CN114356576A - Cloud service analysis management system based on big data - Google Patents
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Abstract
The invention discloses a cloud service analysis management system based on big data, which comprises: the system comprises a correlation data acquisition module, a data processing center, a service resource analysis module, a resource recovery planning module, a service operation monitoring module and a service resource disposal module, wherein user information needing cloud service and service information of a cloud server are acquired through the correlation data acquisition module, all the acquired information is stored through the data processing center, the maximum data capacity stored by a user is analyzed through the service resource analysis module, whether the resource needs to be recovered or not is judged through the resource recovery planning module, the capacity of the recovered resource is planned, the operation condition of a source cloud server is monitored in real time through the service operation monitoring module, the stored data is transferred to a local server related to the user through the service resource disposal module, a proper transfer target is selected, and the phenomenon that the resource storage space is increased again due to the change of user requirements after the resource is recovered is avoided, and convenience is provided for the user to retrieve the query data.
Description
Technical Field
The invention relates to the technical field of cloud service management, in particular to a cloud service analysis management system based on big data.
Background
The cloud service is that the required service is acquired through a network in an on-demand and easily-extensible mode, most of the application of the cloud service in enterprises is used for storing enterprise resources in a cloud mode, more and more enterprises begin to use the cloud service to store the resources along with the increasing popularization of the cloud service, and the cloud server provides services for enterprise users in a simple, efficient, safe and reliable computing form with elastically-extensible processing capacity, so that the cost of the enterprise resources is reduced, and the enterprises can focus more efforts on business and data;
in the prior art, cloud service is managed, the phenomena of blockage, downtime and the like of a cloud server can be avoided, and certain defects still exist: firstly, when the number of people accessing the cloud server exceeds the maximum number of people supported by the cloud server, the problem of blocking or even downtime is easy to occur, the prior art cannot transfer the data stored by the user to a proper transfer target in time, and the pressure of the cloud server cannot be relieved; secondly, resources stored by the cloud service usually have a certain idle resource space, and the cost can be saved by recovering the idle resources, but the prior art cannot avoid the phenomenon that the resource storage space is increased again due to the change of user requirements after the resources are recovered.
Therefore, a cloud service analysis management system based on big data is needed to solve the above problems.
Disclosure of Invention
The invention aims to provide a cloud service analysis management system based on big data to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a cloud service analysis management system based on big data is characterized in that: the system comprises: the system comprises a correlation data acquisition module, a data processing center, a service resource analysis module, a resource recovery planning module, a service operation monitoring module and a service resource disposal module;
the associated data acquisition module is used for acquiring user information needing cloud service and service information of the cloud server; the data processing center is used for storing all the acquired information; the service resource analysis module is used for analyzing the maximum data capacity stored by the user; the resource recovery planning module is used for judging whether resources need to be recovered or not and planning the capacity of the recovered resources; the service operation monitoring module is used for monitoring the operation condition of the source cloud server in real time and sending an early warning signal to the service resource handling module when the source cloud server is down; the service resource handling module is used for transferring the stored data to a local server related to the user and selecting a suitable transfer target.
Further, the associated data acquisition module comprises a service information acquisition unit and a user information acquisition unit, wherein the service information acquisition unit is used for acquiring resource storage capacity data of cloud service; the user information acquisition unit is used for acquiring process information of data storage of users needing cloud service and local server information associated with corresponding users, and transmitting all acquired data to the data processing center.
Further, the service resource analysis module comprises a resource storage prediction unit and an idle resource statistics unit, wherein the resource storage prediction unit is used for drawing a storage behavior curve according to process information of user storage data needing cloud service, and predicting the maximum data capacity stored by a user according to the behavior curve; the idle resource counting unit is used for counting the idle resource capacity in the current cloud server and transmitting the counting data to the resource recovery planning module.
Further, the resource recovery planning module includes an idle resource analysis unit and a resource recovery decision unit, where the idle resource analysis unit is configured to compare the prediction result with the current idle resource capacity, and analyze whether to recover the idle resource of the corresponding user; the resource recovery decision unit is used for completing resource recovery work according to an analysis result: if the idle resources needing to be recovered are analyzed, the capacity of the recovered resources is analyzed, and then the idle resources with the corresponding capacity are recovered.
Further, the service operation monitoring module comprises a cloud service monitoring unit and a downtime early warning unit, and the cloud service monitoring unit is used for monitoring the operation condition of the source cloud server in real time; the downtime early warning unit is used for sending an early warning signal to the service resource handling module when the source cloud server goes down; the service resource handling module comprises a stored data analysis unit, an associated server positioning unit, an access data transfer unit and a transfer target planning unit, wherein the stored data analysis unit is used for analyzing the importance degree of different data according to the collected process information of the user stored data after receiving the early warning signal; the associated server positioning unit is used for positioning a local server associated with a corresponding user; the access data transfer unit is used for transferring the data accessed by the corresponding user to the associated local server; and the transfer target planning unit is used for selecting a proper transfer target according to the server position information and the importance degree of the stored data.
Further, the user information collection unit collects a set of resource capacities purchased by users who purchase the same cloud server resource as M { M1, M2, …, Mk }, and collects local server IP address information associated with corresponding users, where k represents the number of users who purchase the same cloud server resource, the service information collection unit collects a set of data capacities stored by a random user as B { B1, B2, …, Bn } before the data storage capacity in the resource capacity purchased by the random user exceeds 50%, and collects the number of times of retrieving data stored for the corresponding time as M { M1, M2, …, mn }, where n represents the number of times of storing data by the corresponding user before the storage capacity exceeds 50%, and transmits all collected data to the data processing center.
Further, the resource storage prediction unit is used for predicting the variation range of the data capacity stored by the user: the remaining resource capacity Mj' after a random one-time data access by a random one user after the storage capacity exceeds 50% is calculated according to the following formula:
wherein Mi represents the resource capacity purchased by one random user, Bi represents the data amount randomly stored by the corresponding user at one time, Bi' represents that after the storage capacity of the corresponding user exceeds 50%, the data amount stored at one time randomly, Bi' represents the data amount taken out at one time randomly after the storage capacity of the corresponding user exceeds 50%, and after the storage capacity exceeds 50%, the set of resource capacities remaining after each access of data by the corresponding user is M '═ { M1', M2 ', …, MK' }, and K represents the number of times of data access of a corresponding user, and the residual resource capacity before the data storage capacity exceeds 50% is subtracted from the resource capacity increased after the data access of the user exceeds 50% to obtain the residual resource capacity after the data access of one user randomly for one time, so that the aim of drawing a storage behavior curve according to the residual resource capacity is fulfilled, and the accuracy of the result of predicting the maximum data capacity stored by the user is improved.
Furthermore, a storage behavior curve is drawn according to the resource capacity left after the user accesses data each time, and the storage behavior curve is fitted through a function M' ═ Ax + B: the coefficients A, B are calculated according to the formula:
obtaining a fitting function for determining the coefficients: comparing the elements in M ', screening out the maximum remaining resource capacity as Mmax', when the remaining resource capacity is maximum, the user accesses data for the J th time, the distance from the obtained point (J, Mmax ') to a straight line after curve fitting of the storage behavior is d, predicting the maximum capacity of the data stored by the user as Mmax' +2d, and counting the idle resource capacity in the current cloud server corresponding to the user as M by using the idle resource counting unitThe residue is leftComparing M with the idle resource analysis unitThe residue is leftAnd Mi- (Mmax' +2 d): if M isThe residue is leftNot more than Mi- (Mmax' +2d), judging that the idle resources of the corresponding user do not need to be recovered; if M isThe residue is left>Mi- (Mmax' +2d), determining that the idle resource of the corresponding user needs to be recovered, wherein the capacity of the idle resource needs to be recovered is as follows: mThe residue is left-[Mi-(Mmax’+2d)]The idle resource recovery work is finished by utilizing the resource recovery decision unit, and the storage behavior curve of the user is fitted, so that the analysis of the user storage behavior according to the fitted curve is facilitatedThe maximum capacity of data stored by a user is predicted under the condition of change of stored data, the judgment of whether idle resources need to be recovered or not is facilitated, the capacity of the idle resources needing to be recovered is determined, and the phenomenon that the resource storage space is increased again due to change of user requirements after the resources are recovered is avoided.
Further, after receiving the warning signal, the stored data analysis unit analyzes the importance degree of different data according to the collected process information of the user stored data: calculating the importance degree coefficient Wi of random primary data stored by a user according to the following formula
Where mi denotes the number of times of randomly withdrawing the stored data at one time, the associated server positioning unit is used to confirm that the position coordinate set of the local server associated with the corresponding user is (X, Y) { (X1, Y1), (X2, Y2), …, (Xp, Yp) }, and the position coordinate set of the source cloud server is (X ', Y '), where p denotes the number of local servers associated with the corresponding user, the importance degree coefficient and the local server position are transmitted to the access data transfer unit, the greater the number of times of withdrawing the data by the user is, the greater the user's demand for the corresponding data is, the more important the reflected data is, and the stored data importance degree coefficient is calculated to provide data support for transferring the data to a suitable local server, i.e., a transfer target.
Further, the access data transfer unit is used for transferring the data accessed by the corresponding user to the associated local server: calculating a reliability coefficient Qj for transferring the data randomly stored by the user once to a local server according to the following formula:
the method comprises the steps that Xj and Yj respectively represent horizontal and vertical coordinates of a random local server position, reliability coefficient sets of data stored by a user at one time at random are obtained and transferred to the local servers are Q (Q1, Q2, … and Qp), a transfer target planning unit is used for comparing the reliability coefficients, the data stored at the corresponding time are transferred to the local server corresponding to the highest reliability coefficient, the highest reliability coefficient is Qmax, when the source cloud server is monitored to be in fault, important data are transferred in time, convenience is provided for the user to fetch query data, the phenomenon that the user cannot fetch access data is avoided, data importance factors are added on the basis of local server transfer data selected nearby, and data are protected in a targeted mode.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, the residual resource capacity is analyzed before the data storage capacity exceeds 50% and the data resource capacity accessed by the user is analyzed after the data storage capacity exceeds 50%, the residual resource capacity after the user accesses data is judged, the storage behavior curve is drawn according to the residual resource capacity, and the storage behavior curve of the user is fitted, so that the maximum capacity of the data stored by the user can be predicted under the condition that the user stores data and changes according to the fitted curve analysis, whether the idle resource needs to be recovered or not is judged, the capacity of the idle resource needing to be recovered is determined, and the phenomenon that the resource storage space is increased again due to the change of the user requirement after the resource is recovered is avoided; the cloud service operation condition is monitored in real time, the early warning signal is sent in time when the downtime occurs, the appropriate transfer target is selected according to the importance degree of the data and the distance of the transfer target, the data is transferred to the appropriate target, convenience is brought to users to fetch the query data, and the phenomenon that the users cannot fetch the access data is avoided.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a structural diagram of a cloud service analysis management system based on big data according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Referring to fig. 1, the present invention provides a technical solution: a cloud service analysis management system based on big data is characterized in that: the system comprises: the system comprises a correlation data acquisition module S1, a data processing center S2, a service resource analysis module S3, a resource recovery planning module S4, a service operation monitoring module S5 and a service resource disposal module S6;
the associated data acquisition module S1 is used for acquiring user information needing cloud service and service information of the cloud server; the data processing center S2 is used for storing all the collected information; the service resource analysis module S3 is used to analyze the maximum data capacity stored by the user; the resource recovery planning module S4 is configured to determine whether resources need to be recovered and plan the capacity of the recovered resources; the service operation monitoring module S5 is configured to monitor an operation status of the source cloud server in real time, and send an early warning signal to the service resource handling module S6 when the source cloud server goes down; the service resource handling module S6 is used to transfer the stored data to a local server associated with the user and to select an appropriate transfer destination.
The associated data acquisition module S1 includes a service information acquisition unit and a user information acquisition unit, where the service information acquisition unit is used to acquire resource storage capacity data of cloud services; the user information acquisition unit is used for acquiring process information of data storage of users needing cloud services and local server information associated with corresponding users, and transmitting all acquired data to the data processing center S2.
The service resource analysis module S3 includes a resource storage prediction unit and an idle resource statistics unit, where the resource storage prediction unit is configured to draw a storage behavior curve according to process information of user storage data that requires cloud service, and predict a maximum data capacity stored by a user according to the behavior curve; the idle resource counting unit is configured to count an idle resource capacity in the current cloud server, and transmit the statistical data to the resource recycling planning module S4.
The resource recovery planning module S4 includes an idle resource analysis unit and a resource recovery decision unit, where the idle resource analysis unit is configured to compare the prediction result with the current idle resource capacity, and analyze whether to recover the idle resource of the corresponding user; the resource recovery decision unit is used for completing resource recovery work according to the analysis result: if the idle resources needing to be recovered are analyzed, the capacity of the recovered resources is analyzed, and then the idle resources with the corresponding capacity are recovered.
The service operation monitoring module S5 includes a cloud service monitoring unit and a downtime early warning unit, and the cloud service monitoring unit is used for monitoring the operation status of the source cloud server in real time; the downtime early warning unit is used for sending an early warning signal to the service resource handling module S6 when the source cloud server goes down; the service resource handling module S6 comprises a stored data analysis unit, a related server positioning unit, an access data transfer unit and a transfer target planning unit, wherein the stored data analysis unit is used for analyzing the importance degree of different data according to the collected process information of the user stored data after receiving the early warning signal; the associated server positioning unit is used for positioning a local server associated with the corresponding user; the access data transfer unit is used for transferring the data accessed by the corresponding user to the associated local server; and the transfer target planning unit is used for selecting a proper transfer target according to the server position information and the importance degree of the stored data.
The method comprises the steps that a user information acquisition unit is used for acquiring a set of resource capacities purchased by users purchasing the same cloud server resource, wherein the set of the resource capacities is M { M1, M2, … and Mk }, local server IP address information associated with corresponding users is acquired, k represents the number of the users purchasing the same cloud server resource, before the data storage capacity in the resource capacity purchased by a random user exceeds 50%, the set of the data amount stored by the random user is B { B1, B2, … and Bn }, the number of times of retrieving the data stored correspondingly is M { M1, M2, … and mn }, wherein n represents the number of times of storing the data by the corresponding user before the storage capacity exceeds 50%, and all acquired data are transmitted to a data processing center S2.
Predicting the variation range of the data capacity stored by the user by using a resource storage prediction unit: the remaining resource capacity Mj' after a random one-time data access by a random one user after the storage capacity exceeds 50% is calculated according to the following formula:
wherein Mi represents the resource capacity purchased by a random user, Bi represents the data volume randomly stored by a corresponding user at one time, Bi 'represents the data volume randomly stored by the corresponding user at one time after the storage capacity of the corresponding user exceeds 50%, Bi' represents the data volume randomly taken out by the corresponding user at one time after the storage capacity exceeds 50%, and the set of the resource capacity left after the corresponding user accesses the data at each time after the storage capacity exceeds 50% is M '{ M1', M2 ', …, and MK' }, wherein K represents the number of times that the corresponding user accesses the data, and a storage behavior curve is drawn according to the left resource capacity, so that the accuracy of the result of predicting the maximum data capacity stored by the user can be effectively improved.
Drawing a storage behavior curve according to the residual resource capacity after the user accesses data each time, and fitting the storage behavior curve through a function M' ═ Ax + B: the coefficient A, B is calculated according to the following formula:
obtaining a fitting function for determining the coefficients: comparing the elements in M ', screening out the maximum residual resource capacity as Mmax', when the maximum residual resource capacity is maximum, the user is J-th access data, the distance from the obtained point (J, Mmax ') to the straight line after the curve fitting of the storage behavior is d, predicting the maximum data capacity stored by the user as Mmax' +2d, and utilizing an idle resource statistical unit to calculate the systemCounting that the idle resource capacity in the current cloud server corresponding to the user is MThe residue is leftComparing M with an idle resource analysis unitThe residue is leftAnd Mi- (Mmax' +2 d): if M isThe residue is leftNot more than Mi- (Mmax' +2d), judging that the idle resources of the corresponding user do not need to be recovered; if M isThe residue is left>Mi- (Mmax' +2d), determining that the idle resource of the corresponding user needs to be recovered, wherein the capacity of the idle resource needs to be recovered is as follows: mThe residue is left-[Mi-(Mmax’+2d)]And the resource recovery decision unit is used for finishing the work of recovering the idle resources, judging whether the idle resources need to be recovered or not, and determining the capacity of the idle resources needing to be recovered, so that the phenomenon that the resource storage space is increased again due to the change of user requirements after the resources are recovered is avoided.
After the stored data analysis unit receives the early warning signal, the importance degree of different data is analyzed according to the collected process information of the user stored data: calculating the importance degree coefficient Wi of the random primary data stored by the user according to the following formula:
where mi denotes the number of times of randomly retrieving the stored data at one time, the associated server location unit is used to confirm that the location coordinate set of the local server associated with the corresponding user is (X, Y) { (X1, Y1), (X2, Y2), …, (Xp, Yp) }, and the location coordinate set of the source cloud server is (X ', Y'), where p denotes the number of local servers associated with the corresponding user, and the importance coefficient and the location of the local server are transmitted to the access data transfer unit.
Transferring the data accessed by the corresponding user to the associated local server by using the access data transfer unit: calculating a reliability coefficient Qj for transferring the data randomly stored by the user once to a local server according to the following formula:
the reliability coefficient set for transferring the data randomly stored by the user at one time to the local server is Q (Q1, Q2, … and Qp), the reliability coefficient is compared by using a transfer target planning unit, the data stored at the corresponding time is transferred to the local server corresponding to the highest reliability coefficient, the highest reliability coefficient is Qmax, the important data are transferred in time, convenience is brought to the user to fetch the query data, the phenomenon that the user cannot fetch the access data is avoided, the data importance factor is added on the basis of selecting the local server transfer data nearby, and the data are protected in a targeted manner.
The first embodiment is as follows: the method comprises the steps of acquiring that the capacity of resources purchased by a random user who purchases the same cloud server resource is Mi-200 GB, acquiring that the data storage capacity in the capacity of the resources purchased by the random user exceeds 50% by using a service information acquisition unit, wherein the data amount stored by the random user is B-B1, B2, B3-5, 10 and 20, and predicting the change range of the data capacity stored by the user by using a resource storage prediction unit: according to the formula
After the storage capacity exceeds 50%, calculating the remaining resource capacity Mj '60, where Bi' 10 and Bi '20, after a user randomly accesses data once, and obtaining a set of the remaining resource capacities after the storage capacity exceeds 50%, corresponding to the user accesses data each time, which is M' { M1 ', M2', M3 '} {60, 20, 40}, drawing a storage behavior curve according to the remaining resource capacity after the user accesses data each time, and fitting the storage behavior curve through a function M' ═ Ax + B: according to the formula
And
calculating coefficient A, B: a-60 and B-54, resulting in a fitting function that determines the coefficients: m 'Ax + B60 x-54, the maximum resource capacity Mmax' 60 is selected, when the maximum resource capacity is left, the user is stored 1 time JAnd (3) acquiring data, wherein the distance from the acquired point (J, Mmax ') (1, 60) to a straight line after curve fitting of the storage behavior is 54, the maximum capacity of data stored by the user is predicted to be Mmax' +2d 168, and the idle resource capacity in the current cloud server corresponding to the user is counted to be MThe residue is leftCompare M64The residue is leftAnd Mi- (Mmax' +2 d): mi- (Mmax' +2d) ═ 32<And 64, judging that the idle resources of the corresponding users need to be recovered, wherein the capacity of the idle resources needing to be recovered is as follows: mThe residue is left-[Mi-(Mmax’+2d)]When the resource is 32GB, the idle resource recovery work is finished by using a recovery resource decision unit;
example two: the collected times of withdrawing the data stored in the corresponding time are m ═ { m1, m2, m3} ═ 10, 6, 8}, and the stored data analysis unit analyzes the importance degree of different data according to the collected process information of the user stored data after receiving the early warning signal: according to the formula
Calculating an importance degree coefficient W1 of the first data stored by the user to be 0.42, confirming, by the associated server positioning unit, that a position coordinate set of a local server associated with the corresponding user is (X, Y) { (X1, Y1), (X2, Y2), (X3, Y3) } { (10, 50), (100 ), (50, 50) }, and a position coordinate of the source cloud server is (X ', Y') -20, 20 according to a formula
The reliability coefficient set of the data stored for the first time by the user to be transferred to the local server is obtained as Q ═ { Q1, Q2, Q3} - {0.013, 0.004, 0.010}, the highest reliability coefficient is Qmax ═ 0.013, and the data stored for the first time is transferred to the first local server.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A cloud service analysis management system based on big data is characterized in that: the system comprises: the system comprises a correlation data acquisition module (S1), a data processing center (S2), a service resource analysis module (S3), a resource recovery planning module (S4), a service operation monitoring module (S5) and a service resource disposal module (S6);
the associated data acquisition module (S1) is used for acquiring user information needing cloud service and service information of a cloud server; the data processing center (S2) is used for storing all the collected information; the service resource analysis module (S3) is used for analyzing the maximum data capacity stored by the user; the resource recovery planning module (S4) is used for judging whether the resources need to be recovered and planning the capacity of the recovered resources; the service operation monitoring module (S5) is configured to monitor an operation status of a source cloud server in real time, and send an early warning signal to the service resource handling module (S6) when the source cloud server is down; the service resource handling module (S6) is used to transfer the stored data to a local server associated with the user and to select an appropriate transfer target.
2. The big data based cloud service analysis management system according to claim 1, wherein: the associated data acquisition module (S1) comprises a service information acquisition unit and a user information acquisition unit, wherein the service information acquisition unit is used for acquiring resource storage capacity data of cloud service; the user information acquisition unit is used for acquiring process information of data storage of users needing cloud service and local server information associated with corresponding users, and transmitting all acquired data to the data processing center (S2).
3. The big data based cloud service analysis management system according to claim 1, wherein: the service resource analysis module (S3) comprises a resource storage prediction unit and an idle resource statistical unit, wherein the resource storage prediction unit is used for drawing a storage behavior curve according to the process information of user storage data needing cloud service, and predicting the maximum data capacity stored by a user according to the behavior curve; the idle resource counting unit is used for counting the idle resource capacity of the current cloud server and transmitting the statistical data to the resource recovery planning module (S4).
4. The big data based cloud service analysis management system according to claim 3, wherein: the resource recovery planning module (S4) includes an idle resource analysis unit and a resource recovery decision unit, where the idle resource analysis unit is configured to compare the prediction result with the current idle resource capacity, and analyze whether to recover the idle resource of the corresponding user; the resource recovery decision unit is used for completing resource recovery work according to an analysis result: if the idle resources needing to be recovered are analyzed, the capacity of the recovered resources is analyzed, and then the idle resources with the corresponding capacity are recovered.
5. The big data based cloud service analysis management system according to claim 1, wherein: the service operation monitoring module (S5) comprises a cloud service monitoring unit and a downtime early warning unit, wherein the cloud service monitoring unit is used for monitoring the operation condition of the source cloud server in real time; the downtime early warning unit is used for sending an early warning signal to the service resource handling module (S6) when the source cloud server goes down; the service resource handling module (S6) comprises a stored data analysis unit, a related server positioning unit, an access data transfer unit and a transfer target planning unit, wherein the stored data analysis unit is used for analyzing the importance degree of different data according to the collected process information of the user stored data after receiving the early warning signal; the associated server positioning unit is used for positioning a local server associated with a corresponding user; the access data transfer unit is used for transferring the data accessed by the corresponding user to the associated local server; and the transfer target planning unit is used for selecting a proper transfer target according to the server position information and the importance degree of the stored data.
6. The big data based cloud service analysis management system according to claim 2, wherein: the method includes the steps that a user information acquisition unit is used for acquiring a set of resource capacities purchased by users purchasing the same cloud server resource, wherein the set of resource capacities is M (M1, M2, … and Mk), local server IP address information associated with corresponding users is acquired, k represents the number of users purchasing the same cloud server resource, the service information acquisition unit is used for acquiring a set of data capacities stored by one random user, B (B1, B2, … and Bn) before the data storage capacity in the resource capacity purchased by one random user exceeds 50%, the number of times of retrieving data stored correspondingly is M (M1, M2, … and mn), n represents the number of times of storing data by the corresponding users before the storage capacity exceeds 50%, and all acquired data are transmitted to a data processing center (S2).
7. The big data based cloud service analysis management system according to claim 4, wherein: predicting a data capacity variation range of the user storage by using the resource storage prediction unit: the remaining resource capacity Mj' after a random one-time data access by a random one user after the storage capacity exceeds 50% is calculated according to the following formula:
wherein Mi represents the resource capacity purchased by a random user, Bi represents the data amount randomly stored by the corresponding user at one time, Bi ' represents the data amount randomly stored by the corresponding user at one time after the storage capacity exceeds 50%, Bi "represents the data amount randomly taken out by the corresponding user at one time after the storage capacity exceeds 50%, and the set of the resource capacities left after the corresponding user accesses the data at each time after the storage capacity exceeds 50% is M ' { M1 ', M2 ', …, MK ' }, wherein K represents the number of times the corresponding user accesses the data.
8. The big data based cloud service analysis management system according to claim 7, wherein: drawing a storage behavior curve according to the residual resource capacity after the user accesses data each time, and fitting the storage behavior curve through a function M' ═ Ax + B: the coefficients A, B are calculated according to the formula:
obtaining a fitting function for determining the coefficients: comparing the elements in M ', screening out the maximum remaining resource capacity as Mmax', when the remaining resource capacity is maximum, the user accesses data for the J th time, the distance from the obtained point (J, Mmax ') to a straight line after curve fitting of the storage behavior is d, predicting the maximum capacity of the data stored by the user as Mmax' +2d, and counting the idle resource capacity in the current cloud server corresponding to the user as M by using the idle resource counting unitThe residue is leftComparing M with the idle resource analysis unitThe residue is leftAnd Mi- (Mmax' +2 d): if M isThe residue is leftNot more than Mi- (Mmax' +2d), judging that the idle resources of the corresponding user do not need to be recovered; if M isThe residue is left>Mi- (Mmax' +2d), determining that the idle resource of the corresponding user needs to be recovered, wherein the capacity of the idle resource needs to be recovered is as follows: mThe residue is left-[Mi-(Mmax’+2d)]And completing the work of recovering idle resources by using the resource recovery decision unit.
9. The big data based cloud service analysis management system according to claim 5, wherein: after the stored data analysis unit receives the early warning signal, the importance degree of different data is analyzed according to the collected process information of the user stored data: calculating the importance degree coefficient Wi of random primary data stored by a user according to the following formula
Where mi denotes the number of times of randomly retrieving the stored data at one time, the associated server location unit is used to confirm that the location coordinate set of the local server associated with the corresponding user is (X, Y) { (X1, Y1), (X2, Y2), …, (Xp, Yp) }, and the location coordinate set of the cloud server is (X ', Y'), where p denotes the number of local servers associated with the corresponding user, and the importance degree coefficient and the location of the local server are transmitted to the access data transfer unit.
10. The big data based cloud service analysis management system according to claim 9, wherein: transferring data accessed by the corresponding user to the associated local server by using the access data transfer unit: calculating a reliability coefficient Qj for transferring the data randomly stored by the user once to a local server according to the following formula:
and obtaining a reliability coefficient set Q ═ Q1, Q2, … and Qp } for transferring the data stored by the user at one time randomly to the local server, comparing the reliability coefficients by using a transfer target planning unit, and transferring the data stored at the corresponding time to the local server corresponding to the highest reliability coefficient, wherein the highest reliability coefficient is Qmax.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114675789A (en) * | 2022-04-20 | 2022-06-28 | 国韵信息科技(济南)有限公司 | Big data analysis storage system and method based on computer system |
| CN116091175A (en) * | 2023-04-10 | 2023-05-09 | 南京航空航天大学 | Transaction information data management system and method based on big data |
| CN116091000A (en) * | 2023-02-14 | 2023-05-09 | 深圳市万特网络科技有限公司 | OA system-based resource intelligent management system and method |
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2022
- 2022-01-07 CN CN202210014642.1A patent/CN114356576A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114675789A (en) * | 2022-04-20 | 2022-06-28 | 国韵信息科技(济南)有限公司 | Big data analysis storage system and method based on computer system |
| CN116091000A (en) * | 2023-02-14 | 2023-05-09 | 深圳市万特网络科技有限公司 | OA system-based resource intelligent management system and method |
| CN116091000B (en) * | 2023-02-14 | 2023-12-08 | 宁波紫熙物联科技有限公司 | OA system-based resource intelligent management system and method |
| CN116091175A (en) * | 2023-04-10 | 2023-05-09 | 南京航空航天大学 | Transaction information data management system and method based on big data |
| CN116091175B (en) * | 2023-04-10 | 2023-08-22 | 南京航空航天大学 | Transaction information data management system and method based on big data |
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