A VDC resource management system based on big data

Abstract

The invention discloses a VDC resource management system based on big data, which transmits tenant information to a list temporary storage unit through a VDC configuration unit; then, the tenant association unit is used to automatically obtain projects and management personnel in the tenant information from the list temporary storage unit, and transmit the projects and management personnel to an inertia acquisition unit; the inertia acquisition unit is used to receive the projects and management personnel transmitted by the tenant association unit, and perform preliminary screening analysis on them to obtain inertia objects and the projects currently in charge and mark them as object targets; the inertia analysis unit is used to perform inertia analysis on all inertia objects and object targets, and obtain the recognition times and corresponding tremor values, recognition cumulative time and corresponding correction values ​​of 24 time periods corresponding to each object target; the corresponding projects that the user may be more inclined to when logging in are confirmed according to the above data, and are automatically displayed; the invention is simple, effective, and easy to use.

Description

Technical Field

[0001] The present invention relates to the technical field of VDC resource management, and in particular to a VDC resource management system based on big data. Background Art

[0002] The patent with publication number CN110798354A discloses a resource relationship processing method for multi-cloud management. The method of the present invention is: 1. Configure cloud tenant information of different cloud platforms and encrypt and store it in the database; 2. Configure VDC and associate cloud tenants as needed; 3. Associate the project with the required VDC; 4. Obtain the corresponding VDC list according to the project in which the user participates; 5. Obtain cloud tenants according to the VDC list; 6. Call the corresponding cloud platform through the cloud tenant information to obtain resources on the cloud platform; return to the multi-cloud management platform for integration. It solves the resource management problem of the virtual data center of the multi-cloud platform; it can be used for resource collaborative management in a multi-cloud environment.

[0003] However, for this resource allocation method, there is a lack of a method for how to quickly enter the target project, save time, and avoid selection, especially when a user manages multiple projects. Based on this, a solution is provided. Summary of the invention

[0004] The purpose of the present invention is to provide a VDC resource management system based on big data;

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A VDC resource management system based on big data, including

[0007] The VDC configuration unit is used to mark all cloud tenants, corresponding projects and managers as tenant information; the VDC configuration unit is also used to transmit the tenant information to the list temporary storage unit, and the list temporary storage unit receives the tenant information transmitted by the user and stores it in real time;

[0008] The tenant association unit automatically obtains the projects and managers in the tenant information from the list temporary storage unit, and transmits the projects and managers to the inertia acquisition unit;

[0009] The inertia acquisition unit receives the projects and managers transmitted by the tenant association unit, performs preliminary screening and analysis on them, obtains the inertia objects, and the projects they are currently responsible for and marks them as object targets;

[0010] The inertia acquisition unit is used to transmit all the inertia-divided objects and object targets to the inertia analysis unit, which is used to perform inertia analysis on all the inertia-divided objects and object targets, and obtain the identification times and the corresponding tremor values, identification accumulated time and the corresponding correction values ​​corresponding to 24 time periods of each object target;

[0011] The inertial analysis unit is used to store the identification times and the corresponding tremor values, identification cumulative time and the corresponding correction values ​​of each object mark in four time periods in real time;

[0012] The user login unit is used for users to log in to the cloud platform, and after logging in, the user information is transmitted to the processor, and the processor is used to display and analyze the user information. The specific display and analysis methods are as follows:

[0013] SS01: After obtaining user information, all object targets related to the user are automatically obtained;

[0014] SS02: Then get the current time and mark it as login time;

[0015] SS03: The data retrieval unit obtains the identification times and corresponding tremor values, identification cumulative time and corresponding correction values ​​of all target objects at the time of login from the inertial analysis unit;

[0016] SS04: All object labels are marked as Bj, j = 1...m, and the corresponding identification times, tremor values, identification cumulative time and correction values ​​are marked as Rj, Cj, Lj and Oj, j = 1...m; and Bj, Rj, Cj, Lj and Oj are in one-to-one correspondence;

[0017] SS05: Then use the formula to calculate the optional values ​​of all object targets. The specific calculation formula is:

[0018] Kj=0.41*Rj / Cj+0.59*Lj / Oj;

[0019] In the formula, 0.41 and 0.59 are the preset weights;

[0020] SS06: Then sort the object targets in descending order of Kj value, mark the first ranked target as the first displayed target, and the remaining second and third ranked targets as parallel candidates;

[0021] The processor is used to transmit the preferred target and the parallel candidate targets to the self-configuration unit. The self-configuration unit automatically enters the interface of the preferred target first, and simultaneously displays the parallel candidate targets in the corner of the interface to facilitate users to switch quickly.

[0022] Furthermore, the specific method of the initial screening analysis is as follows:

[0023] S1: Get all managers and mark them as the subjects to be scored;

[0024] S2: Then, the subjects to be scored are preliminarily screened to obtain the subjects to be scored and the projects they are currently responsible for, and marked as the target objects; the specific screening method in step S2 is:

[0025] Get all the scored objects, automatically get the projects of all the scored objects, automatically get the number of the corresponding projects, remove the scored objects with a number lower than X1, and mark the remaining scored objects as habitually scored objects; the specific value of X1 here is 2;

[0026] Mark the project that the corresponding object is currently responsible for as the object target.

[0027] Furthermore, the specific method of inertia analysis is as follows:

[0028] Step 1: Get all the objects and their corresponding object labels;

[0029] Step 2: Select any object and obtain all its object targets;

[0030] Step 3: Perform operation analysis on the selected object targets of the habitual division in turn, select any object target, perform operation analysis on it, and obtain 24 time period subgroups and time period cumulative time groups for each object target;

[0031] Step 4: After that, the time period group and time period cumulative time group of each day in the near T1 period of the target are obtained, and the data are reduced to obtain the identification times and the corresponding tremor values, identification cumulative time and the corresponding correction values ​​of the 24 time periods of the corresponding target;

[0032] Step 5: Select any next target object and repeat steps 3-5. After all target objects are processed, the identification times and their corresponding tremor values, identification accumulated time and their corresponding correction values ​​for 24 time periods corresponding to each target object are obtained.

[0033] Furthermore, the specific method of object analysis in step 3 is:

[0034] S01: Get the start time and expected period of the object. The start time refers to the start time of the object, and the expected period is the estimated time the object is expected to be used, which is uploaded by the administrator.

[0035] S02: Then, the via length value is obtained according to the start time, and the via length value is the length of time that has passed from the start to the present;

[0036] S03: When the long value exceeds T1, T1 is a preset value, generally 30 days; obtain the usage data of the corresponding object target every day near T1. If the long value does not exceed T1, obtain the usage data of the object target from the beginning to the present. In order to facilitate analysis in subsequent applications, T1 is used to represent the duration of the analyzed period. The method of obtaining usage data is as follows:

[0037] S031: Usage data includes time period group and time period cumulative time group;

[0038] S032: First, define the time period, divide a day into 24 time periods, mark each hour as a time period, mark it as a time period group, and divide it into the first time period from midnight to one o'clock, and divide it in sequence;

[0039] S033: Then, the number of times the customary object corresponding to each time period uses the object label is obtained. The single use is defined as if the customary object logs in and calls the object label and then goes offline, and does not log in to the object label again within T2, then the number of uses is automatically increased by one, and the number is marked as time period times. The time period times within 24 time periods constitute a time period time group;

[0040] S034: The specific duration of use of the object mark in the 24 time periods is obtained, and it is marked as the accumulated time of the time period, and the accumulated time of the 24 time periods is marked as a time period accumulated time group.

[0041] Furthermore, the specific method of data reduction in step 4 is:

[0042] SS1: Select any time period, obtain the time period times of group T1, and mark the time period times as Di, i=1...T1; Di means T1 days forward from the current time, mark that day as the first day, and Di means the time period times of the selected time period of the i-th day;

[0043] SS2: Then automatically find the mean of Di, mark it as P, and then use the formula to calculate the deviation value W. The specific calculation formula is:

[0044]

[0045] SS3: When the W value exceeds X2, data is filtered out. Specifically, the corresponding Di is selected in order from large to small according to |Di-P|. After each Di is selected, it is deleted and the W value is recalculated until the W value does not exceed X2, where X2 is the preset value.

[0046] The mean value of Di after deletion is marked as the identification time, and the corresponding number of deleted Di is marked as the tremor value;

[0047] SS4: After that, the accumulated time group of the T1 group is obtained, and the accumulated time of any period is selected and processed according to the same principle as steps SS1-SS3. The obtained value is marked as the recognized accumulated time, and the corresponding tremor value is marked as the corrected value;

[0048] SS5: Select any next time period and repeat steps SS1-SS4. After all time periods are processed, the identification times and their corresponding tremor values, identification accumulated time and their corresponding correction values ​​of 24 time periods are obtained.

[0049] Furthermore, it also includes a management unit, which is communicatively connected with the processor and is used to enter all preset values.

[0050] Beneficial effects of the present invention:

[0051] The present invention marks all cloud tenants, corresponding projects and managers as tenant information through a VDC configuration unit; the VDC configuration unit also transmits the tenant information to a list temporary storage unit, which receives the tenant information transmitted by the user and stores it in real time; then the tenant association unit automatically obtains the projects and managers in the tenant information from the list temporary storage unit, and transmits the projects and managers to the inertia acquisition unit;

[0052] The inertia acquisition unit is then used to receive the projects and managers transmitted by the tenant association unit, and perform a preliminary screening analysis on them to obtain the inertia objects and the projects they are currently responsible for and mark them as object targets; the inertia analysis unit is then used to perform inertia analysis on all inertia objects and object targets to obtain the recognition times and corresponding tremor values, recognition cumulative time and corresponding correction values ​​for 24 time periods corresponding to each object target; based on the above data, the corresponding projects that the user may prefer when logging in are confirmed and automatically displayed; the present invention is simple, effective, and easy to use. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention will be further described below in conjunction with the accompanying drawings.

[0054] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1As shown, the present invention is a VDC resource management system based on big data, including a VDC configuration unit, a list temporary storage unit, a tenant association unit, an inertia acquisition unit, an inertia analysis unit, a data retrieval unit, a self-configuration unit, a processor, a user login unit and a management unit;

[0057] The VDC configuration unit is used to configure the cloud tenants related content, and obtain a number of cloud tenants, each cloud tenant corresponds to a number of projects, each project corresponds to a number of managers; the configuration method is implemented in the following way:

[0058] Configure cloud tenant information for different cloud platforms;

[0059] The user configures the cloud tenant information on the cloud platform that needs to be taken over. This method uses JSON format to store the access information of different configuration items on different cloud platforms.

[0060] For example, configure the cloud tenant information of OpenStack: {"domainName":"default","password":"test","projectId":"test","userName":"test"}, configure the cloud tenant information of Alibaba Cloud: {"accessKeyID":"xxxxxxxxx","accessKeySecret":"xxxxxxxxxxxxxxxxx"}, and finally encrypt it using RSA encryption and store it in the database.

[0061] Cloud tenant management saves cloud tenant information in different formats in JSON format and stores it in the database through RSA encryption, which can adapt to the access requirements of different cloud vendors and the security of access information;

[0062] Then configure the tenant information for a single VDC on the cloud.

[0063] For example, create a new VDC (virtual data center), configure the VDC information, and then add multiple cloud platform cloud tenants with business associations to the VDC to form a new VDC;

[0064] By joining cloud tenants from different cloud platforms, cloud computing resources can be managed across clouds, and different VDCs can be built according to different requirements to meet the needs of different projects. It can provide ease of use for multi-cloud management, greatly facilitating operation and maintenance personnel to manage resources on VDCs, and can achieve the effect of isolating resources on different VDCs.

[0065] Finally, associate the project with the VDC; for example, a department manager creates a project, then associates the required VDC based on the needs of project resources, and then adds members corresponding to the project.

[0066] By associating a project with a VDC, you can rebuild a multi-cloud resource grouping based on project needs. Resources between each project can also be isolated, allowing project members to manage their own cloud resources more easily and clearly.

[0067] The VDC configuration unit is used to mark all cloud tenants, corresponding projects and managers as tenant information; the VDC configuration unit is also used to transmit the tenant information to the list temporary storage unit, and the list temporary storage unit receives the tenant information transmitted by the user and stores it in real time;

[0068] The tenant association unit automatically obtains the projects and managers in the tenant information from the list temporary storage unit, and transmits the projects and managers to the inertia acquisition unit;

[0069] The inertia acquisition unit receives the projects and managers transmitted by the tenant association unit and performs preliminary screening and analysis on them. The specific method of preliminary screening and analysis is as follows:

[0070] S1: Get all managers and mark them as the subjects to be scored;

[0071] S2: After that, the subjects are preliminarily screened. The specific screening method is as follows:

[0072] Get all the scored objects, automatically get the projects of all the scored objects, automatically get the number of the corresponding projects, remove the scored objects with a number lower than X1, and mark the remaining scored objects as habitual scoring objects; the specific value of X1 here is 2, of course, the administrator can also set it according to the specific situation;

[0073] Mark the project that the corresponding subject is currently responsible for as the object target;

[0074] The inertia acquisition unit is used to transmit all inertia-divided objects and object targets to the inertia analysis unit. The inertia analysis unit is used to perform inertia analysis on all inertia-divided objects and object targets. The specific method of inertia analysis is as follows:

[0075] Step 1: Get all the objects and their corresponding object labels;

[0076] Step 2: Select any object and obtain all its object targets;

[0077] Step 3: Perform operation analysis on the selected object of the habitual division in turn. Select any object and perform operation analysis on it. The specific method is as follows:

[0078] S01: Get the start time and expected period of the object. The start time refers to the start time of the object, and the expected period is the estimated time the object is expected to be used, which is uploaded by the administrator.

[0079] S02: Then, the via length value is obtained according to the start time, and the via length value is the length of time that has passed from the start to the present;

[0080] S03: When the long value exceeds T1, T1 is a preset value, generally 30 days; obtain the usage data of the corresponding object target every day near T1. If the long value does not exceed T1, obtain the usage data of the object target from the beginning to the present. In order to facilitate analysis in subsequent applications, T1 is used to represent the duration of the analyzed period. The method of obtaining usage data is as follows:

[0081] S031: Usage data includes time period group and time period cumulative time group;

[0082] S032: First, define the time period, divide a day into 24 time periods, mark each hour as a time period, mark it as a time period group, and divide it into the first time period from midnight to one o'clock, and divide it in sequence;

[0083] S033: Then, the number of times the customary object corresponding to each time period uses the object label is obtained. The single use is defined as if the customary object logs in and calls the object label and then goes offline, and does not log in to the object label again within T2, then the number of uses is automatically increased by one, and the number is marked as time period times. The time period times within 24 time periods constitute a time period time group;

[0084] S034: Obtain the specific duration of use of the object mark in the 24 time periods, mark it as the accumulated time of the time period, and mark the accumulated time of the 24 time periods as a time period accumulated time group;

[0085] Step 4: After that, obtain the time period subgroup and time period cumulative time group of each day within the time period near T1 of the target object, and perform data reduction. The specific method of data reduction is as follows:

[0086] SS1: Select any time period, obtain the time period times of group T1, and mark the time period times as Di, i=1...T1; Di means T1 days forward from the current time, mark that day as the first day, and Di means the time period times of the selected time period of the i-th day;

[0087] SS2: Then automatically find the mean of Di, mark it as P, and then use the formula to calculate the deviation value W. The specific calculation formula is:

[0088]

[0089] SS3: When the W value exceeds X2, data is filtered out. Specifically, the corresponding Di is selected in order from large to small according to |Di-P|. After each Di is selected, it is deleted and the W value is recalculated until the W value does not exceed X2, where X2 is the preset value.

[0090] The mean value of Di after deletion is marked as the identification time, and the corresponding number of deleted Di is marked as the tremor value;

[0091] SS4: After that, the accumulated time group of the T1 group is obtained, and the accumulated time of any period is selected and processed according to the same principle as steps SS1-SS3. The obtained value is marked as the recognized accumulated time, and the corresponding tremor value is marked as the corrected value;

[0092] SS5: Select the next time period and repeat steps SS1-SS4. After all time periods are processed, the identification times and their corresponding tremor values, identification accumulated time and their corresponding correction values ​​of 24 time periods are obtained.

[0093] Step 5: Select the next target and repeat steps 3-5. After all targets are processed, the identification times and corresponding tremor values, identification accumulated time and corresponding correction values ​​of 24 time periods corresponding to each target are obtained.

[0094] The inertial analysis unit is used to store the identification times and the corresponding tremor values, identification cumulative time and the corresponding correction values ​​of each object mark in four time periods in real time;

[0095] The user login unit is used for users to log in to the cloud platform, and after logging in, the user information is transmitted to the processor, and the processor is used to display and analyze the user information. The specific display and analysis methods are as follows:

[0096] SS01: After obtaining user information, all object targets related to the user are automatically obtained;

[0097] SS02: Then get the current time and mark it as login time;

[0098] SS03: The data retrieval unit obtains the identification times and corresponding tremor values, identification cumulative time and corresponding correction values ​​of all target objects at the time of login from the inertial analysis unit;

[0099] SS04: All object labels are marked as Bj, j = 1...m, and the corresponding identification times, tremor values, identification cumulative time and correction values ​​are marked as Rj, Cj, Lj and Oj, j = 1...m; and Bj, Rj, Cj, Lj and Oj are in one-to-one correspondence;

[0100] SS05: Then use the formula to calculate the optional values ​​of all object targets. The specific calculation formula is:

[0101] Kj=0.41*Rj / Cj+0.59*Lj / Oj;

[0102] In the formula, 0.41 and 0.59 are the preset weights;

[0103] SS06: Then sort the object targets in descending order of Kj value, mark the first ranked target as the first displayed target, and the remaining second and third ranked targets as parallel candidates;

[0104] The processor is used to transmit the preferred target and the parallel candidate targets to the self-configuration unit. The self-configuration unit automatically enters the interface of the preferred target first, and simultaneously displays the parallel candidate targets in the corner of the interface to facilitate users to switch quickly.

[0105] The management unit is connected to the processor for inputting all preset values.

[0106] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A VDC resource management system based on big data, characterized in that: include: VDC configuration unit: used to mark all cloud tenants, corresponding projects and managers as tenant information and transmit them to the list temporary storage unit for real-time storage; Inertia acquisition unit: receives the projects and managers transmitted by the tenant association unit, performs preliminary screening and analysis on them, obtains the inertia objects and the projects they are currently responsible for, and marks them as object targets; Inertia analysis unit: used to perform inertia analysis on all inertia objects and target targets, obtain the identification times and corresponding tremor values, identification cumulative time and corresponding correction values ​​of 24 time periods corresponding to each target target, and store them in real time; User login unit: used for users to log in to the cloud platform, and transmit user information to the processor after login. The processor is used to display and analyze the user information. The specific display and analysis methods are as follows: SS01: After obtaining user information, all object targets related to the user are automatically obtained; SS02: Then get the current time and mark it as login time; SS03: The data retrieval unit obtains the identification times and corresponding tremor values, identification cumulative time and corresponding correction values ​​of all target objects at the time of login from the inertial analysis unit; SS04: All object labels are marked as Bj, j = 1...m, and the corresponding identification times, tremor values, identification cumulative time and correction values ​​are marked as Rj, Cj, Lj and Oj, j = 1...m; and Bj, Rj, Cj, Lj and Oj are in one-to-one correspondence; SS05: Then use the formula to calculate the optional values ​​of all object targets. The specific calculation formula is: Kj=0.41*Rj / Cj+0.59*Lj / Oj; In the formula, 0.41 and 0.59 are the preset weights; SS06: Then sort the object targets in descending order of Kj value, mark the first ranked target as the first displayed target, and the remaining second and third ranked targets as parallel candidates; The processor is used to transmit the preferred target and the parallel candidate targets to the self-configuration unit. The self-configuration unit automatically enters the interface of the preferred target first, and simultaneously displays the parallel candidate targets in the corner of the interface to facilitate users to switch quickly.

2. A VDC resource management system based on big data according to claim 1, characterized in that: It also includes a tenant association unit: automatically obtains the projects and managers in the tenant information from the list temporary storage unit, and transmits the projects and managers to the inertial acquisition unit.

3. A VDC resource management system based on big data according to claim 1, characterized in that: The specific method of initial screening analysis is as follows: S1: Get all managers and mark them as the subjects to be scored; S2: Then, the subjects to be scored are preliminarily screened to obtain the subjects to be scored and the projects they are currently responsible for, and marked as the target objects; the specific screening method in step S2 is: Get all the scored objects, automatically get the projects of all the scored objects, automatically get the number of the corresponding projects, remove the scored objects with a number lower than X1, and mark the remaining scored objects as habitually scored objects; the specific value of X1 here is 2; Mark the project that the corresponding object is currently responsible for as the object target.

4. A VDC resource management system based on big data according to claim 1, characterized in that: The specific method of inertia analysis is: Step 1: Get all the objects and their corresponding object labels; Step 2: Select any object and obtain all its object targets; Step 3: Perform operation analysis on the selected object targets of the habitual division in turn, select any object target, perform operation analysis on it, and obtain 24 time period subgroups and time period cumulative time groups for each object target; Step 4: After that, the time period group and time period cumulative time group of each day in the near T1 time of the target are obtained, and the data are reduced to obtain the identification times and the corresponding tremor values, identification cumulative time and the corresponding correction values ​​of the 24 time periods of the corresponding target; Step 5: Select the next target object and repeat steps 3-5. After all targets are processed, the identification times and their corresponding tremor values, identification accumulated time and their corresponding correction values ​​for 24 time periods of each target object are obtained.

5. A VDC resource management system based on big data according to claim 4, characterized in that: The specific method of object analysis in step 3 is: S01: Get the start time and expected period of the object. The start time refers to the start time of the object, and the expected period is the estimated time the object is expected to be used, which is uploaded by the administrator. S02: Then, the via length value is obtained according to the start time, and the via length value is the length of time that has passed from the start to the present; S03: When the long value exceeds T1 time, where T1 is a preset value, generally 30 days; obtain the usage data of the corresponding object mark every day near T1 time. If the long value does not exceed T1 time, obtain the usage data of the object mark from the beginning to the present.

6. A VDC resource management system based on big data according to claim 5, characterized in that: The data acquisition method is: S031: Usage data includes time period group and time period cumulative time group; S032: First, define the time period, divide a day into 24 time periods, mark each hour as a time period, mark it as a time period group, and divide it into the first time period from midnight to one o'clock, and divide it in sequence; S033: Then, the number of times the customary object corresponding to each time period uses the object label is obtained. The single use is defined as if the customary object logs in and calls the object label and then goes offline, and does not log in to the object label again within T2, then the number of uses is automatically increased by one, and the number is marked as time period times. The time period times within 24 time periods constitute a time period time group; S034: The specific duration of use of the object mark in the 24 time periods is obtained, and it is marked as the accumulated time of the time period, and the accumulated time of the 24 time periods is marked as a time period accumulated time group.

7. A VDC resource management system based on big data according to claim 5, characterized in that: The specific method of data reduction in step 4 is: SS1: Select any time period, obtain the time period times of group T1, and mark the time period times as Di, i=1...T1; Di means T1 days forward from the current time, mark that day as the first day, and Di means the time period times of the selected time period of the i-th day; SS2: Then automatically find the mean of Di, mark it as P, and then use the formula to calculate the deviation value W. The specific calculation formula is: SS3: When the W value exceeds X2, data is filtered out. Specifically, the corresponding Di is selected in order from large to small according to |Di-P|. After each Di is selected, it is deleted and the W value is recalculated until the W value does not exceed X2, where X2 is the preset value. The mean value of Di after deletion is marked as the identification time, and the corresponding number of deleted Di is marked as the tremor value; SS4: After that, the accumulated time group of the T1 group is obtained, and the accumulated time of any period is selected and processed according to the same principle as steps SS1-SS3. The obtained value is marked as the recognized accumulated time, and the corresponding tremor value is marked as the corrected value; SS5: Select any next time period and repeat steps SS1-SS4. After all time periods are processed, the identification times and their corresponding tremor values, identification accumulated time and their corresponding correction values ​​of 24 time periods are obtained.

8. The VDC resource management system based on big data according to claim 5 is characterized in that: It also includes a management unit, which is connected to the processor for inputting all preset values.

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