CN114296868B - Virtual machine automatic migration decision method based on user experience in multi-cloud environment - Google Patents
Virtual machine automatic migration decision method based on user experience in multi-cloud environment Download PDFInfo
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Abstract
The invention discloses a virtual machine automatic migration decision method based on user experience in a cloud environment, and belongs to the technical field of computers. The automatic migration decision method of the virtual machine comprises the following steps: judging whether static migration exists in a multi-cloud cascade environment, if the migration of the virtual machine can be completed by using the static migration, otherwise, adopting dynamic migration, predicting an access time period of a user accessing the virtual machine on a multi-cloud platform, collecting performance data of parameters of the virtual machine and physical nodes, using the access time period, the parameters of the virtual machine and the performance data of the physical nodes to obtain a fuzzy consistency judgment matrix of FAHP, solving the weight of parameters of field sub-modules in a weight equation according to the fuzzy consistency matrix, determining a migration sequence of the virtual machine according to the weight of the solved parameters of the field sub-modules, and selecting an important virtual machine for preferential migration according to the priority. The automatic migration decision method can be used for automatically screening the virtual machine migration nodes.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a virtual machine automatic migration decision-making method based on user experience in a multi-cloud environment.
Background
Data centers have become the infrastructure of various industries, from data rooms providing business support for small and medium-sized enterprises to IDCs of large-sized companies, and rely on huge hardware infrastructure and complex software for management. Service interruption and other events often occur in various large data centers at home and abroad, the service interruption of a fault node may spread to nodes which normally operate, a data center administrator often develops codes for service recovery during the service interruption, and the stability and customer satisfaction of cloud services become specific indexes of QOS evaluation.
Because unexpected downtime is an inevitable accident, a very effective disaster preparation scheme needs to be adopted to deal with the situation, but currently, if a container service and a virtual machine service are all required to be pulled up quickly in the disaster preparation scheme, a large area of virtual machines need to be deployed quickly, but the direct deployment efficiency is very low, the configuration is also tedious, the simplest method is to quickly migrate the backed-up virtual machines to available servers, which involves large area virtual machine migration, and of course, such migration can cause very many chain reactions.
In recent years, the stability of various cloud services has received wide attention from users. Although the number of catastrophic service outages has been reduced, the impact is still huge, especially in large-scale clustering and multi-cloud environments. These interrupts are highly likely to trigger the migration of a Virtual Machine (VM) located in the failed node. However, unlike a platform incident that can be predicted, the access time for each VM is random. The traditional method adopts an OpenStack virtual machine automatic migration decision method based on user experience to realize high performance and good load balance, a multi-target monitoring system of virtual machines and physical machine nodes and self-adaptive virtual machine migration scheduling aiming at an OpenStack multi-cloud platform. The method adopts the AHP as a core thought of decision scheduling, but the AHP needs to carry out constraint aiming at different scenes and nodes with use functions, so that the obtained weight values are different, the evaluation standards are difficult to unify, and the method realizes the automatic migration of the virtual machine, but the efficiency is not high.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a virtual machine automatic migration decision method based on user experience in a cloud environment, which can be used for automatically screening virtual machine migration nodes so as to realize automatic virtual machine migration.
In order to achieve the technical purpose, the invention adopts the following technical scheme: a virtual machine automatic migration decision-making method based on user experience in a multi-cloud environment specifically comprises the following steps:
(1) Firstly, judging a migration mechanism of a virtual machine in a multi-cloud cascade environment, if the migration of the virtual machine can be completed by using static migration, using the static migration, if the static migration cannot be completed, performing dynamic migration, and executing the step (2);
(2) Predicting the access time period of a user accessing the virtual machine on the multi-cloud platform by using an order determination method, and collecting parameters of the virtual machine and performance data of physical nodes;
(3) And using the access time period, the virtual machine parameters and the physical node performance data to obtain a fuzzy consistency judgment matrix combined with a Fuzzy Analytic Hierarchy Process (FAHP), solving the weight of the field sub-module parameters in the weight equation according to the fuzzy consistency matrix, determining the migration sequence of the virtual machine according to the weight sequence according to the weight of the solved field sub-module parameters, and migrating the virtual machine according to the height sequence.
Further, the migration mechanism of the virtual machine in the step (1) is specifically: when all node resources are larger than or equal to the resources required by the virtual machine, adopting static migration; and when all the node resources are less than the resources required by the virtual machine, performing dynamic migration.
Further, the total node resources include: CPU core number, operation memory RAM, hard disk size ROM, and Network bandwidth Network.
Further, the resources required by the virtual machine include: CPU core number, operation memory RAM, hard disk size ROM, and Network bandwidth Network.
Further, the process of acquiring the fuzzy consistent judgment matrix specifically comprises: calculating the ratio r of the access time period to the virtual machine parameters or the physical node performance data ij ＝a j /b i Wherein a is j Denotes the jth access period, b i Representing the ith virtual machine parameter or physical node performance data; and forming a fuzzy consistent judgment matrix by all the ratios.
Further, the weight equation is specifically:
where n denotes the number of all physical nodes, W j Represents the corresponding weight of the field submodule in the jth visit period, a j Represents the weight W corresponding to the field submodule in the jth visit time period j Resource usage constant of (a) j Is selected from { a } 1 ,a 2 ,…,a n And lambda represents a resource constant in the domain sub-module.
Further, the process of performing the migration sequence of the virtual machines according to the high-low order of the weight in the step (3) specifically includes: and solving the parameter weights of the CPU core number, the GPU, the operation memory RAM, the hard disk size ROM and the Network bandwidth Network in the weight equation according to the fuzzy consistency matrix, averaging the weights of the CPU core number, the GPU, the operation memory RAM, the hard disk size ROM and the Network bandwidth Network in each physical node, sequencing the weight average values of the physical nodes from large to small, and carrying out virtual machine migration according to the high-low sequence.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the virtual machine automatic migration decision method based on user experience in the cloud environment, FAHP is used as the most core algorithm of virtual machine automatic decision, the priority of virtual machine migration is divided by weight, however, when multitask migration is carried out, complex weight needs manual definition, manual configuration cannot be achieved if virtual machine migration standards are different, for the phenomenon, FAHP weight automatic solution is adopted, the number of parameters is changed and unnecessary steps are reduced on the basis of FAHP, the parameters are optimized, support is provided for automatic weight solution, after the weight is obtained, the virtual machine migration decision method is divided from top to bottom according to the weight mode, and therefore support is provided for automatic virtual machine migration;
(2) The automatic migration decision method of the virtual machine predicts the time of a cloud user using the virtual machine through an order determination method in an FAHP mechanism so as to obtain an accurate prediction result, performs constants of a weight equation by taking the prediction result, performance indexes of the virtual machine and network speed fluctuation conditions of physical properties of nodes as parameters, takes a weight value as a variable, solves the weight equation to obtain a weight, divides node resources according to the importance degree of the nodes in the virtual machine, and prepares for automatic migration of the virtual machine;
(3) For the problem that the weight equation redundancy is caused when the parameters are too much when the virtual machine is migrated in a large area, the number of the weight values is reduced by reducing the parameter quota aiming at the problem, and the effective weight values are very necessary to be reserved.
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Fig. 1 is a flowchart of a virtual machine auto-migration decision method based on user experience in a cloud environment according to the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the drawings.
Fig. 1 is a flowchart of a virtual machine automatic migration decision method based on user experience in a cloud environment according to the present invention, where the virtual machine automatic migration decision method specifically includes the following steps:
(1) Firstly, judging a migration mechanism of a virtual machine in a multi-cloud cascade environment, if the migration of the virtual machine can be completed by using static migration, using the static migration, if the static migration cannot be completed, performing dynamic migration, and executing the step (2); specifically, when all the node resources are greater than or equal to the resources required by the virtual machine, the static resource nodes are excessive, and only static migration needs to be adopted; and when all the node resources are less than the resources required by the virtual machine, indicating that the static resource nodes are insufficient, and performing dynamic migration. All node resources in the invention include: CPU core number, operation memory RAM, hard disk size ROM, network bandwidth Network; the resources required by the virtual machine include: CPU core number, operation memory RAM, hard disk size ROM, and Network bandwidth Network.
(2) The order determination method is used for predicting the access time period of the virtual machines accessed by the users on the multi-cloud platform, because one node corresponds to a plurality of virtual machines, when the virtual machines run at full power, hard disk space is consumed, and if the hard disk space resources are insufficient, the virtual machines are blocked during running. Whether the node is worth to be migrated is judged by predicting the access time period of the virtual machine accessed by the user on the multi-cloud platform, after all the nodes are predicted, the nodes can be sequenced according to the advantages and disadvantages of resources, the virtual machine migrated to the node server can exert performance, the condition of busy and idle imbalance among the nodes is avoided, the maximum use of hardware resources is realized, and meanwhile, a better prediction effect is achieved on the access time period of the virtual machine through an order determination method. The virtual machine parameters and the physical nodes are used as quantization parameters which influence the weight change most importantly, so that the collected virtual machine parameters, the physical node performance data and the predicted access of the virtual machine are used as parameters of two dimensions to jointly complete the weight setting of the weight equation.
(3) The method comprises the following steps of using access time period, virtual machine parameters and physical node performance data to obtain a fuzzy consistency judgment matrix combined with a Fuzzy Analytic Hierarchy Process (FAHP), solving the weight of field sub-module parameters in a weight equation according to the fuzzy consistency matrix, and using the resource usage amount of each node in all node resources by a field sub-module according to the weight of the solved field sub-module parameters, wherein the method comprises the following steps: CPU core number, GPU, operation memory RAM, hard disk size ROM, network bandwidth Network, realize the statistical judgement of the self resource usage of the virtual machine through the sub-module of the field, the virtual machine with large resource of the preferential migration; the migration efficiency of the virtual machine is greatly enhanced by the field sub-module; meanwhile, the difficulty of solving the weight can be reduced, so that the problems of low efficiency, complex parameters and the like caused by the traditional method are solved. Determining the migration sequence of the virtual machines according to the high and low sequence of the weight, and performing the virtual machine migration according to the high and low sequence comprises the following specific processes: according to the fuzzy consistency matrix, parameter weights of the CPU core number, the GPU, the operation memory RAM, the hard disk size ROM and the Network bandwidth Network in the weight equation are solved, the weights of the CPU core number, the GPU, the operation memory RAM, the hard disk size ROM and the Network bandwidth Network in each physical node are averaged, the weight average values of the physical nodes are sorted from large to small, and virtual machine migration is carried out according to the sequence of high and low to improve the migration rate of the virtual machine.
The fuzzy consistency judgment matrix is compared according to the importance of the parameters, and specifically, the ratio r of the access time period to the parameters of the virtual machine or the access time period to the performance data of the physical nodes is calculated ij ＝a j /b i Wherein a is j Denotes the jth access period, b i Representing the ith virtual machine parameter or physical node performance data; and (3) forming a fuzzy consistent judgment matrix R by all the ratios:
the weight equation in the invention is specifically as follows:
where n denotes the number of all physical nodes, W j Represents the corresponding weight of the field submodule in the jth visit period, a j Represents the weight W corresponding to the field sub-module in the jth visit time period j Resource usage constant of a j Is selected from { a 1 ,a 2 ,…,a n And the lambda represents a resource constant in the domain submodule and is used for modifying the equation to ensure that the resource quantity solved by the weight equation meets the minimum requirement of migration, so that the problem that the judgment of the equation result is feasible but the real situation is infeasible due to the fact that the resource quantity is too low is solved. When the weight equation is used for solving the weight of the number of the CPU cores of all the physical nodes, the lambda value is the difference value between the residual quantity of the CPU resources of the physical nodes and the CPU resource usage quantity of the virtual machine; when the weight equation is used for solving the weight of the GPUs of all the physical nodes, the lambda value is the difference value between the GPU resource residual quantity of the physical nodes and the GPU resource usage quantity of the virtual machine; when the weight equation is used for solving the weight of the running memory RAM of all the physical nodes, the lambda value is the difference value between the RAM resource residual quantity of the physical nodes and the RAM resource usage quantity of the virtual machine; when the weight equation is solved, the weight of the hard disk size ROM of all the physical nodes is solvedWhen the number λ is the difference between the remaining amount of the ROM resource of the physical node and the usage amount of the ROM resource of the virtual machine; and when the weight of the Network bandwidth Network of all the physical nodes is solved by the weight equation, the lambda value is the difference value between the Network resource residual quantity of the physical nodes and the Network resource usage quantity of the virtual machine.
According to the method, a dynamic migration decision mechanism of the virtual machine is obtained through weight setting, a dynamic node resource scheme of the virtual machine is decided, the nodes are sequentially ordered from top to bottom according to the priorities, and the most important function nodes are matched with the largest dynamic resources by adopting a priority mechanism. Compared with the traditional method of determining the node weight by using the AHP method, the FAHP can realize automatic solution of the node weight, the weight can be obtained according to the weight equation only after the fuzzy consistent judgment matrix is confirmed, and sequencing can be performed according to the weight sequence, so that the virtual machine can be efficiently migrated by a mechanism which is faster than that of the traditional method under the multi-cloud environment.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (5)
1. A virtual machine automatic migration decision-making method based on user experience in a multi-cloud environment is characterized by comprising the following steps:
(1) Firstly, judging a migration mechanism of a virtual machine in a multi-cloud cascade environment, if the migration of the virtual machine can be completed by using static migration, using the static migration, if the static migration cannot be completed, performing dynamic migration, and executing the step (2);
(2) Predicting the access time period of a user accessing the virtual machine on the multi-cloud platform by using an order determination method, and collecting parameters of the virtual machine and performance data of physical nodes;
(3) The access time period, the virtual machine parameters and the physical node performance data are used for acquiring a fuzzy consistency judgment matrix combined with a Fuzzy Analytic Hierarchy Process (FAHP), the weight of field sub-module parameters in a weight equation is solved according to the fuzzy consistency matrix, the migration sequence of the virtual machine is determined according to the weight sequence according to the weight of the solved field sub-module parameters, and the virtual machine is migrated according to the height sequence; the field submodule is the resource usage of each physical node in all node resources, and comprises: CPU core number, GPU, operation memory RAM, hard disk size ROM and Network bandwidth Network;
the acquiring process of the fuzzy consistency judging matrix specifically comprises the following steps: calculating the ratio r of the access time period to the virtual machine parameters or the physical node performance data ij ＝a j /b i Wherein a is j Denotes the jth access period, b i Representing the ith virtual machine parameter or physical node performance data; forming a fuzzy consistent judgment matrix by all the ratios;
the weight equation is specifically:
where n denotes the number of all physical nodes, w j Represents the corresponding weight of the field submodule in the jth visit period, a j Represents the weight w corresponding to the field sub-module in the jth visit time period j Resource usage constant of (a) j Is selected from { a } 1 ,a 2 ,…,a n And the lambda represents a resource constant in the domain submodule.
2. The method for deciding the automatic migration of the virtual machine based on the user experience in the cloud environment according to claim 1, wherein the migration mechanism of the virtual machine in the step (1) is specifically as follows: when all the node resources are greater than or equal to the resources required by the virtual machine, adopting static migration; and when all the node resources are less than the resources required by the virtual machine, performing dynamic migration.
3. The method for automatic migration decision-making of virtual machines based on user experience in a cloud environment according to claim 2, wherein all node resources include: CPU core number, operation memory RAM, hard disk size ROM, and Network bandwidth Network.
4. The method for automatic migration decision-making of virtual machines based on user experience in a cloud environment according to claim 2, wherein the resources required by the virtual machines include: CPU core number, operation memory RAM, hard disk size ROM, and Network bandwidth Network.
5. The method for deciding the automatic migration of the virtual machine based on the user experience in the cloudy environment according to claim 1, wherein the step (3) of performing the migration sequence of the virtual machine according to the sequence of the weights specifically comprises the following steps: and solving the parameter weights of the CPU core number, the GPU, the operation memory RAM, the hard disk size ROM and the Network bandwidth Network in the weight equation according to the fuzzy consistency matrix, averaging the weights of the CPU core number, the GPU, the operation memory RAM, the hard disk size ROM and the Network bandwidth Network in each physical node, sequencing the weight average values of the physical nodes from large to small, and carrying out virtual machine migration according to the high-low sequence.
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| CN115794314B (en) * | 2023-01-29 | 2023-05-09 | 国网信息通信产业集团有限公司 | Virtual machine migration method in cloud computing environment |
| CN117827467B (en) * | 2024-03-05 | 2024-05-10 | 南京群顶科技股份有限公司 | Dynamic portrait-based virtual machine resource allocation method |
| CN118132346B (en) * | 2024-05-08 | 2024-07-09 | 贵州航天云网科技有限公司 | Industrial Internet platform assembly and application reliability optimization system |
| CN118502885B (en) * | 2024-07-15 | 2024-10-15 | 济南浪潮数据技术有限公司 | Thermomigration method, equipment, program product and medium |
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