CN108984344B - Method, device and equipment for dynamically generating snapshot and storage medium - Google Patents
Method, device and equipment for dynamically generating snapshot and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a storage medium for dynamically generating snapshots, wherein the method comprises the steps of firstly, acquiring historical performance data of a current storage system in the past N days; then, predicting a target change trend of the preset operation in the future M days corresponding to each item of performance data in the historical performance data by using a machine learning algorithm; judging whether a target variation trend meeting a preset variation trend exists or not; if so, setting the timing time of the timer to execute the snapshot operation after M days and generate the snapshot. Therefore, on one hand, the method does not need to manually determine the snapshot time, so that the labor cost is saved; on the other hand, compared with the method of taking snapshots at fixed time, the method of taking snapshots at fixed time is a dynamic method of predicting whether snapshots need to be taken in the future according to historical performance data, so that the number of generated snapshots is reduced, the generated snapshots are useful snapshots, and excessive storage resources are not occupied.
Description
Technical Field
The present invention relates to the field of snapshot technologies, and in particular, to a method, an apparatus, a device, and a storage medium for dynamically generating a snapshot.
Background
A snapshot is one of the primary means of disaster-tolerant backup of a storage system, and refers to assigning a fully available copy of a data set that includes an image of the corresponding data at some point in time (the point in time at which the copy began). When the application failure or file damage occurs to the storage system, the data can be quickly restored according to the stored snapshot, and the data can be restored to the state of an available time point.
In the prior art, two methods are mainly used for generating snapshots, one is manual snapshot, namely, a maintenance operator determines the snapshot time according to actual needs; the other is time snapshot. Because the time for manually taking the snapshot is very difficult to determine, a mode of taking the snapshot regularly is usually adopted in practical application, that is, data backup is performed at regular intervals to form a snapshot.
However, the frequency needs to be determined according to the operation change of the storage system in a timing snapshot mode, and operation and maintenance personnel need to pay attention to the operation change of the storage system at any time, so that more labor cost is consumed; more seriously, the timing snapshot mode can generate more same snapshots, so that excessive storage resources are occupied, and the performance of the storage system is reduced. Therefore, how to avoid generating more useless snapshots to save storage resources is a problem that needs to be urgently waited by those skilled in the art.
Disclosure of Invention
The invention aims to provide a method, a device, equipment and a storage medium for dynamically generating snapshots, which are used for avoiding generating more useless snapshots to save storage resources.
To solve the above technical problem, the present invention provides a method for dynamically generating a snapshot, comprising:
acquiring historical performance data of a current storage system in the past N days;
predicting a target change trend about preset operation of each item of performance data in the historical performance data in the future M days by using a machine learning algorithm;
judging whether a target variation trend meeting a preset variation trend exists or not;
if yes, setting the timing time of the timer to execute snapshot operation after M days and generate a snapshot;
the historical performance data comprises performance data related to triggering snapshot operation, and both N and M are positive integers.
Preferably, the preset operation is a write operation on a volume, and the determining whether a target variation trend meeting a preset variation trend exists is specifically:
judging whether the write operation amount corresponding to the target variation trend is larger than or equal to the write operation amount corresponding to the preset variation trend;
if yes, determining that the target variation trend meets the preset variation trend, otherwise, not meeting the preset variation trend.
Preferably, after the target variation trend is judged to conform to the preset variation trend, the method further includes:
judging whether the current storage system has an idle time period after M days;
and if so, entering the step of setting the timing time of the timer, wherein the arrival time corresponding to the timing time is specifically in the idle time period.
Preferably, the machine learning algorithm is specifically a learning algorithm employing LSTM.
Preferably, the historical performance data specifically includes CPU utilization, memory utilization, IOPS of the volume, bandwidth, and latency.
Preferably, N is 30 days.
Preferably, M is 7 days.
To solve the above technical problem, the present invention further provides an apparatus for dynamically generating a snapshot, including:
the acquisition module is used for acquiring historical performance data of the current storage system in the past N days;
the prediction module is used for predicting a target change trend of the preset operation in the future M days corresponding to each item of performance data in the historical performance data by using a machine learning algorithm;
the judging module is used for judging whether a target variation trend meeting a preset variation trend exists or not;
the setting module is used for setting the timing time of the timer so as to execute snapshot operation after M days and generate a snapshot when the judgment result of the judging module is yes;
the historical performance data comprises performance data related to triggering snapshot operation, and both N and M are positive integers.
In order to solve the above technical problem, the present invention further provides an apparatus for dynamically generating a snapshot, including a memory for storing a computer program;
a processor, configured to implement the steps of the method for dynamically generating a snapshot when executing the computer program.
To solve the above technical problem, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method for dynamically generating a snapshot.
The method for dynamically generating the snapshot provided by the invention comprises the steps of firstly, acquiring historical performance data of the current storage system in the past N days; then, predicting a target change trend of the preset operation in the future M days corresponding to each item of performance data in the historical performance data by using a machine learning algorithm; judging whether a target variation trend meeting a preset variation trend exists or not; if so, setting the timing time of the timer to execute the snapshot operation after M days and generate the snapshot. Therefore, compared with the prior art, on one hand, the method does not need to manually determine the snapshot time, so that the labor cost is saved; on the other hand, compared with the method of taking snapshots at regular time, whether snapshot is needed in the future or not is predicted according to historical performance data, if yes, snapshot is executed, and if not, snapshot is not needed, and the method belongs to a dynamic method, so that the number of generated snapshots is reduced, the generated snapshots are useful snapshots, and excessive storage resources are not occupied.
In addition, the device, the equipment and the storage medium for dynamically generating the snapshot provided by the invention also have the beneficial effects.
Drawings
In order to illustrate the embodiments of the present invention more clearly, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained by those skilled in the art without inventive effort.
Fig. 1 is a flowchart of a method for dynamically generating a snapshot according to an embodiment of the present invention;
FIG. 2 is a flow chart of another method for dynamically generating snapshots, according to an embodiment of the invention;
fig. 3 is a block diagram of an apparatus for dynamically generating a snapshot according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without any creative work belong to the protection scope of the present invention.
The core of the invention is to provide a method, a device, equipment and a storage medium for dynamically generating snapshots, which are used for avoiding generating more useless snapshots to save storage resources.
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flowchart of a method for dynamically generating a snapshot according to an embodiment of the present invention. As shown in fig. 1, the method includes:
s10: historical performance data of the current storage system over the past N days is obtained.
In specific implementation, the reliability of the storage system is higher than the price, that is, frequent snapshots have no substantial meaning, and the snapshots need to be stored, which wastes storage resources. In the invention, the operation of the storage system is actually considered to have a genetic rule, in other words, whether the operation needing snapshot is possible to occur in the future or not can be predicted through past historical data. For example, if it is predicted that the storage system may have a large number of write operations in the future, in which case snapshotting may be necessary, the snapshotting may be performed after the write operations are completed.
The historical performance data is to be used as reference data for predicting whether snapshot operation is necessary, and therefore, the historical performance data contains performance data related to triggering the snapshot operation. As a preferred embodiment, the historical performance data specifically includes CPU utilization, memory utilization, IOPS of the volume, bandwidth, and latency. It should be noted that the above performance data is only one embodiment, and does not represent only these data.
N in this step is a positive integer, and in one embodiment, N is 30 days. It is understood that 30 days is only one embodiment and does not represent only 30 days, and the data needs to be determined according to the operation condition of the storage system.
S11: and predicting a target change trend of each item of the historical performance data about preset operation by using a machine learning algorithm in the future M days.
The machine learning algorithm is a learning model obtained by learning by using a large amount of sample data, so that a corresponding output result can be obtained after new data is input. Since the application of the machine learning algorithm is already mature, the detailed model and learning process thereof are not repeated in the present invention. Specifically, a learning algorithm of LSTM (long short term memory network) may be employed. And taking each item of performance data in the historical performance data as an input object, and obtaining a prediction result corresponding to each item of performance data, namely a target change trend of the preset operation in the future M days.
The preset operation in this step needs to be empirically determined which operations need to be snapshot after being completed, such as the write operation mentioned in the above example, and of course, other types of operations may be used besides the write operation. The more predictive operation settings, the greater the probability that a snapshot will be generated. If the historical performance data includes a CPU utilization rate, a memory utilization rate, an IOPS of the volume, a bandwidth, and a time delay, a target change trend related to a preset operation corresponding to the CPU utilization rate, a target change trend related to a preset operation corresponding to the memory utilization rate, a target change trend related to a preset operation corresponding to the IOPS of the volume, a target change trend related to a preset operation corresponding to the bandwidth, and a target change trend related to a preset operation corresponding to the time delay are required to be obtained.
In this step, M is a positive integer, and in one embodiment, M is 7 days. It is understood that 7 days is only one embodiment and does not represent only 7 days, and the data needs to be determined according to the operation condition of the storage system. In addition, M is usually smaller than N, because if M is larger than N, the trend of change of a large number of days in the future is predicted by data of a small number of days, which easily causes the problem of inaccurate prediction result.
S12: and judging whether a target variation trend meeting the preset variation trend exists, if so, entering S13, and if not, ending.
In the step, whether each target variation trend meets the preset variation trend needs to be judged, and each judgment process is independent, can be carried out simultaneously or can be sequentially judged according to a certain sequence, if one or more target variation trends meet the preset variation trend, the judgment result in the step is yes, otherwise, the step is finished. Whether the target variation trend meets the preset variation trend or not may be the amount that the amount of a certain operation reaches the preset variation trend, or the number that the number of times of a certain operation reaches the preset variation trend, which is not limited herein.
S13: the timing time of the timer is set to perform the snapshot operation after M days and generate the snapshot.
The setting of the timer in this step is to perform a snapshot operation when the corresponding time arrives, thereby generating a snapshot. It is to be understood that the timing time here is a time corresponding to the target variation tendency in accordance with the preset variation tendency in S13. For example, if a certain target change trend represents that a preset operation will be generated in 7 days in the future, the timing time is 7 days later, and certainly the timing time is preferably set to be longer than 7 days, because a snapshot needs to be taken after the preset operation is performed, the specific extension may be determined according to actual conditions. After the timing time of the timer is set, when the timing time is reached, the snapshot operation is executed.
As described in detail above for a complete snapshot cycle, when a snapshot is not required in a cycle, i.e., when the determination result of S12 is negative, the determination of the next cycle needs to be performed, i.e., S110-S13 needs to be repeatedly performed. It will be appreciated that as each cycle progresses, and as time increases, the historical performance data of the previous cycle is different from the historical performance data of the next cycle, and therefore, the results obtained for each step are different, and the cycle progresses.
The method for dynamically generating the snapshot provided by the embodiment includes the steps that firstly, historical performance data of a current storage system in the past N days are obtained; then, predicting a target change trend of the preset operation in the future M days corresponding to each item of performance data in the historical performance data by using a machine learning algorithm; judging whether a target variation trend meeting a preset variation trend exists or not; if so, setting the timing time of the timer to execute the snapshot operation after M days and generate the snapshot. Therefore, compared with the prior art, on one hand, the method does not need to manually determine the snapshot time, so that the labor cost is saved; on the other hand, compared with the method of taking snapshots at regular time, whether snapshot is needed in the future or not is predicted according to historical performance data, if yes, snapshot is executed, and if not, snapshot is not needed, and the method belongs to a dynamic method, so that the number of generated snapshots is reduced, the generated snapshots are useful snapshots, and excessive storage resources are not occupied.
In the above embodiment, the preset operation is not limited, and the snapshot range is not limited. As a preferred embodiment, the preset operation is specifically a write operation on the volume, and S12 is specifically:
judging whether the write operation amount corresponding to the target variation trend is larger than or equal to the write operation amount corresponding to the preset variation trend;
if yes, determining that the target variation trend accords with the preset variation trend, and otherwise, not according with the preset variation trend.
In a specific implementation, if a large number of write operations occur to the storage system, a large change occurs to the operation of the storage system, and at this time, a snapshot needs to be taken so as to avoid obtaining from the snapshot when data is lost in a later period. In this step, when a large number of write operations are predicted for a certain volume, the volume is snapshot, and other areas do not require.
Fig. 2 is a flowchart of another method for dynamically generating a snapshot according to an embodiment of the present invention. As shown in fig. 2, on the basis of the previous embodiment, after determining that the target variation trend meets the preset variation trend, the method further includes:
s20: judging whether the current storage system has an idle time period after M days, if so, entering a step of setting the timing time of a timer, wherein the arrival time corresponding to the timing time is specifically in the idle time period.
Because the storage system is not always in a running state, the storage system has an idle time period, and snapshot is performed in the time period, so that other operations can be efficiently processed in the non-idle time period, and resource competition can be reduced.
In specific implementation, it may also be determined whether the time interval between the idle time period and the time when the preset operation is finished is longer, if the time interval is shorter and meets the preset value, the snapshot operation may be performed in the idle time period, otherwise, the snapshot operation is performed after the preset operation is finished and received.
The above detailed description describes embodiments corresponding to the method for dynamically generating a snapshot, and the present invention further provides a device for dynamically generating a snapshot corresponding to the above method. As shown in fig. 3, the apparatus includes:
and the obtaining module 10 is used for obtaining historical performance data of the past N days of the current storage system.
And the prediction module 11 is used for predicting a target change trend of each item of the historical performance data about the preset operation in the future M days corresponding to the historical performance data by using a machine learning algorithm.
And the judging module 12 is configured to judge whether a target variation trend meeting a preset variation trend exists. .
And a setting module 13, configured to set a timing time of the timer to perform a snapshot operation M days later and generate a snapshot when the determination result of the determining module 12 is yes.
The historical performance data comprises performance data related to triggering snapshot operation, and both N and M are positive integers.
Since the embodiments of the apparatus portion and the method portion correspond to each other, please refer to the description of the embodiments of the method portion for the embodiments of the apparatus portion, which is not repeated here.
The device for dynamically generating the snapshot provided by the embodiment firstly acquires historical performance data of the current storage system in the past N days; then, predicting a target change trend of the preset operation in the future M days corresponding to each item of performance data in the historical performance data by using a machine learning algorithm; judging whether a target variation trend meeting a preset variation trend exists or not; if so, setting the timing time of the timer to execute the snapshot operation after M days and generate the snapshot. Therefore, compared with the prior art, the method corresponding to the device does not need to manually determine the snapshot time, so that the labor cost is saved; on the other hand, compared with the method of taking snapshots at regular time, whether snapshot is needed in the future or not is predicted according to historical performance data, if yes, snapshot is executed, and if not, snapshot is not needed, and the method belongs to a dynamic method, so that the number of generated snapshots is reduced, the generated snapshots are useful snapshots, and excessive storage resources are not occupied.
The invention also provides a device for dynamically generating the snapshot, which is mainly described from the hardware perspective. The apparatus comprises a memory for storing a computer program;
a processor for implementing the steps of the method for dynamically generating a snapshot as described in the above embodiments when executing the computer program.
Since the embodiment of the device portion and the embodiment of the method portion correspond to each other, please refer to the description of the embodiment of the method portion for the embodiment of the device portion, which is not repeated here. In some embodiments of the invention, the processor and memory may be connected by a bus or other means.
The device for dynamically generating the snapshot provided by the embodiment comprises a memory and a processor, wherein the processor can realize the following method when executing the program stored in the memory: firstly, acquiring historical performance data of a current storage system in the past N days; then, predicting a target change trend of the preset operation in the future M days corresponding to each item of performance data in the historical performance data by using a machine learning algorithm; judging whether a target variation trend meeting a preset variation trend exists or not; if so, setting the timing time of the timer to execute the snapshot operation after M days and generate the snapshot. Therefore, compared with the prior art, on one hand, the method does not need to manually determine the snapshot time, so that the labor cost is saved; on the other hand, compared with the method of taking snapshots at regular time, whether snapshot is needed in the future or not is predicted according to historical performance data, if yes, snapshot is executed, and if not, snapshot is not needed, and the method belongs to a dynamic method, so that the number of generated snapshots is reduced, the generated snapshots are useful snapshots, and excessive storage resources are not occupied.
Finally, the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method steps of dynamically generating a snapshot as described in the above embodiments. Reference is made to the description of the method section examples.
It is to be understood that the above-described functional modules, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and used for executing all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The present embodiment provides a computer-readable storage medium storing a computer program that, when executed, can implement the following method: firstly, acquiring historical performance data of a current storage system in the past N days; then, predicting a target change trend of the preset operation in the future M days corresponding to each item of performance data in the historical performance data by using a machine learning algorithm; judging whether a target variation trend meeting a preset variation trend exists or not; if so, setting the timing time of the timer to execute the snapshot operation after M days and generate the snapshot. Therefore, compared with the prior art, on one hand, the method does not need to manually determine the snapshot time, so that the labor cost is saved; on the other hand, compared with the method of taking snapshots at regular time, whether snapshot is needed in the future or not is predicted according to historical performance data, if yes, snapshot is executed, and if not, snapshot is not needed, and the method belongs to a dynamic method, so that the number of generated snapshots is reduced, the generated snapshots are useful snapshots, and excessive storage resources are not occupied.
The method, apparatus, device and storage medium for dynamically generating a snapshot provided by the present invention are described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method for dynamically generating snapshots, comprising:
acquiring historical performance data of a current storage system in the past N days;
predicting a target change trend about preset operation of each item of performance data in the historical performance data in the future M days by using a machine learning algorithm;
judging whether a target variation trend meeting a preset variation trend exists or not;
if yes, setting the timing time of the timer to execute snapshot operation after M days and generate a snapshot;
the historical performance data comprises performance data related to triggering snapshot operation, and both N and M are positive integers.
2. The method for dynamically generating a snapshot according to claim 1, wherein the preset operation is a write operation on a volume, and the determining whether the target change trend that meets the preset change trend exists is specifically:
judging whether the write operation amount corresponding to the target variation trend is larger than or equal to the write operation amount corresponding to the preset variation trend;
if yes, determining that the target variation trend meets the preset variation trend, otherwise, not meeting the preset variation trend.
3. The method for dynamically generating snapshots according to claim 1, further comprising, after determining that the target trend of change conforms to the preset trend of change:
judging whether the current storage system has an idle time period after M days;
and if so, entering the step of setting the timing time of the timer, wherein the arrival time corresponding to the timing time is specifically in the idle time period.
4. Method for the dynamic generation of snapshots according to claim 1, characterized in that said machine learning algorithm is in particular a learning algorithm using LSTM.
5. The method of claim 1, wherein the historical performance data specifically includes CPU utilization, memory utilization, IOPS of a volume, bandwidth, and latency.
6. A method for dynamically generating snapshots as in claim 1, where N is 30 days.
7. The method for dynamically generating snapshots of claim 6, wherein M is 7 days.
8. An apparatus for dynamically generating snapshots, comprising:
the acquisition module is used for acquiring historical performance data of the current storage system in the past N days;
the prediction module is used for predicting a target change trend of the preset operation in the future M days corresponding to each item of performance data in the historical performance data by using a machine learning algorithm;
the judging module is used for judging whether a target variation trend meeting a preset variation trend exists or not;
the setting module is used for setting the timing time of the timer so as to execute snapshot operation after M days and generate a snapshot when the judgment result of the judging module is yes;
the historical performance data comprises performance data related to triggering snapshot operation, and both N and M are positive integers.
9. An apparatus for dynamically generating snapshots, comprising a memory for storing a computer program;
processor for implementing the steps of the method of dynamically generating snapshots as claimed in any one of claims 1 to 7 when executing said computer program.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of dynamically generating snapshots as claimed in any one of claims 1 to 7.
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