CN114726728A - Computer storage optimization method for monitoring data - Google Patents
Computer storage optimization method for monitoring data Download PDFInfo
- Publication number
- CN114726728A CN114726728A CN202210639237.9A CN202210639237A CN114726728A CN 114726728 A CN114726728 A CN 114726728A CN 202210639237 A CN202210639237 A CN 202210639237A CN 114726728 A CN114726728 A CN 114726728A
- Authority
- CN
- China
- Prior art keywords
- current user
- user
- files
- uploading
- network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000005457 optimization Methods 0.000 title claims abstract description 9
- 238000012544 monitoring process Methods 0.000 title abstract description 9
- 230000000875 corresponding effect Effects 0.000 claims description 8
- 230000001174 ascending effect Effects 0.000 claims description 3
- 230000002596 correlated effect Effects 0.000 claims description 2
- 238000012545 processing Methods 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 8
- 230000008569 process Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 102000016550 Complement Factor H Human genes 0.000 description 1
- 108010053085 Complement Factor H Proteins 0.000 description 1
- 206010051602 Laziness Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
- H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
- H04L41/083—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability for increasing network speed
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0876—Network utilisation, e.g. volume of load or congestion level
- H04L43/0894—Packet rate
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/20—Traffic policing
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/06—Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Environmental & Geological Engineering (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The invention relates to the technical field of data processing, in particular to a computer storage optimization method for monitoring data. The method comprises the following steps: acquiring the uploading resource priority of the current user, dividing each area of the school garden into groups with preset number, and simultaneously setting a link busy influence factor of each group; obtaining a dependency index of a current user; obtaining a network strain coefficient and a flow control index according to a link busy influence factor and a dependency index corresponding to an area where a current user is located; and if the flow control index is a preset value, obtaining the optimized uploading speed based on the maximum uploading speed of the network hard disk uploaded to the network center by the current user, the uploading resource priority, the network strain coefficient and the lower limit of the uploading speed. The invention optimally controls the final uploading speed of the current user, obtains the uploading speed which is most suitable for the user, reduces the reading and writing pressure of the network hard disk of the network storage center, and simultaneously ensures that each user has good experience when uploading files.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a computer storage optimization method for monitoring data.
Background
Along with the continuous perfection of the network construction of each college, the construction of a campus network center is continuously promoted, and a campus network hard disk becomes a novel file storage and transmission mode in order to improve the convenience of teaching and learning of teachers and students.
However, due to the difference of devices used by different users, the difference of network links, the difference of locations, etc., when uploading data to users in a network storage center, cache areas with different sizes are required as preconditions for data storage, and if the cache is fixed, the large-batch file uploading may limit the writing speed (random reading and writing) of the data, thereby affecting the uploading of subsequent user files. However, if a certain file is too large, the file is occupied by cache resources, uploading of other small files is affected, more people and time are delayed, and the use experience of other people is very poor.
Most of the traditional solutions are to greatly increase the network speed, so that the user cannot get stuck in the uploading process, but the traditional solutions have no fundamental solution, waste of the network speed can be caused in leisure time, once the user is used too much, the network is blocked, and the user can get stuck in the uploading process.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a computer storage optimization method for monitoring data, which adopts the following technical solutions:
one embodiment of the invention provides a computer storage optimization method for monitoring data, which comprises the following steps: acquiring the upper limit and the lower limit of the uploading speed of the network hard disk uploaded to the network center by the current user, the number of files to be uploaded, the sizes of all the files and the busyness degree of the network hard disk at the current moment, and determining the priority weight of the user; obtaining the use time span of the current user recently using the history uploaded files and obtaining the number of the effective access files of the current user in a preset time period;
obtaining the uploading resource priority of the current user based on the number of files to be uploaded by the current user, the sizes of all the files and the priority weight of the user; dividing each area into groups with preset number according to the current access amount of users in each area in the campus to the network hard disk, and simultaneously setting a link busy influence factor of each group; obtaining a dependency index of a current user based on the use time span of the latest history uploaded files of the current user and the number of effective access files in a preset time period;
obtaining a network strain coefficient according to a link busy influence factor and a dependency index corresponding to an area where a current user is located; obtaining a flow control index based on the upper limit of the uploading speed of the network hard disk uploaded to the network center by the current user, the busy degree of the hard disk at the current moment and the average value of the historical maximum uploading speed; and if the flow control index is a preset value, obtaining the optimized uploading speed based on the maximum uploading speed of the network hard disk uploaded to the network center by the current user, the uploading resource priority, the network strain coefficient and the lower limit of the uploading speed.
Preferably, determining the priority weight of the user comprises: and manually marking the priority weight of the user according to the identity characteristics of the user, the minimum time span of the historical uploaded files and the professional type of the user, wherein the priority weight is higher if the priority weight is larger.
Preferably, obtaining the usage time span of the current user's recent usage history upload file comprises: counting a first preset number of history uploading files recently used by a current user; sorting each history uploading file in the first preset number of history uploading files in an ascending order according to the time span between the time used by the current user and the current time, and setting the weight of each time span according to the sorted result, wherein the weight of the time span with the minimum value is the maximum; and obtaining a weighted average value of the time spans based on the weight of each time span, wherein the weighted average value is the use time span of the history uploaded files recently used by the current user.
Preferably, the obtaining of the number of the effective access files of the current user in the preset time period includes: the number of the effective access files represents the number of files successfully uploaded in a preset time period of a current user, and the uploaded files can be successfully downloaded.
Preferably, obtaining the upload resource priority of the current user comprises: the uploading resource priority is in negative correlation with the number of files to be uploaded by the current user and the sizes of all the files; the uploading resource priority is positively correlated with the priority weight of the user.
Preferably, the dividing the respective areas into a preset number of groups, and the setting of the link busy impact factor of each group includes: dividing each area into groups with preset number by using a K-means algorithm based on the difference of the current time of the user in each area to the access quantity of the network hard disk, wherein the preset number is 3 and is respectively a link busy group, a link moderate group and a link idle group; and setting a link busy influence factor of each group, wherein the link busy influence factor of the link busy group is the largest, and the link busy influence factor of the link idle group is the smallest.
Preferably, the dependency index comprises: the dependency index is in a negative correlation with the use time span of the latest historical uploaded files of the current user; the dependency index is in positive correlation with the number of the effective access files in the preset time period of the current user.
Preferably, the network strain coefficients include: the network strain coefficient and the link busy influence factor form a positive correlation; the network strain coefficient is inversely related to the dependency index.
Preferably, the flow control index is:
wherein,
representing a flow control index;
it is shown that the function of taking the maximum value,
representing a symbolic function;
indicating the busy degree of the network hard disk at the current moment;
represents the upper limit of the uploading speed of the current user to the network hard disk of the network center,
representing the average of the historical maximum upload rates.
Preferably, the optimized uploading speed is:
wherein,
representing the optimized uploading speed;
is a function of taking the maximum value;
the upper limit of the uploading speed of the network hard disk uploaded to the network center by the current user is represented;
representing the uploading resource priority of the current user;
represents a natural constant;
representing the network strain coefficient;
represents the lower limit of the upload speed.
The embodiment of the invention at least has the following beneficial effects: according to the method, the importance of the uploaded file is fully considered from the identity of the user by counting the identity of the user, the degree of dependence on the network hard disk, the used network environment, the attribute of the uploaded file and other information, and the priority of the current user uploaded resource is determined; the final uploading speed is optimally controlled by combining the current network strain coefficient and the upper limit and the lower limit of the speed of the user when uploading the file, the uploading speed which is most suitable for the user is obtained, the reading and writing pressure of the network hard disk of the network storage center is reduced, and meanwhile, good experience of each user when uploading the file is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method for optimizing computer storage of monitored data.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description will be given for a method for optimizing computer storage of monitoring data according to the present invention, with reference to the accompanying drawings and preferred embodiments, and the detailed description thereof, the structure, the features and the effects thereof. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following describes a specific scheme of the computer storage optimization method for monitoring data provided by the present invention in detail with reference to the accompanying drawings.
Example (b):
the main application scenarios of the invention are as follows: when the campus user uploads the file to the hard disk of the storage center, the uploading speed is optimized, so that the file uploading experience of each user can be improved.
Referring to fig. 1, a flow chart of a method for optimizing computer storage of monitoring data according to an embodiment of the present invention is shown, where the method includes the following steps:
the method comprises the following steps: acquiring the upper limit and the lower limit of the uploading speed of the network hard disk uploaded to the network center by the current user, the number of files to be uploaded, the sizes of all the files and the busyness degree of the network hard disk at the current moment, and determining the priority weight of the user; and obtaining the use time span of the current user recently using the historical uploaded files and obtaining the number of the effective access files of the current user in a preset time period.
Firstly, the network environment use speed of the current user is counted, and the hard disk read-write busy degree of the current network center is determined at the same time. Because the devices, the positions and the use environments of the users are different, and the transmission rates of the networks have obvious difference, the upper limit and the lower limit of the uploading speed of the current user which is linked to the network center and uploaded to the network hard disk need to be determined; the upper limit and the lower limit of the uploading speed of the current user are tested by the known technology, and are not described in detail; thereby, the upper limit of the current user uploading speed is determined
And lower limit of
。
Meanwhile, the Busy degree detection of network hard disk reading and writing is well known, and can be directly obtained by the personnel in the field through the existing means, namely the Busy degree Busy of the network hard disk at the current moment is obtained and is normalized, so that the value range of the Busy degree Busy is between 0 and 1.
Furthermore, the number of files to be uploaded by the current user needs to be determined
And the size of all files
Since the long-time uploading of large files and the uploading of multiple files affect the use experience of other users, the size and the number of the files uploaded by the user need to be analyzed to determine the uploading priority of the current user, and the statistics of the uploaded files can be directly obtained by the existing technical means.
Then, the priority weight of the user needs to be determined, where it is to be noted that the priority weight of the user means that the uploading priority of the user needs to be adjusted according to the priority weight of the user because the user group corresponding to the network hard disk is huge, the identity characteristics of each specialty or user are different, and the urgency degree of dependence on the uploading task is different; the identity characteristics of the user can be obtained through account information of the user, after the identity characteristics of the user are obtained, the priority weight X of the user is manually marked according to the identity characteristics of the user, the minimum time span of a history uploaded file and the professional type of the user, preferably, in the embodiment, the priority weight range is 1-10, wherein the higher the priority weight is, the higher the priority is; the reason for using the manual labeling is that the situation of each school is different, and the implementer needs to label according to the situation of the school; for example, the specific labeling manner may refer to the teacher level, the professional type, the student identity, etc., and the implementer sets, for example, the teacher priority weight to 10, the student with a large demand for data volume to 7, and the student with a large demand for data volume to 3, etc.
Finally, the usage time span of the current user's recent usage history uploaded file needs to be obtained
And the number n of the effective access files of the current user in a preset time period; usage time span in which current user recently used historical upload file
The obtaining method specifically comprises the following steps: counting the time span of reusing the history uploaded files after the current user uploads the files recently, wherein the use refers to downloading the files, the number of the used history uploaded files in the preferred embodiment is a first preset number, the value is 4, and the time span refers to the time length of the time when each history uploaded file is used and the current time; sorting the used time spans of the 4 historical uploading files in an ascending order, and setting the weight of each time span according to the sorted result, wherein the weight of the time span with the minimum value is the maximum, and the weights are respectively 40%, 30%, 20% and 10%; the weighted average of 4 time spans is the usage time span of the current user using the historical upload file
It should be noted that the history uploaded files used herein, that is, the uploaded files, are not necessarily the same files.
The specific acquisition mode of the number n of the effective access files of the current user in the preset time period is as follows: preferably, the duration of the preset time period is one week, the number of files successfully uploaded by the current user in one week is counted, where successful uploading indicates that the uploaded files can be successfully downloaded, that is, the files are successfully downloaded once after being successfully uploaded to form an effective access file.
Generally, when a user uploads a file, the file is used in a short time, which indicates that the current file is urgent, and if the situation exists many times, the priority of use of the user needs to be increased to ensure user experience.
Step two: obtaining the uploading resource priority of the current user based on the number of files to be uploaded by the current user, the sizes of all the files and the priority weight of the user; dividing each area into groups with preset number according to the current access amount of users in each area in the campus to the network hard disk, and simultaneously setting a link busy influence factor of each group; and obtaining the dependency index of the current user based on the usage time span of the latest historical uploaded files of the current user and the number of the effective access files in a preset period.
Firstly, if the number of files uploaded by a user is large and the size of the uploaded files is large, a large pressure is applied to the final data writing of the server side, and the uploading experience of other users is affected, so that the behavior of the current file uploaded by the user needs to be judged, and the priority is determined, so as to avoid affecting the experience of more people. Secondly, considering the difference of the groups, aiming at different identity attributes, the current priority is adjusted, so that the current system resource utilization is optimized. Thus obtaining upload resource priority
：
Wherein,
representing the uploading resource priority of the current user;
indicating the number of files that the current user needs to upload,
representing the size of all files which need to be uploaded by the current user;
a priority weight of the represented current user;
which represents a correction factor, preferably of 0.3 in this implementation, while the implementer can set the correction parameters according to the current service performance. When the user uploads the number of files, the larger the total size of the files is, the more resources are consumed, which affects the use of others, and therefore, the final priority is close to a small value. Thus, the priority of resource allocation is preliminarily determined according to the identity of the current user and the attributes of the uploaded file.
Further, the uploading position information of the current user and the access amount of the user areas in all areas in the current campus to the network hard disk are determined. In the campus intranet, the network location of the user can be roughly determined by the conventional means, such as inquiring the ip address. The attributes of the represented users in different locations may be classified into a category, such as in a library, a teacher's apartment, a public teaching area, a student's dormitory, a laboratory, etc., and the specific locations of the users in each area may be different, but all may be in the same area, and thus may be considered as the same category. And determining the access amount of the area where the current user is located to the network hard disk, wherein the larger the access amount of the current area is, the larger the load on the total link on the current area is, so that the uploading upper limit of each user is limited. Therefore, the rough using position is determined based on the ip address of the current user, the access amount of the current position is determined, and the access amount of each area in the campus to the network hard disk at the current time is obtained.
Grouping the network hard disk access amount based on each area in the campus at the current moment, dividing all the areas into groups with a preset number, preferably, dividing the areas into three groups in the embodiment, dividing each area into three groups by using a K-means algorithm based on the difference value of the user current moment of each area to the network hard disk access amount, respectively setting a link busy group, a link moderate group and a link idle group, and simultaneously setting link busy influence factors of each group, wherein the link busy influence factors of the link busy group
The size is 0.8; of moderate groups of linksLink busy impact factor
The size is 0.5; link busy impact factor for link idle group
The size is 0.2; the implementer can make appropriate setting for the link busy impact factor according to the specific link state.
And finally, obtaining the laziness index of the current user. Generally speaking, if the dependence of a certain user on the use of the network hard disk is large, that is, after the user uploads the file to the network hard disk, the user accesses the network hard disk again at different positions in a short time, the user can consider that the dependence is large, and the dependence of the current user on the network hard disk is determined by using the use time span of the current user's recent history uploaded file and the number of valid access files in a preset time period. Obtaining a dependency index for a current user
：
Wherein n represents the number of effective access files in a preset time period of a current user;
representing the use time span of the recent history uploaded files of the current user; index of dependence
The method is characterized in that the current user has larger usage amount of the network hard disk in a short period, and the effective downloading time span of the uploaded historical file is smaller, so that the dependence on the network hard disk is larger. Therefore, the dependence degree of the current user on the network hard disk is determined.
Step three: obtaining a network strain coefficient according to a link busy influence factor and a dependency index corresponding to an area where a current user is located; obtaining a flow control index based on the upper limit of the uploading speed of the network hard disk uploaded to the network center by the current user, the busy degree of the hard disk at the current moment and the average value of the historical maximum uploading speed; and if the flow control index is a preset value, obtaining the optimized uploading speed based on the maximum uploading speed of the network hard disk uploaded to the network center by the current user, the uploading resource priority, the network strain coefficient and the lower limit of the uploading speed.
Firstly, determining a link busy influence factor corresponding to the area where the current user is located based on the link busy influence factors of the link busy group, the link moderate group and the link idle group set in the step two, wherein the link busy influence factor is represented by H; obtaining a network strain coefficient based on a link busy influence factor and a dependency index corresponding to an area where a current user is located:
wherein,
representing the network strain coefficient of the current user when uploading the file;
representing a link busy influence factor corresponding to an area where the current user is located; e represents a natural constant;
and representing the dependency indexes corresponding to the current users. If the link busy influence factor H is larger, it indicates that a larger user group is using the network hard disk in the current link, and the system needs to be adjusted integrally. And if the dependence degree of someone on the network hard disk is larger in the area, the current system adjustment amount is weakened.
Further, before uploading files, the priority W of the current uploaded resources is determined based on the size and the number of the files uploaded by the current user, and meanwhile, the network strain coefficient J is determined according to the use state of a network hard disk in the area where the current user is located and the historical data information of the user. Meanwhile, determining whether flow control is needed currently according to the upper limit of the uploading speed of the current user uploading files at the current moment, the busy degree of the current network center network hard disk and the average value of the historical maximum uploading speed, and obtaining a current flow control index K:
wherein,
representing a flow control index;
it is shown that the function of taking the maximum value,
representing a symbolic function, if the value is more than or equal to 0, the function outputs 1, otherwise, the function outputs-1;
indicating the busy degree of the network hard disk at the current moment;
represents the upper limit of the uploading speed of the current user to the network hard disk of the network center,
representing the average of the historical maximum upload rates.
The first item of the formula is the measurement of the busy degree of network central network hard disk reading and writing, the detection of the part is monitored in real time, 0.9 is a set busy threshold value, and an implementer can adjust the threshold value according to the actual network hard disk state.
The second term of the equation is to determine the uploading speed of the current user, and if the size of the uploading speed exceeds the average value of the historical maximum uploading speeds counted by the server, flow control is performed. The average value of the historical maximum uploading speed is defined as the average value of the maximum speed when the current server processes multi-path file uploading historically.
If the final value of K is a preset value, in this embodiment, the preset value is 1, flow control is required, and if the final value is not 1, flow control is not required.
And finally, when the value of K is 1, carrying out flow control on the uploading speed of the current user, and then optimizing the uploading speed after the flow control as follows:
wherein,
representing the optimized uploading speed;
is a function of taking the maximum value;
the upper limit of the uploading speed of the current user to the network hard disk of the network center is represented;
representing the uploading resource priority of the current user;
represents a natural constant;
representing the network strain coefficient;
the lower limit of the uploading speed is indicated, namely the flow control is required to be ensured to the end, and a certain speed is required to be determined for uploading currently.
Therefore, the uploading speeds of different users are subjected to flow control, an optimization effect is achieved, the reading and writing pressure of a network hard disk of a server storage center is reduced, and the speed of each user in uploading files can be guaranteed.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And that specific embodiments have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (10)
1. A method for computer storage optimization of monitored data, the method comprising: acquiring the upper limit and the lower limit of the uploading speed of the network hard disk uploaded to the network center by the current user, the number of files to be uploaded, the sizes of all the files and the busyness degree of the network hard disk at the current moment, and determining the priority weight of the user; obtaining the use time span of the current user recently using the history uploaded files and obtaining the number of the effective access files of the current user in a preset time period;
obtaining the uploading resource priority of the current user based on the number of files to be uploaded by the current user, the sizes of all the files and the priority weight of the user; dividing each area into groups with a preset number according to the current time of the user in each area in the campus on the network hard disk access amount, and simultaneously setting a link busy influence factor of each group; obtaining a dependency index of a current user based on the use time span of the latest history uploaded files of the current user and the number of effective access files in a preset time period;
obtaining a network strain coefficient according to a link busy influence factor and a dependency index corresponding to an area where a current user is located; obtaining a flow control index based on the upper limit of the uploading speed of the network hard disk uploaded to the network center by the current user, the busy degree of the hard disk at the current moment and the average value of the historical maximum uploading speed; and if the flow control index is a preset value, obtaining the optimized uploading speed based on the maximum uploading speed of the network hard disk uploaded to the network center by the current user, the uploading resource priority, the network strain coefficient and the lower limit of the uploading speed.
2. The method of claim 1, wherein determining the priority weight of the user comprises: and manually marking the priority weight of the user according to the identity characteristics of the user, the minimum time span of the historical uploaded files and the professional type of the user, wherein the priority weight is higher if the priority weight is larger.
3. The method of claim 1, wherein the obtaining the usage time span of the current user's recent usage history upload file comprises: counting a first preset number of history uploading files recently used by a current user; sorting each history uploading file in the first preset number of history uploading files in an ascending order according to the time span between the time used by the current user and the current time, and setting the weight of each time span according to the sorted result, wherein the weight of the time span with the minimum value is the maximum; and obtaining a weighted average value of the time spans based on the weight of each time span, wherein the weighted average value is the use time span of the history uploaded files recently used by the current user.
4. The method of claim 1, wherein the obtaining of the number of files that are currently accessed by the user within a predetermined period of time comprises: the number of the effective access files represents the number of files successfully uploaded in a preset time period of a current user, and the uploaded files can be successfully downloaded.
5. The method of claim 1, wherein the obtaining upload resource priority of the current user comprises: the uploading resource priority is in negative correlation with the number of files to be uploaded by the current user and the sizes of all the files; the uploading resource priority is positively correlated with the priority weight of the user.
6. The method of claim 1, wherein the dividing each region into a predetermined number of groups, and the setting of the link busy impact factor for each group comprises: dividing each area into groups with preset number by using a K-means algorithm based on the difference of the current time of the user in each area to the access quantity of the network hard disk, wherein the preset number is 3 and is respectively a link busy group, a link moderate group and a link idle group; and setting a link busy influence factor of each group, wherein the link busy influence factor of the link busy group is the largest, and the link busy influence factor of the link idle group is the smallest.
7. The method of claim 1, wherein the dependency index comprises: the dependency index is in a negative correlation with the use time span of the recent history uploaded files of the current user; the dependency index is in positive correlation with the number of the effective access files in the preset time period of the current user.
8. The method of claim 1, wherein the network strain coefficients comprise: the network strain coefficient and the link busy influence factor form a positive correlation; the network strain coefficient is inversely related to the dependency index.
9. The method of claim 1, wherein the flow control index is:
wherein,
representing a flow control index;
it is shown that the function of taking the maximum value,
representing a symbolic function;
indicating the busy degree of the network hard disk at the current moment;
represents the upper limit of the uploading speed of the current user to the network hard disk of the network center,
representing the average of the historical maximum upload rates.
10. The method of claim 1, wherein the optimized uploading speed is:
wherein,
representing the optimized uploading speed;
is a function of taking the maximum value;
the upper limit of the uploading speed of the current user to the network hard disk of the network center is represented;
representing the uploading resource priority of the current user;
represents a natural constant;
representing the network strain coefficient;
represents the lower limit of the upload speed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210639237.9A CN114726728B (en) | 2022-06-08 | 2022-06-08 | Computer storage optimization method for monitoring data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210639237.9A CN114726728B (en) | 2022-06-08 | 2022-06-08 | Computer storage optimization method for monitoring data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114726728A true CN114726728A (en) | 2022-07-08 |
| CN114726728B CN114726728B (en) | 2022-08-12 |
Family
ID=82232853
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210639237.9A Active CN114726728B (en) | 2022-06-08 | 2022-06-08 | Computer storage optimization method for monitoring data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114726728B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117376955A (en) * | 2023-11-07 | 2024-01-09 | 杭州纵横通信股份有限公司 | Intelligent monitoring method for indoor network signal quality |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150180963A1 (en) * | 2012-08-19 | 2015-06-25 | Box, Inc. | Enhancement of upload and/or download performance based on client and/or server feedback information |
| US9128825B1 (en) * | 2013-05-17 | 2015-09-08 | Google Inc. | Optimizing allocation of flash memory to file groups |
| US20150295972A1 (en) * | 2012-11-16 | 2015-10-15 | Chris Hagan | System for optimising wireless data downloads |
| CN109002543A (en) * | 2018-07-24 | 2018-12-14 | 北京京东尚科信息技术有限公司 | A kind of method and apparatus of file storage |
| CN109361928A (en) * | 2018-09-11 | 2019-02-19 | 华中科技大学 | A kind of information centre's network system and video transmission method |
| CN114374652A (en) * | 2022-01-11 | 2022-04-19 | 同方有云(北京)科技有限公司 | Data transmission speed limiting method and device between thermomagnetic storage and blue light storage |
-
2022
- 2022-06-08 CN CN202210639237.9A patent/CN114726728B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150180963A1 (en) * | 2012-08-19 | 2015-06-25 | Box, Inc. | Enhancement of upload and/or download performance based on client and/or server feedback information |
| US20150295972A1 (en) * | 2012-11-16 | 2015-10-15 | Chris Hagan | System for optimising wireless data downloads |
| US9128825B1 (en) * | 2013-05-17 | 2015-09-08 | Google Inc. | Optimizing allocation of flash memory to file groups |
| CN109002543A (en) * | 2018-07-24 | 2018-12-14 | 北京京东尚科信息技术有限公司 | A kind of method and apparatus of file storage |
| CN109361928A (en) * | 2018-09-11 | 2019-02-19 | 华中科技大学 | A kind of information centre's network system and video transmission method |
| CN114374652A (en) * | 2022-01-11 | 2022-04-19 | 同方有云(北京)科技有限公司 | Data transmission speed limiting method and device between thermomagnetic storage and blue light storage |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117376955A (en) * | 2023-11-07 | 2024-01-09 | 杭州纵横通信股份有限公司 | Intelligent monitoring method for indoor network signal quality |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114726728B (en) | 2022-08-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114726728B (en) | Computer storage optimization method for monitoring data | |
| CN111556165A (en) | Information processing method and system based on cloud computing | |
| CN115884094B (en) | Multi-scene cooperation optimization caching method based on edge calculation | |
| CN118102318B (en) | Data transmission system based on 5G technology | |
| CN109688065B (en) | Parameter processing method and device and storage medium | |
| KR20070008210A (en) | Method and apparatus for blocking the objectionable multimedia information | |
| CN101801036A (en) | Network traffic management method and system and common node | |
| CN112866756B (en) | Code rate control method, device, medium and equipment for multimedia file | |
| CN112559078B (en) | Method and system for hierarchically unloading tasks of mobile edge computing server | |
| CN117081996B (en) | Flow control method based on server-side real-time feedback and soft threshold and related equipment | |
| CN109257633B (en) | Environment-aware HTTP adaptive streaming media QoE (quality of experience) optimization method | |
| CN112637286A (en) | Network architecture adjusting method, device, system and computer readable storage medium | |
| CN110764731A (en) | Multimedia file playing control method, intelligent terminal and server | |
| CN116467069A (en) | Spatial flight information system resource scheduling method and system based on PPO algorithm | |
| CN109325104B (en) | Method for dynamically calculating news acquisition service resources | |
| CN106888237B (en) | Data scheduling method and system | |
| CN113923691A (en) | Client perception evaluation method and device based on 5G signaling data | |
| CN109951329A (en) | A kind of network resource scheduling method and device | |
| KR20220116899A (en) | Real-time IP network performance evaluation method and device using artificial intelligence | |
| CN115277708B (en) | Dynamic load distribution method for streaming media server | |
| Qiao et al. | NEIVA: environment identification based video bitrate adaption in cellular networks | |
| CN118764438A (en) | Data stream priority management method and system for optical communication device | |
| CN117972360B (en) | AI large model optimization method, device, terminal equipment and storage medium | |
| CN116456372B (en) | Wireless monitoring network scheduling method and system based on dynamic reduction policy space | |
| CN110650217B (en) | Load balancing method for download server |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20221223 Address after: No. 001, Huihe Street, Laiwu High tech Zone, Jinan, Shandong 250000 Patentee after: Jinan Gaotou Property Management Service Co.,Ltd. Address before: 271100 Shancai Street, Laiwu High-tech Zone, Jinan City, Shandong Province Patentee before: LAIWU VOCATIONAL AND TECHNICAL College |