KR102622088B1 - A cloud operation evaluation device considering performance guarantee of the cloud operating environment and stability and a sever operating system using the same - Google Patents

Abstract

A virtual machine placement evaluation device according to an embodiment of the present invention is a virtual machine placement evaluation device for calculating a batch evaluation value, which is an evaluation of the placement of a virtual machine running on a physical server. A receiving module that collects operational information, which is information; A first calculation module that calculates an efficiency evaluation value, which is a value for the efficiency of virtual machine placement, based on the operation information; and a batch evaluation module that calculates the batch evaluation value using the efficiency evaluation value.

Description

Cloud operation evaluation device considering performance guarantee of cloud operating environment and server operating system including the same {A CLOUD OPERATION EVALUATION DEVICE CONSIDERING PERFORMANCE GUARANTEE OF THE CLOUD OPERATING ENVIRONMENT AND STABILITY AND A SEVER OPERATING SYSTEM USING THE SAME}

The present invention relates to a cloud operation evaluation device and a server operating system including the same for evaluating operation to ensure the performance of the cloud operating environment and using and promoting cloud resources to provide cloud services.

As Internet network technology increases and space restrictions for computer work, such as working from home, disappear, the cloud environment has recently grown rapidly. From the perspective of a cloud service provider, how to manage virtual machines on physical machines is very important in order to provide cloud services without interruption.

Previously, based on the first-fit decreasing algorithm, recommendations were provided to operate the minimum number of servers by migrating virtual machines from servers with low usage to servers with high usage in stages. However, if server operation is minimized, the remaining operating servers are overloaded, creating an unstable problem in terms of server stability.

The present invention is intended to solve the above-mentioned problems, and in order to efficiently use cloud resources, a cloud operation evaluation device that objectively evaluates and determines cloud resources and recommends optimal cloud resources based on this, and a server including the same We would like to provide an operating system.

A cloud operation evaluation device according to an embodiment of the present invention is a virtual machine placement evaluation device for calculating a placement evaluation value, which is an evaluation of the placement of a virtual machine operating on a physical server, and includes information generated as the virtual machine operates. A receiving module that collects operational information; A first calculation module that calculates an efficiency evaluation value, which is a value for the efficiency of virtual machine placement, based on the operation information; and a batch evaluation module that calculates the batch evaluation value using the efficiency evaluation value.

In addition, it further includes a second calculation module that calculates a stability evaluation value, which is a value for the stability of the deployment of the virtual machine, based on the operation information, and the batch evaluation module further utilizes the stability evaluation value to evaluate the deployment. The value can be calculated.

Additionally, the second calculation module may calculate the stability evaluation value based on the dirty memory rate and the network link rate.

In addition, the first calculation module may calculate the efficiency evaluation value by using the operation information to calculate the integrated power amount based on the operating power amount, which is the amount of power generated as the virtual machine operates.

In addition, the first calculation module calculates the integrated power amount based on the CPU operation rate of the physical server, and the basic power amount, which is the amount of power generated when the CPU is in an idle state, and the additional power amount generated when the virtual machine is operated and the CPU is operated. The operating power amount can be calculated by adding up the power amount.

Additionally, the first calculation module may approximate the basic power amount by dividing the amount of power generated when the CPU is fully operated by a predetermined value.

Additionally, the first calculation module may calculate the amount of additional power based on the ratio of the operating frequency of the CPU to the difference between the frequency when the CPU is fully operated and the frequency when the CPU is in an idle state.

In addition, the first calculation module may calculate the efficiency evaluation value by calculating the integrated power amount based on the mobile power amount, which is the amount of power generated when the virtual machine is migrated, using the operation information.

Additionally, the first calculation module can calculate the amount of moving power by approximating it as a predetermined ratio to the amount of power generated when the CPU is fully operated.

In addition, the first calculation module calculates the ratio of the frequency when the CPU is fully operated to a predetermined ratio to the difference between the frequency when the CPU is fully operated and the frequency when the CPU is idle. As a standard, the amount of moving power can be calculated.

A cloud resource evaluation method according to an embodiment of the present invention is a cloud resource evaluation method for calculating a deployment evaluation value, which is an evaluation of the deployment of a virtual machine operating on a physical server, by utilizing a cloud operation evaluation device. A step of collecting operating information, which is information generated when a virtual machine is operated, by a module; Calculating, by a first calculation module, an efficiency evaluation value, which is a value for the efficiency of virtual machine placement, based on the operation information; It may include a step of calculating the batch evaluation value by utilizing the efficiency evaluation value by the batch evaluation module.

A cloud resource recommendation device according to an embodiment of the present invention is a cloud resource recommendation device for optimal placement of virtual machines operating on a physical server, and collects operation information, which is information generated while an administrator operates a physical server. module; a preference evaluation module that calculates a preference evaluation value related to the manager's management preference using a predetermined preference calculation method based on the operation information; and a recommendation module that calculates recommendation information for recommending cloud resources to the administrator by reflecting the preference evaluation value. The recommendation information may include information about the arrangement of virtual machines on a physical server.

In addition, it further includes a batch calculation module that calculates the optimal placement, which is the placement of virtual machines on the server that satisfies predetermined placement conditions, wherein the batch calculation module determines the optimal placement using a predetermined evaluation method. After receiving the evaluated batch evaluation value, the recommendation module can select the arrangement of the virtual machine to recommend by classifying the optimal arrangement in the order of having the batch evaluation value similar to the preference evaluation value according to a predetermined recommendation method. there is.

In addition, the preference evaluation value and the batch evaluation value can be calculated by considering aspects of stability and efficiency.

Additionally, the predetermined preference calculation method may be a method of calculating the preference evaluation value by classifying the manager's operating pattern of the virtual machine management system based on the operation information.

In addition, the predetermined recommendation method recommends the placement of virtual machines in a similar order by comparing the placement evaluation values based on the preference evaluation values, and if the similar degrees of stability and efficiency are the same, weighting is given to stability This may be a recommended way to place machines.

In addition, it further includes an interface module that calculates an interface that allows the recommended information generated by the recommendation module to be displayed to the administrator, wherein the interface module can calculate an interface so that the optimally arranged information and recommended information are displayed together. there is.

Additionally, the interface module can change the preference evaluation value within a minimum to maximum range, and can calculate an interface that recommends the placement of a virtual machine on a physical server by reflecting the changed preference evaluation value.

In addition, the collection module collects the manager's personal information when the operation information is not collected in a predetermined amount, and the predetermined preference calculation method considers the personal information and the operation information together to evaluate the preference. This may be a method of calculating the value.

In addition, it further includes a proficiency determination module that determines the operator's proficiency in operating the virtual machine management system based on the operation information, and the predetermined preference calculation method includes the personal information and the operation based on the operation proficiency. This may be a method of calculating the preference evaluation value by changing the weight to which information is applied.

The cloud resource recommendation method according to an embodiment of the present invention is implemented by a virtual machine management system, and in the cloud resource recommendation method for operating a virtual machine operating on a physical server, the manager recommends the virtual machine by means of a collection module. A step of collecting operation information, which is information generated while controlling the management system; Calculating, by a preference evaluation module, a preference evaluation value related to the manager's preference using a predetermined preference calculation method based on the operation information; and selecting, by a recommendation module, a virtual machine arrangement on a physical server to be recommended to the administrator by reflecting the preference evaluation value.

The cloud operation evaluation device and the server operating system including the same according to the present invention can maximize the stability of the server.

Additionally, manpower for server management can be minimized.

Additionally, the time required for server management can be minimized.

However, the effects of the present invention are not limited to the effects described above, and effects not mentioned can be clearly understood by those skilled in the art from this specification and the attached drawings.

1 is a relationship diagram of a server operating system according to an embodiment of the present invention.
Figure 2 is a configuration diagram of a server operating system according to an embodiment of the present invention
Figure 3 is a flowchart of a cloud resource recommendation method according to an embodiment of the present invention.
Figure 4 is a diagram illustrating the utilization weight of personal information and motion information among the cloud resource recommendation method according to an embodiment of the present invention.
Figure 5 is an example of an image recommending the placement of a virtual machine among the cloud resource recommendation method according to an embodiment of the present invention.
Figure 6 is an example showing the arrangement of virtual machines according to preference evaluation values among the cloud resource recommendation method according to an embodiment of the present invention.
Figure 7 is a flowchart of a server operation method according to an embodiment of the present invention

Hereinafter, specific embodiments of the present invention will be described in detail with reference to the drawings. However, the spirit of the present invention is not limited to the presented embodiments, and those skilled in the art who understand the spirit of the present invention may add, change, or delete other components within the scope of the same spirit, or create other degenerative inventions or this invention. Other embodiments that are included within the scope of the invention can be easily proposed, but this will also be said to be included within the scope of the invention of the present application.

In addition, components having the same function within the scope of the same idea shown in the drawings of each embodiment will be described using the same reference numerals.

1 is a relationship diagram of a server operating system according to an embodiment of the present invention.

Referring to FIG. 1, the server operating system 100 according to an embodiment of the present invention may be a system that operates, maintains, and transfers virtual machines installed on a physical server.

As a specific example, the server operating system 100 can monitor whether a problem occurs in the virtual machine, and if a problem occurs in the virtual machine, the server operating system 100 can resolve the problem with the virtual machine. Additionally, the server operating system can optimally arrange virtual machines on a physical server in terms of resource utilization.

The server operating system 100 may be connected to the physical server 200 and/or the external server 300 through a wired/wireless network. The server operating system can collect and receive all information generated when a virtual machine operates on a physical server, all information generated when a physical server operates, and/or necessary information from an external server.

The network in the present invention may be a core network integrated with a wired public network, wireless mobile communication network, or mobile Internet, etc., and may include TCP/IP protocols and various services existing at the upper layer thereof, such as HTTP (Hyper Text Transfer Protocol) and HTTPS. (Hyper Text Transfer Protocol Secure), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), etc., which may refer to a worldwide open computer network structure, examples of which include It comprehensively refers to a data communication network that can transmit and receive data in various forms without limitation.

The physical server in the present invention may include other components to perform the server environment. A server can include any type of device.

For example, a server may be a digital device, such as a laptop computer, notebook computer, desktop computer, web pad, or mobile phone, equipped with a processor and equipped with memory and computing power.

In one example, the server may be a web server. However, it is not limited to this, and the type of server can be changed in various ways at a level that is obvious to a person skilled in the art.

Figure 2 is a configuration diagram of a server operating system according to an embodiment of the present invention.

Referring to FIG. 2, the server operating system 100 according to an embodiment of the present invention may include a cloud resource recommendation device 110, a server management device 120, and a cloud operation evaluation device 130.

Each device is connected to each other via a wired/wireless network and can transmit and receive necessary information.

The server management device 120 can perform all processes necessary to operate a physical server.

The server management device 120 includes a storage module 121 that stores information needed to manage and operate the physical server, a monitoring module 122 that monitors in real time whether a virtual machine running on the physical server is operating abnormally, and It may include a migration module 123 that moves virtual machines between physical servers.

In addition, the server management device 120 receives the administrator's device control, generates a control signal, and transmits it to the cloud resource recommendation device 110, the server management device 120, and the cloud operation evaluation device 130. 124) may further be included.

In addition, the server management device 120 is a server management device such as values calculated by the cloud resource recommendation device 110, the server management device 120, and the cloud operation evaluation device 130, the state of the virtual machine, and the state of the physical server. It may further include a display module 125 that displays information required for operation and management.

The storage module 121 may store all data necessary for the server management device 120 to operate.

In one example, the storage module may include internal memory and/or external memory.

For example, internal memory may include volatile memory (e.g., DRAM, SRAM, or SDRAM), non-volatile memory (e.g., one time programmable ROM (OTPROM), PROM, EPROM, EEPROM, mask ROM, flash ROM, flash memory, hard memory, etc. may include at least one of a drive, or solid state drive (SSD)

External memory may be a flash drive, such as compact flash (CF), secure digital (SD), Micro-SD, Mini-SD, extreme digital (xD), multi-media card (MMC), or memory stick. It may include etc.

The monitoring module 122 can receive log data and metric data generated from the virtual machine and monitor in real time whether the virtual machine is operating abnormally, including whether underload or overload occurs.

The migration module 123 can move a virtual machine between physical servers when a problem occurs in the physical server or based on an administrator's control signal.

Before migrating a virtual machine, the migration module 123 can determine whether migration is necessary and whether the physical server is suitable to receive migration.

Through the input module 124, the administrator can input commands and command signals for managing and operating the server operating system 100.

For example, the input module may include a mouse, keyboard, touch panel, (digital) pen sensor, key, or ultrasonic input device. The touch panel may use at least one of, for example, a capacitive type, a resistive type, an infrared type, or an ultrasonic type.

Additionally, the touch panel may further include a control circuit. The touch panel may further include a tactile layer to provide a tactile response to the user. The (digital) pen sensor may be, for example, part of a touch panel or may include a separate recognition sheet.

Additionally, the keys may include, for example, physical buttons, optical keys, or keypads.

Additionally, the ultrasonic input device can detect ultrasonic waves generated from the input tool through a microphone and confirm data corresponding to the detected ultrasonic waves.

The display module 125 may include any device that can display images, such as a display device, screen device, or beam projector.

In one example, a display (eg, display) may include a panel, a holographic device, a projector, or control circuitry for controlling them.

The cloud resource recommendation device 110 according to an embodiment of the present invention is a cloud resource recommendation device 110 for optimal placement of virtual machines operating on a physical server, and includes information generated while an administrator operates a physical server. A collection module 111 that collects motion information, a preference evaluation module 112 that calculates a preference evaluation value related to the manager's management preference by a predetermined preference calculation method based on the motion information, and a preference evaluation module 112 that calculates a preference evaluation value related to the manager's management preference by reflecting the preference evaluation value It may include a recommendation module 113 that selects the virtual machine placement on the physical server to be recommended to the administrator.

In addition, the cloud resource recommendation device 110 may further include a placement calculation module 114 that calculates the optimal placement, which is the placement of virtual machines on the server that satisfies predetermined placement conditions.

In addition, the cloud resource recommendation device 110 includes an interface module 115 that calculates an interface that allows the recommendation information generated by the recommendation module 113 to be displayed to the manager, and the virtual machine of the manager based on the operation information. It may further include a proficiency judgment module 116 that determines the proficiency in operating the management system.

In addition, the cloud resource recommendation device 110 may further include a preference storage module 117 that stores resource evaluation values, which are respective values of efficiency and stability for cloud resources.

Cloud resources can refer to all hardware and software for providing cloud services.

For example, cloud resources may refer to physical servers, virtual machines installed on physical servers, network devices, network resources, etc.

However, it is not limited to this, and the specific examples of the cloud resources can be modified in various ways at a level that is obvious to those skilled in the art.

The cloud resource recommendation device 110 can analyze patterns in which an administrator manages and operates a server operating system and recommend cloud resources appropriate for the pattern.

For example, the cloud resource recommendation device 110 may recommend to the administrator a physical server, network device, network arrangement, physical server placement, placement of a virtual machine within the physical server, etc. suitable for management tendencies.

Hereinafter, the cloud resource recommendation device 110 will be described based on its function of recommending the placement of virtual machines within a physical server, but the present invention is not limited thereto.

The collection module 111 can collect operation information, which is information generated while an administrator manages a physical server.

Operation information may include both log information and/or metric data of the physical server/virtual machine. In addition, the operation information may include control data in which the administrator controls the physical server and all information about the current operation status of the physical server and virtual machine when the control data is generated.

For example, the operation information may also include the methods by which the administrator controlled and controlled the physical server and virtual machine to resolve abnormal situations in the current state of the physical server and virtual machine.

The collection module 111 can collect the manager's personal information when the operation information is not collected in a predetermined amount.

For example, the predetermined collection amount may be 1TB. However, it is not limited to this, and the predetermined collection amount can be modified in various ways at a level that is obvious to those skilled in the art.

Personal information may include information generated when the administrator controls electronic devices from outside and the personal information of the administrator, unrelated to the server operating system 100.

To this end, the collection module 111 can collect necessary information by being connected to a network such as an external server, a public institution server, an open server, etc.

For this purpose, prior approval may be obtained from the administrator.

For example, personal information may include the administrator's navigation route selection information.

When exploring navigation routes, various routes may be recommended, and navigation users can select one route among several routes depending on their preferences.

For this reason, using the manager's navigation application usage information can be helpful in identifying the manager's preferences.

For example, personal information may include the manager's vehicle control information.

Recently, various control information of automobiles is being collected based on the expansion of IOT technology and the trend toward electronic devices in automobiles. The collection module 111 allows the administrator to control the vehicle and collect controlled information from the automobile manufacturing server and/or the telecommunication company server.

The car's control method can be important information in determining whether a manager values stability or efficiency.

Car control information can be combined with navigation information within the car, which can be important information in understanding how the manager controlled the car in traffic situations and locations.

For example, personal information may include information about the administrator's age, gender, school attended, major information, and hometown.

As people get older, they may tend to value stability more than efficiency.

Women may tend to value stability more than efficiency than men.

Because a university's academic culture affects students, the importance placed on efficiency and stability may vary depending on the school from which the student graduated.

Because the major one learns affects the learner, the importance placed on efficiency and stability may vary depending on the major.

Depending on whether one is from a city, a rural area, or a fishing village, the importance placed on efficiency and stability may differ because it affects residents.

For example, people living in urban areas may value efficiency over safety. Additionally, residents in rural/fishing areas may value stability more than efficiency.

The preference evaluation module 112 can calculate a preference evaluation value related to the manager's management preference using a predetermined preference calculation method.

The preference evaluation module 112 may store a pattern classification model that classifies operation information into predetermined operation patterns.

The preference evaluation module 112 may generate a pattern classification model by deep learning based on classification information that classifies past motion information and corresponding motion information.

Deep learning may use the Back Progation algorithm, which is an algorithm that updates the weights of the neural network using labeled data of the output layer, but is not limited to this.

In addition, since the deep neural network and back propagation algorithm are known in the art, detailed description thereof may be omitted.

For example, the preference evaluation value may be composed of values for efficiency and stability, the unit may be %, and the sum of efficiency and stability may be 100.

However, it is not limited to this, and the form of the preference evaluation value can be modified in various ways at a level that is obvious to those skilled in the art.

Operational patterns may be classified by pattern from the perspective of stability and efficiency.

For example, the first operating pattern may be a pattern with 0% stability and 100% efficiency, and the second operating pattern may be a pattern with 2% stability and 98% efficiency. In this way, operating patterns can be classified in all aspects of stability and efficiency.

The predetermined preference calculation method may be a method of calculating the preference evaluation value by classifying the operation pattern of the virtual machine management system of the administrator based on the operation information.

Preference evaluation values can be calculated by considering aspects of stability and efficiency.

The manager's management tendencies can be effectively analyzed using the preference evaluation module 112.

The predetermined preference calculation method may be a method of calculating the preference evaluation value by considering both the personal information and the motion information.

If motion information is not collected in a predetermined amount, the preference evaluation module 112 can calculate a preference evaluation value by using personal information as well.

The preference evaluation module 112 may store a personal calculation model that calculates efficiency and stability through personal information.

The preference evaluation module 112 can calculate a personal calculation model by deep learning based on values classified as efficiency and stability for personal information corresponding to past personal information.

Deep learning algorithms utilize known technologies, and detailed descriptions thereof may be omitted.

The preference evaluation module 112 may calculate a final preference evaluation value by performing a weighted average of the first preference evaluation value calculated through the pattern classification model and the second preference evaluation value calculated by the individual calculation model.

The preference evaluation module 112 may calculate the preference evaluation value by changing the weight of the first preference evaluation value and the second preference evaluation value according to the amount of motion information collected until motion information is collected by a predetermined collection amount. .

For example, as the amount of motion information collected increases, the first preference evaluation value may be given more weight than the second preference evaluation value, and the average may be calculated to calculate the final preference evaluation value.

If the motion information is too little, the first preference evaluation value may not fully reflect the manager's inclinations and preferences.

To compensate for this, the final preference evaluation value can be calculated by further weighting the second preference evaluation value using personal information that has already accumulated a significant amount of data.

As the motion information gradually increases, the weight of the first preference evaluation value may be increased according to the first ratio for the increased amount, and the weight of the second preference evaluation value calculated using personal information may be decreased.

The predetermined preference calculation method may be a method of calculating the preference evaluation value by changing the weight to which the personal information and the motion information are applied based on the manipulation proficiency.

In other words, in the predetermined preference calculation method, when the amount of motion information collected is more than a predetermined standard value, the ratio of the weight decrease of the second preference evaluation value as the weight of the first preference evaluation value increases according to the manipulation proficiency is set to the first ratio. This may be a method of changing the weight at a different ratio.

As a specific example, the preference evaluation module generates a second preference evaluation value according to an increase in the weight of the first preference evaluation value when the amount of motion information collected is more than a predetermined standard value (change standard value) and the manager's manipulation proficiency is more than a predetermined proficiency level. The final preference evaluation value can be calculated by setting the weight reduction ratio as the second ratio, which is a larger ratio than the first ratio.

In addition, if the amount of motion information collected is more than a predetermined standard value and the manager's operating proficiency is less than the predetermined proficiency, the preference evaluation module adjusts the ratio of the weight reduction of the second preference evaluation value according to the increase in the weight of the first preference evaluation value. The final preference evaluation value can be calculated using the third ratio, which is a smaller ratio than the first ratio.

This may be because if the manager's proficiency is low, the reliability of the information is somewhat low because the consistency of the motion information is somewhat low. Conversely, if the manager's proficiency is high, the consistency of motion information may be somewhat high, and thus the reliability of the information may be somewhat high.

If the manipulation proficiency is not calculated by the proficiency judgment module 116, the preference evaluation module may maintain the first ratio and calculate the final preference evaluation value.

The proficiency judgment module 116 can calculate the administrator's proficiency in operating the server operating system 100 based on the operation information.

For this purpose, a proficiency judgment model may be stored in the proficiency judgment module 116.

The proficiency judgment module 116 can generate a proficiency judgment model by deep learning how the physical server and virtual machine were manipulated and their evaluation scores according to the past physical server and virtual machine states.

The specific algorithm for deep learning utilizes known technologies, so detailed descriptions thereof may be omitted.

The proficiency judgment model can calculate the manager's operating proficiency.

However, the proficiency model requires only the control information necessary to determine the operating proficiency and the status information of the physical server and virtual machine at the time of control among the operation information, so just because there is a lot of operation information, it cannot be said that there is a lot of information necessary to judge the operation proficiency. You can.

The proficiency judgment model can calculate operating proficiency only when the amount of data required to judge operating proficiency is greater than a predetermined standard.

Here, the amount of data required to determine manipulation proficiency may be smaller than the change standard value.

This may be so that the above-described algorithm operates effectively.

The batch calculation module 114 can calculate the optimal placement of virtual machines.

To this end, the batch calculation module 114 can predict the future workload of virtual machines and calculate the placement of virtual machines on the physical server that minimizes the preset objective function based on the predicted workload.

Since the method by which the placement calculation module 114 calculates the optimal placement of virtual machines is a known technology, detailed description thereof may be omitted.

The batch calculation module 114 may request an evaluation of the calculated optimal batch from the batch evaluation module 134, which will be described later.

The batch calculation module 114 may receive a batch evaluation value in which the batch evaluation module 134 evaluates the optimal batch using a predetermined evaluation method.

The batch calculation module 114 can transmit the batch evaluation value and optimal placement received from the batch evaluation module 134 to the recommendation module 113.

The batch evaluation value may be in a form that takes stability and efficiency aspects into consideration.

For example, stability and efficiency are expressed as percentages based on an overall value of 100, so batch evaluation values and (final) preference evaluation values can be easily compared.

The recommendation module 113 can select the arrangement of virtual machines to recommend by classifying the optimal arrangement in the order of having the arrangement evaluation value similar to the preference evaluation value according to a predetermined recommendation method.

The recommendation module 113 may compare the preference evaluation value and the placement evaluation value in order to recommend virtual machine deployment by reflecting the administrator's preference among the optimal deployments.

The recommendation module 113 can calculate recommendation information that recommends the recommendation ranking of the optimal arrangement in the order of having the arrangement evaluation value most similar to the preference evaluation value among the optimal arrangements.

The predetermined recommendation method recommends the placement of virtual machines in a similar order by comparing the placement evaluation values based on the preference evaluation values. If the similar degrees of stability and efficiency are the same, the placement of virtual machines is selected by giving weight to stability. This may be a recommended method.

In other words, in the case of virtual machine placement where the distance between the preference evaluation value and the placement evaluation value is the same, more emphasis is placed on stability, and a virtual machine placement with higher stability may be recommended at a higher rank.

The recommendation module 113 may recommend cloud resources in the order of resource evaluation values similar to the preference evaluation value according to a predetermined recommendation method.

To this end, the recommendation module can calculate recommendation information that recommends cloud resources to the manager by reflecting the preference evaluation value.

Here, the predetermined recommendation method recommends cloud resources in a similar order by comparing batch evaluation values or resource evaluation values based on preference evaluation values. If the similar degrees of stability and efficiency are the same, cloud resources are selected by giving weight to stability. This may be a recommended method.

The interface module 115 can calculate an interface so that optimal arrangement information and recommended information are displayed together.

The display module 125 displays the interface calculated by the interface module 115, so that the administrator can easily view the information needed to manage and operate the server operating system 100.

In the preference storage module 117, resource evaluation values may be designated and stored for each cloud resource.

The resource evaluation value can be composed of values for efficiency and stability, and the unit is %, so that the sum of efficiency and stability is 100.

However, it is not limited to this, and the form of the resource evaluation value can be modified in various ways at a level that is obvious to those skilled in the art.

For example, physical server A may have an efficiency of 47% and stability of 53%, and physical server B may have an efficiency of 30% and stability of 70%.

The cloud operation evaluation device 130 may be a device that evaluates whether cloud resources providing cloud services are being appropriately used.

For example, the cloud operation evaluation device 130 can evaluate cloud services in terms of stability and efficiency.

For example, the cloud operation evaluation device 130 may evaluate the placement of virtual machines on a physical server and approximate or replace it with an evaluation of the stability and efficiency of cloud resource operation (cloud service).

Hereinafter, a cloud operation evaluation device and a cloud resource evaluation method will be described based on virtual machine placement evaluation, but the present invention is not limited thereto.

The cloud operation evaluation device 130 according to an embodiment of the present invention is a cloud operation evaluation device 130 for calculating a placement evaluation value, which is an evaluation of the placement of a virtual machine operated on a physical server, and the virtual machine is A receiving module 131 that collects operational information, which is information generated during operation, a first calculation module 132 that calculates an efficiency evaluation value, which is a value for the efficiency of virtual machine placement, based on the operational information, based on the operational information A second calculation module 133 that calculates a stability evaluation value, which is a value for the stability of the deployment of the virtual machine, and a batch evaluation module 134 that calculates the batch evaluation value using the efficiency evaluation value and the stability evaluation value. It can be included.

The receiving module 131 can collect operational information, which is information generated as the virtual machine operates.

The operating information may include hardware operation information of the physical server by the virtual machine.

For example, the operating information may include CPU operation information, memory operation information, network information, etc. The present invention is not limited to the above-described examples, and specific examples of the operating information are provided at a level that is obvious to those skilled in the art. It can be modified in various ways.

The first calculation module can calculate an efficiency evaluation value.

The first calculation module 132 can calculate the efficiency evaluation value by calculating the integrated power amount based on the CPU operation rate of the physical server.

For example, as the integrated power amount increases, the efficiency evaluation value may increase, and as the integrated power amount decreases, the efficiency evaluation value may decrease.

For example, CPU utilization rate can be expressed as CPU frequency in MHZ.

The first calculation module 132 may calculate the integrated power amount by adding the operating power amount, which is the amount of power generated by the physical server as the virtual machine operates, and the moving power amount, which is the amount of power generated when the virtual machine is migrated.

˙ ˙ ˙ ˙ ˙ (Equation 1)

here, means integrated power amount (MW), means operating power (MW), may mean mobile power amount (MW).

In other words, if migration occurs, the integrated power amount can be calculated by adding the power generation for the migration and the power generation of the physical server according to the virtual machine placement after migration.

The first calculation module 132 can calculate the operating power amount by adding the basic power amount, which is the amount of power generated when the CPU is in an idle state, and the additional power amount generated when the CPU is operated by operating the virtual machine.

Additionally, the first calculation module 132 can calculate the basic power amount by dividing the amount of power generated when the CPU is fully operated by a predetermined value.

Additionally, the first calculation module 132 may calculate the amount of additional power based on the ratio of the operating frequency of the CPU to the difference between the frequency when the CPU is fully operated and the frequency when the CPU is in an idle state.

˙ ˙ ˙ ˙ ˙ (Equation 1-2)

here, can refer to the amount of power generated by the physical server when the CPU operation rate is 100%, may mean the basic power amount.

As a result of various experimental data and direct measurement of the amount of power generated by the physical server, the basic power amount decreases as the number of physical servers in operation increases. It was found that it approximates half of .

here, may mean additional power amount.

also, may mean the average of the operation rates of all CPUs. The amount of additional power can be calculated as the ratio of the operating frequency of the CPU to the difference between the frequency when the CPU is fully operational and the frequency when the CPU is idle.

Here, the operating frequency of the CPU may refer to the frequency of the CPU when a purposeful operation is implemented in a virtual machine, such as an application program in a physical server.

in other words, may mean the CPU operating frequency, may refer to the frequency generated when the CPU is operating at 100%, May refer to the CPU frequency generated when the physical machine is only powered on without any purposeful operation being implemented.

Additionally, in Equation 1-2 may refer to the amount of additional power generated when the CPU is operating at 100%.

The first calculation module 132 can calculate the amount of moving power by approximating it as a predetermined ratio to the amount of power generated when the CPU is fully operated.

Specifically, the first calculation module 132 calculates the frequency when the CPU is fully operated at a predetermined ratio based on the difference between the frequency when the CPU is fully operated and the frequency when the CPU is in an idle state. The amount of moving power can be calculated based on the ratio.

˙ ˙ ˙ ˙ ˙ ˙ (Equation 1-3)

here, may mean a predetermined ratio.

The predetermined ratio may be variable depending on the performance of the physical machine, the size of the virtual machine being migrated, the type of virtual machine, etc.

For example, as the performance of the physical machine increases, the predetermined ratio may become smaller.

For example, the larger the size of the virtual machine being migrated, the larger the predetermined ratio may be.

In one example, the predetermined ratio may be 0.4 (40%).

However, it is not limited to this, and the predetermined ratio can be modified in various ways at a level that is obvious to those skilled in the art.

Additionally, in Equation 1-3 may refer to the amount of additional power generated when the CPU is operating at 100%.

can be calculated for each physical server.

The second calculation module 133 can calculate the stability evaluation value based on the dirty memory rate and the network link rate.

As a specific example, the second calculation module 133 may calculate the migration time by comparing the memory of the virtual machine, the dirty memory rate of the virtual machine, and the network link rate.

The second calculation module 133 can evaluate and classify the migration time into five levels (Safety, Stable, Normal, Warning, Danger) as a value for stability by comparing the size of the physical server and the size of the virtual machine. .

Here, the safety level may have a greater stability evaluation value than the danger level.

The larger the memory of the virtual machine, the larger the dirty memory rate of the virtual machine, and the smaller the network link rate, the smaller the stability evaluation value can be.

Conversely, the smaller the memory of the virtual machine, the smaller the dirty memory rate of the virtual machine, and the larger the network link rate, the larger the stability evaluation value can be.

The batch evaluation module 134 can calculate the batch evaluation value using the efficiency evaluation value and the stability evaluation value.

The batch evaluation module 134 calculates the efficiency evaluation value and stability evaluation value for the physical machine at the time of evaluation, and the efficiency evaluation value when the deployment of the virtual machine is changed to the optimal layout received from the cloud resource recommendation device 110. By comparing the and stability evaluation values, the final batch evaluation value can be calculated.

The batch evaluation module 134 calculates the efficiency evaluation value and stability evaluation value for the physical machine at the time of evaluation, and the efficiency evaluation value and By comparing the stability evaluation values, the final batch evaluation value can be calculated.

Efficiency can increase if the efficiency evaluation value of the batch to be changed (optimal or modified layout) is greater than the efficiency evaluation value of the current layout.

Conversely, if the efficiency evaluation value of the batch to be changed is smaller than the efficiency evaluation value of the current layout, the efficiency may decrease.

If the stability evaluation value of the batch to be changed is greater than the stability evaluation value of the current batch, stability can be increased.

Conversely, if the stability evaluation value of the batch to be changed is smaller than the stability evaluation value of the current batch, stability may be reduced.

Batch evaluation values can be made up of values for efficiency and stability, and the unit is %, so that the sum of efficiency and stability is 100.

For this purpose, efficiency evaluation values and batch evaluation values can be numerically processed.

For example, using a predetermined processing table, the differences between the efficiency evaluation value of the current batch and the efficiency evaluation value of the batch to be changed may be classified for each difference between the stability evaluation value of the current batch and the stability evaluation value of the batch to be changed. Efficiency and stability may be pre-specified for each classification of differences between the efficiency evaluation value of the current batch and the efficiency evaluation value of the batch to be changed.

However, it is not limited to this, and the method of processing the efficiency evaluation value and the batch evaluation value can be modified in various ways at a level that is obvious to a person skilled in the art.

Base frequencies are easy to measure, but idle frequencies can be difficult to measure. This may be because in practice, only power is provided and measurement is performed without operating the server in the actual field.

Unlike the above-described content, the batch evaluation module 134 may calculate the batch evaluation value only with the efficiency evaluation value.

Additionally, unlike the above-described content, the batch evaluation module 134 may calculate the batch evaluation value only with the stability evaluation value.

A detailed explanation regarding this may be omitted to the extent that it overlaps with the above-described content.

Figure 3 is a flowchart of a cloud resource recommendation method according to an embodiment of the present invention.

Hereinafter, detailed description may be omitted to the extent that it overlaps with the above-described content.

Referring to Figure 3, the optimal placement recommendation method according to an embodiment of the present invention is implemented by a virtual machine management system, and in the virtual machine management method for operating a virtual machine running on a physical server, the collection module A step in which operation information, which is information generated while the administrator controls the virtual machine management system, is collected, and by a preference evaluation module, a preference evaluation value related to the administrator's preference is generated by a predetermined preference calculation method based on the operation information. It may include a step of selecting a virtual machine arrangement on a physical server to be recommended to the administrator by reflecting the preference evaluation value through the calculation step and recommendation module.

Operation information can be collected by the collection module.

If motion information is not collected in a predetermined amount, the manager's personal information is collected, proficiency is judged, and the personal information and proficiency can be used to calculate a preference evaluation value.

If motion information is collected in a predetermined amount, a preference evaluation index can be calculated using only motion information.

The preference evaluation value can be continuously updated and updated until the administrator requests a recommendation for virtual machine placement through the input module.

When an administrator requests a recommendation for the placement of a virtual machine, the optimal placement can be calculated by the batch calculation module, and the batch calculation module can request a placement evaluation for the optimal placement from the cloud operation evaluation device.

The cloud operation evaluation device can calculate a placement evaluation value by comparing the current virtual machine placement with the optimal placement of the virtual machine.

The recommendation module compares the preference evaluation value and the placement evaluation value, calculates a similar order of optimal arrangements that are suitable for the manager's operating tendencies and patterns, and can recommend the arrangement of virtual machines to the manager through the interface module and display module. there is.

Figure 4 is a diagram for explaining the utilization weight of personal information and motion information among the cloud resource recommendation method according to an embodiment of the present invention.

Referring to FIG. 4 , for example, when motion information on the

If no motion information is collected, only personal information can be 100% used to calculate preference evaluation values.

As motion information gradually increases, the weight of the second preference evaluation value calculated using personal information according to the first ratio may be reduced for the increased amount.

However, the present invention is not limited thereto, and the first ratio can be modified in various ways as will be obvious to those skilled in the art.

FIG. 4(a) is a diagram illustrating a case where the manipulation proficiency is above a predetermined proficiency level. Referring to FIG. 4(a), the predetermined reference value (X10) of the amount of motion information collected may be half of the predetermined collection amount.

However, it is not limited to this, and the predetermined reference value can be modified in various ways at a level that is obvious to those skilled in the art.

When the amount of motion information collected is less than the predetermined reference value (X10), the first preference evaluation value and the second preference evaluation value are adjusted according to the first ratio (B11) to calculate the final preference evaluation value, but the amount of motion information collected When the value is greater than this predetermined reference value, the first preference evaluation value and the second preference evaluation value can be adjusted according to the second ratio (B12) to calculate the final preference evaluation value.

Because of this, for example, even if only 75% of the predetermined collection amount of motion information is collected, the first preference evaluation value is 100% utilized and the final preference evaluation value can be calculated using only the first preference evaluation value.

FIG. 4(b) is a diagram illustrating a case where the operating proficiency is less than a predetermined proficiency. Referring to FIG. 4(b), the predetermined reference value (X10) of the amount of motion information collected may be half of the predetermined collection amount.

However, it is not limited to this, and the predetermined reference value can be modified in various ways at a level that is obvious to those skilled in the art.

When the amount of motion information collected is less than the predetermined reference value (X10), the first preference evaluation value and the second preference evaluation value are adjusted according to the first ratio (B11) to calculate the final preference evaluation value, but the amount of motion information collected If it is greater than this predetermined standard value, the first preference evaluation value and the second preference evaluation value can be adjusted according to the third ratio (B13) to calculate the final preference evaluation value.

Because of this, for example, even if 100% of the motion information is collected of the predetermined collection amount, the first preference evaluation value is used 75% and the second preference evaluation value is utilized 25%, so that the final preference evaluation value can be calculated. .

Figure 5 is an example of a video image recommending the placement of a virtual machine among the cloud resource recommendation method according to an embodiment of the present invention.

Referring to Figure 5, the recommendation module recommends an optimal arrangement and a recommended arrangement that reflects the manager's preferences, and the interface module can display the recommended information to the manager.

The optimal arrangement is plan 1, plan 2, and plan 3. When there are multiple objective functions, three solutions can be calculated by minimizing each objective function.

Here, the preferred evaluation value may be 40% for stability and 60% for efficiency, the batch evaluation value for plan 2 may be 41% for stability and 59% for efficiency, and the batch evaluation value for plan 3 may be 45% for stability. , the efficiency may be 55%, and with the batch evaluation value of the first plan, the stability may be 35% and the efficiency may be 65%.

The recommendation module can recommend plan 2, which has the placement evaluation value closest to the preference evaluation value, as first priority. If the distance between the preference evaluation value and the placement evaluation value is the same, it places greater emphasis on stability and gives plan 3 second priority. You can recommend it, and you can recommend plan 1, which is the next order, as 3rd priority.

Figure 6 is an example showing the arrangement of virtual machines according to preference evaluation values among the cloud resource recommendation method according to an embodiment of the present invention.

The interface module changes the administrator's preference evaluation value, and provides an interface to re-recommend the optimal arrangement in the order of having the placement evaluation value similar to the preference evaluation value according to a predetermined recommendation method as the preference evaluation value changes. can do.

The interface module can change the preference evaluation value within a minimum or maximum range, and can calculate an interface that recommends the placement of a virtual machine on a physical server by reflecting the changed preference evaluation value.

Figure 6 is a diagram showing an image screen where the calculated preference evaluation value can be changed within the minimum and maximum ranges.

The administrator can change the efficiency and stability of the preference evaluation value within the range of 0 to 100 (T11), and the recommendation module ranks recommendations among the optimal arrangements in real time according to a predetermined recommendation method based on the changed preference evaluation value. Recommendation information that re-recommends can be recalculated.

Because of this, if questions arise about the initial preference evaluation value, the administrator can change it and receive recommendations for optimal virtual machine placement.

Figure 7 is a flowchart of a server operation method according to an embodiment of the present invention.

Referring to FIG. 7, the monitoring module can monitor the virtual machine in real time to determine whether the virtual machine and/or the physical machine are operating abnormally.

If the monitoring module determines that the physical machine and/or virtual machine operates abnormally, the migration module may determine whether migration is necessary to solve the problem.

For example, unusual behavior could be an under/overload issue with the virtual machine.

However, it is not limited to this, and the abnormal operation can be modified in various ways at a level that is obvious to a person skilled in the art.

If the migration module determines that migration is necessary, the migration module can adopt a physical machine to which the virtual machine will be migrated to solve the problem and calculate the expected arrangement of the virtual machine.

The migration module can transmit the batch of virtual machines to be changed to the cloud operation evaluation device and request that the batch evaluation values in terms of stability and efficiency be calculated.

The placement evaluation value may be calculated by comparing the placement of the current virtual machine with the placement of the future virtual machine to be migrated.

The migration module ultimately determines whether to migrate based on the batch evaluation value, and if the batch evaluation value satisfies a predetermined evaluation standard, the migration module can proceed with the migration.

For example, the predetermined evaluation standard may be a condition of being above a predetermined standard (20%) such as stability or efficiency.

However, the evaluation criteria are not limited to this and can be modified in various ways at a level that is obvious to those skilled in the art.

A cloud resource evaluation method according to an embodiment of the present invention is a cloud resource evaluation method for calculating a deployment evaluation value, which is an evaluation of the deployment of a virtual machine operating on a physical server, by utilizing a cloud operation evaluation device. A step of collecting operation information, which is information generated when a virtual machine is operated, by a module; A step of calculating an efficiency evaluation value, which is a value for the efficiency of the virtual machine arrangement, based on the operation information by a first calculation module; A step of calculating, by a second calculation module, a stability evaluation value, which is a value for the stability of the deployment of the virtual machine, based on the operation information, and calculating the stability evaluation value by using the efficiency evaluation value and the stability evaluation value by the deployment evaluation module. It may include calculating an evaluation value.

When requested by the cloud resource recommendation device and server management device, the cloud operation evaluation device can evaluate the placement of virtual machines in terms of stability and efficiency according to the situation and transmit the placement evaluation value.

To this end, information necessary for evaluation may be received from the server management device and/or the cloud resource recommendation device.

A cloud operation evaluation device according to an embodiment of the present invention is a cloud operation evaluation device for calculating a placement evaluation value, which is an evaluation of the placement of a virtual machine operated on a physical server, and includes information generated while the virtual machine is running. A receiving module that collects operational information; A first calculation module that calculates an efficiency evaluation value, which is a value for the efficiency of virtual machine placement, based on the operation information; a second calculation module that calculates a stability evaluation value, which is a value for the stability of the virtual machine arrangement, based on the operation information; and a batch evaluation module that calculates the batch evaluation value using the efficiency evaluation value and the stability evaluation value.

Additionally, the first calculation module may calculate the efficiency evaluation value by calculating the integrated power amount based on the CPU operation rate of the physical server.

Additionally, the first calculation module may calculate the integrated power amount by adding the operating power amount, which is the amount of power generated as the virtual machine operates, and the moving power amount, which is the amount of power generated when the virtual machine is migrated.

In addition, the first calculation module may calculate the operating power amount by adding the basic power amount, which is the amount of power generated when the CPU is in an idle state, and the additional power amount generated when the virtual machine is operated and the CPU is operated.

Additionally, the first calculation module may approximate the basic power amount by dividing the amount of power generated when the CPU is fully operated by a predetermined value.

Additionally, the first calculation module may calculate the amount of additional power based on the ratio of the operating frequency of the CPU to the difference between the frequency when the CPU is fully operated and the frequency when the CPU is in an idle state.

Additionally, the first calculation module can calculate the amount of moving power by approximating it as a predetermined ratio to the amount of power generated when the CPU is fully operated.

In addition, the first calculation module calculates the ratio of the frequency when the CPU is fully operated to a predetermined ratio to the difference between the frequency when the CPU is fully operated and the frequency when the CPU is idle. As a standard, the amount of moving power can be calculated.

Additionally, the second calculation module may calculate the stability evaluation value based on the dirty memory rate and the network link rate.

A cloud resource evaluation method according to an embodiment of the present invention is a cloud resource evaluation method for calculating a deployment evaluation value, which is an evaluation of the deployment of a virtual machine operating on a physical server, by utilizing a cloud operation evaluation device. A step of collecting operating information, which is information generated when a virtual machine is operated, by a module; Calculating, by a first calculation module, an efficiency evaluation value, which is a value for the efficiency of virtual machine placement, based on the operation information; Calculating, by a second calculation module, a stability evaluation value, which is a value for the stability of the virtual machine arrangement, based on the operation information; and calculating, by a batch evaluation module, the batch evaluation value using the efficiency evaluation value and the stability evaluation value.

In the attached drawings, in order to more clearly express the technical idea of the present invention, components that are unrelated or less relevant to the technical idea of the present invention are briefly expressed or omitted.

In the above, the configuration and features of the present invention have been described based on the embodiments according to the present invention, but the present invention is not limited thereto, and various changes or modifications may be made within the spirit and scope of the present invention. It is obvious to those skilled in the art, and therefore, it is stated that such changes or modifications fall within the scope of the appended patent claims.

100: Server operating system 110: Cloud resource recommendation device
120: Server management device 130: Cloud operation evaluation device

Claims (11)

In the cloud operation evaluation device for calculating a placement evaluation value, which is an evaluation of the placement of virtual machines operating on a physical server,
A receiving module that collects operational information, which is information generated as the virtual machine operates;
a second calculation module that calculates a stability evaluation value, which is a value for the stability of the virtual machine arrangement, based on the operation information; and
It includes a batch evaluation module that calculates the batch evaluation value using the stability evaluation value,
The second calculation module is,
Comparing the memory of the virtual machine, the dirty memory rate of the virtual machine, and the network link rate to calculate the migration time to calculate the stability evaluation value,
Cloud operation evaluation device.
In the cloud operation evaluation device for calculating a placement evaluation value, which is an evaluation of the placement of virtual machines operating on a physical server,
A receiving module that collects operational information, which is information generated as the virtual machine operates;
A first calculation module that calculates an efficiency evaluation value, which is a value for the efficiency of virtual machine placement, based on the operation information; and
It includes a batch evaluation module that calculates the batch evaluation value using the efficiency evaluation value,
The first calculation module is,
Using the CPU operation rate of the physical server, which is the operation information, the efficiency evaluation value is calculated by calculating the integrated power amount based on the operation power amount, which is the amount of power generated as the virtual machine operates,
The operating power amount is calculated by adding the basic power amount, which is the amount of power generated when the CPU is in an idle state, and the additional power amount generated when the virtual machine is running and the CPU is running,
Calculating the basic power amount by dividing the amount of power generated when the CPU is fully operated by a predetermined value,
Cloud operation evaluation device.
According to paragraph 2,
The first calculation module is,
The basic power is calculated as half of the power generated when the CPU is fully operational.
Cloud operation evaluation device.
In the cloud operation evaluation device for calculating a placement evaluation value, which is an evaluation of the placement of virtual machines operating on a physical server,
A receiving module that collects operational information, which is information generated as the virtual machine operates;
A first calculation module that calculates an efficiency evaluation value, which is a value for the efficiency of virtual machine placement, based on the operation information; and
It includes a batch evaluation module that calculates the batch evaluation value using the efficiency evaluation value,
The first calculation module is,
Using the CPU operation rate of the physical server, which is the operation information, the efficiency evaluation value is calculated by calculating the integrated power amount based on the operation power amount, which is the amount of power generated as the virtual machine operates,
The operating power amount is calculated by adding the basic power amount, which is the amount of power generated when the CPU is idle, and the additional power amount generated when the virtual machine is running and the CPU is running,
Calculating the amount of additional power based on the ratio of the operating frequency of the CPU to the difference between the frequency when the CPU is fully operated and the frequency when the CPU is idle,
Cloud operation evaluation device.
According to paragraph 4,
The operating frequency of the CPU is,
Calculated as the difference between the average frequency of all CPU operation rates and the CPU frequency that occurs when the physical machine is turned on,
Cloud operation evaluation device.
According to clause 5,
The first calculation module is,
For the difference between the frequency when the CPU is fully operated and the frequency when the CPU is idle, the additional power amount is calculated by multiplying the ratio of the operating frequency of the CPU by the amount of power additionally generated when the CPU is fully operated,
Cloud operation evaluation device.
According to clause 5,
The amount of additional power generated when the CPU is fully operated is,
The CPU utilization rate is determined as half of the amount of power generated by the physical server when the CPU operation rate is full.
Cloud operation evaluation device.
In the cloud operation evaluation device for calculating a placement evaluation value, which is an evaluation of the placement of virtual machines operating on a physical server,
A receiving module that collects operational information, which is information generated as the virtual machine operates;
A first calculation module that calculates an efficiency evaluation value, which is a value for the efficiency of virtual machine placement, based on the operation information; and
It includes a batch evaluation module that calculates the batch evaluation value using the efficiency evaluation value,
The first calculation module is,
Calculating the efficiency evaluation value by calculating the integrated power amount based on the mobile power amount, which is the amount of power generated when the virtual machine is migrated, using the operation information,
Cloud operation evaluation device.
According to clause 8,
The first calculation module is,
Calculated by approximating the amount of mobile power as a predetermined ratio to the amount of power generated when the CPU is fully operational.
Cloud operation evaluation device.
According to clause 9,
The first calculation module is,
Calculating the amount of moving power based on the ratio of the frequency when the CPU is fully operated to the difference between the frequency when the CPU is fully operated and the frequency when the CPU is idle, calculated as a predetermined ratio,
Cloud operation evaluation device.
In a cloud resource evaluation method for calculating a placement evaluation value, which is an evaluation of the placement of a virtual machine operating on a physical server, using a cloud operation evaluation device,
A step of collecting operating information, which is information generated when a virtual machine is operated, by a receiving module;
Calculating, by a first calculation module, an efficiency evaluation value, which is a value for the efficiency of virtual machine placement, based on the operation information;
Comprising: using the efficiency evaluation value to calculate the batch evaluation value by a batch evaluation module,
The efficiency evaluation value is,
By the first calculation module, the operating information is used to calculate the integrated power amount calculated based on the mobile power amount, which is the amount of power generated when the virtual machine is migrated.
How to evaluate cloud resources.

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