JP7302439B2 - System analysis method and system analysis program - Google Patents

Description

The present invention relates to a system analysis method and a system analysis program.

2. Description of the Related Art Conventionally, there are cases where it is desired to analyze how much resource of a device is used in each of a plurality of business processing executed using a device. For example, when simulating how much device resources will be used in each business process in accordance with the future business volume expected by the user, the It is desirable to analyze how much it was used.

As a prior art, for example, there is a technique that calculates a solution to an optimization problem that minimizes the sum total of physical resource amounts of all physical machines to which virtual machines are allocated as an objective function, and derives an arrangement configuration. Also, for example, a technology that encodes the load fluctuation direction of a plurality of tasks, calculates a sign product, performs a chi-square test on the sign product, and determines whether or not there is a correlation in the load fluctuation direction. There is

JP 2012-159928 A JP 2014-78160 A

However, with the conventional technology, it is difficult to analyze the resource load of the device for each business process. For example, when multiple business processes are executed using one device, the amount of component corresponding to each business process is unified within the resource load amount of one device, and each business process unit , it is not possible to analyze the resource load of the equipment. In addition, even if a plurality of component amounts corresponding to the number of business processes are specified and presented to the analyst from the equipment resource load amount, which component amount specifically corresponds to which business process It is difficult for the analyst to present to the analyst whether the amount of the component should correspond to which business process specifically.

In one aspect, an object of the present invention is to make it easier to determine to which one of a plurality of business processes the component amount of the resource load corresponds.

According to one embodiment, for any one of a plurality of business processes, time-series data of component amounts related to the one business process among load amounts of resources used for the plurality of business processes. and time-series data of component amounts related to any of the business processes among the processing amounts of the functions used in the plurality of business processes, and any of the acquired load amounts of the resources display the time-series data of the amount of components related to the one of the acquired processing amounts of the function, in association with the time-series data of the amount of components related to the business processing of (1) so that the function can be identified. A system analysis method and system analysis program are proposed.

According to one aspect, it is possible to easily determine which of the plurality of business processes the component amount of the resource load corresponds to.

FIG. 1 is an explanatory diagram of an example of a system analysis method according to an embodiment. FIG. 2 is an explanatory diagram showing an example of the business processing system 200. As shown in FIG. FIG. 3 is a block diagram showing a hardware configuration example of the information processing apparatus 100. As shown in FIG. FIG. 4 is an explanatory diagram showing an example of the structure of the operation table 400. As shown in FIG. FIG. 5 is an explanatory diagram showing an example of the structure of the normalized operation table 500. As shown in FIG. FIG. 6 is an explanatory diagram showing an example of the data structure of the vector table 600. As shown in FIG. FIG. 7 is an explanatory diagram showing an example of the data structure of the component table 700. As shown in FIG. FIG. 8 is an explanatory diagram showing an example of the data structure of the separation result table 800. As shown in FIG. FIG. 9 is an explanatory diagram showing an example of the data structure of the workload table 900. As shown in FIG. FIG. 10 is an explanatory diagram showing an example of the data structure of the prediction table 1000. As shown in FIG. FIG. 11 is an explanatory diagram showing an example of the data structure of the overall prediction table 1100. As shown in FIG. FIG. 12 is a block diagram showing a functional configuration example of the information processing apparatus 100. As shown in FIG. FIG. 13 is an explanatory diagram showing the operation flow of the information processing apparatus 100. As shown in FIG. FIG. 14 is an explanatory diagram (Part 1) showing an example of the operation of the information processing apparatus 100 . FIG. 15 is an explanatory diagram (Part 2) showing an example of the operation of the information processing apparatus 100 . FIG. 16 is an explanatory diagram (part 3) showing an example of the operation of the information processing apparatus 100. As shown in FIG. FIG. 17 is an explanatory diagram (part 4) showing an example of the operation of the information processing apparatus 100. As shown in FIG. FIG. 18 is an explanatory diagram (No. 5) showing an example of the operation of the information processing apparatus 100 . FIG. 19 is an explanatory diagram (No. 6) showing an example of the operation of the information processing apparatus 100 . FIG. 20 is an explanatory diagram (No. 7) showing an example of the operation of the information processing apparatus 100 . FIG. 21 is an explanatory diagram (No. 8) showing an example of the operation of the information processing apparatus 100. As shown in FIG. FIG. 22 is an explanatory diagram (No. 9) showing an example of the operation of the information processing apparatus 100 . FIG. 23 is an explanatory diagram (No. 10) showing an example of the operation of the information processing apparatus 100. As shown in FIG. FIG. 24 is a flow chart showing an example of the overall processing procedure.

Embodiments of a system analysis method and a system analysis program according to the present invention will be described in detail below with reference to the drawings.

(One Example of System Analysis Method According to Embodiment)
FIG. 1 is an explanatory diagram of an example of a system analysis method according to an embodiment. The information processing apparatus 100 makes it easy for the user to determine to which one of a plurality of business processes the information related to the load amount of the resource of each of the one or more devices included in the business processing system corresponds. It is a computer that can

The business processing system is, for example, an ICT (Information and Communication Technology) system. The equipment is, for example, ICT equipment. A device is, for example, a server. A resource is, for example, a resource that forms an infrastructure. Resources are, for example, CPU, memory, and communication bandwidth. A business process is, for example, a series of processes realized by using one or more functions in one or more devices.

Conventionally, an analyst sometimes thinks of simulating the amount of load on a resource when multiple business processes are executed in an ICT system.

For example, an analyst may think of executing a simulation in order to estimate the number of devices that will be required when the amount of business processing increases in the future. Also, the analyst may, for example, think of executing a simulation in order to identify a resource that will become a bottleneck in improving the performance of business processing when the business volume of business processing increases in the future.

In addition, for example, when there is a sudden increase in the amount of work, the analyst may think of simulating the load amount of the resource if the situation is such that actual machine verification is difficult. In addition, analysts should conduct resource load simulations in order to analyze possible patterns of resource load changes over time, in addition to expected patterns of resource load change over time during operation. Sometimes I think.

For this reason, it is desirable to specify how much resource is used in each business process as a criterion for simulation.

Here, if it is possible to measure the resource load amount when each business process is executed independently and to specify how much resource is used in each business process, a simulation can be performed.

However, it is difficult to measure the actual load amount of the resource when each business process is executed independently and to specify how much resource is used in each business process.

For example, in actual business processing systems, there is a tendency for multiple business processes to be executed simultaneously, and there may be cases where multiple business processes are executed using a single device. Specifically, one device may execute a process of receiving queries to a database from multiple business processes. In this case, since the load amount corresponding to each business process is integrated in the resource load amount of one device, it is not possible to measure the resource load amount when each business process is executed independently. .

On the other hand, it is possible to use the nature of the business process to specify the amount of components corresponding to the number of business processes in the resource load amount. This technique divides the entire load into a plurality of component amounts based on the time-series occurrence tendency of the load. However, even with this method, it is difficult to determine which of the divided component amounts specifically corresponds to what kind of business process.

Note that, for example, the following two properties are conceivable as properties of business processing. The usage status of each resource is determined based on the first property that the workload of business processing shows periodic changes at regular time intervals such as days, weeks, and months, and the workload of each business processing. The second property is that

Therefore, in the present embodiment, a system analysis method will be described that makes it easier for an analyst to determine to which business process a predetermined component amount of the resource load specifically corresponds. . As a result, the system analysis method can facilitate the simulation of the resource loads during the execution of a plurality of business processes.

In FIG. 1, the business processing system includes one or more resources, has one or more functions, and realizes multiple business processing. One or more resources are a collection of resources used for at least one of a plurality of business processes. One or more functions is a collection of functions used for at least one of a plurality of business processes. In the example of FIG. 1, the business processing system includes resources 1 and 2 and has functions 1 and 2. FIG.

(1-1) The information processing apparatus 100 divides the load amount of each resource into a plurality of component amounts for any business process, and the time-series data of each component amount, and the processing amount of each function. Time-series data of component amounts related to the business process are acquired.

The time-series data of the component amount related to any business process among the load amount of the resource is data indicating the time change of the component amount. The time-series data of the amount of components involved in any business process out of the amount of processing of functions is data that indicates the time change of the amount of components.

In the example of FIG. 1, the information processing apparatus 100 includes time-series data 101 and 102 of component amounts of load amounts of respective resources related to specific business processing, and respective functions related to the specific business processing. time-series data 103 and 104 of the amount of components in the amount of processing of . Here, the information processing apparatus 100 recognizes that the time-series data 101 to 104 relate to the same business process, but may not specifically recognize which business process they relate to.

(1-2) The information processing apparatus 100 associates any task processing with the time-series data of the amount of components related to the task processing in the load amount of each resource, and sets the task determination information of the task processing. display. The task identification information of task processing is display information for displaying time-series data of component amounts related to the task processing out of the processing amount of each function so that each function can be identified, and is included in the task. 4 is a graph showing the throughput of multiple functions performed;

In the example of FIG. 1, the information processing apparatus 100 associates time-series data 101 and 102 of component amounts corresponding to the specific task processing among the load amounts of the respective resources, and The time-series data 103 and 104 of the amount of components corresponding to the specific task processing are displayed so that their functions can be identified. Here, the correspondence is to display side by side in one line.

For example, the analyst can understand the combination of functions included in the task and the processing amount of each function from the displayed task determination information, so that the analyst can easily grasp the contents of the extracted task processing. For example, if it is found that function 1 is a login function and function 2 is a vacation entry function, it is known that the work including these functions is vacation application work. Therefore, by displaying the resource component amount and the function processing amount component amount side by side as described above, the analyst can grasp how each resource is used in which business process. be able to. As a result, the information processing apparatus 100 can facilitate the simulation of the resource loads during the execution of a plurality of business processes based on the results determined by the analyst.

(Example of business processing system 200)
Next, an example of a business processing system 200 to which the information processing apparatus 100 shown in FIG. 1 is applied will be described with reference to FIG.

FIG. 2 is an explanatory diagram showing an example of the business processing system 200. As shown in FIG. In FIG. 2 , the business processing system 200 includes an information processing device 100 and one or more business processing devices 201 .

In the business processing system 200 , the information processing device 100 and the business processing device 201 are connected via a wired or wireless network 210 . The network 210 is, for example, a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, or the like.

The information processing device 100 is a computer used by an analyst. The information processing device 100, for example, communicates with the business processing device 201 and acquires overall operation data. More specifically, the information processing apparatus 100 collects the resource load amount and function processing amount stored in the business processing apparatus 201 at a predetermined timing, and calculates the collected resource load amount and function processing amount. Generate the summarized overall operation data. The overall operating data is stored, for example, using an operating table 400, which will be described later with reference to FIG.

For example, the information processing apparatus 100 normalizes the resource load amount and the function processing amount of the overall operation data, and generates the overall normalized operation data summarizing the resource load amount and the function processing amount after normalization. to generate The entire normalized operation data is stored, for example, using a normalized operation table 500, which will be described later with reference to FIG.

The information processing apparatus 100, for example, divides the entire normalized operation data at regular time intervals. The fixed time interval is, for example, one day. Specifically, the information processing apparatus 100 divides the entire normalized operation data for each day. The information processing apparatus 100, for example, vectorizes the daily operating data divided for each day to generate a vector for each day. A vector for each day is stored, for example, using a vector table 600 described later in FIG.

The information processing apparatus 100 generates, for example, an operation data matrix that includes columns of vectors for each day. The information processing apparatus 100 performs non-negative matrix factorization on the generated operation data matrix based on the number of bases, for example, to generate a base matrix and a weight matrix. The base matrix is stored, for example, using a component table 700, which will be described later with reference to FIG.

The information processing apparatus 100 separates, for example, one or more component quantities in the unit of task processing included in the base vector corresponding to the unit of task processing, included in the base matrix, into each resource and each function. The separation result is stored using, for example, a separation result table 800, which will be described later with reference to FIG. As a result, the information processing apparatus 100 can generate time-series data in which the component amounts separated for each resource are arranged, and time-series data in which the component amounts separated for each function are arranged.

The information processing apparatus 100 presents the separated result to the analyst, for example. Specifically, the information processing apparatus 100 associates the time-series data in which the amount of components separated for each resource is arranged, and the time-series data in which the amounts of components separated for each function are arranged. It is presented to the analyst by displaying it in an identifiable manner.

The information processing apparatus 100 receives, for example, the setting of the workload of each task processing from the analyst. The set workload is stored using, for example, a workload table 900 described later with reference to FIG.

For example, the information processing apparatus 100 calculates the resource load amount for each task process and the overall resource load amount based on the set task amount. The resource load amount for each business process is stored using, for example, a prediction table 1000, which will be described later with reference to FIG. The overall resource load amount is stored, for example, using an overall prediction table 1100, which will be described later with reference to FIG. The information processing apparatus 100 is, for example, a server or a PC (Personal Computer).

The business processing device 201 is a computer for realizing business processing. The business processing device 201 has, for example, one or more functions, and periodically measures and stores the processing amount of each function. A function is implemented by, for example, a process. Further, the business processing device 201 has, for example, one or more resources, and periodically measures and stores the load amount of each resource. The business processing device 201 is, for example, a server, a PC, a tablet terminal, a smart phone, a wearable terminal, or a household electrical appliance.

Here, the case where the job processing system 200 includes one information processing device 100 has been described, but the present invention is not limited to this. For example, the business processing system 200 may include a plurality of information processing apparatuses 100 . A plurality of information processing apparatuses 100 may cooperate to implement processing.

Moreover, although the case where the information processing apparatus 100 is a device different from the business processing device 201 has been described here, the present invention is not limited to this. For example, the information processing device 100 may be integrated with one of the business processing devices 201 .

(Hardware Configuration Example of Information Processing Device 100)
Next, a hardware configuration example of the information processing apparatus 100 will be described with reference to FIG.

FIG. 3 is a block diagram showing a hardware configuration example of the information processing apparatus 100. As shown in FIG. In FIG. 3 , the information processing apparatus 100 has a CPU (Central Processing Unit) 301 , a memory 302 , a network I/F (Interface) 303 , a recording medium I/F 304 and a recording medium 305 . Also, each component is connected by a bus 300 .

Here, the CPU 301 controls the entire information processing apparatus 100 . The memory 302 has, for example, a ROM (Read Only Memory), a RAM (Random Access Memory), a flash ROM, and the like. Specifically, for example, a flash ROM or ROM stores various programs, and a RAM is used as a work area for the CPU 301 . A program stored in the memory 302 is loaded into the CPU 301 to cause the CPU 301 to execute coded processing.

Network I/F 303 is connected to network 210 through a communication line, and is connected to other computers via network 210 . A network I/F 303 serves as an internal interface with the network 210 and controls input/output of data from other computers. Network I/F 303 is, for example, a modem or a LAN adapter.

A recording medium I/F 304 controls reading/writing of data from/to the recording medium 305 under the control of the CPU 301 . The recording medium I/F 304 is, for example, a disk drive, SSD (Solid State Drive), USB (Universal Serial Bus) port, or the like. A recording medium 305 is a nonvolatile memory that stores data written under control of the recording medium I/F 304 . The recording medium 305 is, for example, a disk, a semiconductor memory, a USB memory, or the like. The recording medium 305 may be removable from the information processing apparatus 100 .

The information processing apparatus 100 may have, for example, a keyboard, a mouse, a display, a printer, a scanner, a microphone, a speaker, etc., in addition to the components described above. Further, the information processing apparatus 100 may have a plurality of recording medium I/Fs 304 and recording media 305 . Further, the information processing apparatus 100 may not have the recording medium I/F 304 and the recording medium 305 .

(Structure of operation table 400)
Next, an example of the structure of the operation table 400 will be described using FIG. The operation table 400 is implemented, for example, by a storage area such as the memory 302 or recording medium 305 of the information processing apparatus 100 shown in FIG.

FIG. 4 is an explanatory diagram showing an example of the structure of the operation table 400. As shown in FIG. As shown in FIG. 4, the operation table 400 has fields of date and time, one or more resource loads, and one or more function processing amounts. By setting information in each field of the operation table 400, history data is stored as a record 400-a. a is any integer.

A combination of the date and time when the resource load amount and the processing amount of the function were measured in the business processing device 201 is set in the date and time field. A resource load is set in the resource load field. The processing amount of the function is set in the function processing amount field.

The resource load field includes, for example, a CPU usage rate [%] field and a disk IO (Input/Output) [IOPS (Input/Output Per Second)] field. The CPU usage rate [%], which is the load amount of the CPU 301 in the business processing device 201, is set in the CPU usage rate [%] field. A disk IO [IOPS], which is the amount of load on the recording medium 305 in the business processing device 201, is set in the disk IO [IOPS] field.

If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a CPU usage rate [%] field. If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a disk IO [IOPS] field.

The functional processing amount field includes, for example, a field for the number of screen A accesses and a field for the number of screen B accesses. In the A screen access count field, the number of times the business processing device 201 has displayed the A screen used for at least one business process is set. In the B screen access count field, the number of times the business processing device 201 has displayed the B screen used for at least one business process is set.

If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a field for the number of screen A accesses. If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a B screen access count field.

(Structure of normalized operation table 500)
Next, an example of the structure of the normalized operation table 500 will be described using FIG. The normalized operation table 500 is realized, for example, by a storage area such as the memory 302 or the recording medium 305 of the information processing apparatus 100 shown in FIG.

FIG. 5 is an explanatory diagram showing an example of the structure of the normalized operation table 500. As shown in FIG. As shown in FIG. 5, the normalized operation table 500 has fields of date and time, one or more resource normalized load amounts, and one or more function normalized processing amounts. The normalized operation table 500 stores normalized history data as a record 500-a by setting information in each field. a is any integer.

A combination of the date and time when the resource load amount and the processing amount of the function were measured in the business processing device 201 is set in the date and time field. A normalized load amount obtained by normalizing the measured load amount of the resource is set in the resource normalized load amount field. A normalized processing amount obtained by normalizing the measured processing amount of the function is set in the function normalized processing amount field.

The resource normalized load amount field includes, for example, a normalized CPU usage rate [%] field and a normalized disk IO [IOPS] field. A value obtained by normalizing the CPU usage rate [%], which is the load amount of the CPU 301 in the business processing device 201, is set in the normalized CPU usage rate [%] field. In the normalized disk IO [IOPS] field, a value obtained by normalizing the disk IO [IOPS], which is the load amount of the recording medium 305 in the business processing device 201, is set.

If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a normalized CPU usage rate [%] field. If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a normalized disk IO [IOPS] field.

The function normalized processing amount field includes, for example, a normalized A screen access count field and a normalized B screen access count field. In the normalized screen A access count field, a value obtained by normalizing the number of times screen A used for at least one task process has been displayed in the business processing device 201 is set. In the normalized screen B access count field, a value obtained by normalizing the number of times screen B used for at least one task process is displayed in the business processing device 201 is set.

If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a normalized screen A access count field. If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a normalized B screen access count field.

(Data structure of vector table 600)
Next, an example of the data structure of the vector table 600 will be described using FIG. The vector table 600 is realized, for example, by a storage area such as the memory 302 or recording medium 305 of the information processing apparatus 100 shown in FIG.

FIG. 6 is an explanatory diagram showing an example of the data structure of the vector table 600. As shown in FIG. As shown in FIG. 6, the vector table 600 has fields for daily data. The vector table 600 stores vector data as a record 600-a by setting information in each field. a is any integer.

Each element of a vector obtained by vectorizing the daily operation data obtained by dividing the entire normalized operation data by day is set in the daily data field. Each element of the vector is the normalized load amount of any resource or the normalized processing amount of any function included in the daily operation data.

(Data structure of component table 700)
Next, an example of the data structure of the component table 700 will be described using FIG. The component table 700 is implemented, for example, by a storage area such as the memory 302 or recording medium 305 of the information processing apparatus 100 shown in FIG.

FIG. 7 is an explanatory diagram showing an example of the data structure of the component table 700. As shown in FIG. As shown in FIG. 7, the component table 700 has one or more component fields. The component table 700 stores component data as a record 700-a by setting information in each field. a is any integer.

Each element of a basis vector corresponding to any business process included in the basis matrix obtained by performing non-negative matrix factorization on the operation data matrix is set in the component field. Each element of the basis vector corresponding to any business process is the component amount of the load amount of any resource or the component amount of the processing amount of any function related to the business process. be.

(Data structure of separation result table 800)
Next, an example of the data structure of the separation result table 800 will be described using FIG. Separation result table 800 is realized, for example, by a storage area such as memory 302 or recording medium 305 of information processing apparatus 100 shown in FIG.

FIG. 8 is an explanatory diagram showing an example of the data structure of the separation result table 800. As shown in FIG. As shown in FIG. 8, the separation result table 800 has fields of component ID, time, one or more resource normalized load amounts, and one or more function normalized processing amounts. Separation result table 800 stores separation result data as record 800-a by setting information in each field. a is any integer.

In the component ID field, a base vector corresponding to any business process is identified, which is the basis for separating the component amount of the load amount of each resource and the component amount of the processing amount of each function. The component ID to be used is set. The component ID is also treated as identification information that identifies any business process itself.

In the time field, time is set to indicate when the component amount of the load amount of each resource and the component amount of the processing amount of each function occur within a certain time interval. be. In the resource normalized load amount field, a component amount of the load amount of any resource is set. A component amount of the processing amount of any function is set in the function normalized processing amount field.

The resource normalized load amount field includes, for example, a normalized CPU usage rate [%] field and a normalized disk IO [IOPS] field. In the normalized CPU usage rate [%] field, the CPU usage rate [%], which is the partial load amount of the CPU 301 in the business processing device 201, is set as the component amount. In the normalized disk IO [IOPS] field, disk IO [IOPS], which is the load amount of a part of the recording medium 305 in the business processing device 201, is set as the component amount.

If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a normalized CPU usage rate [%] field. If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a normalized disk IO [IOPS] field.

The function normalized processing amount field includes, for example, a normalized A screen access count field and a normalized B screen access count field. In the normalized screen A access count field, a part of the total number of times screen A used for at least one task process has been displayed in the business processing device 201 is set as a component quantity. . In the normalized screen B access frequency field, a part of the total number of times screen B used for at least one task process has been displayed in the task processing device 201 is set as a component quantity. .

If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a normalized screen A access count field. If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a normalized B screen access count field.

(Data structure of workload table 900)
Next, an example of the data structure of the workload table 900 will be described with reference to FIG. 9 . The workload table 900 is realized, for example, by a storage area such as the memory 302 or recording medium 305 of the information processing apparatus 100 shown in FIG.

FIG. 9 is an explanatory diagram showing an example of the data structure of the workload table 900. As shown in FIG. As shown in FIG. 9, the workload table 900 has fields of component ID and workload. By setting information in each field of the workload table 900, the workload data is stored as a record 900-a. a is any integer.

A component ID that identifies any business process is set in the component ID field. A business process is represented by, for example, a business pattern. A business pattern is the order of one or more functions used in business processing. The business volume field is set with the business volume of any business process. The amount of work is, for example, the number of executions of work processing.

(Data structure of prediction table 1000)
Next, an example of the data structure of the prediction table 1000 will be described using FIG. The prediction table 1000 is realized, for example, by a storage area such as the memory 302 or the recording medium 305 of the information processing apparatus 100 shown in FIG.

FIG. 10 is an explanatory diagram showing an example of the data structure of the prediction table 1000. As shown in FIG. As shown in FIG. 10, the prediction table 1000 has fields of component ID, time, and one or more resource normalized load amounts. The prediction table 1000 stores prediction data as a record 1000-a by setting information in each field. a is any integer.

A component ID that identifies any business process is set in the component ID field. The time field is set with a time indicating when the predicted value of the normalized load amount for each resource related to any business process is the predicted value within a certain time interval. The resource normalized load amount field is set with a predicted value of the normalized load amount of one of the resources.

The resource normalized load amount field includes, for example, a normalized CPU usage rate [%] field and a normalized disk IO [IOPS] field. The CPU usage rate [%], which is the predicted value of the normalized load amount of the CPU 301 in the business processing device 201, is set in the normalized CPU usage rate [%] field. A normalized disk IO [IOPS] field is set with disk IO [IOPS], which is a predicted value of the normalized load amount of the recording medium 305 in the business processing device 201 .

If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a normalized CPU usage rate [%] field. If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a normalized disk IO [IOPS] field.

(Data structure of overall prediction table 1100)
Next, an example of the data structure of the overall prediction table 1100 will be described using FIG. The overall prediction table 1100 is implemented by, for example, a storage area such as the memory 302 or recording medium 305 of the information processing apparatus 100 shown in FIG.

FIG. 11 is an explanatory diagram showing an example of the data structure of the overall prediction table 1100. As shown in FIG. As shown in FIG. 11, the overall prediction table 1100 has fields of time and one or more resource normalized load amounts. The overall prediction table 1100 stores overall prediction data as a record 1100-a by setting information in each field. a is any integer.

The time field is set with a time indicating when the predicted value of the normalized load amount for each resource in the business processing system 200 is a predicted value within a certain time interval. A predicted value of the normalized load amount of any resource in the business processing system 200 is set in the resource normalized load amount field.

The resource normalized load amount field includes, for example, a normalized CPU usage rate [%] field and a normalized disk IO [IOPS] field. In the normalized CPU usage rate [%] field, the CPU usage rate [%], which is the predicted value of the normalized load amount of the CPU 301 in the task processing device 201 in the task processing system 200, is set. In the normalized disk IO [IOPS] field, disk IO [IOPS], which is the predicted value of the normalized load amount of the recording medium 305 in the business processing device 201 in the business processing system 200, is set.

If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a normalized CPU usage rate [%] field. If there are a plurality of business processing apparatuses 201, each business processing apparatus 201 may have a normalized disk IO [IOPS] field.

(Hardware configuration example of business processing device 201)
A hardware configuration example of the business processing device 201 is the same as the hardware configuration example of the information processing device 100 shown in FIG. 3, and thus description thereof is omitted. The business processing device 201 stores its own operating data in a data structure similar to that of the operating table 400 shown in FIG. The business processing device 201 does not have to store the various tables shown in FIGS.

(Example of functional configuration of information processing apparatus 100)
Next, a functional configuration example of the information processing apparatus 100 will be described with reference to FIG. 12 .

FIG. 12 is a block diagram showing a functional configuration example of the information processing apparatus 100. As shown in FIG. Information processing apparatus 100 includes storage unit 1200 , acquisition unit 1201 , decomposition unit 1202 , integration unit 1203 , extraction unit 1204 , separation unit 1205 , analysis unit 1206 , and output unit 1207 .

The storage unit 1200 is realized by, for example, a storage area such as the memory 302 and the recording medium 305 shown in FIG. A case where the storage unit 1200 is included in the information processing apparatus 100 will be described below, but the present invention is not limited to this. For example, the storage unit 1200 may be included in a device different from the information processing apparatus 100 and the storage contents of the storage unit 1200 may be referenced from the information processing apparatus 100 .

Acquisition unit 1201 to output unit 1207 function as an example of a control unit. Specifically, for example, the acquisition unit 1201 to the output unit 1207 cause the CPU 301 to execute a program stored in a storage area such as the memory 302 or the recording medium 305 shown in FIG. to realize its function. The processing result of each functional unit is stored in a storage area such as the memory 302 or the recording medium 305 shown in FIG. 3, for example.

The storage unit 1200 stores various information that is referred to or updated in the processing of each functional unit. The storage unit 1200 stores, for example, time-series data indicating temporal changes in load amounts of each of one or more resources. Resources are, for example, a CPU, a memory, a communication band, and the like. One or more resources include, for example, different types of resources on the same device. One or more resources include, for example, resources in different devices. The load amount is, for example, the usage amount.

The storage unit 1200 stores, for example, time-series data indicating temporal changes in the processing amount of each of one or more functions. A function is, for example, a function of accessing a predetermined screen. A function may be a function that accepts or processes data. A function may be implemented by an app, for example. The amount of processing is, for example, the number of accesses to the screen. The amount of processing may be, for example, the number of data processed by the application. The load and throughput may be normalized, for example.

The storage unit 1200 stores, for example, a matrix to be subjected to non-negative matrix factorization. The matrix is generated, for example, based on a group of time-series data indicating time-series changes in the load amount of each resource and time-series data indicating time-series changes in the processing amount of each function at regular time intervals. . Specifically, the matrix is a vectorized group of time-series data that indicates time-series data indicating changes in the load amount of each resource and time-series data that indicates time-series changes in the processing amount of each function at regular time intervals. It is generated based on the vector obtained by

The storage unit 1200 stores, for example, a base matrix obtained as a result of non-negative matrix factorization. A basis matrix is a matrix that contains, as columns or rows, each of a predetermined number of basis vectors. Base vectors are time-series data showing time-varying component amounts of load amounts of respective resources and processing amounts of respective functions, such that periodic changes tend to be similar at regular time intervals. It is a vector representing a group with time-series data showing time-varying changes in component amounts.

The storage unit 1200 stores, for example, the amount of work for each work process. The workload is set, for example, by an analyst. The storage unit 1200 stores, for example, time-series data indicating temporal changes in the load amount of each resource for each fixed time interval, which is predicted according to the set work volume for each work process, and each function. A group with time-series data showing changes in the amount of processing over time is stored. The storage unit 1200 is a group of time-series data indicating temporal changes in the load amount of each resource and time-series data indicating temporal changes in the processing amount of each function per fixed time interval in the business processing system 200. memorize The storage unit 1200 specifically stores various tables shown in FIGS. 4 to 11. FIG.

Storage unit 1200 stores, for example, a predetermined number. The given number is a base number. The number of basis determines the number of basis vectors contained in the basis matrix. The predetermined number is set based on the number of business processes executed using at least one of the one or more resources and using at least one of the one or more functions. The predetermined number is, for example, set in advance by the analyst to the same number as the number of business processes. Also, the predetermined number may be set based on the number of dimensions of the vector. The predetermined number is, for example, automatically set to the same number as the number of dimensions of the vector.

The storage unit 1200, for example, vectorizes a group of time-series data indicating temporal changes in the load amount of each resource and time-series data indicating temporal changes in the processing amount of each function at regular time intervals. store a predetermined rule for A fixed time interval is, for example, a day, a week, a month, or the like. A given rule is that the amount of load at any time of any kind of resource will be an element at any position in the vector, and the amount of processing at any time of any kind of function will be at any point in the vector. It is a rule that defines whether it becomes an element at the position of .

Acquisition unit 1201 acquires various types of information used for processing of each functional unit. Acquisition unit 1201 stores various kinds of acquired information in storage unit 1200 or outputs the information to each functional unit. Further, the acquisition unit 1201 may output various information stored in the storage unit 1200 to each functional unit. Acquisition unit 1201 acquires various types of information, for example, based on an analyst's operation input. The acquisition unit 1201 may receive various types of information from a device different from the information processing device 100, for example.

The acquisition unit 1201 acquires, for example, a group of time-series data of the load amount of each resource and time-series data of the processing amount of each function. Specifically, the acquisition unit 1201 acquires a total resource load amount over a predetermined period from one or more business processing apparatuses 201 and a time change in the processing amount of each function. Get the operating data of

Alternatively, the acquiring unit 1201 may acquire a group of time-series data of the load amount of each resource and time-series data of the processing amount of each function at regular time intervals, for example. In this case, specifically, the acquisition unit 1201 indicates, from one or more business processing devices 201, the time change of the load amount of each resource and the time change of the processing amount of each function for each day. , to obtain daily operation data for one day. In this case, the information processing apparatus 100 does not need to have the decomposition unit 1202 .

In addition, the acquisition unit 1201 acquires, for example, time-series data of the component amount of the load amount of each resource and the component amount of the processing amount of each function per fixed time interval related to the same task processing. A group with time-series data may be obtained. In this case, specifically, the acquisition unit 1201 acquires, for each business process, time-series data of the amount of components related to the business process in the load amount of each resource per day, and the processing amount of each function. Acquire a group with the time-series data of the amount of components related to the business process. In this case, the information processing apparatus 100 does not need to have the decomposing unit 1202 to the separating unit 1205 .

In addition, the acquisition unit 1201 may receive a predetermined number of settings, for example, based on an analyst's operation input.

Also, the acquisition unit 1201 may receive the setting of the work volume of each of the plurality of work processes based on the analyst's operation input, for example.

Acquisition unit 1201 may receive a start trigger for starting processing of any of the functional units. The start trigger is, for example, that there is a predetermined operational input by the analyst. The start trigger may be, for example, reception of predetermined information from another computer. The start trigger may be, for example, the output of predetermined information by any of the functional units. Acquisition unit 1201 receives, for example, acquisition of the entire operation data for a predetermined period as a start trigger for starting processing of decomposition unit 1202 .

The decomposition unit 1202 divides the acquired time-series data into time-series data at regular time intervals. The decomposition unit 1202, for example, normalizes the load amount and the processing amount, and converts the obtained operation data into normalized operation data. The decomposing unit 1202 divides the normalized operation data by day to generate daily operation data. As a result, the decomposing unit 1202 can process the acquired time-series data in order to implement non-negative matrix factorization that utilizes the periodic trend of changes in the workload of task processing.

The integration unit 1203 generates a vector for each constant time interval, whose elements are the load amount of each resource and the processing amount of each function for each constant time interval, based on the divided time-series data for each constant time interval. do. For example, the integration unit 1203 arranges the load amount of each resource and the processing amount of each function indicated by the daily operation data as elements at predetermined positions according to a predetermined rule stored in the storage unit 1200. By doing so, the daily operating data is vectorized. This allows the combiner to generate a vector for each day, which can be the columns or rows of the matrix.

Extraction section 1204 generates a matrix that includes, as columns or rows, vectors at regular time intervals. The extraction unit 1204 generates, for example, an operation data matrix that includes columns of vectors for each day. As a result, the extraction unit 1204 can collect the load amount of each resource and the processing amount of each function at regular time intervals and process them as columns or rows of the matrix. The results of matrix factorization can reflect the nature of business processing. For this reason, the extraction unit 1204 uses the non-negative matrix factorization to calculate the load amount of each resource and the processing amount of each function, which correspond to the task processing unit and have similar periodic changes at regular time intervals. can be made extractable.

Next, the extraction unit 1204 performs non-negative matrix factorization on the generated matrix. The extraction unit 1204, for example, defines the operation data matrix by the product of the base matrix and the weight matrix, and performs non-negative matrix factorization. As a result of non-negative matrix factorization, the extraction unit 1204 generates a base matrix that includes a predetermined number of base vectors as columns or rows. As a result, the extraction unit 1204 extracts base vectors representing temporal changes in the load amount of each resource and temporal change in the processing amount of each function, which have similar periodic trends of change and correspond to the unit of work processing. can be extracted.

Separating section 1205 separates a plurality of component amounts represented by each of a predetermined number of basis vectors included in the generated basis matrix for each resource and each function. Here, for example, there is a correspondence relationship in the element positions between the base vectors included in the base matrix and the vectors included in the operational data matrix. For this reason, the separation unit 1205, for example, refers to a predetermined rule stored in the storage unit 1200, and separates a plurality of component quantities indicated by the basis vectors for each resource and each function.

As a result, the separation unit 1205 can acquire the component amount of the load amount of each resource and the component amount of the processing amount of each function in units of business processing. For example, the separation unit 1205 separates time-series data of component amounts of the load amount of each resource per fixed time interval and time-series data of the component amount of the processing amount of each function related to each business process. Series data can be acquired.

The separation unit 1205 sets task determination information for any task process based on the time-series data of the component amount of the processing amount of each function related to the task process. For example, the separation unit 1205 generates time-series data of accumulated amount of component amounts of the processing amount of each function per fixed time interval related to any business process, and Set to discrimination information. For example, the separating unit 1205 may set time-series data of the component amount of the processing amount of each function related to any business process as the business determination information of the business process. As a result, the separation unit 1205 can generate task determination information that serves as a reference for the analyst to determine the type of task content.

The analysis unit 1206 generates, for each task process, prediction time-series data indicating the temporal change in the component amount related to the task process out of the load amount of each resource per fixed time interval in the future. For example, the analysis unit 1206, for each task processing, based on the time-series data of the component amount related to the task processing in the load amount of each resource and the specified workload of the task processing, Generate forecast time series data corresponding to the treatment. Specifically, for each task processing, the analysis unit 1206 multiplies the time-series data of the amount of components related to the task processing in the load amount of each resource by the workload of the task processing, Generate forecast time series data corresponding to . As a result, the analysis unit 1206 can simulate time-varying changes in component amounts of the resource load amounts for each task process.

The analysis unit 1206 generates prediction time-series data indicating temporal changes in the load amount of each resource per certain future time interval for the business processing system 200 . The analysis unit 1206 generates prediction time-series data for the business processing system 200 based on the prediction time-series data for each business processing, for example. Specifically, the analysis unit 1206 superimposes the prediction time-series data for each task processing to generate the prediction time-series data for the task processing system 200 . As a result, the analysis unit 1206 can simulate time-varying changes in resource loads during the execution of a plurality of business processes.

An output unit 1207 outputs the processing result of any one of the functional units. The output format is, for example, display on a display, print output to a printer, transmission to an external device via the network I/F 303, or storage in a storage area such as the memory 302 or recording medium 305. As a result, the output unit 1207 can notify the analyst of the processing result of any of the functional units, and the convenience of the information processing apparatus 100 can be improved.

The output unit 1207 associates the time-series data showing the time-series data representing the time-dependent changes in the component amounts of the load amounts of the respective resources per fixed time interval, related to any of the work processing, and the work determination information of the work processing. display. For example, the output unit 1207 associates the time-series data of the component amount of the load amount of each resource per fixed time interval related to any business process, and the processing amount of each function. Time-series data of component amounts are displayed so that each function can be identified. As a result, the output unit 1207 can enable the analyst to refer to the task determination information that serves as a reference for determining the type of task content.

The output unit 1207, for example, associates time-series data of the amount of components of the load amount of each resource per fixed time interval related to any business process, and outputs the time-series data of the accumulated amount, respectively. identifiable functions of As a result, the output unit 1207 can enable the analyst to refer to the task determination information that serves as a reference for determining the type of task content.

The output unit 1207 displays, for example, prediction time-series data corresponding to each business process so that each business process can be identified. As a result, the output unit 1207 can enable the analyst to refer to the result of simulating the time change of the amount of the component of the load amount of the resources related to the business process for each business process.

The output unit 1207 displays, for example, prediction time-series data for the business processing system 200 . As a result, the output unit 1207 can enable the analyst to refer to the result of simulating the time change of the load amount of the resource during the execution of a plurality of business processes in the business process system 200 .

Although the information processing apparatus 100 includes the acquisition unit 1201 to the output unit 1207 here, the present invention is not limited to this. For example, the information processing apparatus 100 may receive the result of separation by the separating unit 1205 in another computer from another computer that does not include the separating unit 1202 to the separating unit 1205 but does include the separating unit 1202 to the separating unit 1205. may Further, for example, the information processing apparatus 100 may not include the analysis unit 1206 and may transmit the separation result of the separation unit 1205 to another computer including the analysis unit 1206 .

(Embodiment of information processing apparatus 100)
Next, an embodiment of the information processing apparatus 100 will be described with reference to FIGS. 13 to 23. FIG. First, the operation flow of the information processing apparatus 100 will be described with reference to FIG.

FIG. 13 is an explanatory diagram showing the operation flow of the information processing apparatus 100. As shown in FIG. In the embodiment, the information processing apparatus 100 includes a normalization integrating unit 1311, a component amount estimating unit 1312, a job discrimination information creating unit 1313, and a load and a quantity calculator 1321 .

In FIG. 13, the normalization integration unit 1311 collects a load amount history 1301 of each resource and a processing amount history 1302 of each function from one or more business processing apparatuses 201 while the business processing system 200 is in operation. , to get the overall running data. The amount of load is, for example, CPU utilization and disk IO. The amount of processing is, for example, the number of accesses to a predetermined screen. The predetermined screens are screen A and screen B, for example. A predetermined screen is used for business processing.

The normalization integration unit 1311 normalizes and integrates the load amount of each resource and the processing amount of each function based on the acquired operation data, and generates normalized operation data. A specific example of the normalization method will be described later with reference to FIG. 14, for example.

Based on the normalized operation data, the component amount estimation unit 1312 calculates, for each task process, the time-series data of the component amount related to the task process out of the load amount of each resource, and the relevant component amount out of the processing amount of each function. Estimate the time-series data of the amount of components related to business processing. A specific example of the estimation method will be described later with reference to FIGS. 15 to 18, for example.

The task determination information creation unit 1313 creates task determination information corresponding to each task process based on the time-series data of the component amount related to each task process out of the processing amount of each function. The task determination information corresponding to the task processing is, for example, the sum of the amount of each component identifiable and the component amount related to the task processing out of the processing amount of each function identifiable. This is display information for displaying time-series data. A specific example of creating the task determination information will be described later with reference to FIG. 19, for example.

The task determination information creation unit 1313 creates task determination information corresponding to each task process in association with the time-series data of the component amount related to the task process in the load amount of each resource. It is presented to the analyst 1300 by displaying it on the display. A specific example of presenting the job determination information to the analyst 1300 will be described later with reference to FIG. 20, for example.

The analyst 1300 confirms the task determination information corresponding to each task process, predicts the task content of each task process, predicts the type of each task process, and transmits the workload of each task process to the information processing apparatus 100. set. The set workload is stored using, for example, the workload table 900 shown in FIG. A specific example of setting the workload will be described later with reference to FIG. 21, for example.

The load amount calculation unit 1321 calculates the future load amount of each resource based on the set work amount of each task processing and the time-series data of the component amount related to each task process in the load amount of each resource. Calculate the predicted value of the amount of components related to each business process. The calculated predicted values are stored using, for example, the prediction table 1000 shown in FIG. A specific example of calculating the predicted value will be described later with reference to FIGS. 22 and 23, for example.

In addition, the load amount calculation unit 1321 calculates a predicted value of the component amount related to each task processing in the future load amount of each resource in the task processing system 200 . The calculated predicted values are stored using, for example, the overall prediction table 1100 shown in FIG. A specific example of calculating the predicted value will be described later with reference to FIGS. 22 and 23, for example.

The load amount calculation unit 1321 generates time-series data of predicted values of component amounts related to each task process among the load amount of each resource in the future in the task processing system 200, and displays it on the display to perform analysis. presented to person 1300 . A specific example of presenting time-series data of predicted values will be described later with reference to FIGS. 22 and 23, for example.

As a result, the load amount calculation unit 1321 can perform a simulation of changes over time in the load amounts of resources during the execution of a plurality of task processes in the task processing system 200. 1300 can be referenced.

Next, an example of the operation of the information processing apparatus 100 will be described with reference to FIGS. 14 to 23. FIG.

14 to 23 are explanatory diagrams showing an example of the operation of the information processing apparatus 100. FIG. In FIG. 14, (14-1) the information processing apparatus 100 acquires operation data and stores it using the operation table 400. FIG.

(14-2) The information processing apparatus 100 normalizes the load amount of each resource and the processing amount of each function. The information processing apparatus 100 calculates the normalized load _amount nr i (t) by normalizing the load amount r _i (t) of the i-th resource at the time t, for example, by the following equation (1). Normalization is performed so as to satisfy the condition of formula (2) below.

nr _i (t)=r _i (t)/Σ _i,t (r _i (t)) (1)

Σ _i,t (nr _i (t))=1 (2)

The information processing apparatus 100 calculates the normalized load amount ng _i (t) by normalizing the processing amount g _i (t) of the i-th function at the time t, for example, by the following equation (3). Normalization is performed so as to satisfy the condition of formula (4) below.

ng _i (t)=g _i (t)/Σ _i,t (g _i (t)) (3)

Σ _i,t (ng _i (t))=1 (4)

(14-3) The information processing apparatus 100 generates normalized operation data summarizing the normalized load amount of each resource and the normalized processing amount of each function, and stores the normalized operation data using the normalized operation table 500 . As a result, the information processing apparatus 100 can easily handle different scale values of the load amount of each resource and the processing amount of each function.

The information processing apparatus 100 can make it easy to handle the load amount of each resource and the processing amount of each function equally in, for example, non-negative matrix factorization. Therefore, the information processing apparatus 100 can easily avoid the problem that either the value of the evaluation function regarding the load amount of the resource or the value of the evaluation function regarding the processing amount of the function is emphasized in the non-negative matrix factorization. can do. Next, the description of FIG. 15 will be described.

In FIG. 15, (15-1) the information processing apparatus 100 decomposes the normalized operation data stored in the normalized operation table 500 into units of one day to specify daily operation data. The information processing apparatus 100 identifies, for example, the daily operation data of date "20180101" and the daily operation data of date "20180102".

(15-2) The information processing apparatus 100 vectorizes the daily operation data to generate a vector for each day. For example, the information processing apparatus 100 arranges and vectorizes the load amount of each resource and the processing amount of each function indicated by the daily operation data as elements according to a predetermined rule, and stores them using the vector table 600 .

Specifically, the information processing apparatus 100 generates a vector x ₁ in which load amount groups 1501 and 1502 of each resource and processing amount groups 1503 and 1504 of each function indicated by the daily operation data of the date "20180101" are arranged. do. Similarly, the information processing apparatus 100 generates a vector _x2 based on the daily operation data of the date "20180102".

Then, the information processing apparatus 100 sets the vector x ₁ in the first day data field of the vector table 600 and sets the vector x ₂ in the second day data field of the vector table 600 . Next, the description of FIG. 16 will be described.

In FIG. 16, (16-1) the information processing apparatus 100 refers to the vector table 600 and creates an operation data matrix X=(x ₁ , x ₂ , . . . ) whose columns are vectors corresponding to each day. Generate. Here, the vector x _j corresponding to the daily operation data for one day is formed by a linear combination of the base vector u _i and the weighting factor a _ij for each task process. j is a value indicating what day the daily operation data is.

For this reason, if the vector x _j corresponding to the daily operation data for one day is divided into the base vector u _i and the weight vector a _i (weight coefficient a _ij ), information for each resource for each business process can be obtained. and information for each function can be obtained. The vector x _j can be expressed as in the following formula (5) using a formula. N is the base number. The base number N is set in advance, for example. The base number N may, for example, be the number of dimensions of the vectors forming the operational data matrix.

x _j =Σ _{i =1} ^N (a _ij ×u _i ) (5)

If the above equation (5) is expressed in matrix form, it is expressed as in the following equation (6). The operating data matrix X=( _x1 , _x2 ,...). U is the basis matrix. U=(u ₁ , u ₂ , . . . ). A is the weight matrix. A=( _a1 , _a2 ,...)=( _aij ).

X=UA (6)

Here, non-negative matrix factorization has the property of extracting frequent components of each vector of the target matrix. A frequent component is a component that exhibits a periodic tendency to change. For this reason, if non-negative matrix factorization is performed on the operating data matrix, it is considered possible to extract component data showing a periodic change tendency as frequent components. Specifically, the component amount related to the business process out of the load amount of each resource and the component amount related to the business process out of the processing amount of each function corresponding to the frequent component related to each business process. are considered to be extractable as basis vectors of the basis matrix.

(16-2) Therefore, the information processing apparatus 100 performs non-negative matrix factorization on the above equation (6) expressed in matrix form, and obtains a base matrix U including base vectors corresponding to each task process. Generate. The base matrix U is stored using, for example, the component table 700 shown in FIG.

For non-negative matrix factorization, reference can be made, for example, to Reference 1 below. As the Sparsness Constraints in Reference 1 below, for example, the L1 norm is used.

Reference 1: Hoyer, Patrick O.; "Non-negative matrix factorization with sparsness constraints." Journal of machine learning research 5. Nov (2004): 1457-1469.

Specifically, in the non-negative matrix factorization, a weight matrix A and a base matrix U that minimize the following equation (7) are estimated. ||Z|| _FRO is the Frobenius norm. The terms α||A|| ₁ and β||U|| ₁ are the Sparsness Constraints. ||Z|| _FRO is defined by the following equation (8). Z=( _zij ). For the weight matrix A, the following formula (9) holds. Also, for the basis matrix U, the following formula (10) holds.

(A ^* ,U ^* )=argmin _A,U (1/2||X-UA|| _FRO2 )+α||A|| ₁ +β||U ^|| ₁ (7)

||Z|| _FRO ² =Σ _i=1 ^N Σ _j=1 ^M (z _ij ) (8)

A≧0 (9)

U≧0 (10)

Thus, by using the α||A|| ₁ and β||U|| ₁ terms, the weights and basis matrices are induced to be as close to zero as possible. Therefore, according to the non-negative matrix factorization using the above formula (7), the group of the component amount of the load amount of each resource and the component amount of the processing amount of each function in the task processing unit can be facilitated to be generated in a format that is easy for the analyst 1300 to understand.

(16-3) The information processing apparatus 100 separates each base vector u _i of the generated base matrix U into each resource and each function, and the component amount of the load amount of each resource in units of business processing. , and the component amount of the processing amount of each function can be acquired. Separation follows the inverse of the predetermined rules for vectorizing daily operational data.

The information processing apparatus 100, for example, separates the component quantity groups 1601 to 1604 of the basis vector u ₁ into each resource and each function, and stores them using the separation result table 800 shown in FIG. More specifically, the information processing apparatus 100 sets the component quantity group 1601 to the normalized CPU usage rate field, and sets the component quantity group 1602 to the normalized disk IO field. Further, the information processing apparatus 100 specifically sets the component amount group 1603 in the field of the normalized screen A access count, and sets the component amount group 1604 in the field of the normalized screen B access count. Next, the description of FIGS. 17 and 18 will be made.

17 and 18, the information processing apparatus 100 refers to the separation result table 800 to refer to time-series data of the component amount of the load amount of each resource and Identify the group with the time-series data of the component amount of

In _the example of FIG. 17 , the information processing apparatus 100 performs time-series data of the component amount of the load amount of each resource and Identify the group with the time-series data of the component amount of The information processing apparatus 100 identifies, for example, groups of time-series data 1701 and 1702 of component amounts of the load amount of each resource and time-series data 1703 and 1704 of component amounts of the processing amount of each function. .

In _the example of FIG. 18 , the information processing apparatus 100 performs time-series data of the component amount of the load amount of each resource and Identify the group with the time-series data of the component amount of The information processing apparatus 100 identifies, for example, groups of time-series data 1801 and 1802 of the component amounts of the load amount of each resource and time-series data 1803 and 1804 of the component amounts of the processing amount of each function. . Next, the description of FIG. 19 will be described.

In FIG. 19, the information processing apparatus 100 generates task determination information for each task process. For example, the information processing apparatus 100 can identify the component amount of the processing amount of each function based on the time-series data 1703 and 1704 of the component amount of the processing amount of each function of the task processing with the component ID 1. A graph 1901 is generated that shows the change over time of the cumulative amounts of the component amounts that have been color-coded. In the drawing, the color coding is indicated by hatching, for example. A graph 1901 is used as task determination information for task processing with component ID1.

Here, the information processing apparatus 100 has a common component amount unit in the processing amount of each function. It is possible to reduce the number of graphs that the person 1300 refers to. In this way, the information processing apparatus 100 can make it easier to refer to the task determination information, and can reduce the workload of the analyst.

For example, the information processing apparatus 100 can identify the component amount of the processing amount of each function based on the time-series data 1803 and 1804 of the component amount of the processing amount of each function of the task processing with the component ID 2. A color-coded graph 1902 is generated that shows the change over time of the cumulative amounts of the accumulated component amounts. In the drawing, the color coding is indicated by hatching, for example. A graph 1902 is used as task determination information for task processing of component ID1.

Here, the information processing apparatus 100 has a common component amount unit in the processing amount of each function. It is possible to reduce the number of graphs that the person 1300 refers to. In this way, the information processing apparatus 100 can make it easier to refer to the task determination information, and can reduce the workload of the analyst. Next, the description of FIG. 20 will be described.

In FIG. 20 , the information processing apparatus 100 displays the generated task determination information on the display in association with the time-series data of the component amount of the load amount of each resource for each task process, thereby allowing the analyst 1300 to presented to For example, the information processing apparatus 100 displays a generated graph 1901 on the display in association with the time-series data 1701 and 1702 of the component amount of the load amount of each resource of the task processing with the component ID 1 . Here, the correspondence is to display them side by side on the same line.

For example, the information processing apparatus 100 displays a generated graph 1902 on the display in association with time-series data 1801 and 1802 of the amount of components of the load amount of each resource of the task processing with the component ID2. Here, the correspondence is to display them side by side on the same line. As a result, the information processing apparatus 100 makes it easy for the analyst 1300 to determine which type of task processing the component amount of the load amount of each resource specifically corresponds to, based on the task determination information. can do. Next, the description of FIG. 21 will be described.

In FIG. 21, the analyst 1300 sets the workload of each task processing in the information processing apparatus 100 based on the type of task processing determined based on the task determination information. For example, the analyst 1300 predicts that the task processing of the component ID 1 will access only the A screen, so it is expected to be the task processing of vacation application, and sets the future workload of the task processing of the component ID 1 to 130. For example, the analyst 1300 predicts that the business processing of the component ID2 will access both the A screen and the B screen, so that it will be the business processing of vacation approval, and the future workload of the business processing of the component ID2. is set to 100. The information processing apparatus 100 stores the set workload using the workload table 900 shown in FIG. Next, the description of FIG. 22 will be described.

In FIG. 22 , the information processing apparatus 100 calculates, for each business process, a predicted value of the component amount related to the business process out of the load amount of each resource in the future. For example, the information processing apparatus 100 calculates, for each task processing, the predicted value of the component amount related to the task processing out of the load amount of each resource in the future, using the following formula (11), time-series data of predicted values of the amount of components related to the job processing in the load amount of

Predicted value of component amount = work volume _i /component volume _i (11)

The information processing apparatus 100 presents to the analyst 1300 by displaying on the display time-series data of predicted values of future component amounts related to business processing among the load amount of each resource. In this way, the information processing apparatus 100 can enable the analyst 1300 to refer to the result of simulating the temporal change of the amount of the component of the load amount of the resources related to the task process for each task process.

The information processing apparatus 100 also calculates a predicted value of the future load amount of each resource in the business processing system 200 . The information processing apparatus 100 calculates a future predicted value of the load amount of each resource in the business processing system 200 by, for example, the following equation (12), and generates a time series of the future predicted value of the load amount of each resource. Generate data.

Predicted value of load amount = Σ _i work amount _i · component amount _i (12)

More specifically, the information processing apparatus 100 displays time series data 2201 obtained by adding the result of multiplying the time series data 1701 by 130 and the result of multiplying the time series data 1801 by 100 on the display. Further, the information processing apparatus 100 specifically displays time-series data 2202 obtained by adding the result of multiplying the time-series data 1702 by 130 and the result of multiplying the time-series data 1802 by 100 on the display. do.

The information processing apparatus 100 presents to the analyst 1300 the time-series data of the predicted value of the load amount of each resource in the future by displaying it on the display. In this way, the information processing apparatus 100 can enable the analyst 1300 to refer to the result of simulating the time change of the load amount of the resource during the execution of a plurality of business processes in the business process system 200 .

As a result, the analyst 1300 considers the predicted value of the load amount of each resource, and examines the addition of resources in the business processing device 201 and the expansion of the business processing device 201 included in the business processing system 200. be able to. Therefore, the analyst 1300 can secure or improve the convenience, fault tolerance, processing performance, etc. of the business processing system 200 . Next, the description of FIG. 23 will be described.

In FIG. 23 , the information processing apparatus 100 uses a prediction table 1000 to store time-series data of predicted values of future component amounts related to business processing among the load amount of each resource. The information processing apparatus 100 also stores time-series data of future predicted values of the load amount of each resource using the overall prediction table 1100 .

(Overall processing procedure)
Next, an example of an overall processing procedure executed by the information processing apparatus 100 will be described with reference to FIG. 24 . The overall processing is realized by, for example, the CPU 301, storage areas such as the memory 302 and the recording medium 305, and the network I/F 303 shown in FIG.

FIG. 24 is a flow chart showing an example of the overall processing procedure. In FIG. 24, information processing apparatus 100 receives a start trigger (step S2401). The start trigger is, for example, a trigger that is automatically generated at regular timing, a trigger that is generated by the analyst's 1300 operation input, or the like. The operation input by the analyst 1300 is, for example, an operation input for starting estimation of the load amount of future resources.

Next, the information processing apparatus 100 collects the resource load amount and the processing amount of the function from each of the one or more task processing apparatuses 201, and acquires the overall operation data (step S2402). . Then, the information processing apparatus 100 normalizes the resource load amount and the processing amount of the function in the acquired overall operation data (step S2403).

Next, the information processing apparatus 100 integrates the normalized load amount of the resource and the processing amount of the function to generate overall normalized operation data (step S2404). Then, the information processing apparatus 100 divides the generated entire normalized operation data into units of one day (step S2405).

Next, the information processing apparatus 100 vectorizes the daily operation data divided into units of one day, and generates an operation data matrix in which the vectors for each day are arranged as columns (step S2406). The information processing apparatus 100 then performs non-negative matrix factorization on the generated operation data matrix based on the number of bases to generate a base matrix and a weight matrix (step S2407).

Next, the information processing apparatus 100 separates one or more component quantities in the unit of task processing included in the base vector corresponding to the unit of task processing in the base matrix into resources and functions (step S2408). Then, the information processing apparatus 100 generates task determination information based on the group of component amounts separated into functions for each task process (step S2409).

Next, the information processing apparatus 100 assigns task determination information to groups of component amounts separated into resources for each task process (step S2410). Then, the information processing apparatus 100 presents to the analyst 1300 the group of component amounts separated into resources and the task determination information assigned to the group of component amounts separated into resources in association with each task processing unit ( step S2411).

Next, the information processing apparatus 100 receives the setting of the work volume for each work process (step S2412). Then, the information processing apparatus 100 calculates the time change of the load amount of the resource in the future based on the set workload (step S2413). After that, the information processing apparatus 100 presents to the analyst 1300 the time change of the load amount of the future resource (step S2414).

Then, the information processing apparatus 100 ends the overall processing. As a result, the information processing apparatus 100 can enable the analyst 1300 to determine to which type of task processing a group of component amounts separated into resources specifically corresponds. Then, the information processing apparatus 100 can enable the analyst 1300 to analyze the time change of the load amount of the resource in the future.

Here, the information processing apparatus 100 may omit the processing of some steps in FIG. 24 . For example, the processing of steps S2412 to S2414 may be omitted, and the analyst 1300 may not be presented with future changes in resource loads over time. Further, for example, the processing of steps S2412 to S2414 may be executed by a device different from the information processing device 100. FIG.

As described above, according to the information processing apparatus 100, for any business process, the time-series data of the component amount related to the business process among the load amount of each resource and the processing amount of each function It is possible to acquire time-series data of component amounts related to the business process. According to the information processing apparatus 100, for any business process, the load amount of each resource is associated with the time-series data of the component amount related to the business process, and the processing amount of each function is associated with the business process. Time-series data of the amounts of the components involved can be displayed so that each function can be identified. As a result, the information processing apparatus 100 makes it easier for the analyst to determine which type of task processing the component amount related to one of the resource loads specifically corresponds to. can be done.

According to the information processing apparatus 100, it is possible to display the time-series data of the amount of components related to any business process among the amounts of processing of each function so that each function can be identified. As a result, the information processing apparatus 100 can suppress an increase in the number of time-series data that the analyst refers to, and can reduce the workload of the analyst.

According to the information processing apparatus 100, time-series data of the load amount of each resource and time-series data of the processing amount of each function can be acquired. According to the information processing apparatus 100, based on the acquired various time-series data, each vector having the load amount of each resource and the processing amount of each function as elements is included at regular time intervals as columns or rows. A matrix can be generated. According to the information processing apparatus 100, it is possible to perform non-negative matrix factorization on the generated matrix to generate a base matrix that includes a predetermined number of base vectors as columns or rows. According to the information processing apparatus 100, a plurality of component quantities represented by each of a predetermined number of basis vectors included in the generated basis matrix can be separated for each resource and each function. As a result, the information processing apparatus 100 can obtain, for each task processing, time-series data of the component amount related to the task processing out of the load amount of each resource, and the component related to the task processing out of the processing amount of each function. Amount of time-series data can be obtained.

According to the information processing apparatus 100, it is possible to acquire the time-series data of the component amount related to each business process among the load amount of each resource. According to the information processing apparatus 100, it is possible to receive designation of the workload of each task processing. According to the information processing apparatus 100, based on the time-series data of the component amount related to each business process among the acquired load amount of each resource and the business amount of each business process whose designation is received, future , it is possible to generate time-series data of the predicted value of the load amount of each resource. Thereby, the information processing apparatus 100 can simulate the load amount of each resource in the future.

According to the information processing apparatus 100, the load amount and the processing amount can be normalized and handled. Accordingly, the information processing apparatus 100 can easily handle the load amount of each resource and the processing amount of each function equally in the non-negative matrix factorization. Therefore, the information processing apparatus 100 can easily avoid the problem that either the value of the evaluation function regarding the load amount of the resource or the value of the evaluation function regarding the processing amount of the function is emphasized in the non-negative matrix factorization. can do.

According to the information processing apparatus 100, at least one of a CPU, a memory, and a communication band can be employed as one or more resources. Thereby, the information processing apparatus 100 can present the component amount of at least one of the load amounts of the CPU, the memory, and the communication band to the analyst.

According to the information processing apparatus 100, different types of resources in the same device can be employed as one or more resources. As a result, when one business process affects different types of resources in the same device, the information processing apparatus 100 can calculate load amounts of different types of resources in the same device in a format corresponding to one business processing. It is possible to obtain the time-series data of the amount of these components.

According to the information processing apparatus 100, resources in different devices can be employed as one or more resources. As a result, when one business process affects resources in different devices, the information processing apparatus 100 can generate time-series data of component amounts of the load amounts of resources in different devices in a format corresponding to one business process. can be obtained.

According to the information processing apparatus 100, at least a function of accessing a predetermined screen can be employed as one or more functions. As a result, the information processing apparatus 100 can refer to which screen the analyst tends to access in business processing whose type is unknown. It can make it easier to determine if there is

According to the information processing apparatus 100, the predetermined number can be set based on the number of business processes executed using at least one of the one or more resources. As a result, the information processing apparatus 100 can classify the group of the time-series data of the component amount of the load amount of each resource and the time-series data of the component amount of the processing amount of each function for each task processing. can be made available. In addition, the information processing apparatus 100 can reduce the number of basis vectors and reduce the amount of processing required for non-negative matrix factorization.

According to the information processing apparatus 100, the predetermined number can be set based on the number of dimensions of the vector. As a result, even if the number of business processes is unknown, the information processing apparatus 100 can display the time-series data of the component amount of the load amount of each resource and the time series data of the component amount of the processing amount of each function for each business process. Groups with series data can be obtained with high accuracy.

The system analysis method described in this embodiment can be realized by executing a prepared program on a computer such as a personal computer or a workstation. The system analysis program described in the present embodiment is recorded on a computer-readable recording medium such as a hard disk, flexible disk, CD (Compact Disc)-ROM, MO, DVD (Digital Versatile Disc), etc., and is read by a computer. is executed by reading from Also, the system analysis program described in this embodiment may be distributed via a network such as the Internet.

Further, the following additional remarks are disclosed with respect to the above-described embodiment.

(Appendix 1) For any one of a plurality of business processes, time-series data of the component amount related to the one business process among the load amounts of the resources used for the plurality of business processes; Acquire the time-series data of the component amount related to any of the above business processes among the processing amount of the function used for the business process of
of the component amount related to any of the business processes out of the acquired processing amount of the function, in association with the time-series data of the component amount related to any of the business processes out of the acquired load amount of the resource displaying the time-series data so that the features are identifiable;
A system analysis method characterized in that processing is executed by a computer.

(Appendix 2) The process of displaying
The component amount related to any of the business processes out of the acquired processing amount of the function is associated with the time-series data of the component amount related to any of the business processes out of the acquired load amount of the resource. The system analysis method according to appendix 1, characterized in that the time-series data of the piled-up amount is displayed so that the function can be identified.

(Appendix 3) Acquiring the time-series data of the load amount of the resource and the time-series data of the processing amount of the function,
A vector whose elements are the load amount of the resource and the processing amount of the function based on the obtained time-series data of the load amount of the resource and the obtained time-series data of the processing amount of the function. generate a matrix containing as rows,
performing non-negative matrix factorization on the generated matrix to generate a basis matrix containing a predetermined number of basis vectors each as columns or rows;
By separating a plurality of component amounts represented by each of the predetermined number of base vectors included in the generated base matrix with respect to the resource and the function, for each of the plurality of business processes, The computer acquires time-series data of a component amount related to the business process out of the load amount of the resource and time-series data of a component amount related to the business process out of the processing amount of the function. 3. The system analysis method according to appendix 1 or 2, characterized by:

(Appendix 4) acquiring time-series data of component amounts related to each of the plurality of business processes among the load amount of the resource;
Receiving designation of the amount of work for each of the above business processes,
based on the time-series data of the amount of components related to each of the plurality of business processes among the acquired load amount of the resource and the business volume of each of the business processes for which the designation is received; generate time-series data of the loadings of
The system analysis method according to any one of Appendices 1 to 3, wherein the generated time-series data is output, and the computer executes the processing.

(Appendix 5) The system analysis method according to any one of Appendices 1 to 4, wherein the load amount and the processing amount are normalized amounts.

(Appendix 6) The system analysis method according to any one of Appendices 1 to 5, wherein the resource is any one of a CPU, a memory, and a communication band.

(Appendix 7) The system analysis method according to any one of Appendices 1 to 6, wherein the resources are different types of resources in the same device.

(Appendix 8) The system analysis method according to any one of Appendices 1 to 7, wherein the resources are resources in different devices.

(Appendix 9) The system analysis method according to any one of Appendices 1 to 8, wherein the function is a function of accessing a predetermined screen.

(Appendix 10) The system analysis method according to appendix 3, wherein the predetermined number is set based on the number of business processes executed using the resource.

(Appendix 11) The system analysis method according to appendix 3, wherein the predetermined number is set based on the number of dimensions of the vector.

(Supplementary Note 12) For any one of a plurality of business processes, time-series data of component amounts related to the one business process among load amounts of resources used for the plurality of business processes; Acquire the time-series data of the component amount related to any of the above business processes among the processing amount of the function used for the business process of
of the component amount related to any of the business processes out of the acquired processing amount of the function, in association with the time-series data of the component amount related to any of the business processes out of the acquired load amount of the resource displaying the time-series data so that the features are identifiable;
A system analysis program characterized by causing a computer to execute processing.

100 information processing device 101 to 104, 1701 to 1704, 1801 to 1804, 2201, 2202 time series data 200 business processing system 201 business processing device 210 network 300 bus 301 CPU
302 memory 303 network I/F
304 recording medium I/F
305 recording medium 400 operation table 500 normalized operation table 600 vector table 700 component table 800 separation result table 900 workload table 1000 prediction table 1100 overall prediction table 1200 storage unit 1201 acquisition unit 1202 decomposition unit 1203 integration unit 1204 extraction unit 1205 separation unit 1206 analysis unit 1207 output unit 1300 analyst 1301, 1302 history 1311 normalization integration unit 1312 component amount estimation unit 1313 task discrimination information creation unit 1321 load amount calculation unit 1501, 1502 load amount group 1503, 1504 processing amount group 1601 to 1604 component Quantity group 1901, 1902 graph

Claims (5)

time-series data of a component amount related to the one of the business processes among load amounts of resources used for the plurality of business processes, for one of the plurality of business processes; Acquiring time-series data of the amount of components related to any of the business processes out of the amount of processing of the functions used,
of the component amount related to any of the business processes out of the acquired processing amount of the function, in association with the time-series data of the component amount related to any of the business processes out of the acquired load amount of the resource displaying the time-series data so that the features are identifiable;
A system analysis method characterized in that processing is executed by a computer. The displaying process includes:
The component amount related to any of the business processes out of the acquired processing amount of the function is associated with the time-series data of the component amount related to any of the business processes out of the acquired load amount of the resource. 2. The method of system analysis according to claim 1, wherein the time-series data of the accumulated amount is displayed so that the function can be identified. obtaining time-series data of the load amount of the resource and time-series data of the processing amount of the function;
A vector whose elements are the load amount of the resource and the processing amount of the function based on the obtained time-series data of the load amount of the resource and the obtained time-series data of the processing amount of the function. generate a matrix containing as rows,
performing non-negative matrix factorization on the generated matrix to generate a basis matrix containing a predetermined number of basis vectors each as columns or rows;
By separating a plurality of component amounts represented by each of the predetermined number of base vectors included in the generated base matrix with respect to the resource and the function, for each of the plurality of business processes, The computer acquires time-series data of a component amount related to the business process out of the load amount of the resource and time-series data of a component amount related to the business process out of the processing amount of the function. 3. The system analysis method according to claim 1 or 2, characterized by: Acquiring time-series data of a component amount related to each of the plurality of business processes among the load amount of the resource;
Receiving designation of the amount of work for each of the above business processes,
based on the time-series data of the amount of components related to each of the plurality of business processes among the acquired load amount of the resource and the business volume of each of the business processes for which the designation is received; Generate time series data of the loading of
4. The system analysis method according to any one of claims 1 to 3, wherein the computer executes the process of outputting the generated time-series data. time-series data of a component amount related to the one of the business processes among load amounts of resources used for the plurality of business processes, for one of the plurality of business processes; Acquiring time-series data of the amount of components related to any of the business processes out of the amount of processing of the functions used,
of the component amount related to any of the business processes out of the acquired processing amount of the function, in association with the time-series data of the component amount related to any of the business processes out of the acquired load amount of the resource displaying the time-series data so that the features are identifiable;
A system analysis program characterized by causing a computer to execute processing.

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