JP-7854117-B1 - Process control device and process control method

JP7854117B1JP 7854117 B1JP7854117 B1JP 7854117B1JP-7854117-B1

Abstract

[Problem] To more easily manage abnormal program behavior. [Solution] The process management device comprises: a first learning unit configured to learn a first parameter representing the ratio between the probability distribution of first data, which is a series of normal processing resource usage, and the probability distribution of second data, which is a series of processing resource usage that deviates from the range of normal processing resource usage, and to estimate the said ratio; a second learning unit configured to estimate the second parameter of a class corresponding to the first data and a class-specific probability distribution model corresponding to the second data; a third learning unit configured to learn a plurality of generative models, each having an objective function set by the probability distribution of the first data and the probability distribution of the second data estimated by the first learning unit and the second parameter estimated by the second learning unit; and a generation unit configured to generate pseudo-data using each of a plurality of trained generators obtained by the learning of the third learning unit. [Selection Diagram] Figure 1

Inventors

柿島純

Assignees

株式会社インターネットイニシアティブ

Dates

Publication Date: 20260512
Application Date: 20260113

Claims (9)

A first learning unit is configured to learn a first parameter that represents the ratio between a probability distribution of first data, which is a series of processing resource usages that are considered normal for the execution of each process corresponding to a plurality of process IDs, and a probability distribution of second data, which is a series of processing resource usages that deviate from the normal range of processing resource usages, and to estimate the ratio based on the learned first parameter. A second learning unit is configured to take each of the sequences of processing resource usage used in the execution of each process corresponding to each of multiple process IDs as an observed value, treat each observed value as a conditionally independent discrete value, and estimate the second parameter of the class-specific probability distribution model corresponding to the class corresponding to the first data and the class corresponding to the second data, respectively, based on the frequency of occurrence of each observed value. A third learning unit is configured to learn a plurality of generative models, each having an objective function set by the probability distribution of the first data and the probability distribution of the second data determined from the ratio estimated by the first learning unit, and the second parameter estimated by the second learning unit. The third learning unit includes a generator that generates pseudo-data statistically similar to the true data, with each of the sequence of processing resource usage used in the execution of the process corresponding to each of the plurality of process IDs corresponding to the second data being used as true data, and a discriminator that identifies whether the pseudo-data generated by the generator belongs to the class corresponding to the true data or the class corresponding to the pseudo-data, and each having an objective function set by the probability distribution of the first data and the probability distribution of the second data determined from the ratio estimated by the first learning unit, and the second parameter estimated by the second learning unit. A process control device comprising: a generation unit configured to generate the pseudo-data using each of a plurality of trained generators obtained by training by the third learning unit.
In the process control device according to claim 1, Furthermore, an identification unit is configured to use each of the multiple trained classifiers obtained through training by the third learning unit to identify whether each of the pseudo-data generated by the generation unit belongs to the class corresponding to the true data or the class corresponding to the pseudo-data, A process control device comprising: a storage unit configured to store each of the pseudo-data identified by the identification unit as belonging to a class corresponding to true data.
In the process control apparatus according to claim 2, Furthermore, the collection unit is configured to collect a series of processing resource usage amounts used in the execution of each of the multiple process IDs of the managed process, A process management device comprising: a determination unit configured to determine that an operational abnormality has occurred in the managed process when a series of processing resource usage amounts used in the execution of the managed process, collected by the collection unit, matches the pseudo-data stored in the storage unit.
In the process control device according to claim 3, Furthermore, the process control device is characterized by comprising a control unit configured to send an instruction to perform a predetermined action to resolve the operational abnormality of the managed process when the determination unit determines that an operational abnormality has occurred in the managed process.
In the process control device according to claim 1, The probability distribution of the first data is the probability density function of the first data, The probability distribution of the second data is the probability density function of the second data, The process control device is characterized in that the ratio is the density ratio of the probability density function of the first data and the probability density function of the second data.
A first learning step involves learning a first parameter that represents the ratio between a probability distribution of first data, which is a series of processing resource usages that are considered normal for the execution of each process corresponding to multiple process IDs, and a probability distribution of second data, which is a series of processing resource usages that deviate from the normal range of processing resource usage, and estimating the ratio based on the learned first parameter. A second learning step involves taking each of the sequences of processing resource usage used in the execution of each process corresponding to each of multiple process IDs as an observed value, treating each observed value as a conditionally independent discrete value, and estimating the second parameter of the class-specific probability distribution model corresponding to the class corresponding to the first data and the class corresponding to the second data, respectively, based on the frequency of occurrence of each observed value. A third learning step involves learning a plurality of generative models, each having an objective function set by the probability distribution of the first data and the probability distribution of the second data determined from the ratio estimated in the first learning step, and the second parameter estimated in the second learning step. The generator generates pseudo-data that is statistically similar to the true data, with each of the sequence of processing resource usage used in the execution of the process corresponding to each of the plurality of process IDs corresponding to the second data being used as true data, and a discriminator that identifies whether the pseudo-data generated by the generator belongs to the class corresponding to the true data or the class corresponding to the pseudo-data, and each objective function set by the probability distribution of the first data and the probability distribution of the second data determined from the ratio estimated in the first learning step, and the second parameter estimated in the second learning step. A process management method comprising: a generation step of generating pseudo-data using each of a plurality of trained generators obtained through training in the third learning step.
In the process control method described in claim 6, Furthermore, the identification step involves using each of the multiple trained classifiers obtained through the learning in the third learning step to identify whether each of the pseudo-data generated in the generation step belongs to the class corresponding to the true data or the class corresponding to the pseudo-data. A process management method comprising: a storage step of storing each of the pseudo-data identified in the identification step as belonging to a class corresponding to true data in a storage unit.
In the process control method described in claim 7, Furthermore, a collection step is performed to collect a series of processing resource usage amounts used in the execution of each of the multiple process IDs corresponding to the managed process, A process management method characterized by comprising: a determination step of determining that an abnormal operation of the managed process has occurred when the series of processing resource usage used in the execution of the managed process collected in the collection step matches the pseudo data stored in the storage unit.
In the process control method described in claim 8, Furthermore, the process management method is characterized by comprising a management step of sending an instruction to perform a predetermined action to resolve the abnormal operation of the managed process if it is determined in the determination step that an abnormal operation of the managed process has occurred.

Description

This invention relates to a process control apparatus and a process control method. In recent years, the demands on software have become more sophisticated and complex in order to provide high-performance systems and services. Software implemented in high-performance systems consists of programs with vast amounts of source code, and program execution is becoming increasingly complex. When process failures or malfunctions occur in a system in operation, identifying the cause of the program failure or malfunction is complex, time-consuming, and not easy. For example, Patent Document 1 discloses a method for retrospectively identifying the location of the error and the variable values at the time using core files. However, the technology disclosed in Patent Document 1 required source code analysis because it was not possible to identify the cause or location of errors from log information. Japanese Patent Publication No. 2005-301570 Figure 1 is a block diagram showing the configuration of a process management system equipped with a process control device according to an embodiment of the present invention.Figure 2 is a diagram illustrating an operational abnormality of a process managed by the process control device according to this embodiment.Figure 3 is a diagram illustrating the third learning unit included in the process control device according to this embodiment.Figure 4 is a diagram illustrating the third learning unit included in the process control device according to this embodiment.Figure 5 is a diagram illustrating the third learning unit included in the process control device according to this embodiment.Figure 6 is a block diagram showing an example of the hardware configuration of the process control device according to this embodiment.Figure 7 is a flowchart showing the operation of the process control device according to this embodiment.Figure 8 is a flowchart showing the operation of the process control device according to this embodiment.Figure 9 is a flowchart showing the operation of the process control device according to this embodiment.Figure 10 is a flowchart showing the operation of the process control device according to this embodiment. Hereinafter, preferred embodiments of the present invention will be described in detail with reference to Figures 1 to 10. [Process Management System Configuration] First, with reference to Figure 1, an overview of a process management system comprising a process management device 1 according to an embodiment of the present invention will be described. The process management system comprises a process management device 1 and an information processing device 2. The process management device 1 and the information processing device 2 are connected via a network NW. The term "network" (NW) includes, for example, wired networks such as LAN, WAN, the Internet, and ISDN, as well as wireless networks such as mobile communication networks using wireless LAN, LTE/4G, 5G, and 6G wireless communication systems. However, the scope of this invention is not limited to these. The information processing device 2 can be implemented as a server, gateway, desktop computer, embedded device, mobile communication terminal such as a smartphone, tablet computer, laptop computer, etc. In this embodiment, the information processing device 2 is not limited to a single unit, but includes multiple units. In the case of multiple units, each information processing device 2 executes the same process generated by the same application or executable file. Furthermore, the information processing device 2 is uniquely identified by network identification information such as an IP address or MAC address, or by a device ID assigned by a process management system. The information processing device 2 can be implemented using a computer equipped with a processor, main memory, communication interface, auxiliary storage, and input/output (I/O), and a program that controls these hardware resources. The information processing device 2 runs one or more applications (programs) on the OS, and each application operates as one or more processes. Each process is assigned a unique process ID (PID) by the OS. Process IDs are assigned to each execution unit of a process; even the same application may have different process IDs depending on whether it generates multiple processes or depending on the execution environment and timing. The information processing device 2 calculates the CPU usage rate as the amount of processing resources used for each process ID corresponding to the currently running process, and records it in memory. If the configuration of the CPUs of multiple information processing devices 2 are not identical (e.g., number of cores, clock speed), the CPU usage rates recorded by each information processing device 2 are normalized or corrected using each CPU benchmark, etc. The normalization or correction process may be performed by the process management device 1. In this embodiment, the CP