CN108984344A - A kind of dynamic generates method, apparatus, equipment and the storage medium of snapshot - Google Patents
A kind of dynamic generates method, apparatus, equipment and the storage medium of snapshot Download PDFInfo
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- CN108984344A CN108984344A CN201810763934.9A CN201810763934A CN108984344A CN 108984344 A CN108984344 A CN 108984344A CN 201810763934 A CN201810763934 A CN 201810763934A CN 108984344 A CN108984344 A CN 108984344A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/14—Error detection or correction of the data by redundancy in operation
- G06F11/1402—Saving, restoring, recovering or retrying
- G06F11/1446—Point-in-time backing up or restoration of persistent data
- G06F11/1448—Management of the data involved in backup or backup restore
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
- G06F2201/84—Using snapshots, i.e. a logical point-in-time copy of the data
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Abstract
The invention discloses method, apparatus, equipment and storage medium that a kind of dynamic generates snapshot, this method is the History Performance Data for obtaining current storage system and going over N days first；Then the corresponding following M days object variations trend about predetermined registration operation of each single item performance data in machine learning algorithm prediction history performance data is utilized；And judge whether there is the object variations trend for meeting default variation tendency；If it is, the timing of setting timer with after M days execution play snapshot operation and generate snapshot.It can be seen that on the one hand this method does not need manually to determine the opportunity for beating snapshot, human cost has been saved；On the other hand for the mode of snapshot is beaten in timing, it is to predict whether future needs to beat snapshot according to History Performance Data, belong to a kind of dynamic mode, therefore the quantity of the snapshot generated will reduce and the snapshot generated is useful snapshot, will not occupy excessive storage resource.
Description
Technical field
The present invention relates to snapping technique fields, more particularly to a kind of method, apparatus of dynamic generation snapshot, equipment and deposit
Storage media.
Background technique
Snapshot is one of the main means of storage system disaster recovery backup, refers to that one that determines data acquisition system completely available is copied
Shellfish, the copy include the image that corresponding data (copies the time point started) at some time point.When storage system is applied
Can carry out quick data recovery according to the snapshot of storage when failure or file corruption, by data restore some it is available when
Between the state put.
In the prior art, there are mainly two types of the modes for generating snapshot, and one is manually snapshot is beaten, i.e., operation maintenance personnel is according to reality
Border is it needs to be determined that beat the opportunity of snapshot；Another kind is that snapshot is beaten in timing.Opportunity due to manually beating snapshot is very difficult to determine, because
This generallys use the mode of snapshot when beating surely in practical applications, i.e., does a data backup every certain frequency, forms one
Zhang Kuaizhao.
But frequency is determined since the mode that snapshot is beaten in timing needs the operation variation according to storage system, it needs to transport
Dimension personnel's moment pays close attention to storage system operation variation, expends more cost of labor；More seriously, the side of snapshot is beaten in timing
Formula can generate more identical snapshot, to occupy excessive storage resource, lead to the reduced performance of storage system.Thus may be used
See, how to avoid the problem that generating more useless snapshot with save storage resource be those skilled in the art urgently.
Summary of the invention
The object of the present invention is to provide method, apparatus, equipment and storage mediums that a kind of dynamic generates snapshot, for avoiding
More useless snapshot is generated to save storage resource.
In order to solve the above technical problems, the present invention provides a kind of method that dynamic generates snapshot, comprising:
Obtain the History Performance Data that current storage system is gone over N days；
The each single item performance data in the History Performance Data corresponding following M days are predicted using machine learning algorithm
Object variations trend about predetermined registration operation；
Judge whether there is the object variations trend for meeting default variation tendency；
If it is, the timing of setting timer with after M days execution play snapshot operation and generate snapshot；
Wherein, the History Performance Data includes to beat the relevant performance data of snapshot operation to triggering, and N and M are positive
Integer.
Preferably, the predetermined registration operation is specially the write operation about volume, and described judge whether there is meets default variation
The object variations trend of trend specifically:
Judge whether the amount of the write operation in the object variations trend is greater than or equal in the default variation tendency
The amount of write operation；
If it is, determining that the object variations trend meets the default variation tendency and otherwise do not meet.
Preferably, after judging that the object variations trend meets the default variation tendency, further includes:
Judge that current storage system is with the presence or absence of free time section after M days；
If it is, into it is described setting timer timing the step of, arrived wherein the timing is corresponding
The next moment is specifically in the free time section.
Preferably, the machine learning algorithm is specially the learning algorithm for using LSTM.
Preferably, the History Performance Data specifically include cpu busy percentage, memory usage, the IOPS of volume, bandwidth, when
Prolong.
Preferably, N is 30 days.
Preferably, M is 7 days.
In order to solve the above technical problems, the present invention also provides the devices that a kind of dynamic generates snapshot, comprising:
Module is obtained, the History Performance Data gone over N days for obtaining current storage system；
Prediction module, for predicting each single item performance data pair in the History Performance Data using machine learning algorithm
The object variations trend about predetermined registration operation of following M days answered；
Judgment module, for judging whether there is the object variations trend for meeting default variation tendency；
Setup module, for when the judgment result of the judgment module is yes, the timing of timer being arranged in M
It is executed after it and plays snapshot operation and generate snapshot；
Wherein, the History Performance Data includes to beat the relevant performance data of snapshot operation to triggering, and N and M are positive
Integer.
In order to solve the above technical problems, the present invention also provides the equipment that a kind of dynamic generates snapshot, including memory, it is used for
Store computer program；
Processor realizes that dynamic described above generates the step of the method for snapshot when for executing the computer program
Suddenly.
In order to solve the above technical problems, the present invention also provides a kind of computer readable storage medium, it is described computer-readable
Computer program is stored on storage medium, the computer program realizes that dynamic described above generates when being executed by processor
The step of method of snapshot.
The method that dynamic provided by the present invention generates snapshot is the history for obtaining current storage system and going over N days first
Performance data；Then corresponding M days following using each single item performance data in machine learning algorithm prediction history performance data
The object variations trend about predetermined registration operation；And judge whether there is the object variations trend for meeting default variation tendency；Such as
Fruit is that the timing of timer is then arranged to play snapshot operation to execute after M days and generate snapshot.It can be seen that this method
For existing, on the one hand do not need manually to determine the opportunity for beating snapshot, saved human cost；On the other hand relative to
Timing is beaten for the mode of snapshot, is to predict whether future needs to beat snapshot according to History Performance Data ,if needed, then it holds
Row beats snapshot, does not need, does not beat, and belongs to a kind of dynamic mode, therefore the quantity of the snapshot of generation will be reduced and be generated
Snapshot is useful snapshot, will not occupy excessive storage resource.
In addition, device, equipment and storage medium that dynamic provided by the present invention generates snapshot equally have it is above-mentioned beneficial
Effect.
Detailed description of the invention
In order to illustrate the embodiments of the present invention more clearly, attached drawing needed in the embodiment will be done simply below
It introduces, it should be apparent that, drawings in the following description are only some embodiments of the invention, for ordinary skill people
For member, without creative efforts, it is also possible to obtain other drawings based on these drawings.
Fig. 1 is the flow chart for the method that a kind of dynamic provided in an embodiment of the present invention generates snapshot；
Fig. 2 is the flow chart for the method that another dynamic provided in an embodiment of the present invention generates snapshot；
Fig. 3 is the structure chart for the device that a kind of dynamic provided in an embodiment of the present invention generates snapshot.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, rather than whole embodiments.Based on this
Embodiment in invention, those of ordinary skill in the art are without making creative work, obtained every other
Embodiment belongs to the scope of the present invention.
Core of the invention is to provide method, apparatus, equipment and the storage medium of a kind of dynamic generation snapshot, for avoiding
More useless snapshot is generated to save storage resource.
In order to enable those skilled in the art to better understand the solution of the present invention, with reference to the accompanying drawings and detailed description
The present invention is described in further detail.
Fig. 1 is the flow chart for the method that a kind of dynamic provided in an embodiment of the present invention generates snapshot.As shown in Figure 1, the party
Method includes:
S10: the History Performance Data that current storage system is gone over N days is obtained.
In specific implementation, the reliability of storage system is that the rate of exchange are high, that is to say, that frequently beating snapshot, there is no real
The meaning of matter, the snapshot got just need to store, and waste storage resource.In the present invention, it is contemplated that the fortune of storage system
Row has genetic development in fact, in other words, can carry out whether prediction future is possible to out by previous historical data
Now need to beat the operation of snapshot.For example, if predicting storage system may have a large amount of write operation to carry out in future, this
In the case of, it is that then can execute the operation for beating snapshot after completing write operation it is necessary to beat snapshot.
History Performance Data is will be as predicting whether that it is necessary to beat the reference data of snapshot, therefore, History Performance Data
It include to beat the relevant performance data of snapshot operation to triggering.Preferably embodiment, History Performance Data specifically include
Cpu busy percentage, memory usage, the IOPS of volume, bandwidth, time delay.It should be noted that the above performance data is only a kind of tool
Body embodiment, not representing can only be these types of data.
N in this step is positive integer, and in a kind of specific implementation, N is 30 days.It is understood that 30 days are only
A kind of specific embodiment, not representing can only be 30 days, which needs depending on the operating condition of storage system.
S11: it utilizes each single item performance data in machine learning algorithm prediction history performance data corresponding following M days
Object variations trend about predetermined registration operation.
Machine learning algorithm be it is a kind of learnt to obtain a learning model with a large amount of sample data, thus defeated again
After entering new data, it can obtain exporting result accordingly.It is specific since the application of machine learning algorithm is highly developed
Model and the learning process present invention repeat no more.Specifically, the learning algorithm of LSTM (shot and long term memory network) can be used.
Using each single item performance data in History Performance Data as input object, prediction knot corresponding with each single item performance data is obtained
Fruit, the i.e. following M days object variations trend about predetermined registration operation.
Predetermined registration operation in this step needs to need to beat snapshot after the completion of empirically determined which operation out, such as above
The middle write operation mentioned of illustrating other than write operation, can also be other types of operation certainly.Predicted operation setting is got over
More, then the probability that snapshot generates is bigger.If History Performance Data include cpu busy percentage, memory usage, volume IOPS,
If bandwidth, time delay, then need to obtain corresponding object variations trend, the memory usage about predetermined registration operation of cpu busy percentage
It is corresponding corresponding about the object variations trend of predetermined registration operation, band about the object variations trend of predetermined registration operation, the IOPS of volume
The corresponding object variations trend about predetermined registration operation of corresponding object variations trend, the time delay about predetermined registration operation of width.
In this step, M is positive integer, and in a kind of specific implementation, M is 7 days.It is understood that 7 days are only one kind
Specific embodiment, not representing can only be 7 days, which needs depending on the operating condition of storage system.In addition, M is logical
It is less than N in normal situation, this is because being following more by the data prediction of a small amount of number of days if M is greater than N
The variation tendency of number of days is easy to cause the problem of prediction result inaccuracy.
S12: judging whether there is the object variations trend for meeting default variation tendency, if it is, into S13, if
It is no, then terminate.
Need to judge whether each object variations trend meets default variation tendency in this step, and each deterministic process is
It is mutually independent, it can carry out, can also successively judge according to certain sequence, if there is one or more than one mesh simultaneously
Mark variation tendency meets default variation tendency, then the judging result of this step is just yes, is otherwise terminated.Goal variation becomes
Whether gesture meets default variation tendency, and the amount that can be a certain operation has reached the amount or a certain operation of default variation tendency
Number reached the number of default variation tendency, it is not limited here.
S13: the timing of timer is set and plays snapshot operation to be executed after M days and generates snapshot.
The timing of setting timer in this step is to execute to play snapshot operation when the corresponding moment arrives, thus raw
At snapshot.It is understood that timing here is according to the object variations trend pair for meeting default variation tendency in S13
The time answered.For example, some object variations trend characterization future can generate predetermined registration operation within 7 days, then timing be exactly 7 days it
Afterwards, what timing certainly here was preferably provided is ratio 7 days longer, since it is desired that carrying out again after having executed predetermined registration operation
Beat snapshot, it is specific extend how much can according to the actual situation depending on.After setting the timing of timer, when reaching the time
Afterwards, then it executes and plays snapshot operation.
The period for completely beating snapshot above for one is described in detail, fast when not needing to beat in a cycle
According to when, i.e., when the judging result of S12 is no, then needs the judgement into next cycle, that is, need to repeat S110-S13.
It is understood that the time is also being continuously increased with the progress in each period, then previous cycle History Performance Data with it is latter
The History Performance Data in a period is different, and therefore, the result that each step obtains also is different, and recycles progress with this.
The method that dynamic provided in this embodiment generates snapshot is the history for obtaining current storage system and going over N days first
Performance data；Then corresponding M days following using each single item performance data in machine learning algorithm prediction history performance data
The object variations trend about predetermined registration operation；And judge whether there is the object variations trend for meeting default variation tendency；Such as
Fruit is that the timing of timer is then arranged to play snapshot operation to execute after M days and generate snapshot.It can be seen that this method
For existing, on the one hand do not need manually to determine the opportunity for beating snapshot, saved human cost；On the other hand relative to
Timing is beaten for the mode of snapshot, is to predict whether future needs to beat snapshot according to History Performance Data ,if needed, then it holds
Row beats snapshot, does not need, does not beat, and belongs to a kind of dynamic mode, therefore the quantity of the snapshot of generation will be reduced and be generated
Snapshot is useful snapshot, will not occupy excessive storage resource.
In a upper embodiment, predetermined registration operation is not limited, and the range for snapshot of not fighting each other limits.As excellent
Selection of land embodiment, predetermined registration operation are specially the write operation about volume, S12 specifically:
Judge whether the amount of the write operation in object variations trend is greater than or equal to the write operation in default variation tendency
Amount；
If it is, determining that object variations trend meets default variation tendency, otherwise, do not meet.
In specific implementation, if a large amount of write operations occur for storage system, illustrate what the operation of storage system occurred
Biggish variation carries out beating snapshot at this time, in order to avoid obtained from snapshot when later data is lost.In this step, when pre-
When measuring some volume there are a large amount of write operations, then the volume is carried out beating snapshot, other regions are not required.
Fig. 2 is the flow chart for the method that another dynamic provided in an embodiment of the present invention generates snapshot.As shown in Fig. 2,
On the basis of a upper embodiment, after judging that object variations trend meets default variation tendency, further includes:
S20: judge that current storage system is with the presence or absence of free time section after M days, if it is, entering setting timer
Timing the step of, wherein the timing corresponding arrival moment is specifically during idle time in section.
Since storage system is not to be constantly in operating status, free time section is had, in this period, is executed
Snapshot is beaten, allows the busy period efficiently to handle other operations, resource contention can be reduced.
In specific implementation, can also judge time for terminating with predetermined registration operation of section free time whether interval time compared with
It is long, if interval time is shorter, meet preset value, then interior execute of section can play snapshot operation during idle time, otherwise, or
Predetermined registration operation terminates to be carried out the operation for beating snapshot after receiving.
The corresponding embodiment of method that dynamic generates snapshot has been described in detail above, the present invention also provides it is a kind of with it is above-mentioned
The corresponding dynamic of method generates the device of snapshot.As shown in figure 3, the device includes:
Module 10 is obtained, the History Performance Data gone over N days for obtaining current storage system.
Prediction module 11, for corresponding using each single item performance data in machine learning algorithm prediction history performance data
The following M days object variations trend about predetermined registration operation.
Judgment module 12, for judging whether there is the object variations trend for meeting default variation tendency.
Setup module 13 is that when being, the timing of timer is arranged in M for the judging result in judgment module 12
It is executed after it and plays snapshot operation and generate snapshot.
Wherein, History Performance Data includes to beat the relevant performance data of snapshot operation to triggering, and N and M are positive integer.
Since the embodiment of device part is corresponded to each other with the embodiment of method part, the embodiment of device part is asked
Referring to the description of the embodiment of method part, wouldn't repeat here.
Dynamic provided in this embodiment generates the device of snapshot, is the history for obtaining current storage system and going over N days first
Performance data；Then corresponding M days following using each single item performance data in machine learning algorithm prediction history performance data
The object variations trend about predetermined registration operation；And judge whether there is the object variations trend for meeting default variation tendency；Such as
Fruit is that the timing of timer is then arranged to play snapshot operation to execute after M days and generate snapshot.It can be seen that the present apparatus
On the one hand corresponding method does not need manually to determine the opportunity for beating snapshot, has saved human cost for existing；It is another
Aspect is to predict whether future needs to beat snapshot according to History Performance Data, needs for the mode of snapshot is beaten in timing
It if wanting, then executes and beats snapshot, do not need, do not beat, belong to a kind of dynamic mode, therefore the quantity of the snapshot generated will
It reduces and the snapshot generated is useful snapshot, excessive storage resource will not be occupied.
The present invention also provides the equipment that a kind of dynamic generates snapshot, mainly describe from the angle of hardware.The equipment includes depositing
Reservoir, for storing computer program；
Processor is realized when for executing computer program the dynamic as described in above-described embodiment generates the method for snapshot
Step.
Since the embodiment of environment division is corresponded to each other with the embodiment of method part, the embodiment of environment division is asked
Referring to the description of the embodiment of method part, wouldn't repeat here.In some embodiments of the invention, processor and memory
It can be connected by bus or other means.
The equipment that dynamic provided in this embodiment generates snapshot, including memory and processor, processor are executing storage
It can be realized following method when the program of device storage: being the History Performance Data for obtaining current storage system and going over N days first；So
Afterwards using each single item performance data in machine learning algorithm prediction history performance data corresponding following M days about default behaviour
The object variations trend of work；And judge whether there is the object variations trend for meeting default variation tendency；If it is, setting is fixed
When device timing play snapshot operation to be executed after M days and generate snapshot.It can be seen that this method is relative to existing next
It says, does not on the one hand need manually to determine the opportunity for beating snapshot, saved human cost；On the other hand snapshot is beaten relative to timing
It is to predict whether future needs to beat snapshot according to History Performance Data for mode ,if needed, then it executes and beats snapshot, no
It needs, does not beat, belong to a kind of dynamic mode, therefore the quantity of the snapshot generated will reduce and the snapshot generated is useful
Snapshot, excessive storage resource will not be occupied.
Finally, the present invention provides a kind of computer readable storage medium, calculating is stored on computer readable storage medium
Machine program realizes that the dynamic as described in above-described embodiment generates the method and step of snapshot when computer program is executed by processor.
Specific embodiment referring to method section Example description.
It is understood that if above-mentioned functional module is realized in the form of SFU software functional unit and as independent product
When selling or using, it can store in a computer readable storage medium.Based on this understanding, technology of the invention
Substantially all or part of the part that contributes to existing technology or the technical solution can be with software in other words for scheme
The form of product embodies, which is stored in a storage medium, for executing each reality of the present invention
Apply all or part of the steps of the method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory
(Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic or disk
Etc. the various media that can store program code.
Computer readable storage medium provided in this embodiment, is stored with computer program, which is performed can
It realizes following method: being the History Performance Data for obtaining current storage system and going over N days first；Then machine learning algorithm is utilized
The corresponding following M days object variations trend about predetermined registration operation of each single item performance data in prediction history performance data；
And judge whether there is the object variations trend for meeting default variation tendency；If it is, setting timer timing with
It is executed after M days and plays snapshot operation and generate snapshot.It can be seen that this method for existing, is on the one hand not required to very important person
Work determines the opportunity for beating snapshot, has saved human cost；It on the other hand is that foundation is gone through for the mode of snapshot is beaten in timing
Whether history performance data is predicted following to need to beat snapshot ,if needed, then it executes and beats snapshot, do not need, do not beat, belong to one
The dynamic mode of kind, therefore the quantity of the snapshot generated will reduce and the snapshot generated is useful snapshot, will not occupy
More storage resources.
Method, apparatus, equipment and the storage medium for generating snapshot to dynamic provided by the present invention above have carried out in detail
It introduces.Each embodiment is described in a progressive manner in specification, and the highlights of each of the examples are implement with other
The difference of example, the same or similar parts in each embodiment may refer to each other.For the device disclosed in the embodiment,
Since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is referring to method part illustration
It can.It should be pointed out that for those skilled in the art, without departing from the principle of the present invention, may be used also
With several improvements and modifications are made to the present invention, these improvements and modifications also fall within the scope of protection of the claims of the present invention.
It should also be noted that, in the present specification, relational terms such as first and second and the like be used merely to by
One entity or operation are distinguished with another entity or operation, without necessarily requiring or implying these entities or operation
Between there are any actual relationship or orders.Moreover, the terms "include", "comprise" or its any other variant meaning
Covering non-exclusive inclusion, so that the process, method, article or equipment for including a series of elements not only includes that
A little elements, but also including other elements that are not explicitly listed, or further include for this process, method, article or
The intrinsic element of equipment.In the absence of more restrictions, the element limited by sentence "including a ...", is not arranged
Except there is also other identical elements in the process, method, article or apparatus that includes the element.
Claims (10)
1. a kind of method that dynamic generates snapshot characterized by comprising
Obtain the History Performance Data that current storage system is gone over N days；
Using machine learning algorithm predict each single item performance data in the History Performance Data corresponding following M days about
The object variations trend of predetermined registration operation；
Judge whether there is the object variations trend for meeting default variation tendency；
If it is, the timing of setting timer with after M days execution play snapshot operation and generate snapshot；
Wherein, the History Performance Data includes to beat the relevant performance data of snapshot operation to triggering, and N and M are positive integer.
2. the method that dynamic according to claim 1 generates snapshot, which is characterized in that the predetermined registration operation be specially about
The write operation of volume, it is described to judge whether there is the object variations trend for meeting default variation tendency specifically:
Judge whether the amount of the write operation in the object variations trend is greater than or equal in the default variation tendency and writes behaviour
The amount of work；
If it is, determining that the object variations trend meets the default variation tendency and otherwise do not meet.
3. the method that dynamic according to claim 1 generates snapshot, which is characterized in that judging that the object variations become
Gesture meets after the default variation tendency, further includes:
Judge that current storage system is with the presence or absence of free time section after M days；
If it is, into the setting timer timing the step of, wherein when the corresponding arrival of the timing
Pointer body is in the free time section.
4. the method that dynamic according to claim 1 generates snapshot, which is characterized in that the machine learning algorithm is specially
Using the learning algorithm of LSTM.
5. the method that dynamic according to claim 1 generates snapshot, which is characterized in that the History Performance Data specifically wraps
Include cpu busy percentage, memory usage, the IOPS of volume, bandwidth, time delay.
6. the method that dynamic according to claim 1 generates snapshot, which is characterized in that N is 30 days.
7. the method that dynamic according to claim 6 generates snapshot, which is characterized in that M is 7 days.
8. the device that a kind of dynamic generates snapshot characterized by comprising
Module is obtained, the History Performance Data gone over N days for obtaining current storage system；
Prediction module, for predicting that each single item performance data in the History Performance Data is corresponding using machine learning algorithm
The following M days object variations trend about predetermined registration operation；
Judgment module, for judging whether there is the object variations trend for meeting default variation tendency；
Setup module, for when the judgment result of the judgment module is yes, the timing of timer being arranged after M days
Execution plays snapshot operation and generates snapshot；
Wherein, the History Performance Data includes to beat the relevant performance data of snapshot operation to triggering, and N and M are positive integer.
9. the equipment that a kind of dynamic generates snapshot, which is characterized in that including memory, for storing computer program；
Processor realizes that dynamic as described in any one of claim 1 to 7 generates snapshot when for executing the computer program
Method the step of.
10. a kind of computer readable storage medium, which is characterized in that be stored with computer on the computer readable storage medium
Program, the computer program realize that dynamic as described in any one of claim 1 to 7 generates snapshot when being executed by processor
The step of method.
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