 3 | 1 | 1/3 | 1/5 |
Wherein C, M, N, S represents the CPU, memory, network and storage of the node, respectively, rijRepresenting the importance of element i relative to element j, satisfying rij＝1/rjiE.g. rcmRepresenting the relative memory importance of the cpu, RC、RM、RN、RSRespectively representing the importance of the CPU, the memory, the network and the storage of the node. Will matrix RAEach row vector is geometrically averaged and normalizedThe weight vector r is obtained as shown in formula (1.2), where rc＝r1，rm＝r2，rn＝r3，rs＝r4，riAs shown in formula (1.3), n is a contrast matrix RAThe number of rows n is 4.
r＝(rc rm rn rs) (1.2)
Calculating the matrix RAThe maximum characteristic root lambda is shown as a formula (1.4), the consistency of the maximum characteristic root lambda is calculated according to the formula (1.5), the degree that CI is close to 0 represents the satisfaction degree of the consistency, the larger the consistency degree is, the larger the misjudgment degree is, therefore, in order to accurately weight the attribute of each dimension of the node, the matrix R needs to be weightedAAnd carrying out consistency check.
And calculating the comprehensive weighted load of the physical host according to the weight vector and the real-time load vector. Defining a column vector sxReal-time load vectors for attributes of each dimension on a node, as shown in equation (1.6), where pc、pm、pn、psRespectively are the utilization rates of the CPU, the memory, the network and the storage on the node x. Node x Integrated weighted load is defined as PLxAs shown in equation (1.7), where rxThe weight vector of each dimension attribute of node x can be obtained by formula (1.2), where node x can be a physical host or a virtual host.
sx＝(pc pm pn ps)T (1.6)
PLx＝rx×sx (1.7)
And calculating the dynamic weighted load of the physical host and the predicted weighted load of the virtual machine deployed on the physical host according to the comprehensive weighted load.
In some embodiments, said calculating a dynamic weighted load of the physical host and a predicted weighted load of virtual machine deployments on the physical host from the integrated weighted load comprises: and calculating the average value of the comprehensive weighted load of the physical host and the comprehensive weighted load of all the virtual machines deployed on the physical host to obtain the dynamic weighted load of the physical host.
Defining a physical host pxDynamic weighted load DPL ofxDPL as shown in formula (1.8)xConsisting of the composite load of the physical host and the average of the composite loads of all the virtual machines deployed thereon, where XxIs a physical machine pxAnd in the virtual machine set, k is the number of the virtual machines, and beta e (0,1) is a weight coefficient for adjusting the physical host and the virtual host, and when no virtual machine is deployed on the physical machine, the physical dynamic weighting load is the same as the comprehensive weighting load.
When the virtual machine vxPre-deployed in a physical machine pxWhen it is pxIs defined as PRPLxThe formula is shown as (1.9), wherein k represents pxNumber of virtual machines deployed on, rxIs pxWeight vector of each dimension attribute, when pxWhen a virtual machine is deployed, the PRPLxMean load of all virtual machine dimension attributes and rxWhen p is a weighted component ofxWithout deployed virtual machines, PRPLxTotal capacity of each dimension attribute and p applied by virtual machinexThe ratio of the attributes of the respective dimensions (c) above.
And determining the initial deployment position of the virtual machine according to the dynamic weighted load and the predicted weighted load.
In some embodiments, said determining the location of the initial deployment of the virtual machine based on the dynamic weighted load and the predicted weighted load comprises: selecting a physical machine with the sum of the dynamic weighted load and the predicted weighted load smaller than a threshold value as a candidate physical machine; sorting the alternative physical machines from small to large according to the difference value between the sum of the dynamic weighted load and the predicted weighted load and the threshold, and sequentially judging whether the remaining dimensional resources in the alternative physical machines are all larger than the resources applied by the virtual machines; and selecting a first physical machine with the resource of each remaining dimension larger than the resource applied by the virtual machine as the initial deployment position of the virtual machine.
Traversing all physical hosts to calculate their dynamic weighted loads
And the virtual machine is deployed on the physical forecast weighted load
The physical machine that satisfies the following condition is the physical machine to be found. (1) Dynamic weighted load of physical machine
Predicted weighted load with respect to the physical machine from the virtual machine
The sum is less than the physically set threshold and
and
the difference between the sum and the threshold is minimum, so that the maximum resource utilization rate is ensured; (2) physical machine remainsThe remaining resources of each dimension are larger than the resources applied by the virtual machine.
According to the embodiment of the invention, the use conditions of the cpu, the memory, the network and the storage resources are comprehensively considered when the virtual machine is initially deployed, and the balanced utilization and the maximized utilization rate of the basic resources in the cloud data center are ensured, so that the number of physical servers can be reduced, the energy consumption is reduced, and on the other hand, the competition of various basic resources can be reduced, so that the influence on the performance of the virtual machine is reduced.
It should be particularly noted that, steps in the embodiments of the method for initially deploying a virtual machine in cloud computing may be intersected, replaced, added, and deleted, and therefore, these methods for initially deploying a virtual machine in cloud computing, which are transformed by reasonable permutation and combination, should also belong to the scope of the present invention, and should not limit the scope of the present invention to the embodiments.
Based on the above purpose, a second aspect of the embodiments of the present invention provides a system for initial deployment of a virtual machine in cloud computing. As shown in fig. 2, the system 200 includes the following modules: the setting module is configured to set a weight vector according to the importance of the cpu, the memory, the network and the storage on the physical host, and set a real-time load vector of the cpu, the memory, the network and the storage according to the current load condition; the first calculation module is configured to calculate the comprehensive weighted load of the physical host according to the weight vector and the real-time load vector; the second calculation module is configured to calculate a dynamic weighted load of the physical host and a predicted weighted load of the virtual machine deployed on the physical host according to the comprehensive weighted load; and the determining module is configured to determine the initial deployment position of the virtual machine according to the dynamic weighted load and the predicted weighted load.
In some embodiments, the determining module is configured to: selecting a physical machine with the sum of the dynamic weighted load and the predicted weighted load smaller than a threshold value as a candidate physical machine; sorting the alternative physical machines from small to large according to the difference value between the sum of the dynamic weighted load and the predicted weighted load and the threshold, and sequentially judging whether the remaining dimensional resources in the alternative physical machines are all larger than the resources applied by the virtual machines; and selecting a first physical machine with the resource of each remaining dimension larger than the resource applied by the virtual machine as the initial deployment position of the virtual machine.
In some embodiments, the setup module is configured to: and constructing a contrast matrix according to the importance degrees of the cpu, the memory, the network and the storage, carrying out geometric averaging on the vectors of each row of the contrast matrix, and normalizing the vectors of each row after the geometric averaging to obtain a weight vector.
In some embodiments, the second computing module is configured to: and calculating the average value of the comprehensive weighted load of the physical host and the comprehensive weighted load of all the virtual machines deployed on the physical host to obtain the dynamic weighted load of the physical host.
In view of the above object, a third aspect of the embodiments of the present invention provides a computer device, including: at least one processor; and a memory storing computer instructions executable on the processor, the instructions being executable by the processor to perform the steps of: s1, setting weight vectors according to the importance of a cpu, a memory, a network and storage on the physical host, and setting real-time load vectors of the cpu, the memory, the network and the storage according to the current load condition; s2, calculating the comprehensive weighted load of the physical host according to the weight vector and the real-time load vector; s3, calculating the dynamic weighted load of the physical host and the predicted weighted load of the virtual machine on the physical host according to the comprehensive weighted load; and S4, determining the initial deployment position of the virtual machine according to the dynamic weighted load and the predicted weighted load.
In some embodiments, said determining the location of the initial deployment of the virtual machine based on the dynamic weighted load and the predicted weighted load comprises: selecting a physical machine with the sum of the dynamic weighted load and the predicted weighted load smaller than a threshold value as a candidate physical machine; sorting the alternative physical machines from small to large according to the difference value between the sum of the dynamic weighted load and the predicted weighted load and the threshold, and sequentially judging whether the remaining dimensional resources in the alternative physical machines are all larger than the resources applied by the virtual machines; and selecting a first physical machine with the resource of each remaining dimension larger than the resource applied by the virtual machine as the initial deployment position of the virtual machine.
In some embodiments, said setting the weight vector according to importance of cpu, memory, network and storage on the physical host comprises: and constructing a contrast matrix according to the importance degrees of the cpu, the memory, the network and the storage, carrying out geometric averaging on the vectors of each row of the contrast matrix, and normalizing the vectors of each row after the geometric averaging to obtain a weight vector.
In some embodiments, said calculating a dynamic weighted load of the physical host and a predicted weighted load of virtual machine deployments on the physical host from the integrated weighted load comprises: and calculating the average value of the comprehensive weighted load of the physical host and the comprehensive weighted load of all the virtual machines deployed on the physical host to obtain the dynamic weighted load of the physical host.
Fig. 3 is a schematic hardware structural diagram of an embodiment of a computer device initially deployed by a virtual machine in cloud computing according to the present invention.
Taking the device shown in fig. 3 as an example, the device includes a processor 301 and a memory 302.
The processor 301 and the memory 302 may be connected by a bus or other means, such as the bus connection in fig. 3.
The memory 302 is a non-volatile computer-readable storage medium, and can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions/modules corresponding to the method for initial deployment of a virtual machine in cloud computing in this embodiment of the present application. The processor 301 executes various functional applications of the server and data processing by running the nonvolatile software programs, instructions, and modules stored in the memory 302, that is, implements a method for initially deploying a virtual machine in cloud computing.
The memory 302 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of a method of initial deployment of a virtual machine in cloud computing, and the like. Further, the memory 302 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, memory 302 optionally includes memory located remotely from processor 301, which may be connected to a local module via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Any embodiment of the computer device executing the method for initially deploying the virtual machine in the cloud computing can achieve the same or similar effects as any corresponding method embodiment.
The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs a method of initial deployment of a virtual machine in cloud computing.
Fig. 4 is a schematic diagram of an embodiment of a computer storage medium for initial deployment of a virtual machine in cloud computing according to the present invention. Taking the computer storage medium as shown in fig. 4 as an example, the computer readable storage medium 401 stores a computer program 402 which, when executed by a processor, performs the method as described above.
Finally, it should be noted that, as one of ordinary skill in the art can appreciate that all or part of the processes in the methods of the embodiments described above can be implemented by a computer program to instruct related hardware, and the program of the method for initial deployment of a virtual machine in cloud computing can be stored in a computer readable storage medium, and when executed, the program can include the processes of the embodiments of the methods described above. The storage medium of the program may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like. The embodiments of the computer program may achieve the same or similar effects as any of the above-described method embodiments.
The foregoing is an exemplary embodiment of the present disclosure, but it should be noted that various changes and modifications could be made herein without departing from the scope of the present disclosure as defined by the appended claims. The functions, steps and/or actions of the method claims in accordance with the disclosed embodiments described herein need not be performed in any particular order. Furthermore, although elements of the disclosed embodiments of the invention may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated.
It should be understood that, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly supports the exception. It should also be understood that "and/or" as used herein is meant to include any and all possible combinations of one or more of the associated listed items.
The numbers of the embodiments disclosed in the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, of embodiments of the invention is limited to these examples; within the idea of an embodiment of the invention, also technical features in the above embodiment or in different embodiments may be combined and there are many other variations of the different aspects of the embodiments of the invention as described above, which are not provided in detail for the sake of brevity. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of the embodiments of the present invention are intended to be included within the scope of the embodiments of the present invention.
Claims (10)
1. A method for initial deployment of a virtual machine in cloud computing is characterized by comprising the following steps:
setting a weight vector according to the importance of a cpu, a memory, a network and storage on a physical host, and setting a real-time load vector of the cpu, the memory, the network and the storage according to the current load condition;
calculating the comprehensive weighted load of the physical host according to the weight vector and the real-time load vector;
calculating the dynamic weighted load of the physical host and the predicted weighted load of the virtual machine deployed on the physical host according to the comprehensive weighted load; and
and determining the initial deployment position of the virtual machine according to the dynamic weighted load and the predicted weighted load.
2. The method of claim 1, wherein determining the location of the initial deployment of the virtual machine based on the dynamic weighted load and the predicted weighted load comprises:
selecting a physical machine with the sum of the dynamic weighted load and the predicted weighted load smaller than a threshold value as a candidate physical machine;
sorting the alternative physical machines from small to large according to the difference value between the sum of the dynamic weighted load and the predicted weighted load and the threshold, and sequentially judging whether the remaining dimensional resources in the alternative physical machines are all larger than the resources applied by the virtual machines; and
and selecting a first physical machine with the remaining resources of all dimensions larger than the resources applied by the virtual machine as the initial deployment position of the virtual machine.
3. The method of claim 1, wherein setting the weight vector according to importance of cpu, memory, network and storage on the physical host comprises:
and constructing a contrast matrix according to the importance degrees of the cpu, the memory, the network and the storage, carrying out geometric averaging on the vectors of each row of the contrast matrix, and normalizing the vectors of each row after the geometric averaging to obtain a weight vector.
4. The method of claim 1, wherein calculating the dynamic weighted load of the physical host and the predicted weighted load of virtual machine deployment on the physical host based on the integrated weighted load comprises:
and calculating the average value of the comprehensive weighted load of the physical host and the comprehensive weighted load of all the virtual machines deployed on the physical host to obtain the dynamic weighted load of the physical host.
5. A system for initial deployment of virtual machines in cloud computing is characterized by comprising:
the setting module is configured to set a weight vector according to the importance of the cpu, the memory, the network and the storage on the physical host, and set a real-time load vector of the cpu, the memory, the network and the storage according to the current load condition;
the first calculation module is configured to calculate the comprehensive weighted load of the physical host according to the weight vector and the real-time load vector;
the second calculation module is configured to calculate a dynamic weighted load of the physical host and a predicted weighted load of the virtual machine deployed on the physical host according to the comprehensive weighted load; and
and the determining module is configured to determine the initial deployment position of the virtual machine according to the dynamic weighted load and the predicted weighted load.
6. The system of claim 5, wherein the determination module is configured to:
selecting a physical machine with the sum of the dynamic weighted load and the predicted weighted load smaller than a threshold value as a candidate physical machine;
sorting the alternative physical machines from small to large according to the difference value between the sum of the dynamic weighted load and the predicted weighted load and the threshold, and sequentially judging whether the remaining dimensional resources in the alternative physical machines are all larger than the resources applied by the virtual machines; and
and selecting a first physical machine with the remaining resources of all dimensions larger than the resources applied by the virtual machine as the initial deployment position of the virtual machine.
7. The system of claim 5, wherein the setup module is configured to:
and constructing a contrast matrix according to the importance degrees of the cpu, the memory, the network and the storage, carrying out geometric averaging on the vectors of each row of the contrast matrix, and normalizing the vectors of each row after the geometric averaging to obtain a weight vector.
8. The system of claim 5, wherein the second computing module is configured to:
and calculating the average value of the comprehensive weighted load of the physical host and the comprehensive weighted load of all the virtual machines deployed on the physical host to obtain the dynamic weighted load of the physical host.
9. A computer device, comprising:
at least one processor; and
a memory storing computer instructions executable on the processor, the instructions when executed by the processor implementing the steps of the method of any one of claims 1 to 4.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
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| CN115412467A (en) * | 2022-09-01 | 2022-11-29 | 山东正中信息技术股份有限公司 | Tenant cloud resource utilization rate evaluation method and system in E-government cloud |
| CN117591039A (en) * | 2024-01-18 | 2024-02-23 | 济南浪潮数据技术有限公司 | Distributed storage method, system, equipment and medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115412467A (en) * | 2022-09-01 | 2022-11-29 | 山东正中信息技术股份有限公司 | Tenant cloud resource utilization rate evaluation method and system in E-government cloud |
| CN115412467B (en) * | 2022-09-01 | 2023-11-07 | 山东正中信息技术股份有限公司 | Method and system for evaluating tenant cloud resource utilization rate in e-government cloud |
| CN117591039A (en) * | 2024-01-18 | 2024-02-23 | 济南浪潮数据技术有限公司 | Distributed storage method, system, equipment and medium |
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