A method and device for repairing virtual machine abnormality

Abstract

The present application discloses a method and device for repairing virtual machine anomalies, including: obtaining the operation data of the virtual machine in real time, analyzing the operation data, and obtaining the operation status of the virtual machine; if an abnormality occurs in the virtual machine, performing big data analysis on the operation data, and outputting an abnormal state processing method and / or a fallback plan; obtaining historical operation data similar to the current operation data through feature value comparison, comparing the first output abnormal state processing method and / or fallback plan corresponding to the current operation data with the second output abnormal state processing method and / or fallback plan corresponding to the historical operation data, and correcting the first output abnormal state processing method and / or fallback plan according to the comparison result. The present application can quickly determine the abnormality of the virtual machine and quickly repair the virtual machine.

Description

Technical Field

[0001] The present application relates to the field of virtual machine technology, and in particular to a method and device for repairing virtual machine abnormalities. Background Art

[0002] At present, with the increasing popularity of computer and network applications and the increasing variety of business in different fields, the high availability technology of virtual machines (a virtual machine refers to a computer system that runs on a physical machine through software simulation, has complete hardware system functions, and runs in a completely isolated environment) in a distributed environment (that is, after physical machine A encounters problems such as downtime, the virtual machine running on physical machine A can be started on physical machine B without human intervention to ensure the continuous operation of the virtual machine) is becoming more and more important.

[0003] Among the existing technical solutions, there is no solution that can adapt to any platform and quickly provide a method for accurately judging and efficiently processing virtual machine abnormality recovery for virtual machine abnormalities. Summary of the invention

[0004] The present application provides a method and device for repairing a virtual machine abnormality, which can quickly determine the abnormality of the virtual machine and quickly repair the virtual machine.

[0005] In view of this, the first aspect of the present application provides a method for repairing a virtual machine abnormality, the method comprising:

[0006] Acquire the operation data of the virtual machine in real time, analyze the operation data, and obtain the operation status of the virtual machine;

[0007] If an abnormality occurs in the virtual machine, a big data analysis is performed on the operation data to output an abnormal state processing method and / or a fallback plan;

[0008] By comparing characteristic values, historical operating data similar to the current operating data is obtained, and the first output abnormal state processing method and / or fallback plan corresponding to the current operating data is compared with the second output abnormal state processing method and / or fallback plan corresponding to the historical operating data; and the first output abnormal state processing method and / or fallback plan is corrected according to the comparison result.

[0009] Optionally, before acquiring historical operation data similar to the current operation data through the feature value comparison, comparing a first output abnormal state processing method and / or a fallback solution corresponding to the current operation data with a second output abnormal state processing method and / or a fallback solution corresponding to the historical operation data, and revising the first output abnormal state processing method and / or the fallback solution according to the comparison result, the method further includes:

[0010] Obtain the historical operation data corresponding to the virtual machine exception, analyze the second output abnormal state processing method and / or fallback plan corresponding to the historical operation data, and obtain the optimal second output abnormal state processing method and / or fallback plan.

[0011] Optionally, acquiring historical operation data similar to the current operation data through feature value comparison, comparing a first output abnormal state processing method and / or fallback scheme corresponding to the current operation data with a second output abnormal state processing method and / or fallback scheme corresponding to the historical operation data, and modifying the first output abnormal state processing method and / or fallback scheme according to the comparison result, includes:

[0012] Obtaining a characteristic value corresponding to the current operation data through big data analysis, comparing the characteristic value corresponding to the current operation data with the characteristic value corresponding to the historical operation data, and if the comparison result is less than a preset threshold, obtaining a plurality of the historical operation data similar to the current operation data;

[0013] According to the comparison result of the second output abnormal state processing method and / or fallback plan corresponding to the historical operation data and the first output abnormal state processing method and / or fallback plan corresponding to the current operation data, the first output abnormal state processing method and / or fallback plan is corrected.

[0014] Optionally, also include:

[0015] Obtain the current virtual machine's operating data in real time or at regular intervals, input the current operating data into the trained virtual machine status analysis model, and predict the virtual machine's operating status at the next moment;

[0016] If the predicted running state of the virtual machine at the next moment is abnormal, an early warning signal is issued.

[0017] Optionally, before obtaining the operating data of the current virtual machine in real time or at a fixed time, inputting the current operating data into a trained virtual machine state analysis model, and predicting the operating state of the virtual machine at the next moment, the method further includes:

[0018] Obtain multiple historical operation data corresponding to the virtual machine abnormality and the output abnormal state processing method and / or fallback plan corresponding to the historical operation data, use the historical operation data as input and the output abnormal state processing method and / or fallback plan as output, and train the virtual machine state analysis model.

[0019] A second aspect of the present application provides a virtual machine abnormality repair device, the device comprising:

[0020] An operation status acquisition unit, used to acquire the operation data of the virtual machine in real time, analyze the operation data, and obtain the operation status of the virtual machine;

[0021] An analysis unit, configured to perform big data analysis on the operation data and output an abnormal state processing method and / or a fallback plan when an abnormality occurs in the virtual machine;

[0022] The result output unit is used to obtain historical operating data similar to the current operating data through feature value comparison, compare the first output abnormal state processing method and / or fallback plan corresponding to the current operating data with the second output abnormal state processing method and / or fallback plan corresponding to the historical operating data, and modify the first output abnormal state processing method and / or fallback plan according to the comparison result.

[0023] Optionally, also include:

[0024] The optimal solution acquisition unit is used to obtain the historical operation data corresponding to the virtual machine exception, analyze the second output abnormal state processing method and / or fallback solution corresponding to the historical operation data, and obtain the optimal second output abnormal state processing method and / or fallback solution.

[0025] Optionally, the result output unit further includes:

[0026] A data comparison unit, used for obtaining a characteristic value corresponding to the current operation data through big data analysis, comparing the characteristic value corresponding to the current operation data with the characteristic value corresponding to the historical operation data, and if the comparison result is less than a preset threshold, obtaining a plurality of the historical operation data similar to the current operation data;

[0027] A data correction unit is used to correct the first output abnormal state processing method and / or fallback scheme based on a comparison result between the second output abnormal state processing method and / or fallback scheme corresponding to the historical operation data and the first output abnormal state processing method and / or fallback scheme corresponding to the current operation data.

[0028] Optionally, also include:

[0029] A prediction unit is used to obtain the operation data of the current virtual machine in real time or at a fixed time, input the current operation data into the trained virtual machine state analysis model, and predict the operation state of the virtual machine at the next moment;

[0030] The early warning unit is used to send out an early warning signal when the predicted running state of the virtual machine at the next moment is abnormal.

[0031] Optionally, also include:

[0032] A training unit is used to obtain multiple historical operation data corresponding to the virtual machine abnormality and the output abnormal state processing method and / or fallback plan corresponding to the historical operation data, and use the historical operation data as input and the output abnormal state processing method and / or fallback plan as output to train the virtual machine state analysis model.

[0033] It can be seen from the above technical solutions that this application has the following advantages:

[0034] In an embodiment of the present application, a method for repairing virtual machine abnormalities is provided, including: acquiring the operating data of the virtual machine in real time, analyzing the operating data, and obtaining the operating status of the virtual machine; if an abnormality occurs in the virtual machine, performing big data analysis on the operating data, and outputting an abnormal state processing method and / or a fallback plan; acquiring historical operating data similar to the current operating data through feature value comparison, comparing a first output abnormal state processing method and / or a fallback plan corresponding to the current operating data with a second output abnormal state processing method and / or a fallback plan corresponding to the historical operating data, and correcting the first output abnormal state processing method and / or the fallback plan according to the comparison result.

[0035] The present application determines the operation status of the current virtual machine by analyzing real-time operation data. When an abnormality is detected in the virtual machine, a repair plan for the virtual machine is quickly determined through a big data analysis solution, and the current repair plan is modified based on the repair plan when the historical abnormal situation occurred, so as to obtain a repair plan that meets the current abnormal situation, so that the virtual machine abnormality can be quickly determined and the virtual machine can be quickly repaired. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A method flow chart of an embodiment of a method for repairing a virtual machine abnormality according to the present application;

[0037] Figure 2 A method flow chart of another embodiment of a method for repairing a virtual machine abnormality according to the present application;

[0038] Figure 3 A method flow chart of a third embodiment of a method for repairing a virtual machine abnormality according to the present application;

[0039] Figure 4 A structural diagram of an embodiment of a virtual machine abnormality repair device in the present application. DETAILED DESCRIPTION

[0040] The present application determines the operation status of the current virtual machine by analyzing real-time operation data. When an abnormality is detected in the virtual machine, a repair plan for the virtual machine is quickly determined through a big data analysis solution, and the current repair plan is modified based on the repair plan when the historical abnormal situation occurred, so as to obtain a repair plan that meets the current abnormal situation, so that the virtual machine abnormality can be quickly determined and the virtual machine can be quickly repaired.

[0041] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] See also Figure 1 , Figure 1 A method flow chart of an embodiment of a method for repairing a virtual machine abnormality according to the present application is shown as follows: Figure 1 As shown, Figure 1 Included:

[0043] 101. Obtain the operation data of the virtual machine in real time, analyze the operation data, and obtain the operation status of the virtual machine;

[0044] It should be noted that the present application can collect the virtual machine operation data on any platform in real time, and perform operation analysis on the server on the virtual machine operation data uploaded from each platform to determine whether the virtual machine has an abnormality. Specifically, the operation data of each virtual machine can be collected, and an analysis library can be established in the server. By comparing the operation data with the analysis library, the operation status of the virtual machine can be determined.

[0045] 102. If an abnormality occurs in the virtual machine, a big data analysis is performed on the operation data to output an abnormal state processing method and / or a fallback plan;

[0046] It should be noted that when it is determined that a virtual machine is abnormal, a big data analysis can be performed on the operation data of the virtual machine, such as determining the cause of the current abnormality (including abnormal process resource usage, abnormal sequence of processes, etc.), and analyzing the corresponding abnormal state processing method and / or fallback plan based on the cause of the abnormality and the corresponding policy library. The big data analysis process can be executed in the server to obtain a preliminary processing plan.

[0047] 103. Obtain historical operating data similar to the current operating data through feature value comparison, compare the first output abnormal state processing method and / or fallback plan corresponding to the current operating data with the second output abnormal state processing method and / or fallback plan corresponding to the historical operating data, and modify the first output abnormal state processing method and / or fallback plan according to the comparison result.

[0048] It should be noted that after obtaining a preliminary first output abnormal state processing method and / or fallback plan, the characteristic value corresponding to the current operating data can be compared with the characteristic value corresponding to the historical operating data. The server can include the corresponding historical operating data, characteristic values ​​and the corresponding second output abnormal state processing method and / or fallback plan when the virtual machine fails. The present application can select the closest multiple historical operating data based on the characteristic value of the current operating data, and can correct the first output abnormal state processing method and / or fallback plan based on the second output abnormal state processing method and / or fallback plan corresponding to the multiple historical operating data, so as to obtain a virtual machine abnormality repair plan suitable for the current state.

[0049] The present application determines the operation status of the current virtual machine by analyzing real-time operation data. When an abnormality is detected in the virtual machine, a repair plan for the virtual machine is quickly determined through a big data analysis solution, and the current repair plan is modified based on the repair plan when the historical abnormal situation occurred, so as to obtain a repair plan that meets the current abnormal situation, so that the virtual machine abnormality can be quickly determined and the virtual machine can be quickly repaired.

[0050] The present application also provides a method flow chart of another embodiment of a method for repairing a virtual machine abnormality, such as Figure 2 As shown, Figure 2 Included:

[0051] 201. Obtain the operation data of the virtual machine in real time, analyze the operation data, and obtain the operation status of the virtual machine;

[0052] It should be noted that the present application can collect the virtual machine operation data on any platform in real time, and perform operation analysis on the server on the virtual machine operation data uploaded from each platform to determine whether the virtual machine has an abnormality. Specifically, the operation data of each virtual machine can be collected, and an analysis library can be established in the server. By comparing the operation data with the analysis library, the operation status of the virtual machine can be determined.

[0053] 202. If an abnormality occurs in the virtual machine, a big data analysis is performed on the operation data, and an abnormal state processing method and / or a fallback plan are output;

[0054] It should be noted that when it is determined that a virtual machine is abnormal, a big data analysis can be performed on the operation data of the virtual machine, such as determining the cause of the current abnormality (including abnormal process resource usage, abnormal sequence of processes, etc.), and analyzing the corresponding abnormal state processing method and / or fallback plan based on the cause of the abnormality and the corresponding policy library. The big data analysis process can be executed in the server to obtain a preliminary processing plan.

[0055] 203. Obtain historical operation data corresponding to the virtual machine exception, analyze the second output abnormal state processing method and / or fallback solution corresponding to the historical operation data, and obtain the optimal second output abnormal state processing method and / or fallback solution;

[0056] It should be noted that the present application collects multiple historical operation data corresponding to the virtual machine abnormality, and analyzes the second output abnormal state processing method and / or fallback plan corresponding to the multiple historical operation data under the same virtual machine abnormality situation. Since the second output abnormal state processing method and / or fallback plan corresponding to the multiple historical operation data under the same virtual machine abnormality situation are usually relatively similar, the optimal processing method can be selected as the second output abnormal state processing method and / or fallback plan.

[0057] 204. Obtain a characteristic value corresponding to the current operation data through big data analysis, and compare the characteristic value corresponding to the current operation data with the characteristic value corresponding to the historical operation data. If the comparison result is less than a preset threshold, obtain multiple historical operation data similar to the current operation data;

[0058] It should be noted that after obtaining the preliminary first output abnormal state processing method and / or fallback plan, the characteristic value corresponding to the current operation data can be compared with the characteristic value corresponding to the historical operation data. If the comparison result is less than the preset threshold, multiple historical operation data similar to the current operation data are obtained.

[0059] 205. According to the comparison result of the second output abnormal state processing method and / or fallback scheme corresponding to the historical operation data and the first output abnormal state processing method and / or fallback scheme corresponding to the current operation data, the first output abnormal state processing method and / or fallback scheme is modified.

[0060] It should be noted that the server may include historical operating data, characteristic values, and a corresponding second output abnormal state processing method and / or fallback plan corresponding to when a virtual machine fails. The present application can select multiple closest historical operating data based on the characteristic values ​​of the current operating data, and can modify the first output abnormal state processing method and / or fallback plan based on the second output abnormal state processing method and / or fallback plan corresponding to multiple historical operating data, so as to obtain a virtual machine abnormality repair plan suitable for the current state.

[0061] The present application also provides a method flow chart of a third embodiment of a method for repairing a virtual machine abnormality, such as Figure 3 As shown, Figure 3 Included:

[0062] 301. Obtain multiple historical operation data corresponding to the virtual machine exception and the output abnormal state processing method and / or fallback plan corresponding to the historical operation data, take the historical operation data as input, and output the abnormal state processing method and / or fallback plan as output, and train the virtual machine state analysis model.

[0063] It should be noted that the present application can obtain multiple historical operation data corresponding to the virtual machine abnormality and the output abnormal state processing method and / or fallback plan corresponding to the historical operation data, take the historical operation data as input, and output the abnormal state processing method and / or fallback plan as output, and input them into the constructed neural network model for training. The neural network model used in the present application is a common neural network, and a trained virtual machine state analysis model can be obtained.

[0064] 302. Obtain the operating data of the current virtual machine in real time or periodically, input the current operating data into the trained virtual machine state analysis model, and predict the operating state of the virtual machine at the next moment;

[0065] It should be noted that the present application can obtain the operating data of the current virtual machine in real time or at a scheduled time, and can be selected immediately according to actual needs. For example, when the amount of calculation needs to be reduced, the data collection time interval can be set regularly to collect the real-time operating data of the virtual machine at every preset time interval, and then the real-time operating data is input into the virtual machine status analysis model in the server to realize the prediction of the operating status of the virtual machine at the next moment, so that the abnormal condition of the virtual machine can be discovered in advance.

[0066] 303. If the predicted running state of the virtual machine at the next moment is an abnormal state, a warning signal is issued.

[0067] It should be noted that when the virtual machine status analysis model outputs that the virtual machine operation status at the next moment is abnormal, an abnormal warning signal can be issued so that the staff can discover the virtual machine operation status in time, thereby avoiding the occurrence of virtual machine abnormalities.

[0068] The present application also provides an embodiment of a virtual machine abnormality repair device, such as Figure 4 As shown, Figure 4 Included:

[0069] 401. An operation status acquisition unit, used to acquire operation data of the virtual machine in real time, analyze the operation data, and obtain the operation status of the virtual machine;

[0070] It should be noted that the present application can collect the virtual machine operation data on any platform in real time, and perform operation analysis on the server on the virtual machine operation data uploaded from each platform to determine whether the virtual machine has an abnormality. Specifically, the operation data of each virtual machine can be collected, and an analysis library can be established in the server. By comparing the operation data with the analysis library, the operation status of the virtual machine can be determined.

[0071] 402. An analysis unit, configured to perform big data analysis on the operation data when an abnormality occurs in the virtual machine, and output an abnormal state processing method and / or a fallback plan;

[0072] It should be noted that when it is determined that a virtual machine is abnormal, a big data analysis can be performed on the operation data of the virtual machine, such as determining the cause of the current abnormality (including abnormal process resource usage, abnormal sequence of processes, etc.), and analyzing the corresponding abnormal state processing method and / or fallback plan based on the cause of the abnormality and the corresponding policy library. The big data analysis process can be executed in the server to obtain a preliminary processing plan.

[0073] 403. A result output unit is used to obtain historical operation data similar to the current operation data through feature value comparison, compare the first output abnormal state processing method and / or fallback plan corresponding to the current operation data with the second output abnormal state processing method and / or fallback plan corresponding to the historical operation data, and modify the first output abnormal state processing method and / or fallback plan according to the comparison result.

[0074] It should be noted that after obtaining a preliminary first output abnormal state processing method and / or fallback plan, the characteristic value corresponding to the current operating data can be compared with the characteristic value corresponding to the historical operating data. The server can include the corresponding historical operating data, characteristic values ​​and the corresponding second output abnormal state processing method and / or fallback plan when the virtual machine fails. The present application can select the closest multiple historical operating data based on the characteristic value of the current operating data, and can correct the first output abnormal state processing method and / or fallback plan based on the second output abnormal state processing method and / or fallback plan corresponding to the multiple historical operating data, so as to obtain a virtual machine abnormality repair plan suitable for the current state.

[0075] The present application determines the operation status of the current virtual machine by analyzing real-time operation data. When an abnormality is detected in the virtual machine, a repair plan for the virtual machine is quickly determined through a big data analysis solution, and the current repair plan is modified based on the repair plan when the historical abnormal situation occurred, so as to obtain a repair plan that meets the current abnormal situation, so that the virtual machine abnormality can be quickly determined and the virtual machine can be quickly repaired.

[0076] In a specific embodiment, it also includes:

[0077] The optimal solution acquisition unit is used to obtain the historical operation data corresponding to the virtual machine exception, analyze the second output abnormal state processing method and / or fallback solution corresponding to the historical operation data, and obtain the optimal second output abnormal state processing method and / or fallback solution.

[0078] It should be noted that the present application collects multiple historical operation data corresponding to the virtual machine abnormality, and analyzes the second output abnormal state processing method and / or fallback plan corresponding to the multiple historical operation data under the same virtual machine abnormality situation. Since the second output abnormal state processing method and / or fallback plan corresponding to the multiple historical operation data under the same virtual machine abnormality situation are usually relatively similar, the optimal processing method can be selected as the second output abnormal state processing method and / or fallback plan.

[0079] In a specific implementation, the result output unit further includes:

[0080] A data comparison unit is used to obtain a characteristic value corresponding to the current operation data through big data analysis, and compare the characteristic value corresponding to the current operation data with the characteristic value corresponding to the historical operation data. If the comparison result is less than a preset threshold, multiple historical operation data similar to the current operation data are obtained;

[0081] It should be noted that after obtaining the preliminary first output abnormal state processing method and / or fallback plan, the characteristic value corresponding to the current operation data can be compared with the characteristic value corresponding to the historical operation data. If the comparison result is less than the preset threshold, multiple historical operation data similar to the current operation data are obtained.

[0082] The data correction unit is used to correct the first output abnormal state processing method and / or fallback scheme based on the comparison result of the second output abnormal state processing method and / or fallback scheme corresponding to the historical operation data with the first output abnormal state processing method and / or fallback scheme corresponding to the current operation data.

[0083] It should be noted that the server may include historical operating data, characteristic values, and a corresponding second output abnormal state processing method and / or fallback plan corresponding to when a virtual machine fails. The present application can select multiple closest historical operating data based on the characteristic values ​​of the current operating data, and can modify the first output abnormal state processing method and / or fallback plan based on the second output abnormal state processing method and / or fallback plan corresponding to multiple historical operating data, so as to obtain a virtual machine abnormality repair plan suitable for the current state.

[0084] In a specific embodiment, it also includes:

[0085] A prediction unit is used to obtain the operation data of the current virtual machine in real time or at a fixed time, input the current operation data into the trained virtual machine state analysis model, and predict the operation state of the virtual machine at the next moment;

[0086] It should be noted that the present application can obtain the operating data of the current virtual machine in real time or at a scheduled time, and can be selected immediately according to actual needs. For example, when the amount of calculation needs to be reduced, the data collection time interval can be set regularly to collect the real-time operating data of the virtual machine at every preset time interval, and then the real-time operating data is input into the virtual machine status analysis model in the server to realize the prediction of the operating status of the virtual machine at the next moment, so that the abnormal condition of the virtual machine can be discovered in advance.

[0087] The early warning unit is used to send out an early warning signal when the predicted running state of the virtual machine at the next moment is abnormal.

[0088] It should be noted that when the virtual machine status analysis model outputs that the virtual machine operation status at the next moment is abnormal, an abnormal warning signal can be issued so that the staff can discover the virtual machine operation status in time, thereby avoiding the occurrence of virtual machine abnormalities.

[0089] In a specific embodiment, it also includes:

[0090] The training unit is used to obtain multiple historical operation data corresponding to the virtual machine abnormality and the output abnormal state processing method and / or fallback plan corresponding to the historical operation data, take the historical operation data as input, and output the abnormal state processing method and / or fallback plan as output to train the virtual machine state analysis model.

[0091] It should be noted that the present application can obtain multiple historical operation data corresponding to the virtual machine abnormality and the output abnormal state processing method and / or fallback plan corresponding to the historical operation data, take the historical operation data as input, and output the abnormal state processing method and / or fallback plan as output, and input them into the constructed neural network model for training. The neural network model used in the present application is a common neural network, and a trained virtual machine state analysis model can be obtained.

[0092] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0093] The terms "first", "second", "third", "fourth", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0094] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0095] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0096] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0097] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0098] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0099] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for repairing a virtual machine abnormality, characterized in that: include: Acquire the operation data of the virtual machine in real time, analyze the operation data, and obtain the operation status of the virtual machine; If an abnormality occurs in the virtual machine, a big data analysis is performed on the operation data to output an abnormal state processing method and / or a fallback plan; By comparing the characteristic values, historical operation data similar to the current operation data are obtained, a first output abnormal state processing method and / or a fallback solution corresponding to the current operation data is compared with a second output abnormal state processing method and / or a fallback solution corresponding to the historical operation data, and the first output abnormal state processing method and / or the fallback solution is modified according to the comparison result; By comparing the characteristic values, historical operation data similar to the current operation data is obtained, a first output abnormal state processing method and / or a fallback solution corresponding to the current operation data is compared with a second output abnormal state processing method and / or a fallback solution corresponding to the historical operation data, and the first output abnormal state processing method and / or the fallback solution is corrected according to the comparison result, and the above also includes: Acquire the historical operation data corresponding to the virtual machine exception, analyze the second output abnormal state processing method and / or fallback solution corresponding to the historical operation data, and acquire the optimal second output abnormal state processing method and / or fallback solution; The method of acquiring historical operation data similar to the current operation data by comparing the characteristic values, comparing the first output abnormal state processing method and / or fallback solution corresponding to the current operation data with the second output abnormal state processing method and / or fallback solution corresponding to the historical operation data, and revising the first output abnormal state processing method and / or fallback solution according to the comparison result, includes: Obtaining a characteristic value corresponding to the current operation data through big data analysis, comparing the characteristic value corresponding to the current operation data with the characteristic value corresponding to the historical operation data, and if the comparison result is less than a preset threshold, obtaining a plurality of the historical operation data similar to the current operation data; According to the comparison result of the second output abnormal state processing method and / or fallback plan corresponding to the historical operation data and the first output abnormal state processing method and / or fallback plan corresponding to the current operation data, the first output abnormal state processing method and / or fallback plan is corrected.

2. The method for repairing a virtual machine abnormality according to claim 1, characterized in that: Also includes: Obtain the current virtual machine's operating data in real time or at regular intervals, input the current operating data into the trained virtual machine status analysis model, and predict the virtual machine's operating status at the next moment; If the predicted running state of the virtual machine at the next moment is abnormal, an early warning signal is issued.

3. The method for repairing a virtual machine abnormality according to claim 2, characterized in that: Before obtaining the current virtual machine's operating data in real time or at a scheduled time, inputting the current operating data into the trained virtual machine state analysis model, and predicting the virtual machine's operating state at the next moment, the method further includes: Obtain multiple historical operation data corresponding to the virtual machine abnormality and the output abnormal state processing method and / or fallback plan corresponding to the historical operation data, use the historical operation data as input and the output abnormal state processing method and / or fallback plan as output, and train the virtual machine state analysis model.

4. A virtual machine abnormality repair device, characterized in that: include: An operation status acquisition unit, used to acquire the operation data of the virtual machine in real time, analyze the operation data, and obtain the operation status of the virtual machine; An analysis unit, configured to perform big data analysis on the operation data and output an abnormal state processing method and / or a fallback plan when an abnormality occurs in the virtual machine; A result output unit is used to obtain historical operation data similar to the current operation data through feature value comparison, compare a first output abnormal state processing method and / or fallback solution corresponding to the current operation data with a second output abnormal state processing method and / or fallback solution corresponding to the historical operation data, and modify the first output abnormal state processing method and / or fallback solution according to the comparison result; Also includes: an optimal solution acquisition unit, configured to acquire the historical operation data corresponding to the virtual machine abnormality, analyze the second output abnormal state processing method and / or fallback solution corresponding to the historical operation data, and acquire the optimal second output abnormal state processing method and / or fallback solution; The result output unit also includes: A data comparison unit, used for obtaining a characteristic value corresponding to the current operation data through big data analysis, comparing the characteristic value corresponding to the current operation data with the characteristic value corresponding to the historical operation data, and if the comparison result is less than a preset threshold, obtaining a plurality of the historical operation data similar to the current operation data; A data correction unit is used to correct the first output abnormal state processing method and / or fallback scheme based on a comparison result between the second output abnormal state processing method and / or fallback scheme corresponding to the historical operation data and the first output abnormal state processing method and / or fallback scheme corresponding to the current operation data.

5. The virtual machine abnormality repair device according to claim 4, characterized in that: Also includes: A prediction unit is used to obtain the operation data of the current virtual machine in real time or at a fixed time, input the current operation data into the trained virtual machine state analysis model, and predict the operation state of the virtual machine at the next moment; The early warning unit is used to send out an early warning signal when the predicted running state of the virtual machine at the next moment is abnormal.

6. The virtual machine abnormality repair device according to claim 5, characterized in that: Also includes: A training unit is used to obtain multiple historical operation data corresponding to the virtual machine abnormality and the output abnormal state processing method and / or fallback plan corresponding to the historical operation data, and use the historical operation data as input and the output abnormal state processing method and / or fallback plan as output to train the virtual machine state analysis model.

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