JP7360036B2 - Information processing device, information processing system, information processing method and program - Google Patents

Description

The present invention relates to an information processing device, an information processing system, an information processing method, and a program.

There is a computer virtualization technology in which a virtual machine, which is a virtual computer, is placed and executed on a physical machine, which is a physical computer. According to computer virtualization technology, it is possible to dynamically add, delete, and move virtual machines, making it easier to manage information processing operations. A physical machine on which a virtual machine is placed may be one owned by a large-scale information processing facility such as a data center, or a so-called cloud system.

Multiple virtual machines located on different physical machines can execute application programs in a distributed manner, and these multiple virtual machines can work together to provide information processing services in response to user requests. . It is also possible to divide and place multiple virtual machines in different data centers or different cloud systems. In operational management of an information processing service using a plurality of virtual machines, it is preferable to collect operating information indicating the usage status of resources such as processors and memory from each virtual machine. Based on the collected operating information, you can consider adding new virtual machines, deleting some virtual machines, increasing resources allocated to virtual machines, changing the cloud system where virtual machines are placed, etc.

For example, a virtual machine management device that collects measurement data showing the resource usage rate of the entire physical machine and the resource usage rate of each virtual machine, and moves some virtual machines to other physical machines when a resource shortage is detected. is proposed. Furthermore, a virtual machine control device has been proposed that determines whether or not to move two virtual machines in parallel when moving two virtual machines from one physical machine to another. When the proposed virtual machine control device starts moving one virtual machine, it monitors the processing status of two virtual machines and calculates a correlation coefficient, and if the correlation coefficient is positive, it moves the other virtual machine. will also start moving.

International Publication No. 2008/062864 JP2012-88808A

From the viewpoint of accuracy of the collected operating information, it is preferable to collect operating information of a plurality of virtual machines at short intervals. However, there is a problem in that communicating with all the virtual machines used in the information processing service in short cycles increases the amount of communication when collecting operating information. For example, when at least some of a plurality of virtual machines are located in a data center or a cloud system, they communicate in short cycles via a wide area data communication network such as the Internet, and the communication load becomes a problem.

In one aspect, the present invention aims to provide an information processing device, an information processing system, an information processing method, and a program that can reduce the amount of communication when collecting operating information.

In one aspect, an information processing device is provided that includes a communication section, a storage section, and a processing section. The communication unit receives first operating information from the first virtual machine indicating the resource usage status in the first virtual machine, and receives first operating information indicating the resource usage status in the second virtual machine from the second virtual machine. 2 operation information is received. The storage unit stores a history of the received first operating information and second operating information. The processing unit determines the correlation of the resource usage status between the first virtual machine and the second virtual machine based on the history, and when the correlation satisfies a predetermined condition, the processing unit determines the correlation of the resource usage status between the first virtual machine and the second virtual machine, and when the correlation satisfies a predetermined condition, the processing unit determines the correlation of the resource usage status between the first virtual machine and the second virtual machine. The reception frequency is controlled to be lower than the reception frequency of the first operating information. The processing unit estimates the resource usage status of the second virtual machine based on the first operating information and the correlation received from the first virtual machine.

Further, in one aspect, an information processing system includes a first information processing device that executes a first virtual machine, a second information processing device that executes a second virtual machine, and a third information processing device. provided. Further, in one aspect, an information processing method executed by a computer is provided. Further, in one aspect, a program to be executed by a computer is provided.

One aspect is that the amount of communication when collecting operating information can be reduced.

FIG. 1 is a diagram illustrating an example of an information processing system according to a first embodiment. FIG. 3 is a diagram illustrating an example of an information processing system according to a second embodiment. FIG. 2 is a block diagram showing an example of hardware of an information collection server. FIG. 2 is a diagram illustrating an example of correspondence between services, virtual machines, and systems. FIG. 2 is a sequence diagram showing an example of communication between an information collection server and a virtual machine. FIG. 3 is a diagram illustrating an example of grouping virtual machines. FIG. 3 is a diagram (continued 1) illustrating an example of grouping virtual machines. FIG. 3 is a diagram (continued 2) illustrating an example of grouping virtual machines. FIG. 2 is a block diagram showing an example of functions of an information collection server and a virtual machine. It is a figure which shows the example of a service structure table and an operation history table. It is a figure which shows the example of a correlation table and an operation status table. FIG. 3 is a diagram showing an example of a group management table. 3 is a flowchart illustrating an example of a procedure for server start processing. 3 is a flowchart illustrating an example of a procedure for group determination. 3 is a flowchart illustrating an example of a procedure for virtual machine start processing. 3 is a flowchart illustrating an example of a procedure for virtual machine continuation processing. 3 is a flowchart illustrating an example of a procedure for server continuation processing.

The present embodiment will be described below with reference to the drawings.
[First embodiment]
A first embodiment will be described.

FIG. 1 is a diagram illustrating an example of an information processing system according to the first embodiment.
The information processing system according to the first embodiment arranges a plurality of virtual machines in different information processing apparatuses and monitors the resource usage status of each of the plurality of virtual machines. The information processing system of the first embodiment includes information processing devices 10, 20, and 20a.

The information processing device 10 is a computer that monitors virtual machines by collecting operating information indicating the usage status of resources in virtual machines. The information processing device 10 may be a client computer or a server computer.

The information processing devices 20 and 20a are server computers capable of executing virtual machines. The information processing devices 20 and 20a can communicate with the information processing device 10 via a network. The information processing devices 20 and 20a may be computers in a data center or computers in a public cloud system owned by a cloud provider. Furthermore, the information processing devices 20 and 20a may be computers in a private cloud system or on-premises system owned by a service provider that provides services to users using virtual machines. The information processing device 20 and the information processing device 20a may belong to different systems. The information processing devices 20, 20a and the information processing device 10 may communicate via a wide area data communication network such as the Internet.

A virtual machine 21 (first virtual machine) is arranged in the information processing device 20. A virtual machine 22 (second virtual machine) is arranged in the information processing device 20a. The information processing device 10 monitors the resource usage status of each of the virtual machines 21 and 22. For example, the information processing device 10 presents the resource usage status of the virtual machines 21 and 22 to the service administrator by displaying it on a display device or the like. By monitoring resource usage, you can consider configuration changes such as adding new virtual machines, deleting some virtual machines, increasing the resources allocated to virtual machines, and changing the system in which virtual machines are placed.

Resources are hardware resources allocated to virtual machines and used for information processing. Resource usage includes the usage rate of processors such as CPU (Central Processing Unit), memory usage such as RAM (Random Access Memory), access amount of storage such as HDD (Hard Disk Drive), network bandwidth usage rate, etc. can be mentioned.

The virtual machine 21 can transmit performance information 23 (first performance information) indicating the usage status of resources in the virtual machine 21 . The virtual machine 22 can transmit performance information 24 (second performance information) indicating the usage status of resources in the virtual machine 22 . The virtual machines 21 and 22 may transmit operating information in response to a request from the information processing device 10, or may autonomously transmit operating information at a frequency specified directly or indirectly by the information processing device 10. You may. Reducing the transmission frequency, which will be described later, also includes stopping the transmission of operating information. Note that the virtual machines 21 and 22 may be virtual machines in a narrow sense that each executes a guest operating system (OS), or may be a virtual information processing entity that does not execute a guest OS, such as a container in container-type computer virtualization technology. .

The information processing device 10 includes a communication section 11, a storage section 12, and a processing section 13. The communication unit 11 is a communication interface that communicates with the information processing devices 20 and 20a via a network. The communication unit 11 may be a wired communication interface connected to a wired communication device such as a switch or a router, or a wireless communication interface connected to a wireless communication device such as an access point or a wireless base station. The storage unit 12 may be a volatile semiconductor memory such as a RAM, or a nonvolatile storage such as an HDD or flash memory.

The processing unit 13 is, for example, a processor such as a CPU, a GPU (Graphics Processing Unit), or a DSP (Digital Signal Processor). However, the processing unit 13 may include a specific purpose electronic circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The processor executes a program stored in a memory such as a RAM (which may also be the storage unit 12). A collection of multiple processors is sometimes referred to as a "multiprocessor" or simply "processor."

The communication unit 11 receives operating information 23 from the virtual machine 21 and receives operating information 24 from the virtual machine 22. The storage unit 12 stores a history of the performance information 23 received from the virtual machine 21 and the performance information 24 received from the virtual machine 22. The history of the operating information 23 and 24 indicates the resource usage status at each of a plurality of different times. Until sufficient history is accumulated in the storage unit 12, for example, the communication unit 11 receives operation information from each of the virtual machines 21 and 22 at a low predetermined frequency (for example, every minute). The processing unit 13 may cause the virtual machines 21 and 22 to periodically transmit the operating information by notifying the virtual machines 21 and 22 of the initial value of the transmission frequency. Further, the communication unit 11 may collect the operating information by requesting the operating information from the virtual machines 21 and 22 at a predetermined frequency.

The processing unit 13 determines the correlation 14 of the resource usage status between the virtual machine 21 and the virtual machine 22 based on the history stored in the storage unit 12. When the operating information 23 and 24 indicate the usage status of two or more types of resources, the processing unit 13 may determine the correlation 14 for each type of resource. For example, the processing unit 13 associates the operating information 23 and the operating information 24 that are close in time from the history stored in the storage unit 12, and based on the plurality of pairs, the processing unit 13 associates the operating information 23 and the operating information 24 with the numerical value included in the operating information 23 and the operating information 24, based on the plurality of pairs. A correlation coefficient between numerical values included in the information 24 is calculated. Further, for example, the processing unit 13 calculates the average magnification of the numerical value included in the operating information 24 with respect to the numerical value included in the operating information 23, based on the operating information 23 and 24 for the most recent fixed period.

When the correlation 14 satisfies the predetermined condition, the processing unit 13 controls the reception frequency of the operation information 24 of the virtual machine 22 to be lower than the reception frequency of the operation information 23 of the virtual machine 21. The predetermined condition is, for example, that the absolute value of the correlation coefficient is greater than a threshold (eg, 0.8). At this time, the processing unit 13 may lower the reception frequency of the operating information 24 than the initial value before determining the correlation 14, or may stop receiving the operating information 24 from the virtual machine 22. Further, the processing unit 13 may set the reception frequency of the operating information 23 to be higher than the initial value before determining the correlation 14, such as by changing the reception frequency of the operating information 23 to half the one minute interval, that is, 30 seconds.

In controlling the reception frequency, the processing unit 13 may instruct the virtual machine 22 to reduce the frequency of transmission of the operating information 24 through the virtual machine 21 by notifying the virtual machine 21 of information regarding the virtual machine 22. . Furthermore, the processing unit 13 may directly instruct the virtual machine 22 to reduce the frequency of transmission of the operating information 24. The instruction given directly or indirectly to the virtual machine 22 may be an instruction to stop transmission. Further, the frequency with which the communication unit 11 requests the operating information 24 from the virtual machine 22 may be reduced.

The virtual machine whose reception frequency is lowered when the correlation 14 satisfies a predetermined condition may be a virtual machine located on the downstream side of the data flow among the virtual machines 21 and 22. For example, the processing unit 13 detects the order of message communication between the plurality of virtual machines by a method such as the communication unit 11 transmitting a test request and collecting communication logs of the virtual machines 21 and 22. When the virtual machine 21 is the sender of a message and the virtual machine 22 is the sender of the message, the processing unit 13 calculates the reception frequency of the virtual machine 22 located on the downstream side of the data flow among the virtual machines 21 and 22. It is possible to lower it.

When the reception frequency of the operation information 24 becomes lower than the reception frequency of the operation information 23, the operation information 23 indicating the resource usage status of the virtual machine 21 is collected, while the operation information 24 indicating the resource usage status of the virtual machine 22 is collected. There will be periods when data is not collected. Therefore, the processing unit 13 estimates the resource usage status of the virtual machine 22 for the period in which the operating information 24 is not collected. Specifically, the processing unit 13 estimates the resource usage status of the virtual machine 22 based on the operation information 23 and the correlation 14 received from the virtual machine 21 . For example, the processing unit 13 estimates that the numerical value included in the operating information 23 is multiplied by the magnification factor calculated when determining the correlation 14 as the numerical value indicating the resource usage status of the virtual machine 22. .

The processing unit 13 may present a mixture of the numerical values included in the operating information 23 and the estimated numerical values to the service manager. For example, the processing unit 13 displays the numerical value included in the latest operation information 23 regarding the resource usage status of the virtual machine 21, and estimates the resource usage status of the virtual machine 22 based on the correlation 14. Display numbers.

According to the information processing apparatus 10 of the first embodiment, the history of the performance information 23 received from the virtual machine 21 and the performance information 24 received from the virtual machine 22 is accumulated. Based on the history, the correlation 14 of the resource usage status between the virtual machine 21 and the virtual machine 22 is determined, and if the correlation 14 satisfies a predetermined condition, the reception frequency of the operating information 24 of the virtual machine 22 is determined. It gets lower. Then, based on the performance information 23 and the correlation 14 received from the virtual machine 21, the resource usage status of the virtual machine 22 is estimated.

As a result, the frequency of collecting operating information from some virtual machines becomes lower, and the amount of communication can be reduced compared to the case where operating information is frequently collected from all virtual machines. In particular, when multiple virtual machines are located in different systems (eg, different cloud systems), traffic on a wide area data communication network such as the Internet can be reduced. Further, based on the correlation with the performance information of virtual machines that are collected frequently, the performance information of virtual machines that are collected less frequently (including virtual machines for which collection has been stopped) is estimated. Therefore, compared to the case where the collection frequency of all virtual machines is uniformly lowered, it is possible to improve the monitoring accuracy of monitoring the resource usage status.

Note that in the information processing system of the first embodiment, estimation processing is performed to estimate the resource usage status of the virtual machine 22, and adding the estimation processing may slightly increase the load on the information processing system. be. On the other hand, by adopting the estimation process, the monitoring process of transmitting the operating information 24 from the virtual machine 22 to the information processing device 10 and processing the operating information 24 is reduced, and the reduction in the monitoring process greatly reduces the load on the information processing system. . Therefore, the effect of reducing the load on the monitoring process is greater than increasing the load on the estimation process, and the overall load on the information processing system is reduced.

In addition, distributed management is adopted in which the virtual machine 22 is subordinated to the virtual machine 21, and the monitoring of the virtual machine 22 is entrusted to the virtual machine 21, such as by sending various instructions to the virtual machine 22 through the virtual machine 21. You can also do that. Thereby, communication between the information processing device 10 and the virtual machine 22 can be reduced. For example, if the virtual machines 21 and 22 are placed in the same cloud system and the information processing device 10 is located outside the cloud system, communication in the wide area data communication network can be reduced.

[Second embodiment]
Next, a second embodiment will be described.
FIG. 2 is a diagram illustrating an example of an information processing system according to the second embodiment.

The information processing system of the second embodiment is a multi-cloud system in which a plurality of virtual machines used for service provision are distributed and arranged in different systems. Multi-cloud systems in a broad sense include multi-cloud systems in a narrow sense and hybrid cloud systems. A multi-cloud system in a narrow sense is one in which multiple virtual machines are distributed and arranged in two or more public cloud systems. A hybrid cloud system is one in which multiple virtual machines are distributed and placed in a public cloud system and an in-house system (on-premises system). By using multiple systems, the risk of service outages can be reduced, and system-specific functions can be utilized.

The information processing system of the second embodiment includes cloud systems 31 and 32, an in-house system 33, and an information collection server 100. Cloud systems 31 and 32, in-house system 33, and information collection server 100 are connected to network 30. Network 30 is a wide area data communications network such as the Internet.

The cloud system 31 is a public cloud system owned by a certain cloud provider and in which a service provider can deploy virtual machines according to a contract with the cloud provider. The cloud system 31 includes a plurality of servers such as servers 200 and 200a, which are physical machines that execute virtual machines.

The cloud system 32 is a public cloud system owned by a cloud provider different from the cloud system 31, and in which a service provider can deploy virtual machines according to a contract with the cloud provider. The cloud system 32 includes a plurality of servers such as servers 200b and 200c, which are physical machines that execute virtual machines.

The in-house system 33 is an on-premises system owned by a service provider that provides services using virtual machines. The in-house system 33 includes multiple servers such as servers 200d and 200e that execute virtual machines. The service provider distributes and arranges a plurality of virtual machines in two or more of the cloud systems 31 and 32 and the in-house system 33. The plurality of virtual machines cooperate to process information in response to a request from a user, and send a response to the user. For example, one virtual machine receives a request, multiple virtual machines perform distributed data processing while transferring the message in a tree structure, and the one virtual machine aggregates the final results and transmits a response. The placement of these multiple virtual machines is determined by the service administrator.

The information collection server 100 is a server computer that monitors resource usage in a plurality of virtual machines used for services. The information collection server 100 is located outside the cloud systems 31 and 32. The information collection server 100 may be owned by a service provider or may be included in its own system 33.

The information collection server 100 periodically receives operation information indicating resource usage status from at least some of the plurality of virtual machines via the network 30. The resource usage status is the usage status of hardware resources allocated to the virtual machine. Examples of resource usage include CPU usage, RAM usage, access to storage such as HDD, and network bandwidth usage. The information collection server 100 reports the usage status of resources in a plurality of virtual machines to a service administrator. For example, the information collection server 100 displays a screen listing the resource usage status of multiple virtual machines.

The resource usage status of multiple virtual machines is referenced for service operation management. For example, configuration changes such as adding new virtual machines, deleting some virtual machines, increasing resources allocated to virtual machines, and changing the system in which virtual machines are placed are considered. Note that the server 200 corresponds to the information processing device 20 of the first embodiment. The server 200b corresponds to the information processing device 20a of the first embodiment. The information collection server 100 corresponds to the information processing device 10 of the first embodiment.

FIG. 3 is a block diagram showing an example of hardware of the information collection server.
The information collection server 100 includes a CPU 101, a RAM 102, an HDD 103, an image interface 104, an input interface 105, a media reader 106, and a communication interface 107. These units included in the information collection server 100 are connected to a bus. The CPU 101 corresponds to the processing unit 13 of the first embodiment. RAM 102 or HDD 103 corresponds to storage unit 12 in the first embodiment. The communication interface 107 corresponds to the communication unit 11 of the first embodiment. The servers 200, 200a, 200b, 200c, 200d, and 200e also have the same hardware as the information collection server 100.

The CPU 101 is a processor that executes program instructions. The CPU 101 loads at least part of the program and data stored in the HDD 103 into the RAM 102, and executes the program. The CPU 101 may include multiple processor cores, and the information collection server 100 may include multiple processors. A collection of multiple processors is sometimes referred to as a "multiprocessor" or simply "processor."

The RAM 102 is a volatile semiconductor memory that temporarily stores programs executed by the CPU 101 and data used by the CPU 101 for calculations. The information collection server 100 may include a type of memory other than RAM, or may include a plurality of memories.

The HDD 103 is a nonvolatile storage that stores software programs such as an OS (Operating System), middleware, and application software, and data. The information collection server 100 may include other types of storage such as flash memory and SSD (Solid State Drive), or may include multiple storages.

The image interface 104 outputs an image to the display device 111 connected to the information collection server 100 according to instructions from the CPU 101. As the display device 111, any type of display device can be used, such as a CRT (Cathode Ray Tube) display, a Liquid Crystal Display (LCD), an Organic Electro-Luminescence (OEL) display, or a projector. . An output device other than the display device 111, such as a printer, may be connected to the information collection server 100.

The input interface 105 receives input signals from the input device 112 connected to the information collection server 100. Any type of input device can be used as the input device 112, such as a mouse, touch panel, touch pad, keyboard, etc. A plurality of types of input devices may be connected to the information collection server 100.

The medium reader 106 is a reading device that reads programs and data recorded on the recording medium 113. As the recording medium 113, any type of recording medium can be used, such as a magnetic disk such as a flexible disk (FD) or HDD, an optical disk such as a compact disc (CD) or a digital versatile disc (DVD), or a semiconductor memory. I can do it. For example, the media reader 106 copies programs and data read from the recording medium 113 to other recording media such as the RAM 102 and the HDD 103. The read program is executed by the CPU 101, for example. Note that the recording medium 113 may be a portable recording medium, and may be used for distributing programs and data. Further, the recording medium 113 and the HDD 103 are sometimes referred to as computer-readable recording media.

The communication interface 107 is connected to the network 30 and communicates with the servers 200, 200a, 200b, 200c, 200d, and 200e via the network 30. The communication interface 107 is, for example, a wired communication interface connected to a wired communication device such as a switch or a router. However, the communication interface 107 may be a wireless communication interface connected to a wireless communication device such as a base station or an access point.

Next, the arrangement and connection relationships of multiple virtual machines will be explained.
FIG. 4 is a diagram showing an example of correspondence between services, virtual machines, and systems.
One example of a service operated in the second embodiment includes processes 41, 42, 43, 44, 45, and 46. The topology of communication between processes 41, 42, 43, 44, 45, and 46 is a tree structure. Process 41 receives a request from a user. Process 41 sends messages to processes 42 and 43, respectively, requesting data processing from processes 42 and 43. After receiving the message from process 41, process 43 sends a message to process 44 to request data processing from process 44. After process 44 receives the message from process 43, process 44 sends a message to process 45 to request data processing from process 45. After receiving the message from process 41, process 42 sends a message to process 46 requesting process 46 to process the data.

Process 45 performs the requested data processing. The process 44 receives the data processing result from the process 45 as a response to the sent message, and performs its own data processing. The process 43 receives the data processing result from the process 44 as a response to the transmitted message, and performs its own data processing. Process 46 performs the requested data processing. Process 42 receives data processing results from process 46 in response to the sent message and performs its own data processing. Process 41 receives data processing results from processes 42 and 43 as a response to the transmitted message. Finally, process 41 performs its own data processing and sends the response to the user.

Process 41 is executed in virtual machine 51. Process 42 is executed in virtual machine 52. Process 43 is executed in virtual machine 53. Process 44 is executed in virtual machine 54 . Process 45 is executed in virtual machine 55. Process 46 is executed in virtual machine 56. The processing procedures of the processes 41, 42, 43, 44, 45, and 46 are defined by the application program. The virtual machines 51, 52, 53, 54, 55, and 56 of the second embodiment are virtual machines in a narrow sense that execute a guest OS and application programs. However, the virtual machines 51, 52, 53, 54, 55, and 56 may be used as containers using container-type computer virtualization technology, and the virtual machines 51, 52, 53, 54, 55, and 56 may not be running a guest OS. It's okay.

Virtual machines 51, 53, 54, and 55 are placed in the cloud system 31. In the cloud system 31, the virtual machines 51, 53, 54, and 55 may be placed on different servers, or two or more of the virtual machines 51, 53, 54, and 55 may be placed on the same server. It's okay. The virtual machines 52 and 56 are placed in the cloud system 32. In the cloud system 32, the virtual machines 52 and 56 may be placed on different servers or may be placed on the same server. In this example, virtual machines 51, 52, 53, 54, 55, and 56 are divided and arranged in two public cloud systems. However, it is also possible to arrange some of the virtual machines 51, 52, 53, 54, 55, and 56 in the company's system 33. Note that the virtual machine 51 corresponds to the virtual machine 21 of the first embodiment. The virtual machine 52 corresponds to the virtual machine 22 of the first embodiment.

Here, the information collection server 100 monitors the usage status of resources in the virtual machines 51, 52, 53, 54, 55, and 56. In order to increase the accuracy of monitoring the resource usage status and ensure the real-time nature of the monitoring information, it is preferable that the information collection server 100 receives operation information at high frequency. However, if the information collection server 100 receives operating information from all of the virtual machines 51, 52, 53, 54, 55, and 56 with high frequency, there is a problem that traffic on the network 30, which is a wide area data communication network, increases.

Therefore, the information processing system according to the second embodiment stops sending operation information from some of the virtual machines 51, 52, 53, 54, 55, and 56 to the information collection server 100. The virtual machine whose operation information is to be stopped from being sent is a virtual machine whose resource usage status has a strong correlation with other specific virtual machines. The information collection server 100 estimates the resource usage status of a virtual machine for which operating information is not collected, based on the resource usage status of the virtual machine for which operating information is collected and a pre-calculated correlation. Thereby, traffic on the network 30 can be reduced while maintaining monitoring accuracy.

FIG. 5 is a sequence diagram showing an example of communication between the information collection server and the virtual machine.
Here, communication between the virtual machines 51 and 53 as a representative among the virtual machines 51, 52, 53, 54, 55, and 56 will be explained. In the sequence example shown in FIG. 5, the virtual machine 51 continues to send operation information to the information collection server 100, and the virtual machine 53 temporarily stops sending operation information to the information collection server 100.

First, the information collection server 100 sends a test start notification to the virtual machines 51 and 53 (S100). Upon receiving the test start notification, the virtual machines 51 and 53 start monitoring communication with other virtual machines. Communication monitoring may be performed by monitoring transmitted packets at the packet level or by monitoring transmitted messages at the application level. The virtual machines 51 and 53 collect communication logs that include at least the destination and time of the sent message.

The information collection server 100 transmits a test request to the virtual machine 51, which is the entrance to the service (S101). Upon receiving the test request, the virtual machine 51 sends a test message to the virtual machine 53 to request data processing from the virtual machine 53 (S102). At this time, the virtual machine 51 detects the transmission of the test message to the virtual machine 53 and collects a communication log indicating the transmission of the test message to the virtual machine 53. When the test request processing is completed, the virtual machines 51 and 53 transmit communication logs to the information collection server 100 (S103). The virtual machines 51 and 53 autonomously transmit communication logs. However, the information collection server 100 may request communication logs from the virtual machines 51 and 53.

The information collection server 100 similarly receives communication logs from other virtual machines. The information collection server 100 determines the order of message transmission between virtual machines based on these communication logs, and determines the data flow of the service as shown in FIG. 4. Furthermore, the information collection server 100 determines a low collection frequency as the initial value of the performance information collection frequency. A low collection frequency may be, for example, every minute. The information collection server 100 may determine the initial value of the collection frequency according to the total number of virtual machines participating in the service. In that case, the smaller the total number of virtual machines, the higher the collection frequency, and the larger the total number of virtual machines, the lower the collection frequency. Before group classification, which will be described later, the information collection server 100 collects operation information at a low collection frequency, so that it is possible to suppress the communication traffic of the entire information processing system from becoming temporarily excessive.

The information collection server 100 notifies the virtual machines 51 and 53 of the initial value of the collection frequency (S104). The virtual machines 51 and 53 start acquiring resource usage status and transmitting operating information to the information gathering server 100 at a low frequency notified from the information gathering server 100 (S105). Examples of resource usage include CPU usage, RAM usage, HDD access, and network bandwidth usage. In the second embodiment, we will mainly focus on the CPU and RAM as resources.

The information collection server 100 retains the operating information received from the virtual machines 51 and 53 as a history for at least a certain period of time. When the history of operating information for a certain period of time is accumulated, the information collection server 100 classifies the plurality of virtual machines into groups based on the history of operating information and the data flow between the plurality of virtual machines. If two virtual machines have a parent-child relationship on the data flow and there is a strong correlation between the resource usage statuses of the two virtual machines, the two virtual machines are classified into the same group. If the absolute value of the correlation coefficient between the measured values included in the operating information is larger than a predetermined threshold (for example, 0.8) between two virtual machines, it is determined that there is a strong correlation. A parent-child relationship is one in which one virtual machine sends a message to another virtual machine. According to the example of FIG. 4, focusing on the virtual machines 51 and 53, the virtual machine 51 is the parent virtual machine and the virtual machine 53 is the child virtual machine.

If the resource usage status of a grandchild virtual machine that has a parent-child relationship with a child virtual machine has a strong correlation with the parent virtual machine, the grandchild virtual machine is also continuously classified into the same group as the parent virtual machine. The information collection server 100 selects the most upstream virtual machine in each group as the representative virtual machine, and treats virtual machines other than the representative virtual machine as dependent virtual machines. As will be described later, only the representative virtual machine transmits operation information to the information collection server 100, and the subordinate virtual machines do not transmit operation information to the information collection server 100. Note that the information collection server 100 may limit the virtual machines classified into the same group to virtual machines placed in the same system among the cloud systems 31 and 32 and the in-house system 33.

Virtual machines are grouped by resource type. Therefore, two virtual machines may be classified into the same group because their CPU usage rates have a strong correlation, but they may be classified into different groups because their RAM usage amounts have a low correlation. Here, it is assumed that the virtual machine 51 is a representative virtual machine and the virtual machine 53 is a dependent virtual machine. Furthermore, the information collection server 100 calculates a magnification of the resource usage status of the representative virtual machine for the dependent virtual machine. The magnification can be calculated, for example, by dividing the average measured value of the dependent virtual machine over a certain period by the average measured value of the representative virtual machine. However, the magnification may be expressed as a function using a variable representing the measured value of the representative virtual machine.

The information collection server 100 transmits a group notification to the virtual machine 51, which is the representative virtual machine (S106). The group notification includes identification information of the virtual machine 53, which is a dependent virtual machine, and a magnification of the measured value of the virtual machine 53 relative to the measured value of the virtual machine 51. Upon receiving the group notification, the virtual machine 51 transmits a stop notification to the virtual machine 53, which is a subordinate virtual machine belonging to the same group as the virtual machine 51 (S107). When the virtual machine 53 receives the stop notification, it stops sending the operating information to the information collection server 100.

Furthermore, the virtual machine 51 changes the frequency of transmitting operating information to the information collection server 100 to be higher than the initial value. The virtual machine 51 acquires the resource usage status and starts transmitting the operating information to the information collection server 100 at a high collection frequency after the change (S108).

The collection frequency after the change is determined according to the number of dependent virtual machines belonging to the same group. When the total number of virtual machines in a group is n and the number of dependent virtual machines is m, the transmission frequency of the representative virtual machine is increased to be n÷(n−m)×α times the initial value. That is, the transmission interval of the representative virtual machine is shortened to (n−m)÷n÷α times the initial value. The more dependent virtual machines that do not send operating information, the more frequently the representative virtual machine sends information. The adjustment parameter α is a positive real number smaller than 1. As a result, it is possible to increase the frequency of collection of representative virtual machines while reducing the total data amount of operating information within a group.

However, increasing the collection frequency too much does not contribute to improving monitoring accuracy and also increases the network load unnecessarily. Therefore, the collection frequency of the representative virtual machine is set not to be higher than a predetermined upper limit. The upper limit of the collection frequency is, for example, an interval of 1 second. When two virtual machines 51 and 53 form one group, for example, the collection frequency of virtual machine 51, which is the representative virtual machine, is adjusted to between 30 seconds and 1 minute.

The information collection server 100 receives operation information from the virtual machine 51, but no longer receives operation information from the virtual machine 53. Therefore, the information collection server 100 multiplies the measurement value indicated by the latest operation information received from the virtual machine 51 by a pre-calculated magnification factor for the virtual machine 53 to determine the latest resource usage status of the virtual machine 53. Estimate. Calculation of magnification and estimation of resource usage status are performed for each type of resource.

Furthermore, the virtual machine 51 monitors the resource usage status of the virtual machine 53, which is a subordinate virtual machine belonging to the same group. The virtual machine 51 requests operation information from the virtual machine 53 at a low predetermined frequency (S109). The low predetermined frequency may be the same as the initial value of the collection frequency notified in step S104, for example, at one minute intervals. The virtual machine 53 acquires the usage status of resources in the virtual machine 53 in response to a request from the virtual machine 51, and transmits operation information to the virtual machine 51 (S110).

When the virtual machine 51 receives the operation information from the virtual machine 53, it determines whether a strong correlation is maintained in the resource usage status between the virtual machine 51 and the virtual machine 53. Whether a strong correlation is maintained is determined by comparing the most recent operation information of the virtual machine 53 and the corresponding operation information of the virtual machine 51.

For example, the virtual machine 51 extracts the operating information of the virtual machine 51 closest to the reception time of the operating information from the virtual machine 53 from the history, and extracts the measured value included in the operating information of the virtual machine 51 and the operating information of the virtual machine 53. Create a pair with measurements contained in . The virtual machine 51 calculates the correlation coefficient from the plurality of pairs, and determines that a strong correlation is maintained if the absolute value of the correlation coefficient is greater than the threshold, and if the absolute value of the correlation coefficient is less than or equal to the threshold. If so, it is determined that the correlation has become low. Furthermore, when the virtual machine 51 determines that a strong correlation is maintained, it recalculates the magnification of the resource usage status of the virtual machine 53 relative to the virtual machine 51. For example, the virtual machine 51 is calculated by dividing the average of the most recent measured values of the virtual machine 53 by the average of the corresponding measured values of the virtual machine 51.

Here, it is assumed that a strong correlation is maintained between the virtual machines 51 and 53. Then, the virtual machine 51 transmits a correlation update notification including the recalculated magnification to the information collection server 100 (S111). Furthermore, the virtual machine 51 continues to acquire the resource usage status at a high collection frequency and transmits the operating information to the information collection server 100 (S112). The information collection server 100 does not continue to receive operation information from the virtual machine 53. Therefore, the information collection server 100 estimates the resource usage status of the virtual machine 53 using the latest magnification notified in step S111.

The virtual machine 51 requests operation information from the virtual machine 53 again (S113), and receives the operation information from the virtual machine 53 (S114). Here, it is assumed that the absolute value of the correlation coefficient becomes equal to or less than the threshold value, and the correlation between the virtual machines 51 and 53 is lost. Then, the virtual machine 51 transmits a restart notification to the virtual machine 53 (S115). Upon receiving the restart notification, the virtual machine 53 resumes periodically acquiring the resource usage status and transmitting it to the information collection server 100. The collection frequency of the virtual machine 53 at this time may be a low frequency, for example, the initial value notified in step S104.

The virtual machine 51 transmits a group cancellation notification to the information collection server 100 indicating that the virtual machine 53 has left the group (S116). When the information collection server 100 receives the group cancellation notification, the information collection server 100 switches to grasping the resource usage status of the virtual machine 53 that is removed from the group based on the measured value instead of the estimated value.

Further, the virtual machine 51 recalculates the frequency with which the virtual machine 51 transmits operating information to the information collection server 100 as the number of dependent virtual machines belonging to the group decreases. This reduces the collection frequency of the virtual machine 51. As a result, the virtual machines 51 and 53 acquire resource usage status and transmit operating information to the information collection server 100 at a low frequency (S117). The collection frequency reverts to, for example, one minute intervals.

In this way, there is a strong correlation in the usage status of resources such as CPU and RAM between the parent virtual machine that sends messages in a service and the child virtual machine that receives the messages and processes the data. There is. In this case, the traffic on the network 30 can be reduced by stopping the collection of operation information of the child virtual machine and estimating the resource usage status of the child virtual machine from the operation information of the parent virtual machine. Furthermore, by increasing the frequency of collection of parent virtual machines within the range in which the total traffic of the network 30 is reduced, resource usage status can be monitored in real time, and monitoring accuracy can be improved.

Note that even after stopping the collection of operation information of dependent virtual machines, the information collection server 100 periodically determines the correlation between the representative virtual machines for which operation information is being collected. If a strong correlation is newly detected between two representative virtual machines, the information collection server 100 changes the representative virtual machine on the downstream side of the data flow to a dependent virtual machine. In that case, the information collection server 100 transmits a group notification indicating that a dependent virtual machine has been added to the upstream representative virtual machine, as in step S106.

FIG. 6 is a diagram illustrating an example of grouping virtual machines.
As described above, the information collection server 100 sends a test request to the virtual machine 51. Then, according to the structure shown in FIG. 4, the virtual machine 51 transmits a test message to the virtual machines 52 and 53. Virtual machine 53 sends a test message to virtual machine 54, and virtual machine 54 sends a test message to virtual machine 55. Virtual machine 52 sends a test message to virtual machine 56. During this time, the virtual machines 51, 52, 53, 54, 55, and 56 collect communication logs including the virtual machine to which the test message is sent and the time at which the test message was sent, by monitoring packets or the like.

The virtual machines 51, 52, 53, 54, 55, and 56 transmit communication logs to the information collection server 100. The information collection server 100 determines the order of message transmission among the virtual machines 51, 52, 53, 54, 55, and 56 based on the received communication log, and grasps the data flow. In addition, the information collection server 100 collects CPU information, which is operating information indicating the CPU usage rate, and RAM usage from the virtual machines 51, 52, 53, 54, 55, and 56 at low frequency (for example, every 1 minute). RAM information, which is the operating information shown, is received.

FIG. 7 is a diagram (continued 1) illustrating an example of grouping virtual machines.
The information collection server 100 groups virtual machines 51, 52, 53, 54, 55, and 56 for each type of resource, that is, CPU and RAM.

Regarding the CPU, the information collection server 100 uses CPU information received from the virtual machines 51, 52, 53, 54, 55, and 56 at low frequency to calculate the correlation downstream from the virtual machine 51 at the beginning of the data flow. Judge in order.

First, the information collection server 100 calculates the correlation coefficient of the CPU usage rate between the virtual machine 51 and the virtual machine 53. Here, it is assumed that the absolute value of the correlation coefficient is larger than the threshold value. Then, the information collection server 100 classifies the virtual machine 53 and the virtual machine 51 into the same group. Next, the information collection server 100 follows the data flow downstream and calculates the correlation coefficient of the CPU usage rates between the virtual machines 51 and 54. Here, it is assumed that the absolute value of the correlation coefficient is larger than the threshold value. Then, the information collection server 100 classifies the virtual machine 54 into the same group as the virtual machines 51 and 53.

Next, the information collection server 100 follows the data flow downstream and calculates the correlation coefficient of the CPU usage rates between the virtual machines 51 and 55. Here, it is assumed that the absolute value of the correlation coefficient is less than or equal to the threshold value. Then, the information collection server 100 classifies the virtual machine 55 into a different group from the virtual machines 51, 53, and 54. The information collection server 100 also calculates a correlation coefficient of CPU usage rates between the virtual machines 51 and 52. Here, it is assumed that the absolute value of the correlation coefficient is less than or equal to the threshold value. Then, the information collection server 100 classifies the virtual machine 52 into a different group from the virtual machine 51. Next, the information collection server 100 follows the data flow downstream and calculates the correlation coefficient of the CPU usage rates between the virtual machines 52 and 56. Here, it is assumed that the absolute value of the correlation coefficient is larger than the threshold value. Then, the information collection server 100 classifies the virtual machine 56 and the virtual machine 52 into the same group.

As a result, for the CPU, the virtual machines 51, 52, and 55 become representative virtual machines, and the virtual machines 53, 54, and 56 become dependent virtual machines. The virtual machines 51, 52, and 55 periodically transmit CPU information to the information collection server 100. The virtual machines 53, 54, and 56 stop sending CPU information to the information collection server 100. Furthermore, as the virtual machines 53 and 54 stop transmitting CPU information, the collection frequency of the virtual machine 51 increases with an upper limit of three times the initial value (for example, a lower limit of the transmission interval of 20 seconds). Further, as the virtual machine 56 stops transmitting CPU information, the collection frequency of the virtual machine 52 increases with an upper limit of twice the initial value (for example, a lower limit of the transmission interval of 30 seconds).

Similarly, regarding RAM, the information collection server 100 uses RAM information received from the virtual machines 51, 52, 53, 54, 55, and 56 at low frequency to move downstream from the virtual machine 51 at the beginning of the data flow. Determine correlations in order.

First, the information collection server 100 calculates the correlation coefficient of RAM usage between the virtual machine 51 and the virtual machine 53. Here, it is assumed that the absolute value of the correlation coefficient is larger than the threshold value. Then, the information collection server 100 classifies the virtual machine 53 and the virtual machine 51 into the same group. Next, the information collection server 100 follows the data flow downstream and calculates the correlation coefficient of RAM usage between the virtual machines 51 and 54. Here, it is assumed that the absolute value of the correlation coefficient is less than or equal to the threshold value. Then, the information collection server 100 classifies the virtual machine 54 into a different group from the virtual machines 51 and 53.

Next, the information collection server 100 follows the data flow downstream and calculates the correlation coefficient of RAM usage between the virtual machines 54 and 55. Here, it is assumed that the absolute value of the correlation coefficient is larger than the threshold value. Then, the information collection server 100 classifies the virtual machine 55 and the virtual machine 54 into the same group. The information collection server 100 also calculates a correlation coefficient of RAM usage between the virtual machine 51 and the virtual machine 52. Here, it is assumed that the absolute value of the correlation coefficient is less than or equal to the threshold value. Then, the information collection server 100 classifies the virtual machine 52 into a different group from the virtual machine 51. Next, the information collection server 100 follows the data flow downstream and calculates the correlation coefficient of RAM usage between the virtual machines 52 and 56. Here, it is assumed that the absolute value of the correlation coefficient is less than or equal to the threshold value. Then, the information collection server 100 classifies the virtual machine 56 and the virtual machine 52 into a different group.

As a result, regarding the RAM, the virtual machines 51, 52, 54, and 56 become representative virtual machines, and the virtual machines 53 and 55 become dependent virtual machines. The virtual machines 51, 52, 54, and 56 periodically transmit RAM information to the information collection server 100. The virtual machines 53 and 55 stop sending RAM information to the information collection server 100. Furthermore, as the virtual machine 53 stops sending RAM information, the collection frequency of the virtual machine 51 increases, with the upper limit being twice the initial value. Furthermore, as the virtual machine 55 stops sending RAM information, the collection frequency of the virtual machine 54 increases, with the upper limit being twice the initial value.

FIG. 8 is a diagram (continued 2) illustrating an example of grouping virtual machines.
Here, changing the group of CPU information will be explained. Regarding RAM information, group change is performed independently of CPU information. The virtual machine 51, which is the representative virtual machine, requests CPU information from the virtual machines 53 and 54, which are subordinate virtual machines belonging to the same group, at low frequency, and receives the CPU information from the virtual machines 53 and 54. Further, the virtual machine 52, which is the representative virtual machine, requests CPU information from the virtual machine 56, which is a subordinate virtual machine belonging to the same group, at low frequency, and receives the CPU information from the virtual machine 56.

The virtual machine 51 calculates a correlation coefficient of CPU usage rates between the virtual machines 51 and 53, and calculates a correlation coefficient of CPU usage rates between the virtual machines 51 and 54. Here, it is assumed that the absolute value of any correlation coefficient is larger than the threshold value. Then, the virtual machines 53 and 54 continue to operate as subordinate virtual machines to the virtual machine 51. Furthermore, the virtual machine 52 calculates a correlation coefficient of CPU usage rates between the virtual machines 52 and 56. Here, it is assumed that the absolute value of the correlation coefficient has decreased below the threshold value. Then, the virtual machine 56 leaves the group of virtual machines 52 and operates as a representative virtual machine. Therefore, the virtual machine 56 resumes sending the CPU information to the information collection server 100. Furthermore, as the virtual machine 56 resumes transmission, the collection frequency of the virtual machine 52 decreases.

On the other hand, the information collection server 100 monitors the correlation of CPU information periodically received from the virtual machines 51, 52, and 55. The information collection server 100 selects the virtual machine 51 and the virtual machines 55 adjacent to the group according to the data flow. The information collection server 100 calculates a correlation coefficient of CPU usage rates between the virtual machines 51 and 55. Here, it is assumed that the absolute value of the correlation coefficient has become larger than the threshold value. Then, the virtual machine 55 joins the group of virtual machines 51, 53, and 54 and operates as a subordinate virtual machine. Therefore, the virtual machine 55 stops sending CPU information to the information collection server 100.

Further, the information collection server 100 selects the virtual machine 51 and the virtual machines 52 adjacent to the group according to the data flow. The information collection server 100 calculates the correlation coefficient of the CPU usage rate between the virtual machine 51 and the virtual machine 52. Here, it is assumed that the absolute value of the correlation coefficient continues to be less than or equal to the threshold value. Then, the virtual machine 52 continues to operate as a representative virtual machine and continues to send CPU information to the information collection server 100.

In this way, an existing representative virtual machine may be changed to a dependent virtual machine based on the judgment of the information collection server 100. Additionally, an existing dependent virtual machine may be changed to the representative virtual machine based on the judgment of the representative virtual machine.

Next, the functions of the information collection server 100 and each virtual machine will be explained.
FIG. 9 is a block diagram showing an example of functions of an information collection server and a virtual machine.
The information collection server 100 includes a structure information storage section 121, an operation history storage section 122, and a correlation information storage section 123. These storage units are realized using, for example, a storage area of the RAM 102 or the HDD 103. The information collection server 100 also includes a test execution section 124, an operation information reception section 125, a correlation detection section 126, a correlation update section 127, and an operation status display section 128. These processing units are realized using, for example, a program.

The structure information storage unit 121 stores service structure information representing data flows between a plurality of virtual machines used for a service. The service structure information represents a topology in which a plurality of virtual machines are connected in a tree structure. The operation history storage unit 122 stores the history of operation information received from the virtual machines 51, 52, 53, 54, 55, and 56. The operation history storage unit 122 records measured values in association with time, virtual machine, and resource pairs. The operation history storage unit 122 retains the received operation information as a history for at least a certain period of time. The correlation information storage unit 123 stores correlation information indicating a relationship between a representative virtual machine and a dependent virtual machine whose resource usage status is strongly correlated. The correlation information includes a magnification for estimating the numerical value of the dependent virtual machine from the measured value of the representative virtual machine.

The test execution unit 124 executes a test to determine the data flow of the virtual machines 51, 52, 53, 54, 55, and 56. The test execution unit 124 notifies the virtual machines 51, 52, 53, 54, 55, and 56 of the test start, and sends a test request to the virtual machine 51, which is the entrance to the service. The test execution unit 124 collects communication logs indicating the destination virtual machine and transmission time of the test message from the virtual machines 51, 52, 53, 54, 55, and 56, analyzes the communication log, and determines the data flow. do. The test execution unit 124 generates structural information indicating a data flow and stores it in the structural information storage unit 121.

The performance information receiving unit 125 receives performance information periodically transmitted by at least some of the virtual machines 51, 52, 53, 54, 55, and 56. The operating information is divided by resource type, and which resource's operating information is to be sent differs depending on the virtual machine. In the initial state, all of the virtual machines 51, 52, 53, 54, 55, and 56 transmit operating information about all resources at low frequency. The performance information receiving unit 125 overwrites the correlation information storage unit 123 with the latest measurement value included in the received performance information. Additionally, the performance information receiving unit 125 associates the measured value included in the received performance information with the resource set indicated by the identification information of the source virtual machine, the reception time, and the measured value, and stores it in the performance history storage unit 122. Append.

The correlation detection unit 126 periodically transmits operation information for each resource based on the structure information stored in the structure information storage unit 121 and the history of operation information stored in the operation history storage unit 122. Determine the correlation between virtual machines. Of the two virtual machines for which a strong correlation has been detected, the correlation detection unit 126 designates the upstream virtual machine as the representative virtual machine, and designates the downstream virtual machine as the dependent virtual machine. Further, the correlation detection unit 126 calculates the magnification of the resource usage status of the dependent virtual machine with respect to the representative virtual machine. The correlation detection unit 126 records the relationship and magnification between the representative virtual machine and the dependent virtual machine in the correlation information storage unit 123.

Specifically, the correlation detection unit 126 selects the type of resource, selects two virtual machines that are in an upstream and downstream relationship in the data flow, and calculates the most recent certain period of time regarding the resource and the two virtual machines. Operation information is extracted from the operation history storage section 122. The correlation detection unit 126 correlates the measured value of one virtual machine with the measured value of the other virtual machine whose reception time is closest, and calculates a correlation coefficient from a plurality of pairs of measured values at different timings. The correlation detection unit 126 determines that the correlation is strong if the absolute value of the correlation coefficient is greater than the threshold, and determines that the correlation is weak if the absolute value of the correlation coefficient is less than or equal to the threshold. If the correlation is strong, the correlation detection unit 126 calculates the average of the measured values of the representative virtual machine over the most recent certain period and the average of the measured values of the dependent virtual machines over the most recent certain period, and divides the latter by the former. Calculate the magnification.

The correlation update unit 127 receives a correlation update notification indicating that the magnification has been updated from the representative virtual machine. Then, the correlation update unit 127 records the updated magnification in the correlation information storage unit 123. The correlation update unit 127 also receives a group cancellation notification from the representative virtual machine indicating that a certain dependent virtual machine has left the group. Then, the correlation update unit 127 changes the correlation recorded in the correlation information storage unit 123.

The operating status display unit 128 displays a management screen listing the resource usage status of the virtual machines 51, 52, 53, 54, 55, and 56 on the display device 111 in response to a request from a user. At this time, the operating status display unit 128 may display the measured value for the virtual machine and resource pair whose measured value is recorded in the correlation information storage unit 123. On the other hand, for pairs of virtual machines and resources for which measured values are not recorded in the correlation information storage unit 123, the operating status display unit 128 calculates estimated values by multiplying the measured values of the related representative virtual machines by a multiplier, Display estimated values instead of measured values. Note that the operating status display unit 128 may continuously display the management screen without waiting for a request from the user. Further, the operating status display unit 128 may store management information listing resource usage status in a storage device such as the HDD 103, or may transmit it to another information processing device.

The virtual machine 51 has a group information storage section 221. The group information storage unit 221 is realized using, for example, a storage area of a RAM or an HDD. The virtual machine 51 also includes a service processing section 222, a communication log acquisition section 223, an operation information acquisition section 224, and a group control section 225. These processing units are realized using, for example, a program. Other virtual machines also have the same functions as the virtual machine 51.

The group information storage unit 221 stores group information for managing dependent virtual machines belonging to the same group when the virtual machine 51 is a representative virtual machine. The group information includes identification information of the subordinate virtual machine notified from the information collection server 100. The group information also includes measurement values collected from dependent virtual machines. Further, the group information includes the magnification notified from the information collection server 100 or the latest updated magnification.

The service processing unit 222 executes service data processing according to an application program. When the virtual machine 51 corresponds to a service entrance, the service processing unit 222 receives a test request from the information collection server 100. The service processing unit 222 may send messages to other virtual machines. Additionally, the service processing unit 222 may receive messages from other virtual machines.

Upon receiving the test start notification from the information collection server 100, the communication log acquisition unit 223 monitors packets transmitted by the virtual machine 51. Alternatively, the communication log acquisition unit 223 monitors transmission messages at the application level. The communication log acquisition unit 223 detects the destination virtual machine and transmission time of the message during test execution, generates a communication log indicating the destination virtual machine and the transmission time, and transmits it to the information collection server 100.

The performance information acquisition unit 224 acquires a measured value indicating the current resource usage status of the virtual machine 51. Measured values include CPU usage rate, RAM usage amount, HDD access amount, network bandwidth usage rate, and the like. When a guest OS is being executed on the virtual machine 51, the performance information acquisition unit 224 may acquire measured values from the guest OS.

When the information collection server 100 notifies the initial value of the collection frequency, the operating information acquisition unit 224 acquires the measured values of all resources at the specified collection frequency and transmits them to the information collection server 100. When the group control unit 225 decides to change the collection frequency of a certain resource, the performance information acquisition unit 224 acquires and transmits measured values at the changed collection frequency. When receiving a stop notification for a certain resource from another virtual machine, the performance information acquisition unit 224 stops acquiring and transmitting measured values. When receiving a restart notification for a certain resource from another virtual machine, the performance information acquisition unit 224 resumes acquisition and transmission of measured values. When performance information about a certain resource is requested from another virtual machine, the performance information acquisition unit 224 acquires a measured value and sends it back to the other virtual machine.

The group control unit 225 manages dependent virtual machines when the virtual machine 51 is a representative virtual machine. The group control unit 225 records the identification information and magnification of the subordinate virtual machine notified from the information collection server 100 in the group information storage unit 221. The group control unit 225 also sends a stop notification to the dependent virtual machines. Furthermore, the group control unit 225 changes the collection frequency of the virtual machines 51 according to the number of dependent virtual machines.

The group control unit 225 also periodically requests operational information from the dependent virtual machines and collects the operational information of the dependent virtual machines. The group control unit 225 determines whether a strong correlation is maintained between the operation information of the virtual machine 51 and the operation information of the subordinate virtual machines. If a strong correlation is maintained, the group control unit 225 calculates the latest magnification, updates the magnification recorded in the group information storage unit 221, and sends a correlation update notification including the latest magnification to the information collection server 100. Send to. If a strong correlation is not maintained, the group control unit 225 sends a restart notification to the subordinate virtual machine, updates the group information in the group information storage unit 221, and sends a group cancellation notification to the information collection server 100.

FIG. 10 is a diagram showing an example of a service structure table and an operation history table.
The service structure table 131 is stored in the structure information storage section 121. The service structure table 131 includes items for virtual machine ID, parent virtual machine, and child virtual machine. Identification information of a virtual machine is registered in the virtual machine ID field. In the parent virtual machine item, identification information of an upstream virtual machine that is a sender that sends a message to the virtual machine is registered. Identification information of a downstream virtual machine to which the virtual machine sends a message is registered in the child virtual machine item.

The service structure table 131 represents a tree-structured data flow between the virtual machines 51, 52, 53, 54, 55, and 56 as shown in FIG. For example, child virtual machines of the virtual machine 51 (VM1) are the virtual machine 52 (VM2) and the virtual machine 53 (VM3). The parent virtual machine of the virtual machine 52 (VM2) is the virtual machine 51 (VM1). A child virtual machine of the virtual machine 52 (VM2) is the virtual machine 56 (VM6).

The operation history table 132 is stored in the operation history storage section 122. The operation history table 132 includes items such as time, virtual machine ID, resource, and measured value. In the time item, the time when the information collection server 100 receives the operating information is registered. However, as the time, the time included in the operating information may be used, or the time when the virtual machine acquires the measured value or the time when the operating information is transmitted may be used. Identification information of the virtual machine that transmitted the operating information is registered in the virtual machine ID field. In the resource item, the type of resource indicated by the measured value is registered. Types of resources include CPU, RAM, storage, network, etc. Measured values included in the operating information are registered in the measured value item.

FIG. 11 is a diagram showing an example of a correlation table and an operating status table.
The correlation table 133 is stored in the correlation information storage unit 123. The correlation table 133 includes items of virtual machine ID, resource, measured value, dependent virtual machine, and magnification. Identification information of the representative virtual machine transmitting the operating information is registered in the virtual machine ID field. In the resource item, types of resources such as CPU and RAM are registered. In the measured value item, measured values included in the latest operating information received for the representative virtual machine and the resource are registered. In the dependent virtual machine item, identification information of a dependent virtual machine that has a strong correlation with the representative virtual machine for the resource is registered. As the magnification, the magnification of the numerical value of the dependent virtual machine with respect to the measured value of the representative virtual machine is registered.

For example, in the correlation table 133, the latest CPU measurement value of the virtual machine 51 is 50, the CPU magnification of the virtual machine 53 to the virtual machine 51 is 0.50, and the CPU magnification of the virtual machine 54 to the virtual machine 51 is 0.50. It is registered that the CPU magnification is 0.30. Further, for example, it is registered that the latest measured value of the RAM of the virtual machine 51 is 30, and that the ratio of the RAM of the virtual machine 53 to that of the virtual machine 51 is 0.40. Further, for example, it is registered that the latest CPU measurement value of the virtual machine 52 is 70, and that the CPU magnification of the virtual machine 56 with respect to the virtual machine 52 is 1.20.

The operating status table 134 is generated by the operating status display unit 128 and displayed on the display device 111. The operating status table 134 includes items of virtual machine ID, resource, and current value. Identification information of a virtual machine is registered in the virtual machine ID field. In the resource item, types of resources such as CPU and RAM are registered. In the current value field, a numerical value indicating the latest resource usage status is registered. The numerical value registered in the item of current value is the latest measured value for the representative virtual machine, and the estimated value for the dependent virtual machine. The operating status table 134 may include a note indicating that estimated values are estimated values.

For example, a measured value of “50” is displayed as the CPU usage rate of the virtual machine 51. Further, as the RAM usage amount of the virtual machine 51, the measured value “30” is displayed. On the other hand, as the CPU usage rate of the virtual machine 53, "25" is displayed, which is an estimated value calculated by multiplying the measured value of the CPU of the virtual machine 51, 50, by a magnification factor of 0.50. Further, as the RAM usage amount of the virtual machine 53, "12" is displayed, which is an estimated value calculated by multiplying the measured value of the RAM of the virtual machine 51, 30, by a magnification factor of 0.40.

FIG. 12 is a diagram showing an example of a group management table.
Group management table 231 is stored in group information storage section 221. The group management table 231 includes items such as resources, self-measured values, dependent virtual machines, other measured values, and magnification. In the resource item, types of resources such as CPU and RAM are registered. In the self-measured value item, a numerical value indicating the usage status of the resource measured by the virtual machine 51 is registered. Identification information of a dependent virtual machine belonging to the same group as the virtual machine 51 is registered in the item of dependent virtual machine. In the item of other measured values, a numerical value indicating the usage status of the resource measured by the dependent virtual machine is registered. In the magnification field, the latest average magnification of the measured value of the dependent virtual machine with respect to the measured value of the virtual machine 51 is registered.

Next, the processing procedures of the information collection server 100 and the representative virtual machine will be explained.
FIG. 13 is a flowchart illustrating an example of a procedure for server start processing.
(S10) The test execution unit 124 transmits a test request to a predetermined virtual machine. Messages are sent between multiple virtual machines in response to test requests.

(S11) The test execution unit 124 receives communication logs from each virtual machine. The communication log includes the destination virtual machine of the message and the time of transmission.
(S12) The test execution unit 124 determines the data flow between the plurality of virtual machines based on the communication log received in step S11, and generates the service structure table 131.

(S13) The performance information receiving unit 125 determines the initial value of the collection frequency for collecting performance information according to the total number of virtual machines participating in the service.
(S14) The performance information receiving unit 125 notifies each virtual machine of the collection frequency. Thereby, the performance information receiving unit 125 receives the performance information from each virtual machine at the notified frequency. The operation information receiving unit 125 stores the received operation information in the operation history table 132.

(S15) The correlation detection unit 126 groups the plurality of virtual machines for each resource based on the service structure table 131 and the operation history table 132. One virtual machine in the group becomes a representative virtual machine that transmits operating information, and other virtual machines serve as dependent virtual machines that do not transmit operating information. Details of group determination will be described later.

(S16) The correlation detection unit 126 notifies the representative virtual machine determined in step S15 of the identification information of the dependent virtual machines belonging to the same group and its magnification. Since grouping is performed for each resource, the notification is also performed for each resource.

FIG. 14 is a flowchart showing an example of a procedure for group determination.
Group determination is performed in step S15 described above and step S64, which will be described later.
(S20) The correlation detection unit 126 selects one resource. Resources to be monitored are determined in advance, such as the CPU, RAM, HDD, and network.

(S21) The correlation detection unit 126 selects a root (starting point) virtual machine in the tree-structured data flow indicated by the service structure table 131, and designates it as the representative virtual machine.
(S22) The correlation detection unit 126 determines whether there are any virtual machines whose operating information is being collected remaining in the tree-structured data flow. If the applicable virtual machine remains, the process advances to step S23; if the applicable virtual machine does not remain, the process advances to step S29.

(S23) The correlation detection unit 126 traces the data flow downstream from the currently selected virtual machine and selects the next virtual machine. If the data flow is branched, it is sufficient to follow any one branch direction. When the end of the tree structure is reached, the correlation detection unit 126 returns to the previous branch point and then traces the unselected branch direction.

(S24) The correlation detection unit 126 extracts the operation history of the resource selected in step S20 for the virtual machine selected in step S23 from the operation history table 132. Furthermore, the correlation detection unit 126 extracts the operation history of the resource selected in step S20 for the immediately preceding representative virtual machine on the upstream side of the virtual machine from the operation history table 132. The correlation detection unit 126 calculates the correlation coefficient of the measured values included in the extracted operating information between the immediately preceding representative virtual machine and the selected virtual machine.

(S25) The correlation detection unit 126 compares the absolute value of the correlation coefficient calculated in step S24 with a predetermined threshold value. If the absolute value of the correlation coefficient is greater than the threshold, the process proceeds to step S26, and if the absolute value of the correlation coefficient is less than or equal to the threshold, the process proceeds to step S28.

(S26) The correlation detection unit 126 classifies the virtual machine selected in step S23 into the same group as the immediately preceding representative virtual machine, and designates it as a dependent virtual machine.
(S27) The correlation detection unit 126 uses the operation information extracted from the operation history table 132 in step S24 to calculate the magnification of the measured value of the dependent virtual machine with respect to the measured value of the representative virtual machine. The correlation detection unit 126 registers in the correlation table 133 the fact that the representative virtual machine and the dependent virtual machine have a strong correlation and the magnification. Then, the process returns to step S22.

(S28) The correlation detection unit 126 designates the virtual machine selected in step S23 as the representative virtual machine. Then, the process returns to step S22.
(S29) The correlation detection unit 126 determines whether all resources have been selected in step S20. If all resources have been selected, the group determination ends, and if unselected resources remain, the process returns to step S20.

FIG. 15 is a flowchart illustrating an example of a procedure for virtual machine start processing.
(S30) The communication log acquisition unit 223 acquires a communication log during test execution. The communication log includes the destination and sending time of messages sent by the virtual machine 51.

(S31) The communication log acquisition unit 223 transmits the communication log to the information collection server 100.
(S32) The operation information acquisition unit 224 receives the collection frequency notification from the information collection server 100. The collection frequency notification indicates a predetermined low collection frequency as the initial value of the collection frequency.

(S33) The operation information acquisition unit 224 starts acquiring operation information of all resources and transmitting it to the information collection server 100 at the low collection frequency notified in step S32.
(S34) The group control unit 225 determines whether a group notification has been received from the information collection server 100. The group notification includes identification information of dependent virtual machines belonging to the same group and their magnification. If a group notification has been received, the process proceeds to step S35, and if a group notification has not been received, the process proceeds to step S38.

(S35) The group control unit 225 calculates the collection target ratio of virtual machines in the group based on the group notification. If the total number of virtual machines belonging to a group is n, and the number of dependent virtual machines whose operating information is not collected is m, then the collection target ratio is (n−m)÷n.

(S36) The group control unit 225 increases the collection frequency of the operating information acquisition unit 224 from the initial value notified in step S32 according to the collection target ratio calculated in step S35. The smaller the collection target ratio, the higher the collection frequency. The collection frequency after the change is the initial value multiplied by n÷(n−m)×α. Therefore, the transmission interval after the change is shortened to the initial transmission interval multiplied by (n−m)÷n÷α. However, if the collection frequency calculated using this calculation formula exceeds a predetermined upper limit, the collection frequency is determined to be the upper limit.

(S37) The group control unit 225 sends a stop notification to the dependent virtual machine.
(S38) The operation information acquisition unit 224 determines whether a stop notification has been received from the representative virtual machine. If the stop notification is received, the process advances to step S39, and if the stop notification is not received, the virtual machine start process ends. Note that classification of representative virtual machines and dependent virtual machines is performed for each resource. Therefore, the virtual machine 51 may be designated as a representative virtual machine for a certain resource, and designated as a dependent virtual machine for another resource. In that case, the virtual machine 51 may receive a group notification from the information collection server 100 for a certain resource, and receive a stop notification from the representative virtual machine for another resource.

(S39) The performance information acquisition unit 224 stops acquiring and transmitting the performance information.
FIG. 16 is a flowchart illustrating an example of a procedure for virtual machine continuation processing.
(S40) The group control unit 225 determines whether the virtual machine 51 is a representative virtual machine. If the virtual machine is the representative virtual machine, the process advances to step S41; if it is not the representative virtual machine, the process advances to step S50. Note that whether or not a virtual machine is a representative virtual machine is determined for each resource.

(S41) The group control unit 225 refers to the group management table 231 and identifies subordinate virtual machines that belong to the same group as the virtual machine 51. The group control unit 225 requests operational information from the identified dependent virtual machine and receives the operational information.

(S42) The group control unit 225 associates and records the measurement value included in the operation information of the dependent virtual machine received in step S41 and the latest measurement value of the virtual machine 51 acquired by the operation information acquisition unit 224. do. The group control unit 225 calculates the correlation coefficient between the measured value of the virtual machine 51 and the measured value of the dependent virtual machine based on the records of the most recent certain period.

(S43) The group control unit 225 compares the absolute value of the correlation coefficient calculated in step S42 with a predetermined threshold value. If the absolute value of the correlation coefficient is greater than the threshold, the process proceeds to step S44, and if the absolute value of the correlation coefficient is less than or equal to the threshold, the process proceeds to step S46.

(S44) The group control unit 225 calculates the magnification of the measured value of the dependent virtual machine with respect to the measured value of the virtual machine 51, based on the records of the most recent certain period. The group control unit 225 updates the magnification recorded in the group management table 231.

(S45) The group control unit 225 transmits a correlation update notification including the magnification updated in step S44 to the information collection server 100. Then, the process advances to step S50.
(S46) The group control unit 225 sends a restart notification to the dependent virtual machine.

(S47) The group control unit 225 updates the collection target ratio of the virtual machines in the group as one dependent virtual machine has left the group. If the total number of virtual machines belonging to the group at the most recent point in time is n, and the number of dependent virtual machines whose operating information is not collected at the most recent point in time is m, the collection target ratio will increase to (n-m) ÷ (n-1). do.

(S48) The group control unit 225 lowers the collection frequency of the operating information acquisition unit 224 from the previous one according to the collection target ratio calculated in step S47.
(S49) The group control unit 225 transmits to the information collection server 100 a group cancellation notification including the identification information of the virtual machine that has left the group.

(S50) The operation information acquisition unit 224 determines whether a restart notification has been received from the representative virtual machine. If a restart notification is received, the process advances to step S51, and if a restart notification is not received, the virtual machine continuation process ends. Note that the virtual machine 51 may be designated as a representative virtual machine for a certain resource, and designated as a dependent virtual machine for another resource. Therefore, while the virtual machine 51 monitors the dependent virtual machine for a certain resource, it may receive a restart notification from the representative virtual machine for another resource.

(S51) The operating information acquisition unit 224 resumes acquiring and transmitting operating information. The collection frequency after restarting is, for example, the initial value of the low frequency notified in step S32.
FIG. 17 is a flowchart illustrating an example of a procedure for server continuation processing.

(S60) The correlation update unit 127 receives correlation update notifications from each representative virtual machine. The correlation update notification includes the identification information of the dependent virtual machine and the updated magnification.
(S61) The correlation update unit 127 updates the magnification of the correlation table 133.

(S62) The correlation update unit 127 determines whether a group cancellation notification has been received from any representative virtual machine. If the group cancellation notification has been received, the process proceeds to step S63, and if the group cancellation notification has not been received, the process proceeds to step S64.

(S63) The correlation update unit 127 deletes the correlation between the representative virtual machine that sent the group cancellation notification and the notified subordinate virtual machine from the correlation table 133.
(S64) The correlation detection unit 126 re-determines whether any of the current representative virtual machines can be grouped, according to the flowchart shown in FIG.

(S65) The correlation detection unit 126 determines whether the number of dependent virtual machines has increased by changing the representative virtual machine to a dependent virtual machine through the re-determination in step S63. If the number of dependent virtual machines has increased, the process advances to step S66; if the number has not increased, the process advances to step S67.

(S66) The correlation detection unit 126 updates the correlation table 133. Further, the correlation detection unit 126 transmits a group notification including the identification information of the new dependent virtual machine and its magnification to the representative virtual machine of the group to which the new dependent virtual machine belongs.

(S67) The operating status display unit 128 determines whether a request for operating status has been received from the user. If the request for the operating status has been accepted, the process advances to step S68, and if the request for the operating status has not been accepted, the server continuation process ends.

(S68) The operating status display unit 128 estimates a numerical value indicating the operating status of the dependent virtual machine by multiplying the measured value of the representative virtual machine registered in the correlation table 133 by a magnification.
(S69) The operating status display unit 128 generates an operating status table 134 listing numerical values of the operating status of each virtual machine, and displays it on the display device 111. The numerical values in the operating status table 134 include the collected measured values and the estimated values calculated in step S68.

According to the information processing system of the second embodiment, the collection of operation information from some virtual machines is stopped, and the collection of operation information from the remaining virtual machines is continued to the extent that the overall amount of data communication is reduced. The frequency will be increased. For virtual machines for which operating information is not collected, the resource usage status is estimated based on the correlation with virtual machines for which operating information is collected. This makes it possible to ensure real-time monitoring of virtual machines and improve monitoring accuracy while reducing network traffic. Particularly in a multi-cloud environment, traffic on wide area data communication networks such as the Internet can be reduced. Therefore, it is possible to reduce the cost of monitoring virtual machines and suppress delays in monitoring.

The test request is also used to determine the data flow between the plurality of virtual machines within the service, and the correlation between the parent virtual machine and the child virtual machine along the data flow. Since messages are sent from the parent virtual machine to the child virtual machine in the service, there is likely to be a strong correlation between the load on the parent virtual machine and the load on the child virtual machine. Therefore, correlations among multiple virtual machines can be efficiently determined. Further, changes in the correlation between the representative virtual machine and the dependent virtual machines are monitored in a distributed manner by the representative virtual machine. Therefore, traffic on the wide area data communication network can be reduced.

10, 20, 20a Information processing device 11 Communication unit 12 Storage unit 13 Processing unit 14 Correlation 21, 22 Virtual machine 23, 24 Operation information

Claims (12)

receiving first operating information indicating resource usage status in the first virtual machine from a first virtual machine; and receiving second operating information indicating resource usage status in the second virtual machine from a second virtual machine; a communications department that receives operating information;
a storage unit that stores a history of the received first operating information and second operating information;
Based on the history, determine the correlation of the resource usage status between the first virtual machine and the second virtual machine, and if the correlation satisfies a predetermined condition, the second operating information instructing the second virtual machine through the first virtual machine to reduce the frequency of sending the second operating information so that the frequency of receiving the second operating information is lower than the frequency of receiving the first operating information, and a processing unit that estimates resource usage status in the second virtual machine based on the first operating information received from the first virtual machine and the correlation;
An information processing device having: If the correlation satisfies the predetermined condition, the transmission of the second operating information from the second virtual machine to the information processing device is controlled to be stopped;
The information processing device according to claim 1. receiving first operating information indicating resource usage status in the first virtual machine from a first virtual machine; and receiving second operating information indicating resource usage status in the second virtual machine from a second virtual machine; a communications department that receives operating information;
a storage unit that stores a history of the received first operating information and second operating information;
Based on the history, determine the correlation of the resource usage status between the first virtual machine and the second virtual machine, and if the correlation satisfies a predetermined condition, the second operating information control so that the reception frequency of the first operating information is lower than the receiving frequency of the first operating information, and based on the first operating information received from the first virtual machine and the correlation a processing unit that estimates resource usage in the virtual machine;
has
Before determining the correlation, the processing unit receives the first performance information and the first performance information at a predetermined frequency that is lower than the reception frequency of the first performance information when the correlation satisfies the predetermined condition. an information processing device that controls the operation information of No. 2 to be received ; receiving first operating information indicating resource usage status in the first virtual machine from a first virtual machine; and receiving second operating information indicating resource usage status in the second virtual machine from a second virtual machine; a communications department that receives operating information;
a storage unit that stores a history of the received first operating information and second operating information;
Based on the history, determine the correlation of the resource usage status between the first virtual machine and the second virtual machine, and if the correlation satisfies a predetermined condition, the second operating information is lower than the reception frequency of the first operation information, the reception frequency of the second operation information is lower than before determining the correlation, and the reception frequency of the first operation information is lower than the reception frequency of the first operation information. Resource usage in the second virtual machine is controlled so that the correlation is higher than before the determination , and based on the first operating information received from the first virtual machine and the correlation. a processing unit that estimates the situation;
An information processing device having: Among the plurality of virtual machines including the first virtual machine and the second virtual machine, the reception frequency of operation information of some virtual machines including the second virtual machine is higher than before determining the correlation. also becomes lower, and the reception frequency of operation information of other virtual machines including the first virtual machine becomes higher than before determining the correlation,
The reception frequency after the first operating information is changed is determined based on the ratio of the number of the other virtual machines to the number of the plurality of virtual machines.
The information processing device according to claim 4 . receiving first operating information indicating resource usage status in the first virtual machine from a first virtual machine; and receiving second operating information indicating resource usage status in the second virtual machine from a second virtual machine; a communications department that receives operating information;
a storage unit that stores a history of the received first operating information and second operating information;
Based on the history, determine the correlation of the resource usage status between the first virtual machine and the second virtual machine, and if the correlation satisfies a predetermined condition, the second operating information control the reception frequency of the first operating information to be lower than the receiving frequency of the first operating information, update the receiving frequency of the second operating information according to the change in the correlation, and update the receiving frequency of the second operating information from the first virtual machine. a processing unit that estimates a resource usage status in the second virtual machine based on the received first operating information and the correlation;
An information processing device having: The change in the correlation is monitored by the first virtual machine, and the first virtual machine notifies the information processing device.
The information processing device according to claim 6 . receiving first operating information indicating resource usage status in the first virtual machine from a first virtual machine; and receiving second operating information indicating resource usage status in the second virtual machine from a second virtual machine; a communications department that receives operating information;
a storage unit that stores a history of the received first operating information and second operating information;
The order of message communication between a plurality of virtual machines including the first virtual machine and the second virtual machine is detected by sending a test request, and the message transmission source virtual machine is detected by the first virtual machine. a virtual machine to which the message is to be sent as the second virtual machine; and based on the history, the resource allocation between the first virtual machine and the second virtual machine is determined. Determine the correlation of the usage status, and if the correlation satisfies a predetermined condition, control the reception frequency of the second operation information to be lower than the reception frequency of the first operation information, and a processing unit that estimates resource usage status in the second virtual machine based on the first operating information and the correlation received from the virtual machine;
An information processing device having: receiving first operating information indicating resource usage status in the first virtual machine from a first virtual machine; and receiving second operating information indicating resource usage status in the second virtual machine from a second virtual machine; a communications department that receives operating information;
a storage unit that stores a history of the received first operating information and second operating information;
From the first operation information and the second operation information within a predetermined time included in the history, a plurality of pieces of information are obtained in which the first operation information and the second operation information having corresponding reception times are associated with each other. pairs are identified, and based on the plurality of pairs , the first operating information and the second virtual machine are determined as a correlation of resource usage between the first virtual machine and the second virtual machine. Calculate a correlation coefficient with the operating information, and if the absolute value of the correlation coefficient is larger than a threshold , the frequency of receiving the second operating information is lower than the frequency of receiving the first operating information. a processing unit that controls and estimates the resource usage status of the second virtual machine based on the first operating information received from the first virtual machine and the correlation;
An information processing device having: a first information processing device that executes a first virtual machine;
a second information processing device that executes a second virtual machine;
a third information processing device that communicates with the first virtual machine and the second virtual machine;
The third information processing device has:
receiving first operation information indicating the resource usage status in the first virtual machine from the first virtual machine, and receiving first operation information indicating the resource usage status in the second virtual machine from the second virtual machine; Receive the operation information of 2,
retaining a history of the received first operating information and second operating information;
Based on the history, determine the correlation of the resource usage status between the first virtual machine and the second virtual machine, and if the correlation satisfies a predetermined condition, the second operating information instructing the second virtual machine through the first virtual machine to reduce the frequency of transmission of the second operating information so that the frequency of receiving the second operating information is lower than the frequency of receiving the first operating information;
estimating resource usage status in the second virtual machine based on the first operating information received from the first virtual machine and the correlation;
Information processing system. The computer is
first operating information indicating the resource usage status in the first virtual machine received from the first virtual machine; and first operation information indicating the resource usage status in the second virtual machine received from the second virtual machine. obtain the history with the second operating information shown;
Based on the history, determine the correlation of the resource usage status between the first virtual machine and the second virtual machine, and if the correlation satisfies a predetermined condition, the second operating information instructing the second virtual machine through the first virtual machine to reduce the frequency of transmission of the second operating information so that the frequency of receiving the second operating information is lower than the frequency of receiving the first operating information;
estimating resource usage status in the second virtual machine based on the first operating information received from the first virtual machine and the correlation;
Information processing method. to the computer,
first operating information indicating the resource usage status in the first virtual machine received from the first virtual machine; and first operation information indicating the resource usage status in the second virtual machine received from the second virtual machine. obtain the history with the second operating information shown;
Based on the history, determine the correlation of the resource usage status between the first virtual machine and the second virtual machine, and if the correlation satisfies a predetermined condition, the second operating information instructing the second virtual machine through the first virtual machine to reduce the frequency of transmission of the second operating information so that the frequency of receiving the second operating information is lower than the frequency of receiving the first operating information;
estimating resource usage status in the second virtual machine based on the first operating information received from the first virtual machine and the correlation;
A program that executes processing.

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