Method, electronic device and computer program product for exporting a log

Abstract

Embodiments of the present disclosure provide a method, an electronic device and a computer program product related to exporting a log. The method comprises obtaining a first set of attributes representing a target asset in assets protected by a data protection product, a second set of attributes representing a target task performed on the target asset, and a third set of attributes representing a computing resource running the data protection product. The method further comprises determining, based on the first set of attributes, the second set of attributes and the third set of attributes, an export time consumed for exporting a log of the target asset. Through embodiments of the present disclosure, the time required for exporting a log can be accurately estimated when exporting the log.

Description

Technical Field

[0001] Embodiments of this disclosure relate to the field of computers, and more specifically, to methods, electronic devices, apparatuses, media, and computer program products for exporting logs. Background Technology

[0002] With the development of data storage and protection technologies, data protection systems suitable for various data storage environments have been developed. The data being protected (also known as assets) can include data from multiple components within the data storage environment. Therefore, in complex environments, when exporting logs of a target asset, the time required and the size of the logs are difficult to estimate. Consequently, no existing data protection product can provide the ability to estimate the export time and log file size. Therefore, there is an urgent need for a method that can accurately estimate the export time and the size of the exported file when exporting logs. Summary of the Invention

[0003] Embodiments of this disclosure provide a method, electronic device, apparatus, medium, and computer program product for exporting logs.

[0004] According to a first aspect of this disclosure, a method for exporting logs is provided. The method includes obtaining a first set of attributes representing a target asset among assets protected by a data protection product, a second set of attributes representing a target task performed on the target asset, and a third set of attributes representing computing resources running the data protection product. The method further includes determining the export time consumed in exporting the logs of the target asset based on the first set of attributes, the second set of attributes, and the third set of attributes.

[0005] According to a second aspect of this disclosure, an electronic device is also provided. The electronic device includes a processor and a memory coupled to the processor, the memory having instructions stored therein, the instructions causing the device to perform actions when executed by the processor. The actions include acquiring a first set of attributes representing a target asset among assets protected by a data protection product, a second set of attributes representing a target task performed on the target asset, and a third set of attributes representing computing resources running the data protection product. The actions also include determining, based on the first set of attributes, the second set of attributes, and the third set of attributes, the export time consumed in exporting logs of the target asset.

[0006] According to a third aspect of this disclosure, an apparatus for exporting logs is provided. The apparatus includes a set acquisition module configured to acquire a first set of attributes representing a target asset among assets protected by a data protection product, a second set of attributes representing a target task performed on the target asset, and a third set of attributes representing computing resources running the data protection product. The apparatus also includes a log export module configured to determine the export time consumed in exporting logs of the target asset based on the first set of attributes, the second set of attributes, and the third set of attributes.

[0007] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided, including machine-executable instructions that, when executed by a device, cause the device to perform a method according to a first aspect of this disclosure.

[0008] According to a fifth aspect of this disclosure, a computer program product is provided, which is tangibly stored on a computer-readable medium and includes machine-executable instructions that, when executed by a device, cause the device to perform the method according to the first aspect.

[0009] The summary section is provided to present the chosen concepts in a simplified form, which will be further described in the detailed description below. The summary section is not intended to identify key or principal features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter. Attached Figure Description

[0010] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0011] Figure 1 A schematic diagram of an example environment in which embodiments of the present disclosure can be implemented is shown;

[0012] Figure 2 A flowchart illustrating an exemplary implementation of a method for exporting logs is shown schematically.

[0013] Figure 3 A schematic diagram illustrating an exemplary implementation of a machine learning model according to this disclosure is shown.

[0014] Figure 4 A schematic diagram of a hidden layer of a machine learning model according to an exemplary implementation of the present disclosure is shown.

[0015] Figure 5 A block diagram of an apparatus for exporting logs according to an exemplary implementation of the present disclosure is shown schematically.

[0016] Figure 6 A flowchart illustrating an exemplary implementation of a method for training a machine learning model according to this disclosure is shown schematically; and

[0017] Figure 7 A block diagram of a device for exporting logs, according to an exemplary implementation of this disclosure, is shown schematically.

[0018] In all the accompanying figures, the same or similar reference numerals denote the same or similar elements. Detailed Implementation

[0019] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0020] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0021] Furthermore, all specific values ​​in this article are examples only, intended to aid understanding, and are not intended to limit any range.

[0022] The inventors noted that in data protection products, the time consumed for exporting logs varies greatly due to the complexity of data (also known as assets, hereinafter the distinction between data and assets) storage devices, the diversity of users, the different computing systems that allow data protection products, and the different usage environments (e.g., network speed). This leads to a poor user experience, preventing users from making advance arrangements and thus affecting their work.

[0023] In view of this, the method disclosed herein provides a method for exporting logs. This method utilizes various factors associated with exporting logs, each of which can individually or in combination affect the time consumed in exporting the logs. Therefore, by taking these factors into account and using a reasonable algorithm, the time required to export the logs can be accurately estimated.

[0024] Figure 1 A schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented is shown.

[0025] In environment 100, a data protection product 102 is installed on computing resource 101 (e.g., a computing system or server). The data protection product 102 is connected to a user via a network. The assets protected by the data protection product can be stored in multiple storage devices 103-1, 103-2, 103-N (collectively referred to as storage device 103, individually or collectively). Storage device 103 is connected to computing resource 101.

[0026] Figure 2 A flowchart illustrating an exemplary implementation of a method 200 for exporting logs according to this disclosure is shown schematically.

[0027] At box 202, obtain the first set of attributes of the target asset in the assets protected by the data protection product, the second set of attributes of the target task performed on the target asset, and the third set of attributes of the computing resources running the data protection product.

[0028] In some embodiments, the first set of attributes may include the number of Structured Query Language (SQL) assets, the number of file system assets, the number of virtual machine assets, the number of Network Attached Storage (NAS) assets, the number of SAP HANA assets, and the number of Navigator Kubernetes assets. The first set of attributes may also include the number of data protection policies and the number of data protection phases in the entire data protection system.

[0029] In some embodiments, the second set of attributes may include the number of target tasks, the size of the asset (e.g., in bytes), the data size of the compressed asset, the data size of the transmitted asset, the type of target task, the frequency at which the target task is executed, the subtasks of the target task, the number of successful subtask executions, the number of failed subtask executions, and the number of canceled subtasks.

[0030] In some embodiments, the third set of attributes may include attributes of the computing resources running the data protection product, such as hardware attributes, including the number of CPU cores, core clock speed, storage device I / O access speed, PCIe type, network bandwidth, and network latency.

[0031] At box 204, based on the first set of attributes, the second set of attributes, and the third set of attributes, determine the export time consumed by exporting the logs of the target asset.

[0032] In some embodiments, a first set of attributes, a second set of attributes, and a third set of attributes can be input into a machine learning model, which can accurately determine the time consumed in exporting logs based on these input attributes.

[0033] It will be understood that since the attributes that can be included in the first, second, and third attribute sets are all closely related to the time required to export the logs, the time required to export the logs can be determined more accurately by using a reasonably trained machine learning model after considering these important and closely related attributes.

[0034] In some embodiments, the method may further determine the export time consumed in exporting the logs of the target asset based on a fourth set of attributes, including the type of customer owning the target asset, the time when the logs were exported, and the duration of continuous operation of the data protection product. That is, the first, second, third, and fourth attribute sets can all be input into a machine learning model to determine the export time consumed in exporting the logs of the target asset.

[0035] In some embodiments, the time when the log export occurs can be precise to the day (e.g., a day of the week) or more granular (e.g., a specific hour of the day). For example, the log export operation might be performed on Wednesday morning (more specifically, at 10:00 AM).

[0036] In some embodiments, the method may further include using a machine learning model to determine the size of the logs for exporting the target asset.

[0037] In some embodiments, the types of assets may include at least Structured Query Language (SQL) assets, file system assets, virtual machine assets, Network Attached Storage (NAS) assets, SAP HANA assets, and Navigator Kubernetes assets.

[0038] In some embodiments, the method may further include exporting the logs of the target asset during the export time.

[0039] In some embodiments, the machine learning model is a multilayer perceptron, which may include at least a first hidden layer, a second hidden layer, and a third hidden layer. The number of first nodes in the first hidden layer is determined by selecting a specified number of attributes from a first set of attributes, a second set of attributes, a third set of attributes, and a fourth set of attributes, such that the number of first nodes is determined to be a positive integer power of 2 that is greater than or equal to the specified number.

[0040] In some embodiments, at least ten attributes are selected from a first set of attributes, a second set of attributes, a third set of attributes, and a fourth set of attributes; a value is determined that satisfies the following conditions: (i) greater than or equal to the number of selected attributes, and (ii) a positive integer power of 2; the minimum value satisfying the conditions is determined as the number of first nodes. The following will refer to... Figure 3 The machine learning model used in this disclosure is described in detail.

[0041] Figure 3 A schematic diagram of a machine learning model 300 according to an exemplary implementation of the present disclosure is shown.

[0042] As shown in the figure, the machine learning model 300 may include a first module 301 and a second module 302. The first module is used to determine the time required to export the logs. The second module is used to determine the size of the exported logs.

[0043] The structures of the first model 301 and the second module 302 can be the same or different. Figure 3 The diagram illustrates a scenario where the first module 301 and the second module 302 have the same structure, both being fully connected. This structure includes an input layer, a hidden layer, and an output layer. The number of nodes in the input layer is the same as the sum of the attributes in the first, second, third, and fourth attribute sets. Here, attributes can also be referred to as features; the distinction between attributes and features will not be made below. The output layer has one node used to output the time required to export the log or the size of the exported log.

[0044] In some embodiments, the hidden layer can have three layers. The number of nodes in the first hidden layer needs to satisfy the following formula:

[0045]

[0046] Where x represents the number of attributes selected from the first, second, third, and fourth attribute sets, t represents a positive integer, and h1 min This represents the minimum number of elements in the first hidden layer, and min indicates the operation of finding the minimum value.

[0047] In some embodiments, the number of second nodes in the second hidden layer is greater than or equal to the number of first nodes, and the number of third nodes in the third hidden layer is greater than or equal to the number of first nodes.

[0048] Figure 4 A schematic diagram of a hidden layer of a machine learning model according to an exemplary implementation of this disclosure is shown.

[0049] As shown in the figure, for example only, the first hidden layer 401 may include 32 nodes, the second hidden layer 402 may include 64 nodes, and the third hidden layer 403 may include 128 nodes.

[0050] In training this machine learning model, the following training data can be used, which must meet the following conditions: at least ten attributes selected from attributes in the first to fourth attribute sets, representing a subset of the protected assets. The number of assets in the subset is determined proportionally to the total number of assets.

[0051] Understandably, the machine learning model trained in this way fully learns the hidden relationships between various attributes and the time of log export, and possesses generalization ability. Therefore, during the inference phase, it can better determine (e.g., predict) the time and size of the log export for the target asset.

[0052] Figure 5 A block diagram of an apparatus for exporting logs according to an exemplary implementation of the present disclosure is shown schematically.

[0053] The set acquisition module 502 is configured to acquire a first set of attributes representing the target assets in the assets protected by the data protection product, a second set of attributes representing the target tasks performed on the target assets, and a third set of attributes representing the computing resources running the data protection product.

[0054] The log export module 504 is configured to determine the export time consumed by exporting the logs of the target asset based on the first set of attributes, the second set of attributes, and the third set of attributes.

[0055] In some embodiments, the set acquisition module 502 can also be configured to determine the export time based on a first set of attributes, a second set of attributes, a third set of attributes, and a fourth set of attributes, wherein the fourth set of attributes includes the type of customer that owns the target assets, the time when the export logs occurred, and the length of time the data protection product has been running continuously.

[0056] In some embodiments, determining the export time consumed by exporting the logs of the target asset includes using a machine learning model to determine the export time, wherein the machine learning model is a multilayer perceptron, the multilayer perceptron including a first hidden layer, a second hidden layer and a third hidden layer, and the number of first nodes in the first hidden layer is determined by: selecting a specific number based on the total number of attributes in the first set of attributes, the second set of attributes, the third set of attributes and the fourth set of attributes; and determining the number of first nodes as a minimum positive integer power of 2 that is greater than or equal to the specific number.

[0057] In some embodiments, the number of second nodes in the second hidden layer may be greater than or equal to the number of first nodes, and the number of third nodes in the third hidden layer may be greater than or equal to the number of first nodes.

[0058] In some embodiments, the log export module 504 may also be configured to use a machine learning model to determine the size of the logs for the target asset to be exported.

[0059] In some embodiments, the first set of attributes includes the number of Structured Query Language (SQL) assets, the number of file system assets, the number of virtual machine assets, the number of Network Attached Storage (NAS) assets, the number of SAP HANA assets, the number of Navigator Kubernetes assets, the number of data protection policies, and the number of data protection phases.

[0060] In some embodiments, the second set of attributes includes the number of target tasks, the type of target tasks, the frequency at which target tasks are executed, the number of subtasks of the target tasks, the number of successful subtasks, the number of failed subtasks, and the number of canceled subtasks.

[0061] In some embodiments, the third set of attributes includes the number of cores of the central processing unit (CPU) of the computing resources, the core clock frequency, the access speed of the storage devices, the type of high-speed serial computer expansion bus (PCIe), the network bandwidth, and the network latency.

[0062] In some embodiments, the log export module 504 can also be configured to export the logs of the target asset within the export time.

[0063] It is understood that the device 500 can also achieve at least one beneficial technical effect as that achievable by the method 200.

[0064] Figure 6 A flowchart illustrating an exemplary implementation of a method for training a machine learning model according to this disclosure is shown schematically.

[0065] At box 610, select some attributes from the first, second, third, and fourth attribute sets. The number of these attributes should be at least a certain level (e.g., 10-13 attributes in total, and at least one attribute should be selected from each set). At this stage, these selected attributes are also referred to as features. The data obtained at this stage can be called sample data.

[0066] At box 620, selected features can be input into the machine learning model for training. The training process can use the backpropagation algorithm. The training process ends when the parameters of the machine learning model reach the required level.

[0067] At box 630, the machine learning model can enter the inference phase. Input test data, and consider the prediction results for the test data (i.e., the prediction export time and log size).

[0068] At box 640, if the error between the predicted result and the actual result is large, proceed to box 650. If the predicted result meets the accuracy requirements, proceed to box 660.

[0069] At box 650, the machine learning model is retrained using new training data (which may also include previously used sample data), and more features can be used for training.

[0070] At box 660, deploy the machine learning model for real-world use by users.

[0071] Figure 7 A schematic block diagram of a device 700 that can be used to implement embodiments of the present disclosure is shown. Device 700 may be the device or apparatus described in the embodiments of the present disclosure. Figure 7 As shown, device 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) 702 or loaded from storage unit 708 into random access memory (RAM) 703. The RAM 703 may also store various programs and data required for the operation of device 700. The CPU 701, ROM 702, and RAM 703 are interconnected via bus 704. Input / output (I / O) interface 705 is also connected to bus 704. Although not shown in... Figure 7 As shown, device 700 may also include a coprocessor.

[0072] Multiple components in device 700 are connected to I / O interface 705, including: input unit 706, such as keyboard, mouse, etc.; output unit 707, such as various types of monitors, speakers, etc.; storage unit 708, such as disk, optical disk, etc.; and communication unit 709, such as network card, modem, wireless transceiver, etc. Communication unit 709 allows device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0073] The various methods or processes described above can be executed by processing unit 701. For example, in some embodiments, the methods can be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by CPU 701, one or more steps or actions in the methods or processes described above can be performed.

[0074] In some embodiments, the methods and processes described above can be implemented as a computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of this disclosure.

[0075] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0076] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper cables, fiber optic cables, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to computer-readable storage media within the respective computing / processing device.

[0077] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​and conventional procedural programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0078] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0079] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0080] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0081] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

[0082] The following are some example implementations of this disclosure.

[0083] In a first aspect of this disclosure, a method for exporting logs is provided. The method includes obtaining a first set of attributes representing a target asset among assets protected by a data protection product, a second set of attributes representing a target task performed on the target asset, and a third set of attributes representing computing resources running the data protection product. The method further includes determining the export time consumed in exporting the logs of the target asset based on the first set of attributes, the second set of attributes, and the third set of attributes.

[0084] In some embodiments, determining the export time consumed in exporting the logs of the target asset includes determining the export time based on a first set of attributes, a second set of attributes, a third set of attributes, and a fourth set of attributes, wherein the fourth set of attributes includes the type of customer owning the target asset, the time when the logs were exported, and the length of time the data protection product has been running continuously.

[0085] In some embodiments, determining the export time consumed by exporting the logs of the target asset includes using a machine learning model to determine the export time, wherein the machine learning model is a multilayer perceptron, the multilayer perceptron including a first hidden layer, a second hidden layer and a third hidden layer, and the number of first nodes in the first hidden layer is determined by: selecting a specific number based on the total number of attributes in the first set of attributes, the second set of attributes, the third set of attributes and the fourth set of attributes; and determining the number of first nodes as a minimum positive integer power of 2 that is greater than or equal to the specific number.

[0086] In some embodiments, the number of second nodes in the second hidden layer may be greater than or equal to the number of first nodes, and the number of third nodes in the third hidden layer may be greater than or equal to the number of first nodes.

[0087] In some embodiments, the method further includes using a machine learning model to determine the size of the logs for exporting the target asset.

[0088] In some embodiments, the first set of attributes includes the number of Structured Query Language (SQL) assets, the number of file system assets, the number of virtual machine assets, the number of Network Attached Storage (NAS) assets, the number of SAP HANA assets, the number of Navigator Kubernetes assets, the number of data protection policies, and the number of data protection phases.

[0089] In some embodiments, the second set of attributes includes the number of target tasks, the type of target tasks, the frequency at which target tasks are executed, the number of subtasks of the target tasks, the number of successful subtasks, the number of failed subtasks, and the number of canceled subtasks.

[0090] In some embodiments, the third set of attributes includes the number of cores of the central processing unit (CPU) of the computing resources, the core clock frequency, the access speed of the storage devices, the type of high-speed serial computer expansion bus (PCIe), network bandwidth, and network latency.

[0091] In some embodiments, the method further includes exporting the logs of the target asset during the export time.

[0092] In a second aspect of this disclosure, an electronic device is provided. The electronic device includes a processor and a memory coupled to the processor, the memory having instructions stored therein, the instructions causing the device to perform actions when executed by the processor. The actions include acquiring a first set of attributes representing a target asset among assets protected by a data protection product, a second set of attributes representing a target task performed on the target asset, and a third set of attributes representing computing resources running the data protection product. The actions also include determining, based on the first set of attributes, the second set of attributes, and the third set of attributes, the export time consumed in exporting logs of the target asset.

[0093] In some embodiments, determining the export time consumed in exporting the logs of the target asset includes determining the export time based on a first set of attributes, a second set of attributes, a third set of attributes, and a fourth set of attributes, wherein the fourth set of attributes includes the type of customer owning the target asset, the time when the logs were exported, and the length of time the data protection product has been running continuously.

[0094] In some embodiments, determining the export time consumed by exporting the logs of the target asset includes using a machine learning model to determine the export time, wherein the machine learning model is a multilayer perceptron, the multilayer perceptron including a first hidden layer, a second hidden layer and a third hidden layer, and the number of first nodes in the first hidden layer is determined by: selecting a specific number based on the total number of attributes in the first set of attributes, the second set of attributes, the third set of attributes and the fourth set of attributes; and determining the number of first nodes as a minimum positive integer power of 2 that is greater than or equal to the specific number.

[0095] In some embodiments, the number of second nodes in the second hidden layer may be greater than or equal to the number of first nodes, and the number of third nodes in the third hidden layer may be greater than or equal to the number of first nodes.

[0096] In some embodiments, the action also includes using a machine learning model to determine the size of the logs for exporting the target asset.

[0097] In some embodiments, the first set of attributes includes the number of Structured Query Language (SQL) assets, the number of file system assets, the number of virtual machine assets, the number of Network Attached Storage (NAS) assets, the number of SAP HANA assets, the number of Navigator Kubernetes assets, the number of data protection policies, and the number of data protection phases.

[0098] In some embodiments, the second set of attributes includes the number of target tasks, the type of target tasks, the frequency at which target tasks are executed, the number of subtasks of the target tasks, the number of successful subtasks, the number of failed subtasks, and the number of canceled subtasks.

[0099] In some embodiments, the third set of attributes includes the number of cores of the central processing unit (CPU) of the computing resources, the core clock frequency, the access speed of the storage devices, the type of high-speed serial computer expansion bus (PCIe), network bandwidth, and network latency.

[0100] In some embodiments, the action also includes exporting the logs of the target asset during the export time.

[0101] In a third aspect embodiment, an apparatus for exporting logs is provided. The apparatus includes a set acquisition module configured to acquire a first set of attributes representing a target asset among assets protected by a data protection product, a second set of attributes representing a target task performed on the target asset, and a third set of attributes representing computing resources running the data protection product. The apparatus also includes a log export module configured to determine the export time consumed in exporting logs of the target asset based on the first set of attributes, the second set of attributes, and the third set of attributes.

[0102] In an embodiment of the fourth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores one or more computer instructions, wherein the one or more computer instructions are executed by a processor to implement the method according to the first aspect.

[0103] In a fifth aspect embodiment, a computer program product is provided. The computer program product includes one or more computer instructions, wherein the one or more computer instructions, when executed by a processor, implement the method according to the first aspect.

[0104] Although this disclosure has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for exporting logs, comprising: Obtain the set of attributes representing the target assets protected by the data protection product; A machine learning model is used to determine the export time required to export the logs of the target asset, wherein the machine learning model is a multilayer perceptron, and the multilayer perceptron includes: First hidden layer; The second hidden layer; and The third hidden layer, and The number of first nodes in the first hidden layer is determined by selecting a specific number based on the number of attributes in the attribute set, wherein the number of first nodes is a positive integer power of 2 that is greater than or equal to the specific number; and The logs of the target asset are exported based on the determined export time.

2. The method of claim 1, wherein determining the required export time comprises: Based on the attribute set, the required export time is determined, wherein the attribute set includes the type of customer owning the target asset, the time when the logs are exported, and the duration of continuous operation of the data protection product.

3. The method according to claim 1, wherein the number of the first nodes in the first hidden layer is determined by the following method: Based on the total number of attributes in the attribute set, select a number; and The number of the first node is determined to be the smallest positive integer power of 2 that is greater than or equal to the number.

4. The method according to claim 1, wherein the number of second nodes in the second hidden layer is greater than or equal to the number of first nodes, and the number of third nodes in the third hidden layer is greater than or equal to the number of first nodes.

5. The method according to claim 1, further comprising: The machine learning model is used to determine the size of the logs for the target asset.

6. The method according to claim 1, wherein the attribute set further comprises: The number of Structured Query Language (SQL) assets, the number of file system assets, the number of virtual machine assets, the number of Network Attached Storage (NAS) assets, the number of SAP HANA assets, the number of Navigator Kubernetes assets, the number of data protection policies, and the number of data protection phases.

7. The method according to claim 1, wherein the attribute set further includes the number of target tasks, the type of the target tasks, the frequency at which the target tasks are executed, the number of subtasks of the target tasks, the number of successful subtasks, the number of failed subtasks, and the number of canceled subtasks.

8. The method according to claim 1, wherein the attribute set further includes the number of cores of the central processing unit (CPU) of the computing resource, the main frequency of the cores of the CPU of the computing resource, the access speed of the storage device, the type of high-speed serial computer expansion bus (PCIE), network bandwidth, and network latency.

9. The method according to claim 1, further comprising: Export the logs of the target asset within the required export time.

10. An electronic device, comprising: processor; as well as A memory coupled to the processor, the memory having instructions stored therein, the instructions causing the device to perform actions when executed by the processor, the actions including: Obtain the set of attributes representing the target assets protected by the data protection product; A machine learning model is used to determine the export time required to export the logs of the target asset, wherein the machine learning model is a multilayer perceptron, and the multilayer perceptron includes: First hidden layer; The second hidden layer; and The third hidden layer, and The number of first nodes in the first hidden layer is determined by selecting a specific number based on the number of attributes in the attribute set, wherein the number of first nodes is a positive integer power of 2 that is greater than or equal to the specific number; and The logs of the target asset are exported based on the determined export time.

11. The electronic device of claim 10, wherein determining the required export time comprises: Based on the attribute set, the required export time is determined, wherein the attribute set includes the type of customer owning the target asset, the time when the logs are exported, and the duration of continuous operation of the data protection product.

12. The electronic device of claim 10, wherein the number of the first nodes in the first hidden layer is determined by: Based on the total number of attributes in the attribute set, select a number; and The number of the first node is determined to be the smallest positive integer power of 2 that is greater than or equal to the number.

13. The electronic device of claim 10, wherein the number of second nodes in the second hidden layer is greater than or equal to the number of first nodes, and the number of third nodes in the third hidden layer is greater than or equal to the number of first nodes.

14. The electronic device according to claim 10, wherein the action further comprises: The machine learning model is used to determine the size of the logs for the target asset.

15. The electronic device of claim 10, wherein the set of attributes further comprises: The number of Structured Query Language (SQL) assets, the number of file system assets, the number of virtual machine assets, the number of Network Attached Storage (NAS) assets, the number of SAP HANA assets, the number of Navigator Kubernetes assets, the number of data protection policies, and the number of data protection phases.

16. The electronic device of claim 10, wherein the attribute set further includes the number of target tasks, the type of the target tasks, the frequency at which the target tasks are executed, the number of subtasks of the target tasks, the number of successful subtasks, the number of failed subtasks, and the number of canceled subtasks.

17. The electronic device of claim 10, wherein the set of attributes further includes the number of cores of the central processing unit (CPU) of the computing resources, the main frequency of the cores of the CPU of the computing resources, the access speed of the storage device, the type of high-speed serial computer expansion bus (PCIE), network bandwidth, and network latency.

18. The electronic device according to claim 10, wherein the action further comprises: Export the logs of the target asset within the required export time.

19. A non-transient computer-readable storage medium storing instructions thereon, wherein the instructions, when executed by a processor, cause the processor to perform an action, the action comprising: Obtain the set of attributes representing the target assets protected by the data protection product; A machine learning model is used to determine the export time required to export the logs of the target asset, wherein the machine learning model is a multilayer perceptron, and the multilayer perceptron includes: First hidden layer; The second hidden layer; and The third hidden layer, and The number of first nodes in the first hidden layer is determined by selecting a specific number based on the number of attributes in the attribute set, wherein the number of first nodes is a positive integer power of 2 that is greater than or equal to the specific number; and The logs of the target asset are exported based on the determined export time.

20. The non-transient computer-readable storage medium of claim 19, wherein determining the required derivation time comprises: Based on the attribute set, the required export time is determined, wherein the attribute set includes the type of customer owning the target asset, the time when the logs are exported, and the duration of continuous operation of the data protection product.

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