CN110519105B - Bandwidth multiplexing method and system based on peak error degree - Google Patents
Bandwidth multiplexing method and system based on peak error degree Download PDFInfo
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Abstract
The invention provides a bandwidth multiplexing method and system based on peak error degree. The concentration and peak staggering degree of the bandwidth peak value mean value of each user in each period within the statistical days are calculated; matching the peak load error of the users, and distributing the same CDN bandwidth nodes to the users successfully matched; the method of the invention carries out peak staggering matching according to time intervals, realizes bandwidth peak staggering complementation, and makes full use and reuse of bandwidth resources, thereby reducing unit cost and creating economic benefits.
Description
Technical Field
The invention relates to the technical field of data mining, in particular to a bandwidth multiplexing method and system based on peak error degree.
Background
In the cost of the video cloud service, the bandwidth cost is a large part, but actually, most of the current bandwidths are superposed together to perform calculation based on the highest peak value, and are not intelligently allocated, so that the resource utilization rate is not high. The phenomenon that some users regularly have high bandwidth in a certain time period for a long time, but the bandwidth in other time periods is less or even idle exists, but charging and resource allocation are still matched according to the highest peak value, the phenomenon causes resource and cost waste, manual allocation needs to be monitored and calculated at any time, the situation that the user quantity is large is unrealistic, how to better utilize idle bandwidth resources is achieved automatically in a time-saving and labor-saving mode, and the problem that needs to be solved at present is solved.
Disclosure of Invention
The embodiment of the invention aims to provide a bandwidth multiplexing method based on peak error degree, and aims to solve the problems that most of the bandwidth in the prior art is superposed together and calculation is executed on the basis of the highest peak value, intelligent allocation is not performed, and the resource utilization rate is not high.
The embodiment of the invention is realized in such a way that a bandwidth multiplexing method based on the peak error degree comprises the following steps:
calculating the concentration and peak staggering degree of the bandwidth peak value mean value of each user in each time period within the statistical number of days;
matching the peak error degree of the user;
and distributing the same CDN bandwidth nodes to the users successfully matched.
Further, the matching the peak error degree of the user may further include: screening and filtering the users according to the peak staggering degree and the concentration;
further, the matching the peak error degree of the user may further include: and matching the magnitude of the bandwidth peak value average of the user.
Another objective of an embodiment of the present invention is to provide a bandwidth multiplexing system based on peak error degree, which includes:
the concentration and peak error degree calculating device is used for calculating the concentration and peak error degree of the bandwidth peak value mean value of each user in each time period in the statistical days;
the peak error matching module is used for matching the peak error of the user;
and the CDN bandwidth node distribution module is used for distributing the same CDN bandwidth nodes to the users successfully matched.
Furthermore, the system can also comprise a user screening module which is connected with the concentration and peak error degree calculating device and the peak error degree matching module and is used for screening and filtering the user according to the peak error degree and the concentration.
Further, the system may further include a bandwidth peak magnitude matching module, connected to the peak error degree matching module and the CDN bandwidth node allocation module, for matching the magnitude of the bandwidth peak mean value of the user.
The invention has the advantages of
The invention provides a bandwidth multiplexing method and system based on peak error degree. The concentration and peak staggering degree of the average value of the bandwidth peak values of each user in each time period within the statistical number of days are calculated; matching the peak load error of the users, and distributing the same CDN bandwidth nodes to the users successfully matched; the method of the invention carries out peak staggering matching according to time intervals, realizes bandwidth peak staggering complementation, and makes full use and reuse of bandwidth resources, thereby reducing unit cost and creating economic benefits.
Drawings
FIG. 1 is a flow chart of a bandwidth multiplexing method based on peak error according to a preferred embodiment of the present invention;
FIG. 2 is a flowchart of the method of S1 of FIG. 1;
FIG. 3 is a block diagram of a preferred embodiment of the present invention for a bandwidth reuse system based on peak error;
fig. 4 is a block diagram of the concentration ratio and peak error calculation device in fig. 3.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and examples, and for convenience of description, only parts related to the examples of the present invention are shown. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a bandwidth multiplexing method and system based on peak error degree. The concentration and peak staggering degree of the bandwidth peak value mean value of each user in each period within the statistical days are calculated; matching the peak load error of the users, and distributing the same CDN bandwidth nodes to the users successfully matched; the method of the invention carries out peak staggering matching according to time intervals, realizes bandwidth peak staggering complementation, and makes full use and reuse of bandwidth resources, thereby reducing unit cost and creating economic benefits.
Example one
FIG. 1 is a flow chart of a bandwidth multiplexing method based on peak error according to a preferred embodiment of the present invention; the method comprises the following steps:
s1, calculating the concentration and peak staggering degree of the bandwidth peak value mean value of each user in each period in the statistical days;
FIG. 2 is a flowchart of the method of S1 of FIG. 1; the method comprises the following steps:
step A1: calculating the average value of the network bandwidth peak values of each user in each period of the statistical days;
the dividing method of each time interval comprises the following steps: dividing the time of day into n time periods, and recording the time periods as a set T ═ T1，t2，…tn}; n is the total number of time periods; specifically, in the application, n is generally set to 24;
all users are recorded as the set U ═ U1，u2，…um}；
The mean value of the network bandwidth peaks of all users in all periods within the statistical number of days L is recorded as a first data set group:
wherein, G (u)1)、G(u2)、G(um) Respectively represent the 1 st user u12 nd user u2Mth user umNetwork bandwidth peak value mean value data sets in all time periods within the statistical days; m represents the total number of users;
the single-period network bandwidth peak value average value calculation method comprises the following steps:
wherein the content of the first and second substances,
the network bandwidth peak value mean value of the kth user in the ith period is represented; (p)i(uk))1、(pi(uk))2、(pi(uk))LRespectively representing the network bandwidth peak values of the ith day 1, the 2 nd day and the L th day of the ith user; k is more than or equal to 1 and less than or equal to m; i is more than or equal to 1 and less than or equal to n; l represents the number of statistical days, and L is more than or equal to 2;
step A2: calculating the relative frequency of the network bandwidth peak value mean value of each user in each time period;
relative frequency: the ratio of the single-time-period bandwidth peak value average value to the sum of the time-period bandwidth peak value average values;
wherein, F (t)i)(uk) The relative frequency representing the mean value of the network bandwidth peak values of the ith user in the ith time period;
and the relative frequency of the network bandwidth peak value mean value of all the users in each period in the statistical days is recorded as a second data set group:
H(u1)＝{F(t1)(u1)，F(t2)(u1)，…F(tn)(u1)}
H(u2)＝{F(t1)(u2)，F(t2)(u2)，…F(tn)(u2)}……
H(um)＝{F(t1)(um)，F(t2)(um)，…F(tn)(um)}；
wherein, H (u)1)、H(u2)、H(um) Respectively represent the 1 st user u12 nd user u2Mth user umA relative frequency data set of the network bandwidth peak value mean value of each period in the statistical days;
step A3: calculating the mean value and standard deviation of the data in the relative frequency data set of each user;
wherein, mu (u)k) Represents a data set H (u)k) Mean of median data; sigma (u)k) Represents a data set H (u)k) Standard deviation of the median data; h (u)k) Represents the k-th user ukA relative frequency data set of the network bandwidth peak value mean value of each period in the statistical days;
step A4: calculating the concentration and peak staggering degree of the bandwidth peak value mean value of each user;
concentration formula:
peak error formula:
wherein, C (u)k)、S(uk) Respectively representing the concentration and the peak error of the bandwidth peak value mean value of the kth user.
Concentration is an index describing the concentration (i.e., steep or flat) of data distribution, and a positive concentration value means a narrower pattern, larger data, and flat data. The closer to 0, the closer to the positive too distribution. The peak error degree is an index for describing symmetry, and is negative when the data inclines to the right; if skewed to the left, the degree of peak error is positive.
Optionally, step S1 may be followed by the steps of: screening and filtering the users according to the peak staggering degree and the concentration;
the method specifically comprises the following steps: selecting users with retention concentration ratio of [ -1,1] and peak error ratio >0.3, and deleting other users;
s2: matching the peak error degree of the user;
the method specifically comprises the following steps: performing pairwise comparison matching on all users according to a peak-error matching rule, and selecting the best matching user;
misfit matching rule (satisfying both rule 1 and rule 2):
rule 1: peak error degree S (u) of two usersk) And S (u)j) The signs are opposite;
rule 2: select min | S (u)k)+S(uj) Matching users of | is carried out;
wherein, S (u)j) Representing the false peak degree of the bandwidth peak value mean value of the jth user; j is more than or equal to 1 and less than or equal to m;
if there are at least 2 identical min S (u)k)+S(uj) If the concentration ratio is the same (positive or negative), the concentration ratio is closest to min | C (u)k)-C(uj) Matching is performed by the users of l. Wherein C (u)j) Representing the concentration of the bandwidth peak means for the jth user.
Further, step S2 may be followed by: matching the magnitude of the bandwidth peak value average value of the user;
the method specifically comprises the following steps: performing pairwise comparison matching on all users according to the magnitude matching rule of the bandwidth peak value mean value, and selecting the best matching user;
magnitude matching rule of bandwidth peak value mean value:
Wherein G (u)j) Represents the u-th userjJ is more than or equal to 1 and less than or equal to m in the data set of the network bandwidth peak value mean value in each period within the statistical days; max (variable) represents taking the maximum value of the variable; average (variable) represents averaging the variables.
S3: distributing the same CDN bandwidth nodes to the users successfully matched;
the method for allocating bandwidth nodes adopts conventional methods known in the art, and will not be described herein.
Example two
FIG. 3 is a block diagram of a preferred embodiment of the present invention for a bandwidth reuse system based on peak error; the system comprises:
the concentration and peak error degree calculating device is used for calculating the concentration and peak error degree of the bandwidth peak value mean value of each user in each time period in the statistical days;
the peak error matching module is used for matching the peak error of the user;
the CDN bandwidth node distribution module is used for distributing the same CDN bandwidth nodes to the users successfully matched;
further, fig. 4 is a structural diagram of the concentration and peak error calculation apparatus in fig. 3. The concentration and peak error calculation device includes:
the bandwidth peak-to-average calculation module is used for calculating the network bandwidth peak average of each user in each period within the statistical days;
the dividing method of each time interval comprises the following steps: the time of day, etcIs divided into n periods, denoted as set T ═ T1，t2，…tn}; n is the total number of time periods;
all users are recorded as the set U ═ U1，u2，…um}；
The mean value of the network bandwidth peaks of all users in all periods within the statistical number of days L is recorded as a first data set group:
wherein, G (u)1)、G(u2)、G(um) Respectively represent the 1 st user u12 nd user u2Mth user umNetwork bandwidth peak value mean value data sets in all time periods within the statistical days; m represents the total number of users;
the single-period network bandwidth peak value average value calculation method comprises the following steps:
wherein the content of the first and second substances,
the network bandwidth peak value mean value of the kth user in the ith period is represented; (p)i(uk))1、(pi(uk))2、(pi(uk))LRespectively representing the network bandwidth peak values of the ith day 1, the 2 nd day and the L th day of the ith user; k is more than or equal to 1 and less than or equal to m; i is more than or equal to 1 and less than or equal to n; l represents the number of statistical days, and L is more than or equal to 2;
the relative frequency calculation module is used for calculating the relative frequency of the network bandwidth peak value mean value of each user in each time period;
relative frequency: the ratio of the single-time-period bandwidth peak value mean value to the sum of the bandwidth peak value mean values of all the time periods;
wherein, F (t)i)(uk) The relative frequency representing the mean value of the network bandwidth peak values of the ith user in the ith time period;
and the relative frequency of the network bandwidth peak value mean value of all the users in each period in the statistical days is recorded as a second data set group:
H(u1)＝{F(t1)(u1)，F(t2)(u1)，…F(tn)(u1)}
H(u2)＝{F(t1)(u2)，F(t2)(u2)，…F(tn)(u2)}……
H(um)＝{F(t1)(um)，F(t2)(um)，…F(tn)(um)}。
wherein, H (u)1)、H(u2)、H(um) Respectively represent the 1 st user u12 nd user u2Mth user umA relative frequency data set of the network bandwidth peak value mean value of each period in the statistical days;
the relative frequency average and standard deviation calculation module is used for calculating the average and standard deviation of the data in the relative frequency data set of each user;
wherein, mu (u)k) Represents a data set H (u)k) Mean of median data; sigma (u)k) Represents a data set H (u)k) Standard deviation of the median data; h (u)k) Represents the k-th user ukA relative frequency data set of the network bandwidth peak value mean value of each period in the statistical days;
the concentration and peak error calculation module is used for calculating the concentration and peak error of the bandwidth peak value mean value of each user;
concentration formula:
peak error formula:
wherein, C (u)k)、S(uk) Respectively representing the concentration and the peak error of the bandwidth peak value mean value of the kth user.
Further, matching the peak error degree of the user;
the method specifically comprises the following steps: performing pairwise comparison matching on all users according to a peak-error matching rule, and selecting the best matching user;
misfit matching rule (satisfying both rule 1 and rule 2):
rule 1: peak error degree S (u) of two usersk) And S (u)j) The signs are opposite;
rule 2: select min | S (u)k)+S(uj) Matching users of | is carried out;
wherein, S (u)j) Representing the false peak degree of the bandwidth peak value mean value of the jth user; j is more than or equal to 1 and less than or equal to m;
if there are at least 2 identical min S (u)k)+S(uj) If the concentration ratio is the same (positive or negative), the concentration ratio is closest to min | C (u)k)-C(uj) Matching is performed by the users of l. Wherein C (u)j) Representing the concentration of the bandwidth peak means for the jth user.
Further, the system may further include a bandwidth peak magnitude matching module (not shown in the figure), connected to the peak error degree matching module and the CDN bandwidth node allocation module, and configured to match magnitudes of the bandwidth peak mean values of the users;
the method specifically comprises the following steps: performing pairwise comparison matching on all users according to the magnitude matching rule of the bandwidth peak value mean value, and selecting the best matching user;
magnitude matching rule of bandwidth peak value mean value:
Wherein G (u)j) Represents the u-th userjJ is more than or equal to 1 and less than or equal to m in the data set of the network bandwidth peak value mean value in each period within the statistical days; max (variable) represents taking the maximum value of the variable; average (variable) represents averaging the variables.
Further, the system may further include a user screening module (not shown in the figure), connected to the concentration and peak shift degree calculating device and the peak shift degree matching module, for screening and filtering the user according to the peak shift degree and the concentration;
the method specifically comprises the following steps: and selecting users with retention concentration ratio of (-1, 1) and peak error ratio >0.3, and deleting other users.
It will be understood by those skilled in the art that all or part of the steps in the method according to the above embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, such as ROM, RAM, magnetic disk, optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (12)
1. A bandwidth multiplexing method based on peak error degree is characterized by comprising the following steps:
the method for calculating the concentration and peak staggering degree of the bandwidth peak value mean value of each user in each period of the statistical days comprises the following steps:
step A1: calculating the average value of the network bandwidth peak values of each user in each period of the statistical days;
the dividing method of each time interval comprises the following steps: dividing the time of day into n time periods, and recording the time periods as a set T ═ T1，t2，…tn}; n is the total number of time periods;
all users are recorded as the set U ═ U1，u2，…um}；
The mean value of the network bandwidth peaks of all users in all periods within the statistical number of days L is recorded as a first data set group:
wherein, G (u)1)、G(u2)、G(um) Respectively represent the 1 st user u12 nd user u2Mth user umNetwork bandwidth peak value mean value data sets in all time periods within the statistical days; m represents the total number of users;
the single-period network bandwidth peak value average value calculation method comprises the following steps:
wherein the content of the first and second substances,
the network bandwidth peak value mean value of the kth user in the ith period is represented; (p)i(uk))1、(pi(uk))2、(pi(uk))LRespectively representing the network bandwidth peak values of the ith day 1, the 2 nd day and the L th day of the ith user; k is more than or equal to 1 and less than or equal to m;i is more than or equal to 1 and less than or equal to n; l represents the number of statistical days, and L is more than or equal to 2;
step A2: calculating the relative frequency of the network bandwidth peak value mean value of each user in each time period;
relative frequency: the ratio of the single-time-period bandwidth peak value mean value to the sum of the bandwidth peak value mean values of all the time periods;
wherein, F (t)i)(uk) The relative frequency representing the mean value of the network bandwidth peak values of the ith user in the ith time period;
and the relative frequency of the network bandwidth peak value mean value of all the users in each period in the statistical days is recorded as a second data set group:
wherein, H (u)1)、H(u2)、H(um) Respectively represent the 1 st user u12 nd user u2Mth user umA relative frequency data set of the network bandwidth peak value mean value of each period in the statistical days;
step A3: calculating the mean value and standard deviation of the data in the relative frequency data set of each user;
wherein, mu (u)k) Represents a data set H (u)k) Mean of median data; sigma (u)k) Represents a data set H (u)k) Standard deviation of the median data; h (u)k) Represents the k-th user ukOn statistical dayA relative frequency data set of the network bandwidth peak value mean value of each time period in the number;
step A4: calculating the concentration and peak staggering degree of the bandwidth peak value mean value of each user;
concentration formula:
peak error formula:
wherein, C (u)k)、S(uk) Respectively representing the concentration and the peak error of the bandwidth peak value mean value of the kth user;
matching the peak error degree of the user;
and distributing the same CDN bandwidth nodes to the users successfully matched.
2. The method for multiplexing bandwidth based on peak error degree of claim 1, wherein the step of matching the peak error degree of the user further comprises: and screening and filtering the users according to the peak staggering degree and the concentration.
3. The method for multiplexing bandwidth based on peak error degree of claim 1, wherein the step of matching the peak error degree of the user further comprises: and matching the magnitude of the bandwidth peak value average of the user.
4. The method for multiplexing bandwidth based on peak error degree according to claim 1, wherein the matching of the peak error degree of the user specifically comprises: performing pairwise comparison matching on all users according to a peak-error matching rule, and selecting the best matching user;
matching rules of peak error degree: at the same time satisfy
Rule 1: peak error degree S (u) of two usersk) And S (u)j) The signs are opposite;
rule 2: select min | S (u)k)+S(uj) Matching users of | is carried out;
wherein, S (u)j) Representing the false peak degree of the bandwidth peak value mean value of the jth user; j is more than or equal to 1 and less than or equal to m;
if there are at least 2 identical min S (u)k)+S(uj) If the value of | is the same, the concentration is closest to min | C (u) under the premise of selecting the same concentration signk)-C(uj) Matching users of | is carried out; wherein C (u)j) Representing the concentration of the bandwidth peak means for the jth user.
5. The method for multiplexing bandwidth according to claim 2, wherein the filtering for the user according to the peak error and the peak concentration specifically comprises:
and selecting users with retention concentration ratio of (-1, 1) and peak error ratio >0.3, and deleting other users.
6. The method for multiplexing bandwidths according to claim 3 wherein the matching of the magnitudes of the bandwidth peak-to-average values of the users is specifically:
performing pairwise comparison matching on all users according to the magnitude matching rule of the bandwidth peak value mean value, and selecting the best matching user;
magnitude matching rule of bandwidth peak value mean value:
Wherein G (u)j) Represents the u-th userjJ is more than or equal to 1 and less than or equal to m in the data set of the network bandwidth peak value mean value in each period within the statistical days; max (variable) represents taking the maximum value of the variable; average (variable) represents averaging the variables.
7. A system for bandwidth reuse based on degrees of peak error, the system comprising:
concentration and peak error calculation device for calculating the concentration and peak error of the bandwidth peak mean value of each user in each period of statistical days, comprising:
the bandwidth peak-to-average calculation module is used for calculating the network bandwidth peak average of each user in each period within the statistical days;
the dividing method of each time interval comprises the following steps: dividing the time of day into n time periods, and recording the time periods as a set T ═ T1，t2，…tn}; n is the total number of time periods;
all users are recorded as the set U ═ U1，u2，…um}；
The average value of the network bandwidth peak values of all users in all time periods within the statistical days L is recorded as a first data set group:
wherein, G (u)1)、G(u2)、G(um) Respectively represent the 1 st user u12 nd user u2Mth user umNetwork bandwidth peak value mean value data sets in all time periods within the statistical days; m represents the total number of users;
the single-period network bandwidth peak value average value calculation method comprises the following steps:
wherein the content of the first and second substances,
the network bandwidth peak value mean value of the kth user in the ith period is represented; (p)i(uk))1、(pi(uk))2、(pi(uk))LRespectively representing the network bandwidth peak values of the ith day 1, the 2 nd day and the L th day of the ith user; k is more than or equal to 1 and less than or equal to m;i is more than or equal to 1 and less than or equal to n; l represents the number of statistical days, and L is more than or equal to 2;
the relative frequency calculation module is used for calculating the relative frequency of the network bandwidth peak value mean value of each user in each time period;
relative frequency: the ratio of the single-time-period bandwidth peak value mean value to the sum of the bandwidth peak value mean values of all the time periods;
wherein, F (t)i)(uk) The relative frequency representing the mean value of the network bandwidth peak values of the ith user in the ith time period;
and the relative frequency of the network bandwidth peak value mean value of all the users in each period in the statistical days is recorded as a second data set group:
wherein, H (u)1)、H(u2)、H(um) Respectively represent the 1 st user u12 nd user u2Mth user umA relative frequency data set of the network bandwidth peak value mean value of each period in the statistical days;
the relative frequency average and standard deviation calculation module is used for calculating the average and standard deviation of the data in the relative frequency data set of each user;
wherein, mu (u)k) Represents a data set H (u)k) Mean of median data; sigma (u)k) Represents a data set H (u)k) Standard deviation of medium data；H(uk) Represents the k-th user ukA relative frequency data set of the network bandwidth peak value mean value of each period in the statistical days;
the concentration and peak error calculation module is used for calculating the concentration and peak error of the bandwidth peak value mean value of each user;
concentration formula:
peak error formula:
wherein, C (u)k)、S(uk) Respectively representing the concentration and the peak error of the bandwidth peak value mean value of the kth user;
the peak error matching module is used for matching the peak error of the user;
and the CDN bandwidth node distribution module is used for distributing the same CDN bandwidth nodes to the users successfully matched.
8. The peak-error-based bandwidth multiplexing system according to claim 7, wherein the system further comprises a user filtering module, connected to the concentration and peak-error-degree calculating device and the peak-error-degree matching module, for filtering and filtering users according to the peak-error-degree and the concentration.
9. The Peak-miss-based Bandwidth multiplexing system of claim 7, wherein the system further comprises a Bandwidth Peak magnitude matching module connected to the Peak-miss-matching module and the CDN bandwidth node Allocation module for matching magnitudes of the Bandwidth Peak means of the users.
10. The system according to claim 7, wherein the matching of the peak-to-peak error of the user specifically comprises: performing pairwise comparison matching on all users according to a peak-error matching rule, and selecting the best matching user;
the matching rule of the degree of peak error satisfies rule 1 and rule 2 at the same time:
rule 1: peak error degree S (u) of two usersk) And S (u)j) The signs are opposite;
rule 2: select min | S (u)k)+S(uj) Matching users of | is carried out;
wherein, S (u)j) Representing the false peak degree of the bandwidth peak value mean value of the jth user; j is more than or equal to 1 and less than or equal to m;
if there are at least 2 identical min S (u)k)+S(uj) If the concentration is the same, the concentration is the closest to min | C (u) under the premise of selecting the concentration symbolsk)-C(uj) Matching users of | is carried out; wherein C (u)j) Representing the concentration of the bandwidth peak means for the jth user.
11. The system according to claim 8, wherein the filtering for users according to the peak-to-peak ratio and the peak-to-peak ratio specifically comprises:
and selecting users with retention concentration ratio of (-1, 1) and peak error ratio >0.3, and deleting other users.
12. The system according to claim 9, wherein the matching of the magnitudes of the bandwidth peak-to-average values of the users is specifically:
performing pairwise comparison matching on all users according to the magnitude matching rule of the bandwidth peak value mean value, and selecting the best matching user;
magnitude matching rule of bandwidth peak value mean value:
Wherein G (u)j) Represents the u-th userjJ is more than or equal to 1 and less than or equal to m in the data set of the network bandwidth peak value mean value in each period within the statistical days; max (variable) represents taking the maximum value of the variable; average (variable) represents averaging the variables.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1756143A (en) * | 2004-09-27 | 2006-04-05 | 华为技术有限公司 | Time period multiplex system and method for transmission network bandwidth |
| EP1947810A1 (en) * | 2005-10-28 | 2008-07-23 | Shanghai Jiao Tong University | A method and equipment for admitting and controlling the integration service model |
| CN107124375A (en) * | 2017-03-27 | 2017-09-01 | 网宿科技股份有限公司 | Flood peak staggered regulation method, system and the server of CDN bandwidth resources |
| CN108667658A (en) * | 2018-04-28 | 2018-10-16 | 厦门白山耘科技有限公司 | A kind of bandwidth reuse method and device |
-
2019
- 2019-09-18 CN CN201910881361.4A patent/CN110519105B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1756143A (en) * | 2004-09-27 | 2006-04-05 | 华为技术有限公司 | Time period multiplex system and method for transmission network bandwidth |
| EP1947810A1 (en) * | 2005-10-28 | 2008-07-23 | Shanghai Jiao Tong University | A method and equipment for admitting and controlling the integration service model |
| CN107124375A (en) * | 2017-03-27 | 2017-09-01 | 网宿科技股份有限公司 | Flood peak staggered regulation method, system and the server of CDN bandwidth resources |
| CN108667658A (en) * | 2018-04-28 | 2018-10-16 | 厦门白山耘科技有限公司 | A kind of bandwidth reuse method and device |
Non-Patent Citations (1)
| Title |
|---|
| LTE回传PTN场景下基于OLP技术的光通道保护;黄志军;《移动通信》;20150228;全文 * |
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