CN114448818A - CDN node data resource analysis method and computer equipment - Google Patents
CDN node data resource analysis method and computer equipment Download PDFInfo
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Abstract
The invention discloses a CDN node data resource analysis method and computer equipment, wherein the method comprises the following steps: acquiring the flow capacity value of CDN node data distributed in each group of IP networks and the predicted required flow of a target customer in a target area; collecting real-time flow of a target client in a target area based on each group of IP networks and peak flow in preset time; calculating the predicted redundant flow, the actual redundant flow and the peak value redundant flow of the target customer in the target area based on each group of IP networks, and generating a CDN data distribution table of the target customer in the target area; and analyzing whether the target client has the risk of exceeding the standard of the flow use in the target area based on each group of IP networks. The method and the system can acquire the data resource operation condition of the CDN node in real time and accurately position the CDN node data with the abnormality so as to quickly capture the root of the CDN node data with the abnormality, and can quickly adjust and control the global resource in a targeted manner based on the problems generated when the CDN node distributes the data resource.
Description
Technical Field
The invention relates to the technical field of CDN (content Delivery network) node data resource service, in particular to an analysis method of CDN node data resources and computer equipment.
Background
CDN (content delivery network), namely a content delivery network, forms a layer of intelligent virtual network on the basis of the Internet by placing node servers at each position of the network, and can redirect the request of a user to a service node closest to the user according to comprehensive information such as network flow, connection of each node, load condition, distance to the user, response time and the like in real time, so that the user can obtain required content nearby, the crowded condition of the Internet network is solved, and the response speed of the user for accessing a website is improved.
At present, the number of CDN nodes and clients is increasing, which leads to an increase in the overall operation complexity of CDN node data resources. Moreover, when a global CDN node is operated and global customer coverage data is scheduled, it is difficult to accurately locate the cause of a problem occurring when the CDN node delivers data resources and how to adjust the data resources covered in specific areas is difficult to achieve due to the huge number of nodes and a large amount of customer coverage data.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the problems in the prior art that it is difficult to accurately locate the cause of the problem when the CDN node delivers the data resource and how to adjust the data resource covered by specific areas, thereby providing an analysis method of the data resource of the CDN node and a computer device.
According to a first aspect, the present invention provides a method for analyzing CDN node data resources, including the following steps:
acquiring the flow capacity value of CDN node data distributed in each group of IP networks and the predicted required flow of a target customer in a target area;
collecting real-time flow of a target client in a target area based on each group of IP networks and peak flow in preset time;
calculating the predicted redundant flow, the actual redundant flow and the peak redundant flow of the target customer in the target area based on the IP networks according to the flow capacity value, the predicted required flow of the target customer in the target area, the real-time flow and the peak flow, and generating a CDN data distribution table of the target customer in the target area;
and analyzing whether the target customer has a traffic use overproof risk in the target area based on each group of IP networks according to each data recorded by the CDN data distribution table.
In one embodiment, the peak flow rate during the preset time includes: the peak flow rate of the flow meter is measured by the flow meter, the corresponding first peak flow rate in a first time and the corresponding second peak flow rate in a second time, and the time of the first time adjacent to the current time is less than the time of the second time adjacent to the current time.
In one embodiment, calculating the predicted redundant traffic, the actual redundant traffic and the peak redundant traffic of the target client in the target area based on the sets of IP networks according to the traffic capacity value, the predicted demand traffic of the target client in the target area, and the real-time traffic and the peak traffic is performed by the following formulas:
Yapij＝Za-[Tpi/mpi+Tpj/npj]；
wherein, YapijPredicted redundant traffic based on group a IP network in target area p for target clients i, j, ZaTraffic capacity value, T, distributed in group a IP network for CDN nodespiPredicted demand flow, m, for target customer i in target area ppiNumber of client coverage based on group a IP network in target area p for target client i, TpjPredicted demand flow for target customer j in target area p, npjThe coverage number based on the group a IP network in the target area p for the target client j;
Sapij＝Za-[Wpi/mpi+Wpj/npj]；
wherein S isapijActual redundant traffic based on group a IP network in target area p for target clients i, j, ZaTraffic capacity values distributed over group a IP networks for CDN nodes, WpiReal-time traffic, m, for target client i in target area ppiNumber of client overlays, W, based on group a IP network in target area p for target client ipjReal-time traffic for target customer j in target area p, npjThe coverage number based on the group a IP network in the target area p for the target client j;
Fapij＝Za-[Epi/mpi+Epj/npj]；
wherein, FapijPeak redundant traffic, Z, based on group a IP network in target area p for target clients i, jaDistributed in a-group IP network for CDN nodeFlow capacity value of collateral EpiFor the second peak flow, m, corresponding to the target client i in the second time of the target area ppiNumber of client coverage based on group a IP network in target area p for target client i, EpjA second peak flow corresponding to the target client j in the second time of the target area p, npjThe coverage number based on the group a IP network in the target area p for the target client j;
and a is a positive integer.
In one embodiment, generating a CDN data distribution table of a target customer in a target area according to each piece of data recorded by the CDN data distribution table includes:
and generating the CDN data distribution table according to the flow capacity value of the CDN nodes distributed in each group of IP networks, the real-time flow of the target customer in the target area based on each group of IP networks and the peak flow in preset time according to the coverage quantity of the target customer and the target customer based on each group of IP networks and the name of the target area.
In an embodiment, analyzing whether a target customer has a risk of exceeding traffic usage in a target area based on each group of IP networks according to each data recorded by the CDN node data distribution table includes:
calculating a first difference between actual redundant traffic of the target client based on the sets of IP networks in a target area and expected redundant traffic of the target client based on the sets of IP networks in the target area;
calculating a first ratio of the first difference to an expected demand flow of the target customer in a target area;
and when the first ratio is a positive number, the target client has the risk of exceeding the standard of the traffic use in the target area based on the IP networks of all the groups.
In an embodiment, analyzing whether a target customer has a risk of exceeding a standard in traffic usage in a target area based on each group of IP networks according to each data recorded by the CDN node data distribution table further includes:
selecting a maximum flow value between the real-time flow of the target client in the target area based on the IP networks and the peak flow within the preset time;
calculating a second difference between the maximum flow value and an expected demand flow of the target customer in a target area;
calculating a percentage between the second difference and an expected demand flow of the target customer in a target area;
and when the percentage exceeds a preset proportion, the target client has the risk of exceeding the standard of the flow use in the target area based on each group of IP networks.
In an embodiment, analyzing whether a target customer has a risk of exceeding a standard in traffic usage in a target area based on each group of IP networks according to each data recorded by the CDN node data distribution table further includes:
calculating whether a second ratio between the real-time flow of the target client in the target area based on the IP networks and the flow energy value is smaller than a preset value or not;
and when the second ratio is smaller than a preset value, the target client has the risk of exceeding the traffic use standard in the target area based on each group of IP networks.
In an embodiment, analyzing whether a target customer has a risk of exceeding a standard in traffic usage in a target area based on each group of IP networks according to each data recorded by the CDN node data distribution table further includes:
checking whether each data recorded by the CDN node data distribution table has a negative number in real time;
and if the negative number exists, the target client has the risk of exceeding the standard of the flow use in the target area based on each group of IP networks.
In an embodiment, the method for analyzing CDN node data resources further includes:
and when the target client has the risk of exceeding the standard of the flow use in the target area based on the IP networks of all the groups, early warning prompt is carried out.
According to a second aspect, an embodiment of the present invention further provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to cause the computer to execute the method for analyzing CDN node data resources according to the first aspect or any implementation manner of the first aspect.
According to a third aspect, an embodiment of the present invention further provides a computer device, including: the CDN node data resource analysis method includes a memory and a processor, where the memory and the processor are communicatively connected to each other, where the memory stores computer instructions, and the processor executes the computer instructions to execute the CDN node data resource analysis method described in the first aspect or any embodiment of the first aspect.
The technical scheme of the invention has the following advantages:
the invention discloses a CDN node data resource analysis method and computer equipment, wherein the method comprises the following steps: acquiring the flow capacity value of CDN node data distributed in each group of IP networks and the predicted required flow of a target customer in a target area; collecting real-time flow of a target client in a target area based on each group of IP networks and peak flow in preset time; calculating the predicted redundant flow, the actual redundant flow and the peak value redundant flow of the target customer in the target area based on each group of IP networks according to the flow capacity value, the predicted required flow, the real-time flow and the peak value flow of the target customer in the target area, and generating a CDN data distribution table of the target customer in the target area; and analyzing whether the target customer has a traffic use standard exceeding risk in a target area based on each group of IP networks according to each data recorded by the CDN data distribution table. The method and the system can acquire the data resource operation condition of the CDN node in real time and accurately position the abnormal CDN data, can quickly capture the root of the abnormal CDN data, and can quickly perform targeted adjustment and control on the global resources based on the problems generated when the CDN node distributes the data resources. Moreover, each CDN node data does not need to be manually inspected, the efficiency of analyzing the CDN node data and the efficiency of processing abnormal data of the CDN nodes are improved, and the using requirements of customers on the CDN node data can be finally met.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a specific example of an analysis method for CDN node data resources in the embodiment of the present invention;
fig. 2 is a flowchart of another specific example of the analysis method for CDN node data resources in the embodiment of the present invention;
fig. 3 is a flowchart of another specific example of an analysis method for CDN node data resources in the embodiment of the present invention;
fig. 4 is a flowchart of another specific example of the analysis method for CDN node data resources in the embodiment of the present invention;
fig. 5 is a flowchart of another specific example of the analysis method for CDN node data resources in the embodiment of the present invention;
fig. 6 is a flowchart of another specific example of the analysis method for CDN node data resources in the embodiment of the present invention;
fig. 7 is a block diagram of an analysis apparatus for CDN node data resources according to an embodiment of the present invention;
fig. 8 is a hardware diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The embodiment of the invention provides an analysis method of CDN node data resources, which is applied to the technical field of CDN node data resource service and comprises the following steps as shown in FIG. 1:
step S11: and acquiring the traffic capacity value of CDN node data distributed in each group of IP networks and the expected demand traffic of target customers in the target area.
The CDN nodes are servers used for caching data, after a website joins CDN service, multiple CDN nodes are involved, when a user accesses the website, a request points to the CDN node closest to the user, so that the access speed is improved, and the closer the CDN node is to the user, the faster the response speed is. The traffic capacity value of the CDN node distributed in each group of IP networks is a broadband traffic value that broadband data may cover or be distributed on each group of IP networks. For example: the target client (map) is test1.cd n. cn, the IP network traffic capability value distributed in the a group is 20M, and a can represent 11 groups, 12 groups, 13 groups and the like.
In an implementation manner, the traffic capacity value of the CDN node data distributed in any group of IP networks may be determined according to the machine room outlet of the CDN node and the number of CDN nodes serving a customer.
For example: the IP network traffic capacity values distributed in the 11 groups, the 12 groups, the 13 groups and the 14 groups are all 20M.
The predicted demand traffic of the target customer in the target area is the traffic that the target customer plans to meet the distribution demand soon. For example: the flow rate required by the target client Test1 according to the current business demand plan in the target area d is 10G, and the flow rate required by the target client Test2 according to the current business demand plan in the target area d is 8G. The target area may be provinces or cities or villages or streets or any marked area on a map.
Step S12: and acquiring real-time flow of the target client in the target area based on each group of IP networks and peak flow in preset time.
The peak flow rate in the preset time includes: the peak flow rate of the flow meter is measured by the flow meter, the corresponding first peak flow rate in the first time and the corresponding second peak flow rate in the second time, and the time of the first time adjacent to the current time is less than the time of the second time adjacent to the current time. For example: the first time may be yesterday next to the current day and the second time may be within 7 days next to the current day. As shown in table 1 below.
TABLE 1
Step S13: and calculating the predicted redundant flow, the actual redundant flow and the peak value redundant flow of the target customer in the target area based on each group of IP networks according to the flow capacity value, the predicted required flow, the real-time flow and the peak value flow of the target customer in the target area, and generating a CDN node data distribution table of the target customer in the target area.
The flow capacity value, the expected demand flow of the target client in the target area, the real-time flow and the peak flow are counted as shown in the following table 2.
TABLE 2
In one embodiment, the step S13 is executed by the following equations (1) - (3) according to the traffic capacity value, the predicted demand traffic and the real-time traffic and the peak traffic of the target client in the target area, and calculating the predicted redundant traffic, the actual redundant traffic and the peak redundant traffic of the target client based on each set of IP networks in the target area:
Yapij＝Za-[Tpi/mpi+Tpj/npj]； (1)
wherein, YapijPredicted redundant traffic based on group a IP network in target area p for target clients i, j, ZaTraffic capacity value, T, distributed in group a IP network for CDN nodespiPredicted demand flow, m, for target customer i in target area ppiNumber of client coverage based on group a IP network in target area p for target client i, TpjPredicted demand traffic for target customer j in target area p, npjThe coverage number based on the group a IP network in the target area p for the target client j;
Sapij＝Za-[Wpi/mpi+Wpj/npj]； (2)
wherein S isapijActual redundant traffic based on group a IP network in target area p for target clients i, j, ZaTraffic capacity values distributed over group a IP networks for CDN nodes, WpiReal-time traffic, m, for target client i in target area ppiNumber of client overlays, W, based on group a IP network in target area p for target client ipjIn target area p for target client jReal time flow, npjThe coverage number based on the group a IP network in the target area p for the target client j;
Fapij＝Za-[Epi/mpi+Epj/npj]； (3)
wherein, FapijPeak redundant traffic, Z, based on group a IP network in target area p for target clients i, jaTraffic capacity values distributed in group a IP network for CDN nodes, EpiFor the second peak flow, m, corresponding to the target client i in the second time of the target area ppiNumber of client coverage based on group a IP network in target area p for target client i, EpjA second peak flow, n, corresponding to the target client j in the second time of the target area ppjThe coverage number based on the group a IP network in the target area p for the target client j;
a is a positive integer. The "a" may represent the following 11, 12, and 13 groups.
Calculating Y by the above equations (1), (2) and (3)apijPredicted redundant traffic based on group a IP network in target area p for target clients i, j, SapijActual redundant traffic based on group a IP network in target area p for target clients i, j, FapijPeak redundant traffic based on group a IP networks at the target area p for the target clients i, j is shown in table 3 below.
TABLE 3
In one embodiment, generating a CDN node data distribution table of a target customer in a target area according to each data recorded in the CDN node data distribution table includes:
and generating a CDN node data distribution table of the CDN data distribution table according to the flow capacity value of the CDN node distributed in each group of IP networks, the real-time flow of the target customer in the target area based on each group of IP networks and the peak flow in preset time according to the coverage quantity of the target customer and the target customer based on each group of IP networks and the name of the target area. Specifically, as shown in table 4 below.
TABLE 4
In table 4 above:
the predicted distributed traffic of each group of IP networks in the Test1 is Test1 predicted required traffic/the coverage number of the IP networks distributed on the Test 1;
the predicted distribution flow of each group of IP networks of the Test2 is Test2 predicted demand flow/the coverage number of the IP networks distributed on the Test 2;
the real-time distributed traffic of each group of IP networks of the Test1 is Test1 real-time coverage traffic/the coverage number of the IP networks distributed on the Test 1;
the real-time distributed traffic of each group of IP networks of the Test2 is Test2 real-time coverage traffic/the coverage number of the IP networks distributed on the Test 2;
also recorded in table 4 are yesterday peak redundancy flows for each set of IP networks.
Step S14: and analyzing whether the target customer has a traffic use standard exceeding risk in a target area based on each group of IP networks according to each data recorded by the CDN node data distribution table.
In an embodiment, as shown in fig. 2, the step S14, analyzing whether the target customer has a risk of exceeding traffic usage standards in the target area based on each group of IP networks according to each data recorded in the CDN node data distribution table, includes:
step S21: a first difference between actual redundant traffic of the target client based on the sets of IP networks in the target area and expected redundant traffic of the target client based on the sets of IP networks in the target area is calculated.
For example: target client Test1 actual redundant traffic S based on 11 groups of IP networks in the target area, guangdong provinceapij＝Za-[Wpi/mpi+Wpj/npj]＝20-[8/2+4/2]14G, target customer Test1 predicts redundant traffic Y based on 11 sets of IP networks in the target area, guangdong provinceapij＝Za-[Tpi/mpi+Tpj/npj]＝20-[10/2+8/2]And 14-11 ═ 3G.
Step S22: a first ratio of the first difference to an expected demand flow for the target customer in the target area is calculated.
The first difference is calculated to be 3G through step S21, and the predicted required flow rate of Test1 in the target area, guangdong province, is 10G, and 3/10 is 0.3.
Step S23: when the first ratio is a positive number, the target client has the risk of exceeding the standard of the traffic use in the target area based on each group of IP networks.
The ratio calculated in the step S22 is 0.3, where 0.3 > 0, which indicates that the target client Test1 has a risk of exceeding the traffic usage standard in the target area, Guangdong province, based on 11 IP networks.
On the contrary, when the first ratio is a non-negative number, the target customer does not have the risk of exceeding the standard of traffic use in the target area based on each group of IP networks, that is, the data traffic distribution of the current CDN nodes is in a safe state.
In another embodiment, as shown in fig. 3, in step S14, analyzing whether the target customer has a risk of exceeding traffic usage standards in the target area based on each group of IP networks according to each data recorded in the CDN node data distribution table, further includes:
step 31: and selecting a maximum flow value between the real-time flow of the target client in the target area based on each group of IP networks and the peak flow within the preset time.
For example: the real-time traffic of the target client Test2 based on the 11 groups of IP networks in Guangdong province of the target area is 4G, the peak traffic of the target client Test2 based on the 11 groups of IP networks in 7 days in Guangdong province of the target area is 6G, and the maximum traffic value of 6G is selected between 4G and 6G.
Step 32: a second difference between the maximum flow value and the expected demand flow for the target customer in the target area is calculated.
The maximum flow value determined in step S31 is 6G, the predicted required flow of the target customer Test2 in Guangdong province in the target area is 8G, and the second difference is 6-8 to-2G;
step S33: a percentage is calculated between the second difference and the expected demand flow of the target customer in the target area.
The second difference calculated through step S32 is-2G, and the predicted demand flow of the target customer Test2 in the target zone, guangdong province, is 8G, (-2/8 x 100%) -25%.
Step 34: and when the percentage exceeds the preset proportion, the target client has the risk of exceeding the standard of the flow use in the target area based on each group of IP networks.
On the contrary, when the percentage is smaller than or equal to the preset ratio, the target client does not have the risk of exceeding the traffic usage standard in the target area based on each group of IP networks, that is, the data traffic distribution of the current CDN nodes is in a safe state.
For example: the percentage between the second difference calculated in the above step S32 and the expected demanded traffic of the target client in the target area is-25%, the preset ratio may be 5%, and-25% < 5%, which indicates that the target client Test2 has no risk of exceeding the traffic usage standard based on 11 IP networks in the target area, Guangdong province.
In another embodiment, as shown in fig. 4, in step S34, analyzing whether the target customer has a risk of exceeding traffic usage standards in the target area based on each group of IP networks according to each data recorded in the CDN node data distribution table, further includes:
step S41: and calculating whether a second ratio between the real-time flow and the flow energy value of the target client in the target area based on each group of IP networks is smaller than a preset value.
For example: the real-time traffic of the target client Test2 based on 13 groups of IP networks in the target area, guangdong province, is 4G, the traffic capacity value of CDN node data distributed in each group of IP networks is 20G, 4/20 is 0.2, and the preset value is 0.5.
Step S42: and when the second ratio is smaller than the preset value, the target client has the risk of exceeding the standard of the flow use in the target area based on each group of IP networks.
The second ratio is 0.2, the preset value of 0 is 0.5, and 0.2 is less than 0.5, which indicates that the target client Test2 has a risk of exceeding the traffic usage standard in the target area, Guangdong province, based on 13 groups of IP networks through calculation in the step S41.
On the contrary, when the ratio is greater than or equal to the preset value, the target client does not have the risk of exceeding the traffic use standard in the target area based on each group of IP networks, that is, the data traffic distribution of the current CDN nodes is in a safe state.
In another embodiment, as shown in fig. 5, in step S14, analyzing whether the target customer has a risk of exceeding traffic usage standards in the target area based on each group of IP networks according to each data recorded in the CDN node data distribution table, further includes:
step S51: checking whether each data recorded by the CDN node data distribution table has a negative number in real time;
step S52: if the negative number exists, the target client has the risk of exceeding the standard of the flow use in the target area based on each group of IP networks.
For example: and detecting each data recorded in the table 4 in real time, and confirming whether a negative number exists, wherein when the negative number exists, the traffic use overproof risk exists in the target area based on each group of IP networks of the target client.
On the contrary, when each data recorded by the CDN node data distribution table is a non-negative number, the target customer does not have a risk that traffic usage exceeds the standard in the target area based on each group of IP networks, that is, the current CDN node data traffic distribution is in a safe state.
Therefore, through different analysis manners of the step S14, the data resource operation condition of the CDN node can be known in real time and the abnormal CDN data can be accurately located, so that the root cause of the abnormal CDN data can be captured quickly.
In a specific implementation manner, as shown in fig. 6, the method for analyzing CDN node data resources in the embodiment of the present invention further includes:
step S15: and when the target client has the risk of exceeding the standard of the traffic use in the target area based on each group of IP networks, early warning prompt is carried out.
The target client is prompted to have the risk of exceeding the standard of flow use in the target area based on each group of IP networks through early warning, so that the external world can be informed in time to quickly adjust the CDN data resources, the risk processing time can be shortened, and the global resources can be quickly and pertinently adjusted and controlled based on the problems occurring when the CDN nodes distribute the data resources.
Therefore, in the analysis method of the CDN node data resources in the embodiment of the present invention, by executing the steps S11-S15, the data resource operation condition of the CDN node can be known in real time and the CDN data that is abnormal is accurately located, the root cause of the abnormal CDN data is quickly captured, and based on a problem that occurs when the CDN node distributes the data resources, the global resources can be quickly adjusted and controlled in a targeted manner. The CDN node data is not required to be manually inspected, the CDN node data analysis efficiency and the CDN node abnormal data processing efficiency are improved, and the using requirements of customers on the CDN node data can be finally met.
Based on the same concept, an embodiment of the present invention further provides an analysis apparatus for CDN node data resources, as shown in fig. 7, including the following modules:
the traffic obtaining module 71 is configured to obtain traffic capacity values of CDN node data distributed in each group of IP networks and expected demand traffic of a target customer in a target area;
the traffic collection module 72 is configured to collect real-time traffic of the target client in the target area based on each group of IP networks and peak traffic within a preset time;
the flow table generating module 73 is configured to calculate predicted redundant flow, actual redundant flow and peak redundant flow of the target customer in the target area based on each group of IP networks according to the flow capacity value, the predicted required flow, the real-time flow and the peak flow of the target customer in the target area, and generate a CDN data distribution table of the target customer in the target area;
and a risk analysis module 74, configured to analyze whether a target customer has a traffic usage overproof risk in a target area based on each group of IP networks according to each data recorded by the CDN data distribution table.
In one embodiment, the peak flow rate over the preset time comprises: the peak flow rate of the first time is corresponding to the peak flow rate of the second time, and the time of the first time adjacent to the current time is less than the time of the second time adjacent to the current time.
In one embodiment, the calculation of the predicted redundant traffic, the actual redundant traffic and the peak redundant traffic of the target client in the target area based on the respective sets of IP networks is performed by the above equations (1) - (3) according to the traffic capacity value, the predicted demand traffic and the real-time traffic and the peak traffic of the target client in the target area.
In one embodiment, the flow table generation module 73 includes:
and the flow table generation submodule is used for generating a CDN data distribution table according to the flow capacity value of the CDN nodes distributed in each group of IP networks, the real-time flow of the target customer in the target area based on each group of IP networks and the peak flow in preset time and the coverage quantity of the target customer and the target customer based on each group of IP networks and the name of the target area.
In one embodiment, risk analysis module 74 includes:
the first calculation submodule is used for calculating a first difference value between the actual redundant flow of the target client based on each group of IP networks in the target area and the expected redundant flow of the target client based on each group of IP networks in the target area;
the second calculation submodule is used for calculating a first ratio of the first difference value to the predicted required flow of the target customer in the target area;
and the first risk determination submodule is used for determining that the target client has the risk of exceeding the standard of the traffic utilization in the target area based on each group of IP networks when the first ratio is a positive number.
In another embodiment, the risk analysis module 74 further includes:
the flow selection submodule is used for selecting a maximum flow value between the real-time flow of the target client in the target area based on each group of IP networks and the peak flow within the preset time;
a third calculation submodule for calculating a second difference between the maximum flow value and the expected demand flow of the target customer in the target area;
a fourth calculation submodule for calculating a percentage between the second difference and the expected demand flow of the target customer in the target area;
and the second risk determination submodule is used for determining that the target client has the risk of exceeding the standard of the traffic utilization in the target area based on each group of IP networks when the percentage exceeds the preset proportion.
In another embodiment, the risk analysis module 74 further includes:
the fifth calculation submodule is used for calculating whether a second ratio between the real-time flow and the flow energy value of the target client based on each group of IP networks in the target area is smaller than a preset value or not;
and the third determining submodule is used for determining that the target client has the risk of exceeding the standard of the flow use in the target area based on each group of IP networks when the second ratio is smaller than the preset value.
In another embodiment, the risk analysis module 74 further includes:
the data checking submodule is used for checking whether each piece of data recorded by the CDN node data distribution table has a negative number or not in real time;
and the fourth risk determination submodule is used for determining that the target client has the risk of exceeding the standard of the traffic utilization in the target area based on each group of IP networks if the negative number exists.
In another specific embodiment, the apparatus for analyzing CDN node data resources according to the embodiment of the present invention further includes:
and an early warning prompting module 75 for performing early warning prompting when the target client has a traffic use overproof risk in the target area based on each group of IP networks.
Based on the same concept, the embodiment of the present invention further provides a computer device, as shown in fig. 8, the electronic device may include a processor 81 and a memory 82, where the processor 81 and the memory 82 may be connected by a bus or in another manner, and fig. 8 illustrates an example of connection by a bus.
The memory 82, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 81 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 82, that is, implements the analysis method of the CDN node data resource in the above embodiment.
The memory 82 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 81, and the like. Further, the memory 82 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 82 may optionally include memory located remotely from the processor 81, which may be connected to the processor 81 via a network. Examples of such networks include, but are not limited to, the power grid, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 82, and when executed by the processor 81, perform an analysis method of CDN node data resources as in the embodiment shown in the drawings.
The details of the electronic device may be understood by referring to the corresponding related descriptions and effects in the embodiments shown in the drawings, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. A CDN node data resource analysis method is characterized by comprising the following steps:
acquiring the flow capacity value of CDN node data distributed in each group of IP networks and the predicted required flow of a target customer in a target area;
collecting real-time flow of a target client in a target area based on each group of IP networks and peak flow in preset time;
calculating the predicted redundant flow, the actual redundant flow and the peak redundant flow of the target customer in the target area based on the IP networks according to the flow capacity value, the predicted required flow of the target customer in the target area, the real-time flow and the peak flow, and generating a CDN data distribution table of the target customer in the target area;
and analyzing whether the target customer has a traffic use overproof risk in the target area based on each group of IP networks according to each data recorded by the CDN data distribution table.
2. The method of analyzing CDN node data resources of claim 1, wherein the peak traffic within a preset time comprises: the peak flow rate of the flow meter is measured by the flow meter, the corresponding first peak flow rate in a first time and the corresponding second peak flow rate in a second time, and the time of the first time adjacent to the current time is less than the time of the second time adjacent to the current time.
3. The method for analyzing CDN node data resources of claim 1, wherein calculating the predicted redundant traffic, actual redundant traffic, and peak redundant traffic of the target customer in the target area based on the respective sets of IP networks is performed according to the traffic capability value, the predicted required traffic of the target customer in the target area, the real-time traffic, and the peak traffic by using the following formulas:
Yapij＝Za-[Tpi/mpi+Tpj/npj]；
wherein, YapijPredicted redundant traffic based on group a IP networks in target area p for target clients i, j, ZaTraffic capacity value, T, distributed in group a IP network for CDN nodepiPredicted demand flow, m, for target customer i in target area ppiNumber of client coverage based on group a IP network in target area p for target client i, TpjPredicted demand flow for target customer j in target area p, npjThe coverage number based on the group a IP network in the target area p for the target client j;
Sapij＝Za-[Wpi/mpi+Wpj/npj]；
wherein S isapijActual redundant traffic based on group a IP network in target area p for target clients i, j, ZaTraffic energy distributed in group a IP network for CDN nodesForce value, WpiReal-time traffic, m, for target client i in target area ppiNumber of client overlays, W, based on group a IP network in target area p for target client ipjReal-time traffic for target customer j in target area p, npjThe coverage number based on the group a IP network in the target area p for the target client j;
Fapij＝Za-[Epi/mpi+Epj/npj]；
wherein, FapijPeak redundant traffic, Z, based on group a IP network in target area p for target clients i, jaTraffic capacity values distributed over group a IP networks for CDN nodes, EpiFor the second peak flow, m, corresponding to the target client i in the second time of the target area ppiNumber of client coverage based on group a IP network in target area p for target client i, EpjA second peak flow, n, corresponding to the target client j in the second time of the target area ppjThe coverage number based on the group a IP network in the target area p for the target client j;
and a is a positive integer.
4. The method for analyzing CDN node data resources of claim 1, wherein analyzing whether a target customer has a risk of exceeding traffic usage in a target area based on each set of IP networks according to each data recorded by the CDN node data distribution table includes:
calculating a first difference between actual redundant traffic of the target client based on the sets of IP networks in a target area and expected redundant traffic of the target client based on the sets of IP networks in the target area;
calculating a first ratio of the first difference to an expected demand flow of the target customer in a target area;
and when the first ratio is a positive number, the target client has the risk of exceeding the standard of the traffic use in the target area based on the IP networks of all the groups.
5. The method for analyzing CDN node data resources of claim 1, wherein analyzing whether a target customer has a risk of exceeding traffic usage in a target area based on each set of IP networks according to each data recorded by the CDN node data distribution table, further comprises:
selecting a maximum flow value between the real-time flow of the target client in the target area based on the IP networks and the peak flow within the preset time;
calculating a second difference between the maximum flow value and the expected demand flow of the target customer in the target area;
calculating a percentage between the second difference and an expected demand flow of the target customer in a target area;
and when the percentage exceeds a preset proportion, the target client has the risk of exceeding the standard of the flow use in the target area based on each group of IP networks.
6. The method for analyzing CDN node data resources of claim 1, wherein analyzing whether a target customer has a risk of exceeding traffic usage in a target area based on each set of IP networks according to each data recorded by the CDN node data distribution table, further comprises:
calculating whether a second ratio between the real-time flow of the target client in the target area based on the IP networks and the flow energy value is smaller than a preset value or not;
and when the second ratio is smaller than a preset value, the target client has the risk of exceeding the traffic use standard in the target area based on each group of IP networks.
7. The method for analyzing CDN node data resources of claim 1, wherein analyzing whether a target customer has a risk of exceeding traffic usage in a target area based on each set of IP networks according to each data recorded by the CDN node data distribution table, further comprises:
checking whether each data recorded by the CDN node data distribution table has a negative number in real time;
and if the negative number exists, the target client has the risk of exceeding the standard of the flow use in the target area based on each group of IP networks.
8. The method for analyzing CDN node data resources of any one of claims 1 to 7, further comprising:
and when the target client has the risk of exceeding the standard of the flow use in the target area based on the IP networks of all the groups, early warning prompt is carried out.
9. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of analyzing CDN node data resources of any one of claims 1 to 8.
10. A computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing therein computer instructions, and the processor executing the computer instructions to perform the method for analyzing CDN node data resources according to any one of claims 1 to 8.
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