CN117272207A - Data center anomaly analysis method and system - Google Patents
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Abstract
The embodiment of the invention provides a data center abnormality analysis method and system, and provides a data center operation abnormality analysis and prediction method. First, operation data of a target data center is acquired. And then, loading the operation data of the target data center into an anomaly analysis prediction network generated through priori learning, wherein the anomaly analysis prediction network performs knowledge learning generation based on the associated data center operation data and static scheduling data. The anomaly analysis prediction network can generate a corresponding operation anomaly class. Finally, according to the determined operation abnormality category, abnormality diagnosis data is determined. The method can effectively analyze and predict the possible abnormal situation in the running process of the data center, and provides powerful support for the management and maintenance of the data center.
Description
Technical Field
The invention relates to the technical field of data centers, in particular to a data center anomaly analysis method and system.
Background
With the development of information technology, a data center is used as a core facility for storing and processing a large amount of data, and the running state of the data center is critical to the stability of the whole IT system. However, due to the complexity of the data center and the variability of the operating environment, various types of anomalies may occur during operation, such as hardware failures, software errors, network outages, and the like.
Conventional anomaly detection methods generally rely on manual monitoring or preset threshold alarms, and have certain limitations. For example, manual monitoring is inefficient and prone to error; the method of presetting the threshold cannot adapt to the dynamic change of the operation environment of the data center, and a large number of false positives or false negatives may be generated.
In addition, when an abnormality occurs in the data center, it is necessary to rapidly and accurately perform fault diagnosis in order to minimize service interruption time due to the fault. However, current fault diagnosis methods often require the reliance of specialized maintenance personnel experience and may not be able to make an effective diagnosis in the face of complex or unknown types of anomalies.
Therefore, a new method is urgently needed, which can effectively analyze and predict the abnormal situation in the running process of the data center and perform rapid and accurate fault diagnosis according to the abnormal type.
Disclosure of Invention
In view of the foregoing, an objective of an embodiment of the present invention is to provide a data center anomaly analysis method and system. First, operation data of a target data center is acquired. And then, loading the operation data of the target data center into an anomaly analysis prediction network generated through priori learning, wherein the anomaly analysis prediction network performs knowledge learning generation based on the associated data center operation data and static scheduling data. The anomaly analysis prediction network can generate a corresponding operation anomaly class. Finally, according to the determined operation abnormality category, abnormality diagnosis data is determined. The method can effectively analyze and predict the possible abnormal situation in the running process of the data center, and provides powerful support for the management and maintenance of the data center.
According to an aspect of the embodiment of the present invention, there is provided a data center anomaly analysis method and system, the method including:
acquiring target data center operation data;
loading the target data center operation data into an abnormality analysis prediction network which is learned in advance, generating an operation abnormality category determined by the abnormality analysis prediction network, and performing knowledge learning generation by the abnormality analysis prediction network according to the associated data center operation data and static scheduling data;
and determining abnormality diagnosis data according to the operation abnormality type.
In an alternative embodiment, the anomaly analysis prediction network includes a self-attention unit and a fully-connected output unit, the loading the target data center operation data into an anomaly analysis prediction network learned a priori, generating an operation anomaly category determined by the anomaly analysis prediction network, including:
loading the target data center operation data into the self-attention unit, and generating target self-attention characteristics determined by the self-attention unit;
and loading the target self-attention characteristic into the fully-connected output unit, and generating the abnormal operation type determined by the fully-connected output unit.
In an alternative embodiment, the training step of the anomaly analysis prediction network includes:
generating positive learning features and negative learning features according to linked operation scheduling event data in log data of a data center, and generating template learning data according to the positive learning features and the negative learning features;
and updating parameters of the initialized anomaly analysis prediction network according to the template learning data, and generating the anomaly analysis prediction network after updating the parameters.
In an alternative embodiment, the generating positive learning features and negative learning features from linked operational schedule event data in the data center log data includes:
acquiring linked operation scheduling event data in log data of a data center as basic training template data;
performing regularization conversion on the basic training template data to generate the positive learning features;
and randomly scrambling data center operation data and noise characteristics in the data center log data to generate the passive learning characteristics.
In an alternative embodiment, the self-attention unit includes a first self-attention unit and a second self-attention unit, and the parameter updating is performed on the initialized anomaly analysis prediction network according to the template learning data to generate a anomaly analysis prediction network after parameter updating, which includes:
for an operation scheduling event data pair in the template learning data, loading a static scheduling event of the operation scheduling event data pair into the first self-attention unit, generating a target static self-attention characteristic determined by the first self-attention unit, loading a dynamic scheduling event of the operation scheduling event data pair into the second self-attention unit, and generating a target dynamic self-attention characteristic determined by the second self-attention unit;
determining a target training error parameter according to the target static self-attention characteristic and the target dynamic self-attention characteristic, and carrying out parameter updating on the self-attention unit by taking the target training error parameter as a target to generate a self-attention unit after parameter updating;
and carrying out parameter updating on the fully-connected output unit according to the self-attention unit after parameter updating, and generating the fully-connected output unit after parameter updating.
In an alternative embodiment, the running schedule event data pair includes a plurality of dynamic schedule events, the loading the dynamic schedule events of the running schedule event data pair into the second self-attention unit, generating a target dynamic self-attention feature determined by the second self-attention unit, including:
integrating each dynamic scheduling event to generate integrated data center operation data;
and loading the integrated data center operation data into the second self-attention unit to generate target dynamic self-attention characteristics determined by the second self-attention unit.
In an alternative embodiment, the operation schedule event data pair includes a plurality of dynamic schedule events, the loading the dynamic schedule events of the operation schedule event data pair into the second self-attention unit, generating the dynamic self-attention feature determined by the second self-attention unit, including:
loading each dynamic scheduling event into the second self-attention unit respectively, and generating a dynamic self-attention characteristic of each dynamic scheduling event determined by the second self-attention unit;
and summing the dynamic self-attention characteristics of each dynamic scheduling event to generate the target dynamic self-attention characteristics.
In an alternative embodiment, the first self-attention unit and the second self-attention unit are respectively connected with the fully-connected output unit, and the self-attention unit after parameter update performs parameter update on the fully-connected output unit to generate a fully-connected output unit after parameter update, which includes:
determining static self-attention characteristics of the static scheduling data according to the first self-attention unit aiming at the static scheduling data in the template learning data;
constructing target abnormal learning template data according to the static self-attention characteristics and the abnormal categories of the static scheduling data;
and updating parameters of the initialized full-connection output unit according to the target abnormal learning template data, and generating the full-connection output unit after parameter updating.
According to another aspect of the embodiment of the present invention, there is provided a data center anomaly analysis method and system, the system including:
the acquisition module is used for acquiring the operation data of the target data center;
the generation module is used for loading the target data center operation data into an abnormality analysis prediction network which is learned a priori, generating an operation abnormality category determined by the abnormality analysis prediction network, and performing knowledge learning generation by the abnormality analysis prediction network according to the associated data center operation data and static scheduling data;
and the determining module is used for determining the abnormality diagnosis data according to the operation abnormality type.
According to another aspect of an embodiment of the present invention, there is provided a server including: the device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus; the memory is used for storing a computer program; the processor is configured to implement the data center anomaly analysis method step described in any one of the above when executing the computer program.
According to another aspect of the embodiments of the present invention, there is provided a readable storage medium having stored thereon a computer program which, when executed by a processor, can perform the steps of the data center anomaly analysis method described above.
The foregoing objects, features and advantages of embodiments of the invention will be more readily apparent from the following detailed description of the embodiments taken in conjunction with the accompanying drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows a schematic diagram of components of a server provided by an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a data center anomaly analysis method according to an embodiment of the present invention;
FIG. 3 is a functional block diagram of an anomaly analysis system in accordance with an embodiment of the present invention.
Detailed Description
In order to enable those skilled in the art to better understand the present invention, a technical solution of the present embodiment of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiment of the present invention, and it is apparent that the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. All other embodiments, which can be made by those skilled in the art without the benefit of the teachings of this invention, are intended to fall within the scope of the invention.
The terms first, second, third and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 shows an exemplary component diagram of a server 100. The server 100 may include one or more processors 104, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. The server 100 may also include any storage medium 106 for storing any kind of information such as code, settings, data, etc. For example, and without limitation, storage medium 106 may include any one or more of the following combinations: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage medium may store information using any technique. Further, any storage medium may provide volatile or non-volatile retention of information. Further, any storage medium may represent fixed or removable components of server 100. In one case, the server 100 may perform any of the operations of the associated instructions when the processor 104 executes the associated instructions stored in any storage medium or combination of storage media. The server 100 also includes one or more drive units 108, such as a hard disk drive unit, an optical disk drive unit, etc., for interacting with any storage media.
The server 100 also includes input/output 110 (I/O) for receiving various inputs (via input unit 112) and for providing various outputs (via output unit 114). One particular output mechanism may include a presentation device 116 and an associated Graphical User Interface (GUI) 118. The server 100 may also include one or more network interfaces 120 for exchanging data with other devices via one or more communication units 122. One or more communication buses 124 couple the components described above together.
The communication unit 122 may be implemented in any manner, for example, via a local area network, a wide area network (e.g., the internet), a point-to-point connection, etc., or any combination thereof. The communication unit 122 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers 100, etc., governed by any protocol or combination of protocols.
Fig. 2 is a schematic flow chart of a data center anomaly analysis method and system according to an embodiment of the present invention, where the data center anomaly analysis method and system may be executed by the server 100 shown in fig. 1, and detailed steps of the data center anomaly analysis method are described below.
Step S110, acquiring target data center operation data;
step S120, loading the target data center operation data into an abnormality analysis prediction network which is learned a priori, and generating an operation abnormality category determined by the abnormality analysis prediction network, wherein the abnormality analysis prediction network carries out knowledge learning generation according to the associated data center operation data and static scheduling data;
step S130, determining abnormality diagnosis data according to the operation abnormality type.
Based on the above steps, the embodiment provides an analysis and prediction method for abnormal operation of a data center. First, operation data of a target data center is acquired. And then, loading the operation data of the target data center into an anomaly analysis prediction network generated through priori learning, wherein the anomaly analysis prediction network performs knowledge learning generation based on the associated data center operation data and static scheduling data. The anomaly analysis prediction network can generate a corresponding operation anomaly class. Finally, according to the determined operation abnormality category, abnormality diagnosis data is determined. The method can effectively analyze and predict the possible abnormal situation in the running process of the data center, and provides powerful support for the management and maintenance of the data center.
In an alternative embodiment, the anomaly analysis prediction network includes a self-attention unit and a fully-connected output unit, the loading the target data center operation data into an anomaly analysis prediction network learned a priori, generating an operation anomaly category determined by the anomaly analysis prediction network, including:
loading the target data center operation data into the self-attention unit, and generating target self-attention characteristics determined by the self-attention unit;
and loading the target self-attention characteristic into the fully-connected output unit, and generating the abnormal operation type determined by the fully-connected output unit.
In an alternative embodiment, the training step of the anomaly analysis prediction network includes:
generating positive learning features and negative learning features according to linked operation scheduling event data in log data of a data center, and generating template learning data according to the positive learning features and the negative learning features;
and updating parameters of the initialized anomaly analysis prediction network according to the template learning data, and generating the anomaly analysis prediction network after updating the parameters.
In an alternative embodiment, the generating positive learning features and negative learning features from linked operational schedule event data in the data center log data includes:
acquiring linked operation scheduling event data in log data of a data center as basic training template data;
performing regularization conversion on the basic training template data to generate the positive learning features;
and randomly scrambling data center operation data and noise characteristics in the data center log data to generate the passive learning characteristics.
In an alternative embodiment, the self-attention unit includes a first self-attention unit and a second self-attention unit, and the parameter updating is performed on the initialized anomaly analysis prediction network according to the template learning data to generate a anomaly analysis prediction network after parameter updating, which includes:
for an operation scheduling event data pair in the template learning data, loading a static scheduling event of the operation scheduling event data pair into the first self-attention unit, generating a target static self-attention characteristic determined by the first self-attention unit, loading a dynamic scheduling event of the operation scheduling event data pair into the second self-attention unit, and generating a target dynamic self-attention characteristic determined by the second self-attention unit;
determining a target training error parameter according to the target static self-attention characteristic and the target dynamic self-attention characteristic, and carrying out parameter updating on the self-attention unit by taking the target training error parameter as a target to generate a self-attention unit after parameter updating;
and carrying out parameter updating on the fully-connected output unit according to the self-attention unit after parameter updating, and generating the fully-connected output unit after parameter updating.
In an alternative embodiment, the running schedule event data pair includes a plurality of dynamic schedule events, the loading the dynamic schedule events of the running schedule event data pair into the second self-attention unit, generating a target dynamic self-attention feature determined by the second self-attention unit, including:
integrating each dynamic scheduling event to generate integrated data center operation data;
and loading the integrated data center operation data into the second self-attention unit to generate target dynamic self-attention characteristics determined by the second self-attention unit.
In an alternative embodiment, the operation schedule event data pair includes a plurality of dynamic schedule events, the loading the dynamic schedule events of the operation schedule event data pair into the second self-attention unit, generating the dynamic self-attention feature determined by the second self-attention unit, including:
loading each dynamic scheduling event into the second self-attention unit respectively, and generating a dynamic self-attention characteristic of each dynamic scheduling event determined by the second self-attention unit;
and summing the dynamic self-attention characteristics of each dynamic scheduling event to generate the target dynamic self-attention characteristics.
In an alternative embodiment, the first self-attention unit and the second self-attention unit are respectively connected with the fully-connected output unit, and the self-attention unit after parameter update performs parameter update on the fully-connected output unit to generate a fully-connected output unit after parameter update, which includes:
determining static self-attention characteristics of the static scheduling data according to the first self-attention unit aiming at the static scheduling data in the template learning data;
constructing target abnormal learning template data according to the static self-attention characteristics and the abnormal categories of the static scheduling data;
and updating parameters of the initialized full-connection output unit according to the target abnormal learning template data, and generating the full-connection output unit after parameter updating.
Fig. 3 shows a functional block diagram of a data center anomaly analysis system 200 according to an embodiment of the present invention, where functions implemented by the data center anomaly analysis system 200 may correspond to steps performed by the above-described method. The data center based anomaly analysis system 200 can be understood as the server 100, or the processor of the server 100, or can be understood as a component which is independent of the server 100 or the processor and implements the functions of the present invention under the control of the server 100, as shown in fig. 3, and the functions of the functional modules of the data center based anomaly analysis system 200 are described in detail below.
An acquisition module 210 for acquiring target data center operation data;
the generating module 220 is configured to load the target data center operation data into an abnormality analysis prediction network that is learned a priori, and generate an operation abnormality category determined by the abnormality analysis prediction network, where the abnormality analysis prediction network performs knowledge learning generation according to the associated data center operation data and static scheduling data;
a determining module 230 is configured to determine abnormality diagnostic data according to the class of operational abnormalities.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (10)
1. A data center anomaly analysis method, the method comprising:
acquiring target data center operation data;
loading the target data center operation data into an abnormality analysis prediction network which is learned in advance, generating an operation abnormality category determined by the abnormality analysis prediction network, and performing knowledge learning generation by the abnormality analysis prediction network according to the associated data center operation data and static scheduling data;
and determining abnormality diagnosis data according to the operation abnormality type.
2. The data center anomaly analysis method of claim 1, wherein the anomaly analysis prediction network comprises a self-attention unit and a fully-connected output unit, wherein the loading the target data center operation data into the anomaly analysis prediction network learned a priori, generating the operation anomaly category determined by the anomaly analysis prediction network, comprises:
loading the target data center operation data into the self-attention unit, and generating target self-attention characteristics determined by the self-attention unit;
and loading the target self-attention characteristic into the fully-connected output unit, and generating the abnormal operation type determined by the fully-connected output unit.
3. The data center anomaly analysis method of claim 2, wherein the anomaly analysis prediction network training step comprises:
generating positive learning features and negative learning features according to linked operation scheduling event data in log data of a data center, and generating template learning data according to the positive learning features and the negative learning features;
and updating parameters of the initialized anomaly analysis prediction network according to the template learning data, and generating the anomaly analysis prediction network after updating the parameters.
4. The data center anomaly analysis method of claim 3, wherein the generating positive learning features and negative learning features from linked operational scheduling event data in data center log data comprises:
acquiring linked operation scheduling event data in log data of a data center as basic training template data;
performing regularization conversion on the basic training template data to generate the positive learning features;
and randomly scrambling data center operation data and noise characteristics in the data center log data to generate the passive learning characteristics.
5. The data center anomaly analysis method of claim 3, wherein the self-attention unit includes a first self-attention unit and a second self-attention unit, the initializing anomaly analysis prediction network is updated with parameters according to the template learning data, and the generating the updated anomaly analysis prediction network includes:
for an operation scheduling event data pair in the template learning data, loading a static scheduling event of the operation scheduling event data pair into the first self-attention unit, generating a target static self-attention characteristic determined by the first self-attention unit, loading a dynamic scheduling event of the operation scheduling event data pair into the second self-attention unit, and generating a target dynamic self-attention characteristic determined by the second self-attention unit;
determining a target training error parameter according to the target static self-attention characteristic and the target dynamic self-attention characteristic, and carrying out parameter updating on the self-attention unit by taking the target training error parameter as a target to generate a self-attention unit after parameter updating;
and carrying out parameter updating on the fully-connected output unit according to the self-attention unit after parameter updating, and generating the fully-connected output unit after parameter updating.
6. The data center anomaly analysis method of claim 5, wherein the pair of operational schedule event data comprises a plurality of dynamic schedule events, wherein loading the dynamic schedule events of the pair of operational schedule event data into the second self-attention unit generates the target dynamic self-attention feature determined by the second self-attention unit, comprising:
integrating each dynamic scheduling event to generate integrated data center operation data;
and loading the integrated data center operation data into the second self-attention unit to generate target dynamic self-attention characteristics determined by the second self-attention unit.
7. The data center anomaly analysis method of claim 5, wherein the pair of operational schedule event data comprises a plurality of dynamic schedule events, wherein loading the dynamic schedule events of the pair of operational schedule event data into the second self-attention unit generates the dynamic self-attention feature determined by the second self-attention unit, comprising:
loading each dynamic scheduling event into the second self-attention unit respectively, and generating a dynamic self-attention characteristic of each dynamic scheduling event determined by the second self-attention unit;
and summing the dynamic self-attention characteristics of each dynamic scheduling event to generate the target dynamic self-attention characteristics.
8. The data center anomaly analysis method of claim 5, wherein the first self-attention unit and the second self-attention unit are respectively connected with the fully-connected output unit, the self-attention unit updated according to the parameters updates the fully-connected output unit to generate the fully-connected output unit updated according to the parameters, comprising:
determining static self-attention characteristics of the static scheduling data according to the first self-attention unit aiming at the static scheduling data in the template learning data;
constructing target abnormal learning template data according to the static self-attention characteristics and the abnormal categories of the static scheduling data;
and updating parameters of the initialized full-connection output unit according to the target abnormal learning template data, and generating the full-connection output unit after parameter updating.
9. A data center anomaly analysis system, comprising:
the acquisition module is used for acquiring the operation data of the target data center;
the generation module is used for loading the target data center operation data into an abnormality analysis prediction network which is learned a priori, generating an operation abnormality category determined by the abnormality analysis prediction network, and performing knowledge learning generation by the abnormality analysis prediction network according to the associated data center operation data and static scheduling data;
and the determining module is used for determining the abnormality diagnosis data according to the operation abnormality type.
10. A server, comprising: the device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus; the memory is used for storing a computer program; the processor, when configured to execute the computer program, implements the data center anomaly analysis method steps of any one of claims 1-8.
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