CN110968397A - Analysis method and device for virtual machine capacity management - Google Patents
Analysis method and device for virtual machine capacity management Download PDFInfo
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- CN110968397A CN110968397A CN201911132180.8A CN201911132180A CN110968397A CN 110968397 A CN110968397 A CN 110968397A CN 201911132180 A CN201911132180 A CN 201911132180A CN 110968397 A CN110968397 A CN 110968397A
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- 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
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- 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
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Abstract
The application is applicable to the technical field of computer management, and provides an analysis method and device for virtual machine capacity management, wherein the analysis method and device comprise the following steps: the method comprises the steps of obtaining a first capacity state of a virtual machine power supply, outputting a first capacity analysis result when the first capacity state is a shutdown state, obtaining a second capacity state corresponding to a network port and a storage port when the first capacity state is a non-shutdown state, obtaining a third capacity state of virtual machine hardware configuration when the second capacity state is a non-idle state, obtaining a fourth capacity state of a storage space when the third capacity state is a normal capacity state, outputting a fourth capacity analysis result when the fourth capacity state is a resource shortage state or a resource surplus state, and outputting the fourth capacity analysis result when the fourth capacity state is a normal capacity state. According to the method, the multi-dimensional capacity management analysis result is obtained, and analysis of the capacity management of the virtual machine is achieved.
Description
Technical Field
The present application belongs to the technical field of computer management, and in particular, to an analysis method and apparatus for virtual machine capacity management, and a computer-readable storage medium.
Background
As IT gradually turns to the cloud environment, making distributed servers and virtual machines easier to deploy and less expensive, capacity and cost management are critical to modern digital enterprises to ensure adequate resources and budgets to support new, existing, and growing business services. As the application of virtual machines in the environment is increased, users do not know the capacity or utilization rate of each virtual machine, and the cost expenditures are increasingly expanded as time goes by, so that the current virtual machines lack an analysis means for capacity management.
Disclosure of Invention
In view of this, embodiments of the present application provide an analysis method and an analysis device for virtual machine capacity management, which can solve the technical problem that the utilization rate of computer resources of a host machine by a current virtual machine is low.
A first aspect of an embodiment of the present application provides an analysis method for virtual machine capacity management, including:
acquiring a first capacity state of a virtual machine power supply, wherein the first capacity state is used for representing the working state of the virtual machine power supply;
when the first capacity state is determined to be a shutdown state, outputting a first capacity analysis result, wherein the first capacity analysis result comprises identification information of the virtual machines and a capacity analysis result of the first capacity state which is the shutdown state, and the identification information is used for distinguishing a plurality of virtual machines;
when the first capacity state is determined to be a non-power-off state, acquiring a second capacity state corresponding to the network port and the storage port, wherein the second capacity state is used for representing the working states of the network port and the storage port;
when the second capacity state is determined to be an idle state, outputting a second capacity analysis result, wherein the second capacity analysis result comprises identification information of the virtual machine and the second capacity state;
when the second capacity state is determined to be a non-idle state, acquiring a third capacity state of the virtual machine hardware configuration, wherein the third capacity state is used for representing the service condition of the virtual machine hardware configuration;
when the third capacity state is determined to be the resource shortage state or the resource excess state, outputting a third capacity analysis result, wherein the third capacity analysis result comprises identification information of the virtual machine and a capacity analysis result of which the third capacity is the resource shortage state or the resource excess state;
when the third capacity state is determined to be a normal capacity state, acquiring a fourth capacity state corresponding to the storage space, wherein the fourth capacity state is used for representing the use condition of the storage space;
when the fourth capacity state is determined to be the resource shortage state or the resource excess state, outputting a fourth capacity analysis result, wherein the fourth capacity analysis result comprises identification information of the virtual machine and a capacity analysis result of the fourth capacity state which is the resource shortage state or the resource excess state;
and when the fourth capacity state is determined to be the normal capacity state, outputting a fourth capacity analysis result, wherein the fourth capacity analysis result comprises identification information of the virtual machine and a capacity analysis result of which the fourth capacity state is the normal state.
A second aspect of the embodiments of the present application provides an analysis apparatus for virtual machine capacity management, including:
the virtual machine power supply control device comprises a first obtaining unit, a second obtaining unit and a control unit, wherein the first obtaining unit is used for obtaining a first capacity state of a virtual machine power supply, and the first capacity state is used for representing the working state of the virtual machine power supply;
a first judging unit, configured to output a first capacity analysis result when it is determined that the first capacity state is a shutdown state, where the first capacity analysis result includes identification information of a virtual machine and a capacity analysis result that the first capacity state is the shutdown state, and the identification information is used to distinguish multiple virtual machines;
a second obtaining unit, configured to obtain a second capacity state corresponding to the network port and the storage port when it is determined that the first capacity state is an off state, where the second capacity state is used to indicate working states of the network port and the storage port;
a second judging unit, configured to output a second capacity analysis result when it is determined that the second capacity state is an idle state, where the second capacity analysis result includes identification information of a virtual machine and a capacity analysis result that the second capacity state is an idle state;
a third obtaining unit, configured to obtain a third capacity state of the virtual machine hardware configuration when it is determined that the second capacity state is a non-idle state, where the third capacity state is used to indicate a use condition of the virtual machine hardware configuration;
a third judging unit, configured to output a third capacity analysis result when it is determined that the third capacity state is a resource shortage state or a resource excess state, where the third capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the third capacity is a state resource shortage state or a resource excess state;
a fourth obtaining unit, configured to obtain a fourth capacity state corresponding to the storage space when it is determined that the third capacity state is a normal capacity state, where the fourth capacity state is used to indicate a usage situation of the storage space;
a fourth judging unit, configured to, when it is determined that the fourth capacity state is a resource shortage state or a resource excess state, output a fourth capacity analysis result, where the fourth capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the fourth capacity state is the resource shortage state or the resource excess state;
a fifth judging unit, configured to output a fourth capacity analysis result when it is determined that the fourth capacity state is a capacity normal state, where the fourth capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the fourth capacity state is a normal state.
A third aspect of embodiments of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect when executing the computer program.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the method according to the first aspect.
Compared with the prior art, the embodiment of the application has the advantages that: in the application, by acquiring a first capacity state of a virtual machine power supply, when the first capacity state is determined to be a shutdown state, outputting a first capacity analysis result, when the first capacity state is determined to be a non-shutdown state, acquiring a second capacity state corresponding to a network port and a storage port, when the second capacity state is determined to be an idle state, outputting a second capacity analysis result, when the second capacity state is determined to be a non-idle state, acquiring a third capacity state of a virtual machine hardware configuration, when the third capacity state is determined to be a resource shortage state or a resource surplus state, outputting a third capacity analysis result, when the third capacity state is determined to be a normal capacity state, acquiring a fourth capacity state corresponding to a storage space, when the fourth capacity state is determined to be a resource shortage state or a resource surplus state, outputting a fourth capacity analysis result, and outputting the fourth capacity analysis result when the fourth capacity state is determined to be the normal capacity state, wherein the fourth capacity analysis result comprises the identification information of the virtual machine and the capacity analysis result when the fourth capacity state is determined to be the normal capacity state. According to the method, the multi-dimensional capacity management analysis result is obtained, and analysis of the capacity management of the virtual machine is achieved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without any inventive effort.
Fig. 1 illustrates a schematic flow chart of an implementation of an analysis method for capacity management of a virtual machine according to an embodiment of the present application;
FIG. 2 shows a schematic flow chart of step 101 of a method for analysis of virtual machine capacity management provided by the present application;
FIG. 3 is a schematic flow chart diagram illustrating step 103 of a method for analysis of virtual machine capacity management provided by the present application;
FIG. 4 shows a schematic flow chart of step 105 of a method for analysis of virtual machine capacity management provided by the present application;
FIG. 5 is a schematic flow chart diagram illustrating step 107 of a method for analysis of virtual machine capacity management provided by the present application;
FIG. 6 is a schematic flow chart diagram illustrating another analysis method for virtual machine capacity management provided herein;
fig. 7 is a schematic diagram illustrating an analysis apparatus for virtual machine capacity management according to an embodiment of the present application;
fig. 8 shows a schematic diagram of a terminal device provided in the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As the application of virtual machines in the environment is increased, users do not know the capacity or utilization rate of each virtual machine, and the cost expenditures are increasingly expanded as time goes by, so that the current virtual machines lack an analysis means for capacity management. In order to solve the above problem, the present application provides an analysis method for virtual machine capacity management, please refer to fig. 1, and fig. 1 shows a schematic implementation flow diagram of the analysis method for virtual machine capacity management provided in an embodiment of the present application. An analysis method for capacity management of a virtual machine as shown in fig. 1 includes:
step 101, obtaining a first capacity state of a virtual machine power supply, wherein the first capacity state is used for representing the working state of the virtual machine power supply.
A virtual machine is a software implementation that can create an environment between a computer platform and an end user, implementing a computer that runs programs like a real machine. The analysis method for virtual machine capacity management in this embodiment may be applied to capacity analysis between single or multiple virtual machines, and an execution subject of the capacity analysis may be a host, or a server associated with the host, and other capacity analysis devices, which are not limited herein. Capacity management refers to providing the required capacity for data processing and storage at the right time in an economical manner to achieve and maintain the IT service capacity requirements needed for the business at a reasonable cost. Types of capacity management include, but are not limited to, power supply capacity management, network input/output capacity management, storage space capacity management, and virtual machine configuration capacity management.
The capacity analysis equipment acquires resource data corresponding to different capacity management types according to preset acquisition frequency and fineness, wherein the preset acquisition frequency and the fineness can be set according to specific service scenes and user requirements. For example: and acquiring different resource data at the frequency of 1 time in 2 hours by taking 30 days as a time node. And carrying out data preprocessing on the acquired original data. The data preprocessing comprises the elimination and noise suppression of idle data, the processing of heterogeneous data and the data format of unified capacity management. And judging different capacity states of the virtual machine according to the preprocessed original data.
Specifically, the acquiring the first capacity state of the virtual machine power supply includes the following steps, please refer to fig. 2, and fig. 2 shows a schematic flowchart of step 101 in the analysis method for virtual machine capacity management provided in the present application.
In step 1011, the number of times of shutdown and the shutdown duration within the preset duration are obtained.
The capacity analysis equipment obtains the shutdown times and the shutdown duration of the virtual machine within the preset duration.
Step 1012, if at least one of the number of times of shutdown and the duration of shutdown is greater than the corresponding first threshold and second threshold, determining that the first capacity state is the shutdown state.
The power-off state represents the utilization condition of the power supply of the virtual machine within a preset time length and does not represent the power-on and power-off state of the current virtual machine. And when at least one of the shutdown times and the shutdown duration is greater than the corresponding first threshold and second threshold, indicating that the current virtual machine is in a shutdown mode within a preset duration for most of time, and determining that the first capacity state is a shutdown state. The first threshold and the second threshold may be determined according to a specific application scenario and a user requirement.
Step 1013, if the number of times of shutdown is less than or equal to the first threshold and the shutdown duration is less than or equal to the second threshold, determining that the first capacity state is a non-shutdown state.
Step 102, when it is determined that the first capacity state is the shutdown state, outputting a first capacity analysis result, where the first capacity analysis result includes identification information of the virtual machines and a capacity analysis result that the first capacity state is the shutdown state, and the identification information is used to distinguish multiple virtual machines.
And when the first capacity state is determined to be the shutdown state, directly outputting a first capacity analysis result without performing the next analysis. The first capacity analysis result includes, but is not limited to, identification information of a virtual machine and the first capacity status. Since the object of the capacity management analysis may be single or multiple, it is necessary to distinguish the multiple virtual machines by the identification information. For example, the first capacity analysis result may be: the power capacity state of the virtual machine 2 in the host a is a shutdown state. The above examples are merely exemplary and do not limit the format of the capacity analysis results.
Step 103, when it is determined that the first capacity state is the non-power-off state, acquiring a second capacity state corresponding to the network port and the storage port, where the second capacity state is used to represent the working states of the network port and the storage port.
And when the first capacity state is determined to be the non-power-off state, performing next capacity management analysis, namely acquiring second capacity states of the network port and the storage port.
Specifically, the obtaining of the second capacity status corresponding to the network port and the storage port includes the following steps, please refer to fig. 3, and fig. 3 shows a schematic flowchart of step 103 in the analysis method for virtual machine capacity management provided in the present application.
And step 1031, acquiring the input/output flow of the network port, and storing the input/output flow of the port.
The capacity analysis equipment acquires input and output flows of the network port and input and output flows of the storage port of the virtual machine in historical unit time.
As an embodiment of the present application, the capacity analysis device may also obtain an error packet number of the network port to determine the second capacity state. The capacity analysis device can also obtain the virtual disk read-write speed, the peak value, the average value and the minimum value of the partition read-write speed, the jitter and the virtual disk space size increase so as to judge the second capacity state.
Step 1032, if the input/output flow of the network port or the input/output flow of the storage port is greater than a third threshold, determining that the second capacity state is a non-idle state.
Step 1033, if the input/output traffic of the network port is not greater than the corresponding third threshold, and the input/output traffic of the storage port is not greater than the corresponding fourth threshold, determining that the second capacity state is an idle state.
If the input/output flow of the network port is not greater than the corresponding third threshold and the input/output flow of the storage port is not greater than the corresponding fourth threshold, it indicates that the resource utilization rate of the current virtual machine is low, and the capacity state of the current virtual machine can be calibrated to be an idle state. The third threshold and the fourth threshold may be determined according to specific application scenarios and user requirements.
And 104, when the second capacity state is determined to be the idle state, outputting a second capacity analysis result, wherein the second capacity analysis result comprises identification information of the virtual machine and a capacity analysis result of which the second capacity state is the idle state.
And when the second capacity state is determined to be the idle state, directly outputting the capacity analysis result with the second capacity analysis result being the idle state without carrying out the next analysis.
And 105, when the second capacity state is determined to be the non-idle state, acquiring a third capacity state of the virtual machine hardware configuration, wherein the third capacity state is used for representing the service condition of the virtual machine hardware configuration.
And when the second capacity state is determined to be a non-idle state, performing next capacity management analysis, namely a third capacity state of the hardware configuration of the virtual machine.
Specifically, the obtaining of the third capacity state of the hardware configuration of the virtual machine includes the following steps, please refer to fig. 4, and fig. 4 shows a schematic flowchart of step 105 in the analysis method for virtual machine capacity management provided in this application.
Step 1051, acquiring a hardware configuration utilization rate, wherein the hardware configuration comprises a central processing unit or a memory capacity, and the hardware configuration utilization rate represents a maximum peak value of a virtual machine hardware load.
And the capacity analysis equipment acquires the hardware configuration utilization rate of the virtual machine.
As an embodiment of the present application, the capacity analysis device may also obtain the number of cores of the CPU, the memory capacity and the channel type, the number of disks, the capacity and the provisioning mode of each disk, the historical load state of the CPU and the memory, and the like, so as to determine the specific state of the third capacity state.
Step 1052, determining the third capacity state as a resource excess state if the hardware configuration utilization rate is less than a fifth threshold.
And if the hardware configuration utilization rate is smaller than a fifth threshold, determining that the third capacity state is a resource surplus state. For example: and when the load of the central processing unit is less than 30% for 150 times continuously in the preset time length, determining that the third capacity state of the central processing unit is an excess resource state. The fifth threshold may be determined according to specific application scenarios and user requirements.
Step 1053, if the hardware configuration utilization rate is greater than a sixth threshold, it is determined that the third capacity state is a resource shortage state.
If the hardware configuration utilization rate is greater than a sixth threshold, it is determined that the third capacity state is a resource shortage state, for example: and when the load of the central processing unit is continuously more than 80% for 10 times within the preset time length, determining that the third capacity state of the central processing unit is a resource shortage state. The sixth threshold may be determined according to specific application scenarios and user requirements.
Step 1054, if the hardware configuration utilization rate is not less than the fifth threshold and not greater than the sixth threshold, determining that the third capacity state is a capacity normal state.
Step 106, when it is determined that the third capacity state is the resource shortage state or the resource excess state, outputting a third capacity analysis result, where the third capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the third capacity is the state resource shortage state or the resource excess state.
And when the third capacity state is determined to be the resource shortage state or the resource excess state, directly outputting a capacity analysis result of which the third capacity is the state resource shortage state or the resource excess state without carrying out next capacity analysis.
And 107, when the third capacity state is determined to be the normal capacity state, acquiring a fourth capacity state corresponding to the storage space, wherein the fourth capacity state is used for representing the use condition of the storage space.
Specifically, the obtaining of the fourth capacity state of the storage space includes the following steps, please refer to fig. 5, and fig. 5 shows a schematic flowchart of step 107 in the analysis method for virtual machine capacity management provided in this application.
Step 1071, obtaining storage space utilization.
And the capacity analysis equipment acquires the utilization rate of the storage space of the virtual machine.
As an embodiment of the present application, the storage space resource data includes, but is not limited to, the amount of storage space remaining in the virtual disk and the system partition, the utilization rate increase, and the like.
Step 1072, if the storage space utilization rate is less than a seventh threshold, determining that the third capacity state is an excess resource state.
If the utilization rate of the storage space is smaller than the seventh threshold, the utilization rate of the storage space of the current virtual machine is low, and the capacity state of the virtual machine can be calibrated to be the resource surplus state. The seventh threshold may be determined according to specific application scenarios and user requirements.
Step 1073, if the storage space utilization is greater than the seventh threshold, determining that the third capacity state is a resource shortage state.
If the utilization rate of the storage space is greater than the seventh threshold, it is indicated that the current virtual machine storage space cannot meet the current demand, and the capacity state of the virtual machine storage space can be calibrated to be a resource shortage state.
Step 1074, if the storage space utilization rate is equal to the seventh threshold, determining that the third capacity state is a capacity normal state.
Step 108, when it is determined that the fourth capacity state is the insufficient resource state or the excessive resource state, outputting a fourth capacity analysis result, where the fourth capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the fourth capacity state is the insufficient resource state or the excessive resource state.
Step 109, when it is determined that the fourth capacity state is the capacity normal state, outputting a fourth capacity analysis result, where the fourth capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the fourth capacity state is the normal state.
In this embodiment, by obtaining a first capacity state of a power supply of a virtual machine, when it is determined that the first capacity state is a shutdown state, outputting a first capacity analysis result, when it is determined that the first capacity state is a non-shutdown state, obtaining a second capacity state of an input/output of a network port, obtaining a second capacity state of an input/output of a storage port, when it is determined that the second capacity state is an idle state, outputting a second capacity analysis result, when it is determined that the second capacity state is a non-idle state, obtaining a third capacity state of a hardware configuration of the virtual machine, when it is determined that the third capacity state is a resource shortage state or a resource surplus state, outputting a third capacity analysis result, when it is determined that the third capacity state is a normal capacity state, obtaining a fourth capacity state corresponding to a storage space, when it is determined that the fourth capacity state is a resource shortage state or a resource surplus state, outputting a fourth capacity analysis result, and outputting the fourth capacity analysis result when the fourth capacity state is determined to be the normal capacity state, wherein the fourth capacity analysis result comprises the identification information of the virtual machine and the capacity analysis result when the fourth capacity state is determined to be the normal capacity state. According to the method, the multi-dimensional capacity management analysis result is obtained, and analysis of the capacity management of the virtual machine is achieved.
Optionally, on the basis of the embodiment shown in fig. 1, after determining that the third capacity state is the resource shortage state or the resource surplus state, the method further includes the following step, please refer to fig. 6, where fig. 6 shows a schematic flowchart of another analysis method for virtual machine capacity management provided by the present application, by way of example and not by way of limitation. In this embodiment, steps 601 to 602 and steps 606 to 612 are the same as steps 101 to 109 in the previous embodiment, and specific reference is made to the related description of steps 101 to 109 in the previous embodiment, which is not repeated herein.
Step 601, obtaining a first capacity state of the virtual machine power supply, where the first capacity state is used to represent a working state of the virtual machine power supply.
Step 602, when it is determined that the first capacity state is the shutdown state, outputting a first capacity analysis result, where the first capacity analysis result includes identification information of a virtual machine and a capacity analysis result that the first capacity state is the shutdown state, and the identification information is used to distinguish multiple virtual machines.
Step 603, when it is determined that the first capacity state is the non-power-off state, acquiring a second capacity state corresponding to the network port and the storage port, where the second capacity state is used to represent working states of the network port and the storage port.
Step 604, when it is determined that the second capacity state is the idle state, outputting a second capacity analysis result, where the second capacity analysis result includes identification information of the virtual machine and the second capacity state.
Step 605, when it is determined that the second capacity state is a non-idle state, obtaining a third capacity state of the virtual machine hardware configuration, where the third capacity state is used to represent a use condition of the virtual machine hardware configuration.
Step 606, when it is determined that the third capacity state is the insufficient resource state or the excessive resource state, calculating a recommended value of hardware configuration, where the recommended value of hardware configuration is a value that needs to be adjusted for the hardware configuration of the current virtual machine.
And when the third capacity state is determined to be the resource shortage state or the resource excess state, calculating a hardware configuration recommended value so that the user can adjust the capacity of the virtual machine to a normal state through the hardware configuration recommended value.
Specifically, the calculating the recommended value of the hardware configuration includes the following steps.
The capacity analysis equipment obtains the number of the central processor cores and the first maximum load of the central processor in unit time, and calculates the hardware configuration recommended value according to a first formula, wherein the first formula is as follows:
N＝a×(1-b)/2，
wherein a represents the number of the central processor cores, and the unit of the number is the number; b represents the first maximum load in percent.
Specifically, the calculating the recommended value of the hardware configuration includes the following steps.
The capacity analysis device obtains the memory capacity and a second maximum load of the memory capacity in unit time, and calculates the hardware configuration recommended value according to a second formula, wherein the second formula is as follows:
M＝c×(1-d)-1024，
wherein c represents the memory capacity in units of MB (megabits); d represents the second maximum load in percent.
The hardware configuration recommended value algorithm is not limited to the formula one or the formula two, and different algorithms can be set according to specific application scenarios and user requirements.
Step 607, outputting a third capacity analysis result, where the third capacity analysis result includes a recommended value of the computer hardware configuration, identification information of the virtual machine, and a capacity analysis result where the third capacity state is a resource shortage state or a resource surplus.
Step 608, when it is determined that the third capacity state is the capacity normal state, acquiring a fourth capacity state corresponding to the storage space, where the fourth capacity state is used to indicate a usage situation of the storage space.
Step 609, when it is determined that the fourth capacity state is the insufficient resource state or the excessive resource state, outputting a fourth capacity analysis result, where the fourth capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the fourth capacity state is the insufficient resource state or the excessive resource state.
Step 610, when it is determined that the fourth capacity state is the capacity normal state, outputting a fourth capacity analysis result, where the fourth capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the fourth capacity state is the normal state.
In this embodiment, after determining that the fourth capacity state is the insufficient resource state or the excessive resource state, the recommended hardware configuration value is calculated, and a fourth capacity analysis result is output. By the method, a multi-dimensional capacity management analysis result is obtained, and analysis of virtual machine capacity management is achieved.
Fig. 7 is a schematic diagram of an analysis apparatus for virtual machine capacity management according to an embodiment of the present application, and fig. 7 is a schematic diagram of an analysis apparatus for virtual machine capacity management, where the analysis apparatus for virtual machine capacity management shown in fig. 7 includes:
a first obtaining unit 71, configured to obtain a first capacity state of the virtual machine power supply, where the first capacity state is used to represent an operating state of the virtual machine power supply;
a first judging unit 72, configured to output a first capacity analysis result when it is determined that the first capacity state is a shutdown state, where the first capacity analysis result includes identification information of a virtual machine and a capacity analysis result that the first capacity state is the shutdown state, and the identification information is used to distinguish multiple virtual machines;
a second obtaining unit 73, configured to obtain a second capacity state corresponding to the network port and the storage port when it is determined that the first capacity state is the non-power-off state, where the second capacity state is used to indicate working states of the network port and the storage port;
a second judging unit 74, configured to output a second capacity analysis result when it is determined that the second capacity state is an idle state, where the second capacity analysis result includes identification information of a virtual machine and a capacity analysis result that the second capacity state is an idle state;
a third obtaining unit 75, configured to obtain a third capacity state of the virtual machine hardware configuration when it is determined that the second capacity state is a non-idle state, where the third capacity state is used to indicate a usage situation of the virtual machine hardware configuration;
a third judging unit 76, configured to, when it is determined that the third capacity state is the insufficient resource state or the excessive resource state, output a third capacity analysis result, where the third capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the third capacity is the insufficient resource state or the excessive resource state;
a fourth obtaining unit 77, configured to obtain a fourth capacity state corresponding to the storage space when it is determined that the third capacity state is a normal capacity state, where the fourth capacity state is used to indicate a usage of the storage space;
a fourth judging unit 78, configured to, when it is determined that the fourth capacity state is the insufficient resource state or the excessive resource state, output a fourth capacity analysis result, where the fourth capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the fourth capacity state is the insufficient resource state or the excessive resource state;
a fifth judging unit 79, configured to output a fourth capacity analysis result when it is determined that the fourth capacity state is a capacity normal state, where the fourth capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the fourth capacity state is a normal state.
The device, still include:
the computing unit is used for computing a recommended value of hardware configuration, wherein the recommended value of hardware configuration is a numerical value which needs to be adjusted by the hardware configuration of the current virtual machine; and outputting a fourth capacity analysis result, wherein the fourth capacity analysis result comprises a recommended value of the computer hardware configuration, identification information of the virtual machine and the fourth capacity state.
The application provides an analysis device for virtual machine capacity management, which outputs a first capacity analysis result by obtaining a first capacity state of a virtual machine power supply when determining that the first capacity state is a shutdown state, obtains a second capacity state of input and output of a network port when determining that the first capacity state is a non-shutdown state, obtains the second capacity state of input and output of a storage port, outputs the second capacity analysis result when determining that the second capacity state is an idle state, obtains a third capacity state of virtual machine hardware configuration when determining that the second capacity state is a non-idle state, outputs the third capacity analysis result when determining that the third capacity state is a resource shortage state or a resource excess state, and obtains a fourth capacity state corresponding to a storage space when determining that the third capacity state is a normal capacity state, and outputting a fourth capacity analysis result when the fourth capacity state is determined to be the insufficient resource state or the excessive resource state, and outputting the fourth capacity analysis result when the fourth capacity state is determined to be the normal capacity state, wherein the fourth capacity analysis result comprises identification information of the virtual machine and the capacity analysis result when the fourth capacity state is the normal capacity state. According to the method, the multi-dimensional capacity management analysis result is obtained, and analysis of the capacity management of the virtual machine is achieved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 8 shows a schematic diagram of a terminal device according to an embodiment of the present application. As shown in fig. 8, a terminal device 8 of this embodiment includes: a processor 80, a memory 81, and a computer program 82, such as a pipeline inspection scheduling program, stored in the memory 81 and executable on the processor 80. The processor 80, when executing the computer program 82, implements the steps in each of the above-described embodiments of the analysis method for virtual machine capacity management, such as the steps 101 to 109 shown in fig. 1. Alternatively, the processor 80, when executing the computer program 82, implements the functions of the units in the device embodiments described above, such as the functions of the units 71 to 79 shown in fig. 7.
Illustratively, the computer program 82 may be divided into one or more units, which are stored in the memory 81 and executed by the processor 80 to accomplish the present application. The one or more units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 82 in the kind of terminal device 8. For example, the computer program 82 may be divided into an acquisition unit and a calculation unit, each unit having the following specific functions:
the virtual machine power supply control device comprises a first obtaining unit, a second obtaining unit and a control unit, wherein the first obtaining unit is used for obtaining a first capacity state of a virtual machine power supply, and the first capacity state is used for representing the working state of the virtual machine power supply;
a first judging unit, configured to output a first capacity analysis result when it is determined that the first capacity state is a shutdown state, where the first capacity analysis result includes identification information of a virtual machine and a capacity analysis result that the first capacity state is the shutdown state, and the identification information is used to distinguish multiple virtual machines;
a second obtaining unit, configured to obtain a second capacity state of the network input/output and the storage input/output when it is determined that the first capacity state is the non-power-off state, where the second capacity state is used to indicate an operating state of the network input/output and the storage input/output;
a second judging unit, configured to output a second capacity analysis result when it is determined that the second capacity state is an idle state, where the second capacity analysis result includes identification information of a virtual machine and the second capacity state;
a third obtaining unit, configured to obtain a third capacity state of the virtual machine hardware configuration when it is determined that the second capacity state is a non-idle state, where the third capacity state is used to indicate a use condition of the virtual machine hardware configuration;
a third judging unit, configured to output a third capacity analysis result when it is determined that the third capacity state is a resource shortage state or a resource excess state, where the third capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the third capacity is a state resource shortage state or a resource excess state;
a fourth obtaining unit, configured to obtain a fourth capacity state corresponding to the storage space when it is determined that the third capacity state is a normal capacity state, where the fourth capacity state is used to indicate a usage situation of the storage space;
a fourth judging unit, configured to, when it is determined that the fourth capacity state is a resource shortage state or a resource excess state, output a fourth capacity analysis result, where the fourth capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the fourth capacity state is the resource shortage state or the resource excess state;
a fifth judging unit, configured to output a fourth capacity analysis result when it is determined that the fourth capacity state is a capacity normal state, where the fourth capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the fourth capacity state is a normal state.
The terminal device 8 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 80, a memory 81. Those skilled in the art will appreciate that fig. 8 is merely an example of one type of terminal device 8 and is not intended to limit one type of terminal device 8 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the one type of terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 80 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 81 may be an internal storage unit of the terminal device 8, such as a hard disk or a memory of the terminal device 8. The memory 81 may also be an external storage device of the terminal device 8, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like provided on the terminal device 8. Further, the memory 81 may also include both an internal storage unit and an external storage device of the terminal device 8. The memory 81 is used for storing the computer program and other programs and data required by the kind of terminal equipment. The memory 81 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed terminal device and method may be implemented in other ways. For example, the above-described terminal device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical function division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer memory, Read-only memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. An analysis method for capacity management of a virtual machine, comprising:
acquiring a first capacity state of a virtual machine power supply, wherein the first capacity state is used for representing the working state of the virtual machine power supply;
when the first capacity state is determined to be a shutdown state, outputting a first capacity analysis result, wherein the first capacity analysis result comprises identification information of the virtual machines and a capacity analysis result of the first capacity state which is the shutdown state, and the identification information is used for distinguishing a plurality of virtual machines;
when the first capacity state is determined to be a non-power-off state, acquiring a second capacity state corresponding to the network port and the storage port, wherein the second capacity state is used for representing the working states of the network port and the storage port;
when the second capacity state is determined to be the idle state, outputting a second capacity analysis result, wherein the second capacity analysis result comprises identification information of the virtual machine and a capacity analysis result of which the second capacity state is the idle state;
when the second capacity state is determined to be a non-idle state, acquiring a third capacity state of the virtual machine hardware configuration, wherein the third capacity state is used for representing the service condition of the virtual machine hardware configuration;
when the third capacity state is determined to be the resource shortage state or the resource excess state, outputting a third capacity analysis result, wherein the third capacity analysis result comprises identification information of the virtual machine and a capacity analysis result of which the third capacity is the resource shortage state or the resource excess state;
when the third capacity state is determined to be a normal capacity state, acquiring a fourth capacity state corresponding to the storage space, wherein the fourth capacity state is used for representing the use condition of the storage space;
when the fourth capacity state is determined to be the resource shortage state or the resource excess state, outputting a fourth capacity analysis result, wherein the fourth capacity analysis result comprises identification information of the virtual machine and a capacity analysis result of the fourth capacity state which is the resource shortage state or the resource excess state;
and when the fourth capacity state is determined to be the normal capacity state, outputting a fourth capacity analysis result, wherein the fourth capacity analysis result comprises identification information of the virtual machine and a capacity analysis result of which the fourth capacity state is the normal state.
2. The method of claim 1, wherein the obtaining the first capacity state of the virtual machine power supply comprises:
obtaining the shutdown times and the shutdown duration in the preset duration;
if at least one of the shutdown frequency and the shutdown duration is greater than the corresponding first threshold and second threshold, determining that the first capacity state is a shutdown state;
if the power-off times are less than or equal to the first threshold and the power-off duration is less than or equal to the second threshold, determining that the first capacity state is a non-power-off state;
correspondingly, the obtaining of the second capacity states corresponding to the network port and the storage port includes:
acquiring input and output flows of a network port, and storing the input and output flows of the port;
if the input/output flow of the network port or the input/output flow of the storage port is greater than a third threshold, determining that the second capacity state is a non-idle state;
if the input/output flow of the network port is not greater than the corresponding third threshold value and the input/output flow of the storage port is not greater than the corresponding fourth threshold value, determining that the second capacity state is an idle state;
correspondingly, the obtaining of the third capacity state of the hardware configuration of the virtual machine includes:
acquiring a hardware configuration utilization rate, wherein the hardware configuration comprises a central processing unit or a memory capacity, and the hardware configuration utilization rate represents the maximum peak value of the hardware load of the virtual machine;
if the hardware configuration utilization rate is smaller than a fifth threshold, determining that the third capacity state is a resource surplus state;
if the hardware configuration utilization rate is greater than a sixth threshold, determining that the third capacity state is a resource shortage state;
if the hardware configuration utilization rate is not less than the fifth threshold and not greater than the sixth threshold, determining that the third capacity state is a capacity normal state;
correspondingly, the obtaining the fourth capacity state of the storage space includes:
acquiring the utilization rate of a storage space;
if the storage space utilization rate is smaller than a seventh threshold, determining that the third capacity state is a resource surplus state;
if the storage space utilization rate is greater than the seventh threshold, determining that the third capacity state is a resource shortage state;
and if the storage space utilization rate is equal to the seventh threshold, determining that the third capacity state is a capacity normal state.
3. The method of claim 1, wherein after determining that the third capacity state is an insufficient resource state or an excessive resource state, further comprising:
calculating a recommended value of hardware configuration, wherein the recommended value of hardware configuration is a numerical value which needs to be adjusted by the hardware configuration of the current virtual machine;
and outputting a third capacity analysis result, wherein the third capacity analysis result comprises a recommended value of computer hardware configuration, identification information of the virtual machine and a capacity analysis result of which the third capacity state is a resource shortage state or a resource surplus state.
4. The method of claim 3, wherein the calculating the hardware configuration recommendation comprises:
acquiring the number of the central processor cores and a first maximum load of the central processor in unit time;
calculating the recommended hardware configuration value according to a formula I, wherein the formula I is as follows:
N＝a×(1-b)/2，
wherein a represents the number of central processor cores and b represents the first maximum load.
5. The method of claim 3, wherein the calculating the hardware configuration recommendation comprises:
acquiring the memory capacity and a second maximum load of the memory capacity in unit time;
calculating the recommended value of the hardware configuration according to a second formula, wherein the second formula is as follows:
M＝c×(1-d)-1024，
wherein c represents the memory capacity and d represents the second maximum load.
6. An analysis device for virtual machine capacity management, comprising:
the virtual machine power supply control device comprises a first obtaining unit, a second obtaining unit and a control unit, wherein the first obtaining unit is used for obtaining a first capacity state of a virtual machine power supply, and the first capacity state is used for representing the working state of the virtual machine power supply;
a first judging unit, configured to output a first capacity analysis result when it is determined that the first capacity state is a shutdown state, where the first capacity analysis result includes identification information of a virtual machine and a capacity analysis result that the first capacity state is the shutdown state, and the identification information is used to distinguish multiple virtual machines;
a second obtaining unit, configured to obtain a second capacity state corresponding to the network port and the storage port when it is determined that the first capacity state is an off state, where the second capacity state is used to indicate working states of the network port and the storage port;
a second judging unit, configured to output a second capacity analysis result when it is determined that the second capacity state is an idle state, where the second capacity analysis result includes identification information of a virtual machine and a capacity analysis result that the second capacity state is an idle state;
a third obtaining unit, configured to obtain a third capacity state of the virtual machine hardware configuration when it is determined that the second capacity state is a non-idle state, where the third capacity state is used to indicate a use condition of the virtual machine hardware configuration;
a third judging unit, configured to output a third capacity analysis result when it is determined that the third capacity state is a resource shortage state or a resource excess state, where the third capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the third capacity is a state resource shortage state or a resource excess state;
a fourth obtaining unit, configured to obtain a fourth capacity state corresponding to the storage space when it is determined that the third capacity state is a normal capacity state, where the fourth capacity state is used to indicate a usage situation of the storage space;
a fourth judging unit, configured to, when it is determined that the fourth capacity state is a resource shortage state or a resource excess state, output a fourth capacity analysis result, where the fourth capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the fourth capacity state is the resource shortage state or the resource excess state;
a fifth judging unit, configured to output a fourth capacity analysis result when it is determined that the fourth capacity state is a capacity normal state, where the fourth capacity analysis result includes identification information of the virtual machine and a capacity analysis result that the fourth capacity state is a normal state.
7. The apparatus according to claim 6, wherein the first obtaining unit is specifically configured to obtain a shutdown number and a shutdown duration within a preset duration, determine that the first capacity state is a shutdown state if at least one of the shutdown number and the shutdown duration is greater than a corresponding first threshold and a corresponding second threshold, and determine that the first capacity state is a non-shutdown state if the power state is on;
the second obtaining unit is specifically configured to obtain input/output traffic of a network port and input/output traffic of a storage port, determine that the second capacity state is a non-idle state if the input/output traffic of the network port is greater than a third threshold, determine that the second capacity state is the non-idle state if the input/output traffic of the storage port is greater than the third threshold, and determine that the second capacity state is an idle state if the input/output traffic of the network port is not greater than the corresponding third threshold and the input/output traffic of the storage port is not greater than the corresponding fourth threshold;
the third obtaining unit is specifically configured to obtain a hardware configuration utilization rate, where the hardware configuration includes a central processing unit or a memory capacity, the hardware configuration utilization rate indicates a maximum peak value of a hardware load of a virtual machine, if the hardware configuration utilization rate is smaller than a fifth threshold, it is determined that the third capacity state is an excess resource state, if the hardware configuration utilization rate is larger than a sixth threshold, it is determined that the third capacity state is an insufficient resource state, and if the hardware configuration utilization rate is not smaller than the fifth threshold and is not larger than the sixth threshold, it is determined that the third capacity state is a normal capacity state;
the fourth obtaining unit is specifically configured to obtain a storage space utilization rate, determine that the third capacity state is an excess resource state if the storage space utilization rate is smaller than a seventh threshold, determine that the third capacity state is an insufficient resource state if the storage space utilization rate is larger than the seventh threshold, and determine that the third capacity state is a normal capacity state if the storage space utilization rate is equal to the seventh threshold.
8. The apparatus of claim 6, wherein the apparatus further comprises:
the computing unit is used for computing a recommended value of hardware configuration, wherein the recommended value of hardware configuration is a numerical value which needs to be adjusted by the hardware configuration of the current virtual machine; and outputting a third capacity analysis result, wherein the third capacity analysis result comprises a recommended value of computer hardware configuration, identification information of the virtual machine and a capacity analysis result of which the third capacity state is a resource shortage state or a resource surplus state.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 5 when executing the computer program.
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 5.
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