CN114356661A

CN114356661A - Recovery point target adjustment backup method and device and storage medium

Info

Publication number: CN114356661A
Application number: CN202210010967.2A
Authority: CN
Inventors: 徐智成; 徐冬; 杨波
Original assignee: Huayun Data Holding Group Co ltd
Current assignee: Huayun Data Holding Group Co ltd
Priority date: 2022-01-06
Filing date: 2022-01-06
Publication date: 2022-04-15

Abstract

The invention provides a method, a device and a storage medium for adjusting and backing up a recovery point target, wherein the method comprises the following steps: acquiring current feature data of equipment to be adjusted, and determining a corresponding current feature vector according to the current feature data; selecting a target characteristic data item with the similarity meeting preset requirements with the current characteristic vector from a historical database based on a similarity algorithm, wherein the target characteristic data item comprises target historical characteristic data and time length information consumed by corresponding backup data; determining the pre-judgment consumed duration corresponding to the current characteristic data according to the selected target characteristic data item; on the basis of a mathematical model, judging whether the pre-judgment consumed duration corresponding to the current characteristic data is effective or not according to the time of starting the last backup, the RPO interval and the current time; and if the pre-judgment consumed time corresponding to the current characteristic data is effective, starting to transmit the backup data. The scheme effectively reduces the risk of data loss.

Description

Recovery point target adjustment backup method and device and storage medium

Technical Field

The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for adjusting backup of a recovery point target, and a storage medium.

Background

The RPO is called Recovery Point Object, Recovery Point Object. RPO refers to a past point in time to which data can be restored when a disaster or emergency occurs, and is the amount of data loss that can be tolerated by the business system. For example, 00 per day: 00, then if a down event occurs today, the point in time (RPO) to which the data can be restored is today's 00: 00, if a disaster or downtime event occurs 3 am, the lost data is three hours, if 23: 59, the lost data is about 24 hours, so the user's RPO is 24 hours, i.e., the user's maximum amount of data lost is 24 hours. RPO refers to the maximum amount of data that a user is allowed to lose. The RPO index mainly reflects the effectiveness of the backup data in the service continuity management system, i.e., the smaller the RPO value, the stronger the guarantee capability of the system on the data integrity.

The inventors have discovered that if all or most of the data is backed up in fixed large time period increments, then in the worst case, the enterprise will lose all of the data for a corresponding large time. For some applications that are not important, the missing data may be acceptable, but for most applications, the missing data may be important even for a relatively small period of time. For example, if all applications of enterprise a have 4 hours of RPO, the interval between backup and data loss would be 4 hours. If the word processing application a of enterprise a stops running at midnight and fails in the morning, then not much (or any) data may be lost. However, if it is a busy application b for data interaction, enterprise a may lose 4 hours of high value and may not be able to replace the data if application b shuts down at 11 am and does not recover until 3 pm. In this case, application b should need to make more frequent backups in order to access the RPO specific to application b.

Assume that the corresponding device RPO is set to 15 minutes for application b. If the backup operation starts at 10:00 and takes 5 minutes to transfer the backup data to the target site, the instance is available at the storage server at 10:05, but it only reflects the data copy for the corresponding device at 10: 00. If the calculation formula of the RPO policy is adopted, that is, the next backup start time is the last backup start time + the RPO interval — transmission elapsed time, the next synchronization start time will not be later than 10: 10. If the first data copy, beginning at 10:00, expires at 10:15, this data copy will be available.

And taking fixed RPO time as a period to perform regular data backup. The interval is fixed, but the transmission time cannot be accurately estimated, so that the time required for performing one backup cannot be accurately determined. In order to ensure the safety and integrity of user data, it is necessary to shorten the RPO time, increase the execution frequency of backup, and increase unnecessary performance loss. The same data faces different storage servers, and when the performance, network and configuration exist among the storage servers, the backup cost is different. Therefore, the RPO based on the fixed numerical value has no universality, and the risk of data loss is high when the data is backed up and restored.

Disclosure of Invention

In view of the above, the present invention provides a method, an apparatus, and a storage medium for adjusting backup of a recovery point target.

In a first aspect, an embodiment of the present invention provides a method for adjusting backup of a recovery point target, where the method includes the following steps:

acquiring current feature data of equipment to be adjusted, and determining a corresponding current feature vector according to the current feature data;

selecting a target characteristic data item with the similarity meeting preset requirements with the current characteristic vector from a historical database based on a similarity algorithm, wherein the target characteristic data item comprises target historical characteristic data and time length information consumed by corresponding backup data;

determining the pre-judgment consumed duration corresponding to the current characteristic data according to the selected target characteristic data item;

on the basis of a mathematical model, judging whether the pre-judgment consumed duration corresponding to the current characteristic data is effective or not according to the time of starting the last backup, the RPO interval and the current time;

and if the pre-judgment consumed time corresponding to the current characteristic data is effective, starting to transmit the backup data.

In one embodiment, the obtaining current feature data of a device to be adjusted and determining a corresponding current feature vector according to the current feature data includes:

acquiring current characteristic data of equipment to be adjusted, and generating vector data corresponding to each element in the current characteristic data;

and based on all the generated vector data, carrying out normalization processing to obtain corresponding current feature vector data.

In one embodiment, the selecting, based on a similarity algorithm, a target feature data item from a historical database, the similarity of which to the current feature vector meets a preset requirement, where the target feature data item includes target historical feature data and time length information consumed by corresponding backup data, includes:

determining a historical characteristic vector corresponding to each historical characteristic data item in a historical database, wherein each historical characteristic data item comprises historical characteristic data and time consumed by corresponding backup data;

based on a similarity algorithm, selecting historical feature vectors meeting a preset similarity threshold from all historical feature vectors;

and correspondingly selecting corresponding target characteristic data items from the historical database according to the selected historical characteristic vectors.

In one embodiment, the determining the pre-judgment consumed duration corresponding to the current feature data according to the selected target feature data item includes:

if the number of the selected target characteristic data items is 1, determining the pre-judgment consumed duration corresponding to the current characteristic data based on the consumed duration of the corresponding backup data in the target characteristic data items;

and if the number of the selected target characteristic data items is more than 1, determining the pre-judgment consumed time length corresponding to the current characteristic data based on the consumed time lengths of the corresponding backup data in all the selected target characteristic data items.

In one embodiment, if the number of the selected target feature data items is greater than 1, determining the pre-determined consumed duration corresponding to the current feature data based on the consumed durations of the corresponding backup data in all the selected target feature data items, includes:

calculating the sum of the time lengths consumed by corresponding backup data in all the selected target characteristic data items;

calculating to obtain a corresponding consumption duration average value based on the number of the target characteristic data items selected by the sum;

and determining the average value of the consumed time length as the pre-judged consumed time length corresponding to the current characteristic data.

In one embodiment, after the backup data is completely transmitted, the method for adjusting backup by the recovery point target further includes:

and obtaining a current characteristic data item according to the current characteristic data and the actual backup consumption duration, and storing the current characteristic data item into a historical database.

In one embodiment, the method for adjusting backup by the recovery point target further includes:

and if the pre-judgment consumed time corresponding to the current characteristic data is invalid, sleeping for a preset fixed period and then waiting for next execution.

In a second aspect, an embodiment of the present invention further provides a device for adjusting backup of a recovery point target, where the device includes the following modules:

the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring current characteristic data of equipment to be adjusted and determining a corresponding current characteristic vector according to the current characteristic data;

the selecting module is used for selecting a target characteristic data item from a historical database, wherein the similarity of the target characteristic data item and the current characteristic vector meets a preset requirement, and the target characteristic data item comprises target historical characteristic data and time length information consumed by corresponding backup data;

the prejudgment module is used for determining prejudgment consumed time corresponding to the current characteristic data according to the selected target characteristic data item;

the efficiency judging module is used for judging whether the pre-judging consumed duration corresponding to the current characteristic data is effective or not according to the time for starting the last backup, the RPO interval and the current time based on a mathematical model;

and the execution module is used for starting to transmit the backup data if the pre-judgment consumed duration corresponding to the current characteristic data is effective.

In a third aspect, an apparatus for adjusting backup of a recovery point target according to an embodiment of the present invention includes: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing therein computer instructions, and the processor implementing the recovery point target adjustment backup method according to any one of the first aspect by executing the computer instructions.

In a fourth aspect, a non-transitory computer-readable storage medium is provided according to an embodiment of the present invention, which stores computer instructions that, when executed by a processor, implement the recovery point target adjusted backup method according to any one of the first aspect.

The method, the device and the storage medium for adjusting and backing up the recovery point target provided by the embodiment of the invention at least have the following beneficial effects:

according to the method, the device and the storage medium for adjusting the backup of the recovery point target, provided by the embodiment of the invention, the information carrying the consumed time of the backup data is determined from the historical database by acquiring the current characteristic number of the equipment to be adjusted and a similarity calculation method, so that the consumed time of the backup data corresponding to the current equipment to be adjusted is pre-judged, and the effectiveness of the pre-judged consumed time is judged by a mathematical model corresponding to an RPO strategy. And transmitting the backup data for backup after the prejudged consumed time length is determined to be effective. The risk of data loss is reduced by predicting the time consumed by the backup data, and the loss caused by data loss is reduced. The RPO is adapted to the equipment through the time consumed by the backup data from the current characteristic number of the equipment to be adjusted, so that the applicability of the RPO is enhanced, the efficiency of RPO backup data is further improved, the risk of data loss is reduced, and the safety of the data is improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a flowchart of a method for adjusting backup of a recovery point target according to an embodiment of the present invention;

fig. 2 is a flowchart of another method for adjusting backup of a recovery point target according to an embodiment of the present invention;

fig. 3 is a block diagram of a recovery point target adjustment backup apparatus according to an embodiment of the present invention;

fig. 4 is a schematic structural diagram of a recovery point target adjustment backup device according to an embodiment of the present invention.

Detailed Description

The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

Although the processes described below include multiple operations that occur in a particular order, it should be clearly understood that the processes may include more or fewer operations that are performed sequentially or in parallel.

Example 1

Fig. 1 is a flowchart of a method for adjusting backup of a recovery point target according to an embodiment of the present invention,

the embodiment provides a recovery point target adjustment backup method, as shown in fig. 1, the method includes the following steps:

s101, obtaining current feature data of equipment to be adjusted, and determining a corresponding current feature vector according to the current feature data;

step S102, selecting a target characteristic data item from a historical database, wherein the similarity of the target characteristic data item and the current characteristic vector meets a preset requirement, and the target characteristic data item comprises target historical characteristic data and time length information consumed by corresponding backup data;

step S103, determining the pre-judgment consumed duration corresponding to the current characteristic data according to the selected target characteristic data item;

step S104, based on a mathematical model, judging whether the pre-judgment consumed duration corresponding to the current characteristic data is effective according to the time of starting the last backup, the RPO interval and the current time;

step S105, if the pre-determined consumed duration corresponding to the current characteristic data is effective, starting to transmit backup data.

In the above embodiment, the relevant feature data affecting the time consumed by the backup data and the time duration information consumed by the corresponding backup data are collected in advance, and a history database is established. And acquiring current characteristic data of the equipment to be adjusted, wherein the current characteristic data is adaptive to the characteristic data in the historical database. Further, the feature data may include, but is not limited to, the following elements: the number of cpu cores; memory size; 3. a load condition, wherein the load condition comprises: load within 1min, load within 5min, load within 15min, and the like; 4. network io read-write conditions; 5. memory conditions may be used; total cpu usage; 7. the number of file handles; 8, cache and buffer cache use condition; 9. the number of processes in the current environment; cpu frequency; 11. memory read-write conditions; 12. number of simultaneous backups. And determining a corresponding current feature vector according to the current feature data. In other words, that is, the current feature data is converted into the current feature vector, it should be noted that the more elements are involved in the current feature data, the more accurate the corresponding current feature vector is obtained. And after the current feature vector is correspondingly obtained, selecting a target feature data item with the similarity meeting the preset requirement with the current feature vector from a historical database. Calculating the distance of the vector through the characteristic vector corresponding to each characteristic data item in the historical database and the current characteristic vector, and judging whether the distance is within the range of the threshold value, namely, when the distance between the characteristic vector corresponding to any characteristic data item in the historical database and the current characteristic vector is smaller than the preset threshold value, determining that the similarity between the corresponding characteristic data in the characteristic data item and the current characteristic data meets the preset similarity requirement. And after the corresponding characteristic data item is selected, determining the pre-judgment consumed duration corresponding to the current characteristic data according to the selected target characteristic data item.

And based on a mathematical model, judging whether the pre-judgment consumed duration corresponding to the current characteristic data is effective or not according to the time of starting the last backup, the RPO interval and the current time. Specifically, the mathematical model is:

current time < ═ time since last backup started + RPO Interval-backup elapsed time

And correspondingly substituting the pre-judgment consumed time length corresponding to the current characteristic data into the backup consumed time in the backup mathematical model, and judging whether the pre-judgment consumed time length corresponding to the current characteristic data is effective or not after substitution. After substituting, if the mathematical model logic is correct, determining that the pre-judgment consumed duration corresponding to the current characteristic data is valid; and if the mathematical model logic is incorrect, determining that the pre-judgment consumed time corresponding to the current characteristic data is invalid. And if the pre-judgment consumed time corresponding to the current characteristic data is effective, starting to transmit the backup data.

In the above manner, by obtaining the current feature number of the device to be adjusted, and by a similarity algorithm, the corresponding information carrying the consumed time length of the backup data is determined from the historical database, so as to pre-judge the consumed time length of the backup data corresponding to the current device to be adjusted, and by a mathematical model corresponding to the RPO policy, the effectiveness of the pre-judged consumed time length is judged. And transmitting the backup data for backup after the prejudged consumed time length is determined to be effective. The risk of data loss is reduced by predicting the time consumed by the backup data, and the loss caused by data loss is reduced. The RPO is adapted to the equipment through the time consumed by the backup data from the current characteristic number of the equipment to be adjusted, so that the applicability of the RPO is enhanced, the efficiency of RPO backup data is further improved, the risk of data loss is reduced, and the safety of the data is improved.

In the above embodiment, specifically, all the generated data are normalized, for example, all the vector data are linearly transformed, and the result is mapped into the interval of [0-1 ]. So as to facilitate the subsequent similarity calculation processing, count the characteristics and accelerate the convergence, thereby further improving the RPO adjustment efficiency. For example, through the normalization process, the difference between the current feature vector and the feature vector corresponding to any feature data item in the historical database is calculated by using a similarity distance function, wherein the similarity distance function may be a cosine similarity distance function. The data processing amount is reduced, vector data corresponding to each element in the current feature data do not need to be compared one by one, and the calculation efficiency is improved. And further, the efficiency of selecting the target feature data item with the similarity meeting the preset requirement with the current feature vector from the historical database is improved. The prejudgment efficiency and accuracy of the prejudgment consumed time length are improved, the risk of data loss is further reduced, the loss caused by data loss is reduced, the efficiency of RPO data backup is further improved, the risk of data loss is reduced, and the safety of data is improved.

In the above embodiment, specifically, the number of the selected target feature data items may be plural, or may be one. When the number of the target feature data items corresponds to one, the similarity between the selected target feature data item and the current feature data item is the maximum. When there are a plurality of correspondences, in other words, there are a plurality of feature data items that are highly similar to the current feature data at the same time, so in this case, the pre-judgment consumed time length corresponding to the corresponding current feature data is determined by an averaging method. By the method, the accuracy of the pre-judged consumed time length is further improved. And further, the risk of data loss is further reduced, the loss caused by data loss is reduced, the efficiency of RPO data backup is further improved, the risk of data loss is reduced, and the safety of data is improved.

In the above embodiment, specifically, by continuously correcting and iterating the data in the historical database, the accuracy of the predicted consumed time duration is further improved. And further, the risk of data loss is further reduced, the loss caused by data loss is reduced, the efficiency of RPO data backup is further improved, the risk of data loss is reduced, and the safety of data is improved.

For further understanding of the present application, reference is now made to FIG. 2 for further description.

For the equipment to be adjusted, the equipment to be adjusted comprises a machine A and a corresponding storage server, and the characteristic extraction device is installed for extracting the current characteristic of the machine A from the machine A in a fixed period and extracting the corresponding storage server characteristic with the stored server. And the implementation module compares the data with the historical characteristic database data through a similarity algorithm, selects and determines a corresponding target data item from the historical characteristic database, calculates the time consumed by similar backup according to the determined target data item, and obtains the time consumed by prejudgment. Substituting the pre-judged time consumption into the data model, judging whether the formula of the corresponding mathematical model is satisfied, and determining the effectiveness of the pre-judged time consumption. If the corresponding formula is met, the corresponding pre-judgment time consumption is effective, the transmission of backup data is started, namely the machine A backs up the corresponding backup data to a storage server, after the transmission is finished and the corresponding backup is finished, the historical feature library is filled with the feature data of the current equipment to be adjusted, and the iterative feature data is corrected. If the formula is not satisfied, the adjustment backup is correspondingly finished, and the sleep is performed for the next execution after the preset fixed period. By predicting the backup consumption time, the efficiency of RPO data backup is improved, the risk of data loss is reduced, and the safety of data is improved.

Example 2

Fig. 3 is a block diagram of a recovery point target adjustment backup apparatus according to an embodiment of the present invention, and this embodiment describes that the apparatus is applied to the recovery point target adjustment backup method shown in fig. 1. The recovery point target adjustment backup device at least comprises the following modules:

an obtaining module 31, configured to obtain current feature data of a device to be adjusted, and determine a corresponding current feature vector according to the current feature data;

a selecting module 32, configured to select, based on a similarity algorithm, a target feature data item from a historical database, where a similarity between the target feature data item and the current feature vector meets a preset requirement, where the target feature data item includes target historical feature data and time information consumed by corresponding backup data;

the prejudgment module 33 is configured to determine a prejudgment consumed duration corresponding to the current feature data according to the selected target feature data item;

the efficiency judging module 34 is configured to judge whether the pre-judgment consumed duration corresponding to the current feature data is valid according to the time when the last backup starts, the RPO interval, and the current time based on the mathematical model;

and the executing module 35 is configured to start to transmit the backup data if the pre-determined consumed duration corresponding to the current feature data is valid.

The recovery point target adjustment backup device provided in the embodiment of the present application may be used in the method performed in embodiment 1, and for the relevant details, reference is made to the above method embodiment, and the implementation principle and the technical effect are similar, which are not described herein again.

It should be noted that: in the above embodiment, when performing the adjustment backup of the recovery point target, the recovery point target adjustment backup apparatus is described by taking the division of the functional modules as an example, and in practical applications, the function allocation may be completed by different functional modules according to needs, that is, the internal structure of the recovery point target adjustment backup apparatus is divided into different functional modules to complete all or part of the functions described above. In addition, the recovery point target adjustment backup device and the recovery point target adjustment backup method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein again.

Example 3

As shown in fig. 4, the electronic device includes a processor 401 and a memory 402, where the processor 401 and the memory 402 may be connected by a bus or in other manners, and the processor 401 and the memory 402 are connected by a bus as an example in the illustration.

The Processor 401 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), a Graphics Processing Unit (GPU), an embedded Neural Network Processor (NPU), other dedicated deep learning coprocessor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like, or a combination thereof.

The memory 402, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the recovery point target adjustment backup method in the embodiment of the present invention. The processor 401 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 402, that is, implements the recovery point target adjustment backup method in the above method embodiment 1.

The memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 401, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 402 may optionally include memory located remotely from processor 401, which may be connected to processor 401 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules are stored in the memory 402 and, when executed by the processor 401, perform a recovery point target adjustment backup method as shown in FIG. 1.

An embodiment of the present invention further provides a non-transitory computer-readable storage medium, where computer-executable instructions are stored in the non-transitory computer-readable storage medium, and the computer-executable instructions may execute the method for adjusting and backing up the recovery point target in any of the above method embodiments. The non-transitory computer readable storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid-State Drive (SSD), or the like; the non-transitory computer readable storage medium may also include a combination of memories of the above kind.

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, apparatus or non-transitory computer readable storage medium, all relating to or comprising a computer program product.

Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

Obviously, the above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that various changes and modifications to the above description could be made by those skilled in the art without departing from the spirit of the present application. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. A recovery point target adjustment backup method, the method comprising:

2. The method for restoring point target adjustment and backup according to claim 1, wherein the obtaining current feature data of a device to be adjusted and determining a corresponding current feature vector according to the current feature data comprises:

3. The recovery point target adjustment backup method according to claim 2, wherein the selecting, based on a similarity algorithm, a target feature data item whose similarity to the current feature vector meets a preset requirement from a historical database, wherein the target feature data item includes target historical feature data and time length information consumed by corresponding backup data, includes:

4. The recovery point target adjustment backup method according to claim 3, wherein the determining a pre-determined consumption duration corresponding to the current feature data according to the selected target feature data item comprises:

5. The method of claim 4, wherein if the number of the selected target feature data items is greater than 1, determining the pre-determined consumed duration corresponding to the current feature data based on the consumed durations of the corresponding backup data in all the selected target feature data items comprises:

6. A recovery point target adjustment backup method according to any of claims 1-5, characterized in that after the backup data transmission is completed, the method further comprises:

7. The recovery point target adjustment backup method according to claim 6, further comprising:

8. A recovery point target adjustment backup apparatus, the apparatus comprising:

9. A recovery point target adjustment backup apparatus, comprising: a memory and a processor communicatively coupled to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the recovery point target adjustment backup method of any of claims 1-7.

10. A non-transitory computer readable storage medium storing computer instructions which, when executed by a processor, implement the recovery point target adjusted backup method of any of claims 1-7.