CN114422600B - File scheduling system based on cloud storage and file scheduling method based on cloud storage - Google Patents
File scheduling system based on cloud storage and file scheduling method based on cloud storage Download PDFInfo
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Abstract
The application discloses a file scheduling system based on cloud storage and a file scheduling method based on cloud storage, relates to the technical field of computers, and can solve the technical problems that the operation efficiency is low and the file is inconvenient to search when cloud storage is performed at present. The system comprises: the information acquisition module is used for acquiring state information of a user operating system, wherein the state information comprises file state information of any application program in the user operating system; the task generating module is connected with the information acquisition module and used for receiving the file state information sent by the information acquisition module and generating a file scheduling task by analyzing the file state information, wherein the file scheduling task comprises a file cloud storage task and a file recall task stored by the file cloud; the file scheduling module is connected with the task generating module and the cloud storage module and is used for responding to the file scheduling task sent by the task executing module based on the cloud storage module, wherein the cloud storage module is used for storing the cold file identified by the task generating module.
Description
Technical Field
The application relates to the technical field of computers, in particular to a file scheduling system based on cloud storage and a file scheduling method based on cloud storage.
Background
The Internet infrastructure of China is mature, the user network speed is faster and faster, and most of computer equipment is always connected with the Internet almost only by starting up the computer equipment. During networked use, large amounts of file data may be generated in a short period of time, such as: software installation packages, compressed files, documents, codes, multimedia files and the like, and in order to ensure the stable operation of computer equipment and ensure the safety of the files, the files with low use frequency or long-term storage need to be transferred to cloud storage so as to release disk space.
At present, when the files are transferred to cloud storage, users are required to manually screen and upload files to be archived, and the operation is complex and the efficiency is low. In addition, after the user uploads the file to the cloud disk, when the file needs to be used, the user often needs to take a long time to find the file, and the user experience is poor.
Disclosure of Invention
In view of the above, the application provides a file scheduling system based on cloud storage and a file scheduling method based on cloud storage, which can solve the technical problems that at present, when a file is converted from a local storage to a cloud storage, a user is required to manually screen and upload the file to be archived, the operation efficiency is low, the file is inconvenient to search, and the user experience is poor.
According to one aspect of the present application, there is provided a file scheduling system based on cloud storage, the system comprising: the system comprises an information acquisition module, a task generation module, a file scheduling module and a cloud storage module;
the information acquisition module is arranged in a user operating system and used for acquiring state information of the user operating system, wherein the state information comprises file state information of any application program in the user operating system;
the task generating module is connected with the information acquisition module and is used for receiving the file state information sent by the information acquisition module and generating a file scheduling task by analyzing the file state information, wherein the file scheduling task comprises a file cloud storage task and a file recall task after file cloud storage;
the file scheduling module is connected with the task generating module and the cloud storage module and is used for responding and executing the file scheduling task sent by the task generating module based on the cloud storage module, wherein the cloud storage module is used for storing the cold file identified by the task generating module.
Optionally, the file status information includes file access information and file attribute information, and the task generating module includes:
The associated file mining module is connected with the information acquisition module, and is provided with an associated analysis model for determining a first file set with the association higher than a preset threshold value corresponding to the history access files in the last x days according to the associated analysis model and the file access information sent by the information acquisition module;
the cold file identification module is connected with the information acquisition module and the associated file mining module, a Bayesian prediction model is arranged in the cold file identification module and is used for determining a second file set which cannot be accessed in y days in the future according to the Bayesian prediction model, the file access information and the file attribute information sent by the information acquisition module, removing the first file set sent by the associated file mining module from the second file set, removing a system file set and a specified file set to obtain a cold file set, and the cold file set at least comprises one cold file;
and the storage task generating module is connected with the cold file identifying module and is used for generating a file cloud storage task carrying cold file attribute information according to the cold file set.
Optionally, the file scheduling module includes:
the cloud storage system comprises a storage task generating module, a cold file transferring module, a cloud storage module and a storage module, wherein one end of the storage task generating module is connected with the storage task generating module, the other end of the storage task generating module is connected with the cloud storage module and is used for receiving a file cloud storage task which is sent by the storage task generating module and carries cold file attribute information, extracting cold files from a local disk according to the cold file attribute information, transferring the cold files to the cloud storage module and releasing local disk space occupied by the cold files;
the file replacement generating module is connected with the cold file transfer module and is used for generating a file replacement of the cold file at an original storage position of the cold file and creating a transfer mapping relation between the file replacement and the cold file.
Optionally, the task generating module further includes:
the first recall file analysis module is connected with a user client through a client interface and is used for receiving a file access request sent by the user client, analyzing a file to be accessed corresponding to the file access request and a first file state of the file to be accessed in a local disk space;
and the first recall task generating module is connected with the first recall file analyzing module and is used for receiving and identifying the first file state, and if the first file state is judged to be the file replacement, a first file recall task related to the file to be accessed is generated.
Optionally, the task generating module further includes:
the second recall file analysis module is connected with the associated file mining module and connected with a user client through a client interface, and is used for determining an associated file set corresponding to a file to be accessed by using the associated file mining module when a file access request sent by the user client is received, and identifying a second file state of each associated file in the associated file set in a local disk space;
and the second recall task generating module is connected with the second recall file analyzing module and is used for receiving and identifying the second file state, and generating a second file recall task related to the associated file if the second file state is judged to be a file replacement, wherein the task priority of the second file recall task is smaller than the task priority of the first file recall task corresponding to the file to be accessed.
Optionally, the file scheduling module further includes:
and one end of the first file recall module is connected with the first recall task generation module, the other end of the first file recall module is connected with the cloud storage module and is used for receiving the first file recall task sent by the first recall task generation module, carrying out recall processing on the file to be accessed in the cloud storage module according to the task priority and recovering the file to be accessed to the original storage position in the local disk space according to the transfer mapping relation between the file replacement and the cold file.
Optionally, the file scheduling module further includes:
and one end of the second file recall module is connected with the second recall task generation module, the other end of the second file recall module is connected with the cloud storage module and is used for receiving the second file recall task sent by the second recall task generation module, carrying out recall processing on the associated file in the cloud storage module according to the task priority and restoring the associated file to the original storage position in the local disk space according to the transfer mapping relation between the file replacement and the cold file.
Optionally, the system further comprises:
the resource management module is connected with the local disk and the cloud storage module and is used for calculating the occupied space of the local disk and the occupied space of the cloud storage module;
the interactive presentation module is connected with the task generation module, the file scheduling module and the resource management module and is used for displaying the file cloud storage task, the file recall task, the file state of the file corresponding to the file recall task, the occupied space of the local disk and the occupied space of the cloud storage module on a human-computer interaction page.
Optionally, the state information of the user operating system further includes network state information and system state information;
the task generating module is further configured to receive the network state information and/or the system state information sent by the information collecting module, and generate a delay task related to the file scheduling task by analyzing the network state information and/or the system state information, so as to control to delay execution of the file cloud storage task and/or the file recall task after file cloud storage.
According to another aspect of the present application, there is provided a cloud storage-based file scheduling method, which is applied to the above-mentioned cloud storage-based file scheduling system, and the method includes:
the information acquisition module acquires state information of a user operating system, wherein the state information comprises file state information of any application program in the user operating system;
the task generating module receives the file state information sent by the information acquisition module, and generates a file scheduling task by analyzing the file state information, wherein the file scheduling task comprises a file cloud storage task and a file recall task stored by the file cloud;
The file scheduling module is based on a cloud storage module, and responds to the execution of the file scheduling storage task sent by the task generating module, wherein the cloud storage module is used for storing the cold file identified by the task generating module.
By means of the technical scheme, the file scheduling system based on cloud storage and the file scheduling method based on cloud storage provided by the application acquire file state information of a user operating system through the information acquisition module and send the file state information to the task generation module; the task generating module can make a decision based on the file state information through a series of rules and methods, judges whether a file needs to be dumped from the local to the cloud or re-downloaded from the cloud to the local, generates a relevant file scheduling task according to a decision result, and then issues the file scheduling task to the file scheduling module; and finally, automatically executing the file scheduling task by using the file scheduling module and the cloud storage module, and creating a transfer mapping relation between the file replacement and the cold file when executing the file cloud storage task, so that the file to be recalled can be quickly positioned based on the transfer mapping relation when executing the file recall task later, and the file retrieval efficiency is ensured. According to the technical scheme, an artificial intelligence technology is integrated in the file scheduling process, so that the file scheduling efficiency can be ensured, a user can release disk space without feeling and learning cost as far as possible, and the local disk space can store valuable and recently used hot files as far as possible.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
fig. 1 shows a schematic structural diagram of a file scheduling system based on cloud storage according to an embodiment of the present application;
fig. 2 shows a flow diagram of a file scheduling method based on cloud storage according to an embodiment of the present application;
in the figure:
1-an information acquisition module;
the system comprises a task generation module, a related file mining module, a cold file identification module, a storage task generation module, a first recall file analysis module, a first recall task generation module, a second recall file analysis module and a second recall task generation module, wherein the task generation module, the related file mining module, the cold file identification module, the storage task generation module, the first recall file analysis module, the first recall task generation module and the second recall file analysis module are respectively connected with the storage task generation module, the first recall file analysis module and the second recall file analysis module;
the system comprises a 3-file scheduling module, a 31-cold file transfer module, a 32-file replacement generation module, a 33-first file recall module and a 34-second file recall module;
4-cloud storage module;
5-a resource management module;
6-an interactive presentation module.
Detailed Description
In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.
The application will be described in detail hereinafter with reference to the drawings in conjunction with embodiments. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
In this embodiment, a file scheduling system based on cloud storage is provided, as shown in fig. 1, where the system includes: the system comprises an information acquisition module 1, a task generation module 2, a file scheduling module 3 and a cloud storage module 4; the information acquisition module 1 is arranged in the user operating system and is used for acquiring state information of the user operating system, wherein the state information comprises file state information of any application program in the user operating system; the task generating module 2 is connected with the information acquisition module 1 and is used for receiving the file state information sent by the information acquisition module 1 and generating a file scheduling task by analyzing the file state information, wherein the file scheduling task comprises a file cloud storage task and a file recall task after file cloud storage; the file scheduling module 3 is connected with the task generating module 2 and the cloud storage module 4, and is configured to respond to the execution of the file scheduling task sent by the task generating module 2 based on the cloud storage module 4, where the cloud storage module 4 is configured to store the cold file identified by the task generating module 2, and the cloud storage module 4 is docked with any cloud service that provides a similar capability, such as: the method comprises the following steps of an OSS of the Arian cloud, an S3 of the Amazon AWS, a COS of the Tencent cloud, an OBS of the Cinna, and the like, and is responsible for specific operations of uploading and downloading files to a cloud storage service.
Wherein, the information acquisition module 1 can be used for sensing several parts of information, and the first part is "file attribute information": the module will read and parse the information stored in the file system (e.g., the NTFS file system of the Windows mainstream) to obtain information for each file/directory (including but not limited to volume, path, file creation time, last modification time, last access time, actual size of file, file flags, etc.) in order to make this process speed faster, a caching mechanism may also be introduced. When the file attribute is changed, the change can be packaged into an event and transmitted to other modules for use; the second part is "file access information": the information acquisition module 1 can detect whether certain other software in the operating system needs to access/open certain file, whether the file is in an exclusive editing state or not at present and other information, and package the changed information into events to be transmitted to other modules for use; the third part is "network environment information": the information acquisition module 1 can confirm the network environment where the current computer is located, and package network delay and information of a cloud server into states (including but not limited to whether the network environment is strong or weak, whether the network is disconnected or networked, whether a wired network or a wireless network is used, whether the network is a hundred mega network or a gigabit network, and whether the delay from the local computer to the cloud storage server is high or low) for other modules to use; the fourth part is "system state information": the information acquisition module 1 can determine whether cloud storage operation is suitable according to the current state of the computer (including but not limited to whether network transmission operation is allowed to be performed under the conditions of battery endurance and dormancy, whether the local disk IO condition is in a busy state, whether heavy calculation tasks are being performed, and whether network card load is busy) for other modules to use. For this embodiment, after the information collection module 1 collects the file access information and the file attribute information of any application program in the user operating system, the file access information and the file attribute information may be further sent to the task generation module 2, and the task generation module 2 may make a decision through a series of rules and methods to determine whether there is a file that needs to be dumped locally to the cloud or re-downloaded locally from the cloud, and arrange related task plans, so that the subsequent modules may operate according to these plans. When the task planning is carried out, the method specifically comprises the step of planning the file cloud storage task and the step of planning the file recall task after the file cloud storage.
For the embodiment, in a specific application scenario, when a file cloud storage task is planned, cold files which cannot be used for a long time can be identified in a plurality of files based on a machine learning technology, and then the cloud storage task is formulated for the cold files, and the cold files are transferred from a local disk to a cloud storage module. Correspondingly, the task generating module 2, as shown in fig. 1, may specifically include an associated file mining module 21, a cold file identifying module 22, and a storage task generating module 23; the associated file mining module 21 is connected with the information acquisition module 1, and an associated analysis model is arranged in the associated file mining module 21 and is used for determining a first file set with the corresponding association with the history access files in the last x days higher than a preset threshold according to the associated analysis model and the file access information sent by the information acquisition module 1; the cold file identification module 22 is connected with the information acquisition module 1 and the associated file mining module 21, a Bayesian prediction model is arranged in the cold file identification module 22, and is used for determining a second file set which cannot be accessed in y days in the future according to the Bayesian prediction model and file access information and file attribute information sent by the information acquisition module 1, removing a first file set sent by the associated file mining module 21 from the second file set, removing a system file set and a specified file set to obtain a cold file set, wherein the cold file set at least comprises one cold file; the storage task generating module 23 is connected to the cold file identifying module 22, and is configured to generate a file cloud storage task carrying cold file attribute information according to the cold file set.
Note that, the association analysis model configured in the association file mining module 21 for performing association prediction between files may include any one of available machine learning models, such as a hidden markov model, a K nearest neighbor algorithm model, a naive bayes model, a decision tree model, a random forest model, a support vector machine model, a neural network model, and the like, which are not particularly limited herein. In the steps of the embodiment of the present application, the technical solution of the present application will be described by taking the correlation analysis model as a hidden Markov model, wherein the hidden Markov model is assumed that the probability distribution of each state St in the random process is related to only the previous state St-1. For the embodiment, before determining a first file set with a correlation higher than a preset threshold value corresponding to a historical access file in the last x days by using a hidden Markov model, pre-training the correlation analysis model, specifically, acquiring a sample file of which a file scheduling task is executed at least once in a preset historical time period, scanning file attribute information of the sample file, and clustering each sample file by using a preset clustering algorithm to obtain a clustering result; configuring relevance labels for each sample file according to the clustering result; then, training a hidden Markov model by taking each sample file as an input characteristic and taking a relevance label as label data; and obtaining the predicted association degree output by the hidden Markov model, calculating a loss function of the hidden Markov model according to the association degree label and the predicted association degree, and judging that the training of the hidden Markov model is completed by minimizing the loss function so that the hidden Markov model reaches a convergence state.
Correspondingly, the preset clustering algorithm may be any clustering algorithm that can be realized in machine learning, and in this embodiment, the technical scheme in the present application is described by taking the preset clustering algorithm as a k-nearest neighbor algorithm as an example, but the technical scheme in the present application is not limited. The idea of the k-nearest neighbor algorithm is: in the feature space, if most of the k nearest (i.e., nearest in the feature space) samples near a sample belong to a certain class, then that sample also belongs to that class. In the application, when clustering is carried out on each sample file according to a k-nearest neighbor algorithm to obtain a clustering result and relevance labels are configured for each sample file according to the clustering result, the operation of generating, modifying or accessing by the same father process can be used as a clustering basis in a very short time, namely, the closer the time of generating, modifying or accessing by the same father process is, the higher the relevance of the same attribute file is. On this basis, the parent process information of the operation files is combined, and if the parent processes of the files are identical, the association degree is highest. If the parent processes operating the files are not the same program but the programs are in the same directory, then the association is next highest. In this way, each file in the disk space can be labeled with the association degree, so that it can be determined which files are "batch, associated, generated simultaneously, and accessed frequently simultaneously". When the files are subjected to cloud storage operation, the files often need to be processed in batches.
Correspondingly, when cold file identification is performed, a Bayesian prediction model and file access information and file attribute information sent by an information acquisition module can be adopted to determine a second file set which cannot be accessed in the future y days. And then the first file set which is correlated with the history access file and can be accessed can be removed from the second file set, the system file set is removed again from the files in the rest file sets, the files set specified in the manual operation strategy are judged as cold files which can be stored in the cloud. The Bayesian prediction model is a prediction by using Bayesian statistics, and is different from a general statistical method, and not only utilizes model information and data information, but also fully utilizes prior information. When the second file set is counted, the Bayesian formula applied is as follows:
wherein, P (A) is the prior probability, P (B) is the posterior probability, and P (B|A) is the conditional probability, which are three elements of Bayesian statistics.
In a specific application scenario, after the task generating module 2 determines the cold file and generates a file cloud storage task for storing the cold file, the file cloud storage task may be further sent to the file scheduling module 3, so as to implement the dump processing of the cold file by using the file scheduling module 3. Correspondingly, as shown in fig. 1, the file scheduling module 3 may include a cold file transfer module 31 and a file replacement generating module 32; one end of the cold file transfer module 31 is connected with the storage task generating module 23, and the other end is connected with the cloud storage module 4, and is used for receiving a file cloud storage task carrying cold file attribute information sent by the storage task generating module 23, extracting cold files from a local disk according to the cold file attribute information, transferring the cold files to the cloud storage module 4, and releasing the local disk space occupied by the cold files; the file replacement generating module 32 is connected to the cold file transferring module 31, and is configured to generate a file replacement of the cold file at an original storage location of the cold file, and create a transferring mapping relationship between the file replacement and the cold file.
In a specific application scenario, after the cold file is transferred to the cloud storage, when the file is accessed, recall processing of the file can be achieved according to the created transfer mapping relationship between the file replacement and the cold file. Accordingly, as shown in fig. 1, the task generating module 2 further includes: a first recall file analysis module 24 and a first recall task generation module 25; the first recall file analysis module 24 is connected with the user client through a client interface, and is used for receiving a file access request sent by the user client, and analyzing a file to be accessed corresponding to the file access request and a first file state of the file to be accessed in the local disk space; the first recall task generating module 25 is connected to the first recall file analyzing module 24, and is configured to receive and identify a first file status, and if it is determined that the first file status is a file replacement, generate a first file recall task related to the file to be accessed. Otherwise, if the first file state is judged to be the file normally stored in the local disk space, the subsequent operation is not required to be executed, and the extraction and the display of the access file can be directly carried out locally.
In a specific application scenario, when a file is accessed, in order to avoid that when a certain software is found that the file which is attempted to be read is currently in a 'transferred state', an operation of reading the file from the cloud storage is immediately arranged, so that poor user experience is caused. Therefore, in the application, the machine learning technology can be fully combined to pre-judge the habit of the user for accessing the file, and the influence of 'immediately pulling the file from the cloud storage' on experience is reduced. Accordingly, as shown in fig. 1, the task generating module 2 further includes: a second recall file analysis module 26, a second recall task generation module 27; the second recall file analysis module 26 is connected with the association file mining module 21 and is connected with the user client through a client interface, and is used for determining an association file set corresponding to a file to be accessed by using the association file mining module 21 and identifying a second file state of each association file in the association file set in the local disk space when a file access request sent by the user client is received; and a second recall task generating module 27, connected to the second recall file analyzing module 26, for receiving and identifying a second file status, and if the second file status is determined to be a file replacement, generating a second file recall task related to the associated file, wherein the task priority of the second file recall task is smaller than the task priority of the file to be accessed corresponding to the first file recall task.
In a specific application scenario, corresponding to the task generating module 2, as shown in fig. 1, the file scheduling module 3 further includes: a first file recall module 33; one end of the first file recall module 33 is connected with the first recall task generating module 25, and the other end is connected with the cloud storage module 4, and is used for receiving the first file recall task sent by the first recall task generating module 25, carrying out recall processing on the file to be accessed in the cloud storage module 4 according to the task priority and restoring the file to be accessed to the original storage position in the local disk space according to the transfer mapping relation between the file replacement and the cold file.
Accordingly, as shown in fig. 1, the file scheduling module 3 further includes: a second file recall module 34; one end of the second file recall module 34 is connected with the second recall task generating module 27, and the other end is connected with the cloud storage module 4, and is used for receiving the second file recall task sent by the second recall task generating module 27, carrying out recall processing on the associated file in the cloud storage module 4 according to the task priority and restoring the associated file to the original storage position in the local disk space according to the transfer mapping relation between the file replacement and the cold file.
In a specific application scenario, in order to implement statistics on the occupation condition of resources and display corresponding interaction information of each module, as shown in fig. 1, the system further includes: a resource management module 5; an interactive presentation module 6; the resource management module 5 is connected with the local disk and the cloud storage module 4 and is used for calculating the occupied space of the local disk and the occupied space of the cloud storage module; the interactive presentation module 6 is connected with the task generation module 2, the file scheduling module 3 and the resource management module 5, and is used for displaying file cloud storage tasks and file recall tasks on the human-computer interaction page, file states of the files corresponding to the file cloud storage tasks and the file recall tasks, and occupied space of the local disk and occupied space of the cloud storage module. Among these, the interactive presentation module 6 has several functions: the first part is: the task generation module processes the presentation of the result, when the task generation module judges that certain files can execute automatic dump operation, the plans are presented to a user through the interaction module, and the user can intervene in the plans through the interaction interface or know details; the second part is: the resource use condition of the local disk space and the cloud storage space is presented; the third part is: for presenting the state of the file in the resource manager. When the file is in the states of uploading, downloading, unloading, storing in local and the like, the states are presented to the user through the expansion of the resource manager.
In a specific application scenario, when the status information of the user operating system further includes network status information (whether the network environment is strong, whether the network is disconnected or networked, whether the wired network or wireless network is used, whether the hundred meganetwork or giga network is used, whether the delay from the local device to the cloud storage server is high or low, etc.), system status information (whether the battery endurance and dormancy conditions allow the network transmission operation to be performed, whether the local disk IO is in a busy state, whether a heavy computing task is being performed, whether the network card load is busy, etc.). In view of the fact that the network state information and the system state information have large interference on file scheduling task execution, when a specific file scheduling task is planned, the task generating module 2 is used for ensuring that the file scheduling is normally and rapidly carried out and avoiding being influenced by an external network environment or a system operation environment, so that after the file scheduling task is generated, feasibility analysis of the file scheduling can be further carried out according to the network state information and the system state information, when the current external network environment and the system operation environment are determined to support the current file scheduling, the task generating module 2 can be used for sending the file scheduling task to the file scheduling module 3, and the file scheduling module 3 is used for immediately responding to the file scheduling task execution; when the current network state is poor and/or the current system is in a busy state, in order to ensure smooth execution of file scheduling and avoid increasing the burden of the system, the file scheduling task can be further sent to the file scheduling module 3 for generating a delay task related to the file scheduling task, for example, ten seconds delay, the file scheduling task is further responded and executed by the file scheduling module 3, or the file scheduling task and the delay task are sent to the file scheduling module 3, wherein the task priority of the delay task is greater than that of the file scheduling task, so the file scheduling module 3 can firstly execute the delay task, the delay task is executed and ended when the network state information and the system state information are judged to meet the execution condition of the file scheduling task, and the file scheduling task is further executed after the execution of the delay task is ended. The delay task may specifically be a countdown task that does not need any data processing.
Correspondingly, the task generating module 2 is further configured to receive the network state information and/or the system state information sent by the information collecting module 1, and generate a delay task related to the file scheduling task by analyzing the network state information and/or the system state information, so as to control and delay execution of the file cloud storage task and/or the file recall task after the file cloud storage.
According to the file scheduling system based on cloud storage, the information acquisition module can acquire the file state information of the user operating system and send the file state information to the task generation module; the task generating module can make a decision based on the file state information through a series of rules and methods, judges whether a file needs to be dumped from the local to the cloud or re-downloaded from the cloud to the local, generates a relevant file scheduling task according to a decision result, and then issues the file scheduling task to the file scheduling module; and finally, automatically executing the file scheduling task by using the file scheduling module and the cloud storage module, and creating a transfer mapping relation between the file replacement and the cold file when executing the file cloud storage task, so that the file to be recalled can be quickly positioned based on the transfer mapping relation when executing the file recall task later, and the file retrieval efficiency is ensured. According to the technical scheme, an artificial intelligence technology is integrated in the file scheduling process, so that the file scheduling efficiency can be ensured, a user can release disk space without feeling and learning cost as far as possible, and the local disk space can store valuable and recently used hot files as far as possible.
In this embodiment, a file scheduling method based on cloud storage is provided, where the file scheduling method based on cloud storage is applied to a file scheduling system based on cloud storage as shown in fig. 1, and as shown in fig. 2, the method includes:
101. the information acquisition module acquires state information of a user operating system, wherein the state information comprises file state information of any application program in the user operating system.
The file status information may include file access information and file attribute information.
102. The task generating module receives the file state information sent by the information collecting module, and generates a file scheduling task by analyzing the file state information, wherein the file scheduling task comprises a file cloud storage task and a file recall task after the file cloud storage.
For the embodiment, as an optional application scenario, when the task generating module generates a file cloud storage task by analyzing file state information, a first file set with a correlation higher than a preset threshold corresponding to a history access file in the last x days can be determined by using a correlation analysis model set in the correlation file mining module and file access information sent by the information acquisition module; further utilizing a Bayesian prediction model set in the cold file identification module and file access information and file attribute information sent by the information acquisition module to determine a second file set which cannot be accessed in y days in future, removing a first file set sent by the associated file mining module from the second file set, removing a system file set and a specified file set to obtain a cold file set, wherein the cold file set at least comprises one cold file; and finally, generating a file cloud storage task carrying cold file attribute information according to the cold file set.
Corresponding to file cloud storage, as another optional application scenario, when the task generating module generates a file recall task by analyzing file state information, a first recall file analyzing module can be utilized to receive a file access request sent by a user client, analyze a file to be accessed corresponding to the file access request, and analyze a first file state of the file to be accessed in a local disk space; and receiving and identifying the first file state by using the first recall task generating module, and if the first file state is judged to be the file replacement, generating a first file recall task related to the file to be accessed.
As another optional application scenario for generating file recall task, a second recall file analysis module may be further utilized to determine an associated file set corresponding to the file to be accessed by using an associated file mining module when receiving a file access request sent by the user client, and identify a second file state of each associated file in the associated file set in the local disk space; and receiving and identifying a second file state by using a second recall task generation module, and generating a second file recall task related to the associated file if the second file state is determined to be the file replacement, wherein the task priority of the second file recall task is smaller than the task priority of the first file recall task corresponding to the file to be accessed.
In a specific application scenario, the state information of the user operating system may further include network state information and system state information; accordingly, in order to ensure smooth execution of the file scheduling task, as an alternative way, after the task generating module receives the network state information and/or the system state information, the task generating module may further generate a delay task related to the file scheduling task to control and delay execution of the file cloud storage task and/or the file recall task after the file cloud storage by analyzing the network state information and/or the system state information when determining that the network state is poor or the system is in a busy state.
103. The file scheduling module is based on the cloud storage module and responds to the file scheduling storage task sent by the execution task generating module, wherein the cloud storage module is used for storing the cold file identified by the task generating module.
For this embodiment, as an optional application scenario, when the file scheduling module is based on the cloud storage module and responds to executing a file cloud storage task, the cold file transfer module may be used to receive the file cloud storage task carrying cold file attribute information sent by the storage task generating module, extract a cold file from the local disk space according to the cold file attribute information, transfer the cold file to the cloud storage module, and release the local disk space occupied by the cold file; and generating a file replacement body of the cold file at an original storage position of the cold file by using the file replacement body generating module, and creating a transfer mapping relation between the file replacement body and the cold file.
Corresponding to file cloud storage, as another optional application scenario, when the file scheduling module responds to executing a file recall task based on the cloud storage module, the first file recall module can be utilized to receive the first file recall task sent by the first recall task generating module, recall processing is carried out on the file to be accessed in the cloud storage module according to the task priority and the file to be accessed is restored to the original storage position in the local disk space according to the transfer mapping relation between the file replacement and the cold file; and/or, receiving a second file recall task sent by the second recall task generating module by utilizing the second file recall module, carrying out recall processing on the associated file in the cloud storage module according to the task priority and the transfer mapping relation between the file replacement and the cold file, and restoring the associated file to the original storage position in the local disk space.
By means of the technical scheme, the file scheduling method based on cloud storage is characterized in that after the information acquisition module acquires file state information of a user operating system, the task generation module can be used for receiving the file state information sent by the information acquisition module and generating a file scheduling task by analyzing the file state information, wherein the file scheduling task comprises a file cloud storage task and a file recall task stored by the file cloud; further, the file scheduling module is capable of scheduling the storage task in response to the file sent by the execution task generating module based on the cloud storage module, wherein the cloud storage module is used for storing the cold file identified by the task generating module. According to the technical scheme, an artificial intelligence technology is integrated in the file scheduling process, so that the file scheduling efficiency can be ensured, a user can release disk space without feeling and learning cost as far as possible, and the local disk space can store valuable and recently used hot files as far as possible.
Those skilled in the art will appreciate that the drawing is merely a schematic illustration of a preferred implementation scenario and that the modules or flows in the drawing are not necessarily required to practice the application. Those skilled in the art will appreciate that modules in an apparatus in an implementation scenario may be distributed in an apparatus in an implementation scenario according to an implementation scenario description, or that corresponding changes may be located in one or more apparatuses different from the implementation scenario. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The above-mentioned inventive sequence numbers are merely for description and do not represent advantages or disadvantages of the implementation scenario. The foregoing disclosure is merely illustrative of some embodiments of the application, and the application is not limited thereto, as modifications may be made by those skilled in the art without departing from the scope of the application.
Claims (10)
1. A cloud storage-based file scheduling system, the system comprising: the system comprises an information acquisition module, a task generation module, a file scheduling module and a cloud storage module;
the information acquisition module is arranged in a user operating system and used for acquiring state information of the user operating system, wherein the state information comprises file state information of any application program in the user operating system;
The task generating module is connected with the information acquisition module and is used for receiving the file state information sent by the information acquisition module and generating a file scheduling task by analyzing the file state information, wherein the file scheduling task comprises a file cloud storage task and a file recall task after file cloud storage;
the file cloud storage task is used for representing a cloud storage task for a cold file, and specifically comprises the following steps: determining a first file set with the corresponding relevance to the history access files in the last x days higher than a preset threshold value; determining a second set of files that will not be accessed for a future y days; removing the first file set, the system file set and the specified file set from the second file set to obtain a cold file set so as to generate a file cloud storage task carrying cold file attribute information;
the file recall task is used for representing recall processing of the cold file when the file is accessed after the cold file is transferred to cloud storage;
the file scheduling module is connected with the task generating module and the cloud storage module and is used for responding and executing the file scheduling task sent by the task generating module based on the cloud storage module, wherein the cloud storage module is used for storing the cold file identified by the task generating module.
2. The system of claim 1, wherein the file status information includes file access information, file attribute information, and the task generation module includes:
the associated file mining module is connected with the information acquisition module, and is provided with an associated analysis model for determining a first file set with the association higher than a preset threshold value corresponding to the history access files in the last x days according to the associated analysis model and the file access information sent by the information acquisition module;
the cold file identification module is connected with the information acquisition module and the associated file mining module, a Bayesian prediction model is arranged in the cold file identification module and is used for determining a second file set which cannot be accessed in y days in the future according to the Bayesian prediction model, the file access information and the file attribute information sent by the information acquisition module, removing the first file set sent by the associated file mining module from the second file set, removing a system file set and a specified file set to obtain a cold file set, and the cold file set at least comprises one cold file;
And the storage task generating module is connected with the cold file identifying module and is used for generating a file cloud storage task carrying cold file attribute information according to the cold file set.
3. The system of claim 2, wherein the file scheduling module comprises:
the cloud storage system comprises a storage task generating module, a cold file transferring module, a cloud storage module and a storage module, wherein one end of the storage task generating module is connected with the storage task generating module, the other end of the storage task generating module is connected with the cloud storage module and is used for receiving a file cloud storage task which is sent by the storage task generating module and carries cold file attribute information, extracting cold files from a local disk according to the cold file attribute information, transferring the cold files to the cloud storage module and releasing local disk space occupied by the cold files;
the file replacement generating module is connected with the cold file transfer module and is used for generating a file replacement of the cold file at an original storage position of the cold file and creating a transfer mapping relation between the file replacement and the cold file.
4. The system of claim 1, wherein the task generation module further comprises:
the first recall file analysis module is connected with a user client through a client interface and is used for receiving a file access request sent by the user client, analyzing a file to be accessed corresponding to the file access request and a first file state of the file to be accessed in a local disk space;
And the first recall task generating module is connected with the first recall file analyzing module and is used for receiving and identifying the first file state, and if the first file state is judged to be the file replacement, a first file recall task related to the file to be accessed is generated.
5. The system of claim 2, wherein the task generation module further comprises:
the second recall file analysis module is connected with the associated file mining module and connected with a user client through a client interface, and is used for determining an associated file set corresponding to a file to be accessed by using the associated file mining module when a file access request sent by the user client is received, and identifying a second file state of each associated file in the associated file set in a local disk space;
and the second recall task generating module is connected with the second recall file analyzing module and is used for receiving and identifying the second file state, and generating a second file recall task related to the associated file if the second file state is judged to be a file replacement, wherein the task priority of the second file recall task is smaller than the task priority of the first file recall task corresponding to the file to be accessed.
6. The system of claim 4, wherein the file scheduling module further comprises:
and one end of the first file recall module is connected with the first recall task generation module, the other end of the first file recall module is connected with the cloud storage module and is used for receiving the first file recall task sent by the first recall task generation module, carrying out recall processing on the file to be accessed in the cloud storage module according to the task priority and recovering the file to be accessed to the original storage position in the local disk space according to the transfer mapping relation between the file replacement and the cold file.
7. The system of claim 5, wherein the file scheduling module further comprises:
and one end of the second file recall module is connected with the second recall task generation module, the other end of the second file recall module is connected with the cloud storage module and is used for receiving the second file recall task sent by the second recall task generation module, carrying out recall processing on the associated file in the cloud storage module according to the task priority and restoring the associated file to the original storage position in the local disk space according to the transfer mapping relation between the file replacement and the cold file.
8. The system of claim 1, wherein the system further comprises:
the resource management module is connected with the local disk and the cloud storage module and is used for calculating the occupied space of the local disk and the occupied space of the cloud storage module;
the interactive presentation module is connected with the task generation module, the file scheduling module and the resource management module and is used for displaying the file cloud storage task, the file recall task, the file state of the file corresponding to the file recall task, the occupied space of the local disk and the occupied space of the cloud storage module on a human-computer interaction page.
9. The system according to any one of claims 1 to 7, wherein the state information of the user operating system further comprises network state information, system state information;
the task generating module is further configured to receive the network state information and/or the system state information sent by the information collecting module, and generate a delay task related to the file scheduling task by analyzing the network state information and/or the system state information, so as to control to delay execution of the file cloud storage task and/or the file recall task after file cloud storage.
10. A cloud storage-based file scheduling method, wherein the cloud storage-based file scheduling method is applied to the cloud storage-based file scheduling system according to any one of claims 1 to 9, and the method comprises:
the information acquisition module acquires state information of a user operating system, wherein the state information comprises file state information of any application program in the user operating system;
the task generating module receives the file state information sent by the information acquisition module, and generates a file scheduling task by analyzing the file state information, wherein the file scheduling task comprises a file cloud storage task and a file recall task stored by the file cloud;
the file cloud storage task is used for representing a cloud storage task for a cold file, and specifically comprises the following steps: determining a first file set with the corresponding relevance to the history access files in the last x days higher than a preset threshold value; determining a second set of files that will not be accessed for a future y days; removing the first file set, the system file set and the specified file set from the second file set to obtain a cold file set so as to generate a file cloud storage task carrying cold file attribute information;
The file recall task is used for representing recall processing of the cold file when the file is accessed after the cold file is transferred to cloud storage;
the file scheduling module is based on a cloud storage module, and responds to the execution of the file scheduling storage task sent by the task generating module, wherein the cloud storage module is used for storing the cold file identified by the task generating module.
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