CN112165691B - Content delivery network scheduling method, device, server and medium - Google Patents
Content delivery network scheduling method, device, server and medium Download PDFInfo
- Publication number
- CN112165691B CN112165691B CN202011018566.9A CN202011018566A CN112165691B CN 112165691 B CN112165691 B CN 112165691B CN 202011018566 A CN202011018566 A CN 202011018566A CN 112165691 B CN112165691 B CN 112165691B
- Authority
- CN
- China
- Prior art keywords
- value
- distribution network
- content distribution
- task
- bandwidth
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/24—Accounting or billing
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/06—Testing, supervising or monitoring using simulated traffic
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W8/00—Network data management
- H04W8/22—Processing or transfer of terminal data, e.g. status or physical capabilities
- H04W8/24—Transfer of terminal data
- H04W8/245—Transfer of terminal data from a network towards a terminal
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Databases & Information Systems (AREA)
- Information Transfer Between Computers (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The application discloses a content distribution network scheduling method, a content distribution network scheduling device, a content distribution network scheduling server and a content distribution network scheduling medium, and relates to the field of computers. The specific implementation scheme is as follows: receiving a task downloading request and determining a downloading task; according to the downloading task and a preset flow prediction model, predicting a flow value generated in a preset time length after the execution starting moment of the downloading task; determining a target bandwidth value and a target flow value according to a preset time length and a flow value; and executing a downloading task through the first content distribution network and/or the second content distribution network according to the target bandwidth value, the target traffic value and the first preset bandwidth threshold value, wherein the first content distribution network and the second content distribution network have different charging modes. The implementation mode provides a content distribution network scheduling method, which can improve the upgrading effect of equipment.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to the field of resource management, and in particular, to a method, an apparatus, a server, and a medium for scheduling a content delivery network.
Background
At present, in an Over The Air (OTA) project, a Content Delivery Network (CDN) can be used to deliver an upgrade package, and a device requiring upgrade can realize device upgrade by using The upgrade package.
In practice, it is found that CDN service cost becomes a large cost in the OTA project. In order to reduce the CDN service cost, a 95-month peak charging method is usually adopted to calculate the CDN service cost. Specifically, the bandwidth value of each month is counted, and the maximum bandwidth value obtained by removing the first 5% of the bandwidth value is used as a bandwidth charging point, and charging is performed according to the size of the bandwidth charging point. Therefore, in controlling the cost of the OTA project, the size of the bandwidth accounting point needs to be controlled.
However, if the control strength of the bandwidth charging point is too high, the issuing speed is slow, and the upgrading effect of the device is poor.
Disclosure of Invention
A content delivery network scheduling method, apparatus, server and medium are provided.
According to a first aspect, there is provided a content delivery network scheduling method, comprising: receiving a task downloading request and determining a downloading task; according to the downloading task and a preset flow prediction model, predicting a flow value generated in a preset time length after the execution starting moment of the downloading task; determining a target bandwidth value and a target flow value according to a preset time length and a flow value; and executing a downloading task through the first content distribution network and/or the second content distribution network according to the target bandwidth value, the target traffic value and the first preset bandwidth threshold value, wherein the first content distribution network and the second content distribution network have different charging modes.
According to a second aspect, there is provided a content distribution network scheduling apparatus, comprising: the task determining unit is configured to receive a task downloading request and determine a downloading task; a flow prediction unit configured to predict a flow value generated within a preset time period after an execution start time of the download task according to the download task and a preset flow prediction model; the numerical value determining unit is configured to determine a target bandwidth value and a target flow value according to a preset time length and a flow value; and the task execution unit is configured to execute the downloading task through the first content distribution network and/or the second content distribution network according to the target bandwidth value, the target flow value and the first preset bandwidth threshold value, wherein the first content distribution network and the second content distribution network have different charging modes.
According to a third aspect, there is provided a content delivery network scheduling server comprising: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a content distribution network scheduling method as any one of above.
According to a fourth aspect, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method as described in the first aspect.
According to the technology of the application, the content delivery network scheduling method is provided, and the equipment upgrading effect can be improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for scheduling a content distribution network according to the present application;
FIG. 3 is a schematic diagram of an application scenario of a content distribution network scheduling method according to the present application;
FIG. 4 is a flow diagram of another embodiment of a content distribution network scheduling method according to the present application;
fig. 5 is a schematic structural diagram of an embodiment of a scheduling apparatus of a content distribution network according to the present application;
fig. 6 is a block diagram of a server for implementing the scheduling method of the content distribution network according to the embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows an exemplary system architecture 100 to which embodiments of the content distribution network scheduling method or content distribution network scheduling apparatus of the present application may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, and 103 may be electronic devices such as televisions, computers, and tablets in public places, on which various types of client applications may be installed, and in a case where the terminal devices 101, 102, and 103 need to upgrade the client applications or upgrade a terminal system, an upgrade request may be sent to the server 105 through the network 104.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices including, but not limited to, televisions, smart phones, tablet computers, e-book readers, car-mounted computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 105 may be a server that provides various services, for example, obtains an upgrade request of the terminal devices 101, 102, and 103, obtains an upgrade task matching the upgrade request by analyzing the upgrade request, obtains a content distribution network download link corresponding to the upgrade task based on the upgrade task, and sends the content distribution network download link to the corresponding terminal devices 101, 102, and 103 through the network 104, so that the terminal devices 101, 102, and 103 complete the upgrade task through the content distribution network download link.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 105 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the scheduling method for a content distribution network provided in the embodiment of the present application is generally performed by the server 105. Accordingly, the content distribution network scheduling apparatus is generally provided in the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow 200 of one embodiment of a content distribution network scheduling method according to the present application is shown. The content distribution network scheduling method of the embodiment comprises the following steps:
In this embodiment, the task downloading request may be used to request to execute a downloading task, that is, to execute a downloading operation for the downloading task, where the downloading task may include, but is not limited to, an upgrade package download of a device, a client application download, a network resource download, and the like, which is not limited in this embodiment. Further, the terminal device may respond to the task downloading request triggered by the user, or the terminal device may periodically trigger the task downloading request and transmit the task downloading request to the server, so that the server receives the task downloading request and determines the downloading task requested to be downloaded by the task downloading request.
And step 202, predicting a flow value generated within a preset time after the execution starting moment of the downloading task according to the downloading task and a preset flow prediction model.
In this embodiment, the preset flow prediction model is a model trained in advance, used for predicting a flow value generated within a preset time after the start time of execution of a download task according to an input task parameter value of the download task. The downloading task has a plurality of task parameters, and the task parameters may include, but are not limited to, a task issuing area, a task issuing package size, a task issuing number, a product line identifier corresponding to the task, a product line active number corresponding to the task, a per-second query rate of a product line corresponding to the task, and the like. Each task parameter has a corresponding task parameter value. The preset flow prediction model may store a weight value corresponding to each task parameter, and based on the task parameter value of the download task and the corresponding weight value, a flow value generated within a preset time after the execution start time of the download task may be calculated.
Further, the flow rate prediction model outputs a flow rate value generated within a preset time period after the execution start time of the predicted downloading task, where the preset time period may be a preset time period, for example, 60 minutes, or other values may be selected, for example, 50 minutes, 70 minutes, and the like are selected as the preset time period. The specific preset time length value is set by a training standard adopted in the flow prediction model training, and this embodiment does not limit this. In the case that the preset time is 60 minutes, the flow prediction model outputs the flow value which can be predicted to be generated in the case that the downloading task is executed for 60 minutes.
And step 203, determining a target bandwidth value and a target flow value according to the preset time length and the flow value.
In this embodiment, according to the preset duration and the flow value, a target bandwidth value per unit time and a target flow value per unit period can be determined. Specifically, the target bandwidth value may be determined by using a ratio of the flow value to a preset time period, and the target flow value may be determined by using a ratio of the flow value to a target cycle number. The target cycle number may be a cycle number of the delivered traffic, for example, if the cycle of the delivered traffic is that the traffic is delivered every 5 minutes, the cycle number of the delivered traffic within 60 minutes is 12. The target bandwidth value obtained in the above manner is used to represent a bandwidth value per unit time, and the target flow rate value obtained in the above manner is used to represent a flow rate value per unit cycle.
And step 204, executing a downloading task through the first content distribution network and/or the second content distribution network according to the target bandwidth value, the target traffic value and the first preset bandwidth threshold value.
In this embodiment, the first content distribution network and the second content distribution network have different charging methods. The first content distribution network may be a content distribution network that performs charging in a 95-month peak charging manner, and the second content distribution network may be a content distribution network that performs charging in a traffic charging manner. The 95-month peak charging mode is that effective bandwidth values are obtained every 5 minutes in a natural month, the effective bandwidth values are sorted from high to low, then the effective bandwidth values are removed from the first 5%, the highest bandwidth in the remaining effective bandwidth values is used as a charging bandwidth, and charging is carried out based on the bandwidth values of the charging bandwidth. And the flow charging mode is a mode of charging based on the used flow value.
It can be understood that the content distribution network is an intelligent virtual network constructed on the basis of the existing network, and users can obtain required content nearby by means of the functional modules of load balancing, content distribution, scheduling and the like of the central platform by means of the edge servers deployed in various places, so that network congestion is reduced, and the access response speed and hit rate of the users are improved. In this embodiment, the downloading task may be issued to a remote service point closest to the user through the content distribution network, so that the user downloads the downloading task, thereby improving the downloading speed.
In this embodiment, a first content distribution network and a second content distribution network with different charging manners may be selected to implement scheduling of the content distribution network, and a scheduling scheme matching the download task may be determined based on the download task, for example, it may be determined that the download task is executed by using the first content distribution network and the second content distribution network in cooperation, or it is determined that the download task is executed only by using the first content distribution network. And automatic adaptation is carried out based on the condition of the downloading task, the most appropriate content distribution network scheduling mode is selected, and the downloading effect is improved on the premise of ensuring controllable cost of the downloading task.
With continued reference to fig. 3, a schematic diagram of one application scenario of a content distribution network scheduling method according to the present application is shown. In the application scenario of fig. 3, the content distribution network scheduling method may be applied in a scenario of an over-the-air technology, where the over-the-air technology refers to a technology for implementing remote management on mobile terminal equipment and SIM card data through an air interface of mobile communication. As shown in fig. 3, the a device 301, the B device 302, the C device 303, and the D device 304 may be different mobile terminal devices, and in a case that the a device 301, the B device 302, the C device 303, and the D device 304 need to implement device upgrade through an over-the-air download technology, the a device 301, the B device 302, the C device 303, and the D device 304 may send a device upgrade request to an issuing node by calling an upgrade interface, for example, the a device 301 may send a device upgrade request to an E issuing node 305, the B device 302 and the C device 303 may send a device upgrade request to an F issuing node 306, and the D device 304 may send a device upgrade request to a G issuing node 307. The E issuing node 305, the F issuing node 306 and the G issuing node 307 may be different issuing nodes. Further, the OTA console 308 may transmit a delivery task to the E delivery node 305, the F delivery node 306, and the G delivery node 307, where the delivery task may include a content distribution network download link corresponding to device upgrade, and the E delivery node 305, the F delivery node 306, and the G delivery node 307 may send a content distribution network download link matching to a related device, so that the device completes upgrade based on the content distribution network download link.
In the process of device upgrade, by using the content distribution network scheduling method in this embodiment, the task download requests sent by the device a 301, the device B302, the device C303, and the device D304 can be received, and a download task is determined, where the download task at this time is a device upgrade download task. The method comprises the steps of predicting a flow value generated in a preset time after the execution start time of the equipment upgrading and downloading task according to the equipment upgrading and downloading task and a preset flow prediction model, determining a target bandwidth value and a target flow value according to the preset time and the flow value, selecting a scheduling content distribution network to execute the equipment upgrading and downloading task according to the target bandwidth value, the target flow value and a first preset bandwidth threshold value, and returning a link generated by the selected scheduling content distribution network to the equipment so that the equipment can finish equipment upgrading by using the first content distribution network through the link, or finish equipment upgrading by using the first content distribution network and the second content distribution network through the link.
The content distribution network scheduling method provided in the foregoing embodiment of the present application may receive a task downloading request, determine a downloading task, predict a traffic value generated within a preset time period after an execution start time of the downloading task according to the downloading task and a preset traffic prediction model, determine a target bandwidth value and a target traffic value according to the preset time period and the traffic value, and execute the downloading task through a first content distribution network and/or a second content distribution network according to the target bandwidth value, the target traffic value, and a first preset bandwidth threshold. The process can schedule the first content distribution network and the second content distribution network to execute the downloading task on the premise of controlling the bandwidth charging point to control the cost, determine the type of the scheduled content distribution network based on the prediction of the predicted consumption flow value of the downloading task and the preset bandwidth control condition, and improve the flow issuing speed to a certain extent, thereby improving the upgrading effect of equipment.
With continued reference to fig. 4, a flow 400 of another embodiment of a content distribution network scheduling method according to the present application is shown. As shown in fig. 4, the content distribution network scheduling method of the present embodiment may include the following steps:
In this embodiment, steps 401 to 403 may be referred to for training the flow prediction model. First, task parameters of the historical download task related to the historical bandwidth may be determined based on the historical download task and the historical bandwidth generated within a preset time period after the execution start time of the historical download task. For the determination of the task parameter, a pearson correlation coefficient method may be used for the determination, and other correlation coefficient methods capable of determining the degree of correlation between the task parameter and the historical bandwidth may also be used for the determination, which is not limited in this embodiment. Optionally, the preset duration may be determined by the confidence of the task duration with high bandwidth, for example, if a one-year upgrade task and a bandwidth corresponding to the upgrade task are selected, the task duration with high bandwidth is listed in a normal distribution diagram, and the task duration corresponding to a 90% confidence interval may be selected as the preset duration.
The method for determining the task parameters of the historical downloading task related to the historical bandwidth may specifically be: determining a plurality of task parameters of historical downloading tasks; listing a plurality of task parameters and a correlation coefficient matrix of historical bandwidth; and selecting task parameters of the historical downloading tasks which are highly related to the historical bandwidth based on the correlation coefficient matrix, and determining the task parameters as the task parameters of the historical downloading tasks which are related to the historical bandwidth.
And 402, determining a correlation coefficient of the task parameter and the historical bandwidth.
In this embodiment, after determining a plurality of task parameters related to the historical bandwidth, a correlation coefficient between the task parameters and the historical bandwidth may be further determined. Optionally, the manner of determining the correlation coefficient between the task parameter and the historical bandwidth may specifically be: determining historical task traffic based on historical bandwidth; and substituting the historical task flow and the task parameter values of the task parameters into a preset linear regression formula, and calculating to obtain a correlation coefficient of the task parameters and the historical bandwidth. Wherein, the linear regression formula is as follows:
hθ(x)＝θTx
wherein h isθ(x) Representing historical task flow, x representing a parameter value of a task parameter, theta representing a correlation coefficient, thetaTA transposed matrix representing the correlation coefficients.
And 403, constructing a flow prediction model based on the correlation coefficient and the task parameter.
In this embodiment, the linear regression formula may be referred to, a calculation relationship among the flow value, the correlation coefficient, and the task parameter generated within the preset time after the execution start time of the download task is constructed, and a flow prediction model is constructed based on the calculation relationship, so that the constructed flow prediction model can input the task parameter, obtain the correlation coefficient corresponding to the task parameter, and calculate the flow value generated within the preset time after the execution start time of the download task based on the task parameter and the correlation coefficient.
In this embodiment, the task parameter value of the download task is a value corresponding to the task parameter, and if there are multiple task parameters in the download task, each task parameter has a corresponding task parameter value, for example, the task parameter is a task issuing packet size, and the task parameter value corresponding to the task parameter is a specific issuing packet size value, or the task parameter is a task issuing amount, and the task parameter value corresponding to the task parameter value is a specific issuing amount.
In this embodiment, by inputting the task parameter value of the download task into the preset flow prediction model, the flow prediction model may determine a correlation coefficient corresponding to the task parameter value of the download task, and output a flow value generated within a preset time period after the execution start time of the download task based on the correlation coefficient and the task parameter value.
In this embodiment, the flow value is a flow value generated within a preset time period after the execution start time of the download task, and if the bandwidth value per unit time needs to be determined, the target bandwidth value needs to be determined based on the ratio of the flow value to the element and the time period. Alternatively, the unit of the preset time length may be converted from minutes to seconds, and then the ratio may be calculated. Assuming that the preset time is 60 minutes, the 60 minutes can be converted into 3600 seconds, and then the ratio of the flow value to 3600 is calculated to determine the target bandwidth value.
In this embodiment, the target cycle number is a cycle number for issuing the traffic, for example, the cycle for issuing the traffic may be set to issue the traffic every 5 minutes, at this time, a cycle number included in the preset duration may be determined, and the cycle number is determined as the target cycle number corresponding to the preset duration. For example, assuming that the preset time duration is 60 minutes and the flow rate issuing period is once every 5 minutes, the preset time duration includes 12 periods, and the period number is determined as the target period number.
In this embodiment, the target flow value is a flow value issued in a unit period, and may be calculated by dividing the total flow value by the target period number.
In response to determining that the target bandwidth value is greater than or equal to the first preset bandwidth threshold, a download task is performed over the first content distribution network, step 410.
In this embodiment, if the target bandwidth value is greater than or equal to the first preset bandwidth threshold, it indicates that the bandwidth value that needs to be consumed by the download task exceeds the traffic value to be generated corresponding to the first 5% of the bandwidth point charged by using the peak-of-95 month, and at this time, the download task is executed by using the first content distribution network charged by using the peak-of-95 month, so that the bandwidth value consumed by the task is controlled to be located at the first 5% of the peak-of-95 month charging, so as to avoid charging, and achieve the cost control effect.
In some optional implementations of this embodiment, the first preset bandwidth threshold is calculated by: obtaining a flow value to be output based on the flow budget cost and a preset input-output ratio; determining the flow value to be generated of each bandwidth point according to the ratio of the flow value to be generated to the number of the bandwidth points; and obtaining the bandwidth value to be generated of each bandwidth point based on the flow value to be generated of each bandwidth point, and determining the bandwidth value to be generated of each bandwidth point as a first preset bandwidth threshold value.
In this implementation, the traffic budget cost may be monthly traffic budget cost, for example, the traffic budget cost may be 10 ten thousand yuan, and in this case, the monthly traffic budget cost is 10 ten thousand yuan. Alternatively, the traffic budget cost may also be a quarterly traffic budget cost or an annual traffic budget cost, and the time unit of the traffic budget cost is not limited in this implementation manner. The preset input-output ratio is a ratio between preset input and output, for example, the preset input-output ratio may be a ratio between output flow and input flow budget cost. Assuming that the preset input-output ratio is 2:1, the ratio of the output traffic cost to the input traffic budget cost needs to be greater than or equal to the preset input-output ratio to achieve the cost control effect of the content distribution network. For example, assuming that monthly traffic budget costs 10 ten thousand dollars and a predetermined input-to-output ratio of 2:1, 20 ten thousand dollars of traffic are required to be produced. Based on the flow budget cost and the preset input-output ratio, the flow cost to be output can be determined firstly, and then the flow value to be output is determined based on the flow cost to be output. Further, the traffic value to be generated of each bandwidth point may be determined according to a ratio of the traffic value to be generated to the number of bandwidth points. The number of the bandwidth points is the number of the first 5% bandwidth values which need to be removed when the 95-month peak charging mode is adopted. Because the charging is not needed for the first 5% of the bandwidth value when the 95 month peak charging mode is adopted, the flow value to be produced is produced by using the first 5% of the bandwidth value, and the cost control effect corresponding to the preset input-output ratio can be achieved. Therefore, the traffic values to be generated can be averaged to the bandwidth points corresponding to the number of the bandwidth points to obtain the bandwidth value to be generated of each bandwidth point, and the bandwidth value to be generated of each bandwidth point is determined as the first preset bandwidth threshold. Specifically, the download task may be executed using the first content distribution network when the target bandwidth value is greater than or equal to the first preset bandwidth threshold, and the download task may be executed using the first content distribution network and the second content distribution network when the target bandwidth value is less than the first preset bandwidth threshold.
In step 411, in response to determining that the target bandwidth value is smaller than the first preset bandwidth threshold, a download task is executed through the first content distribution network and the second content distribution network.
In this embodiment, if the target bandwidth value is smaller than the first preset bandwidth threshold, it indicates that the bandwidth value that needs to be consumed by the download task does not exceed the traffic value to be generated corresponding to the first 5% bandwidth point charged using the 95 month peak, and at this time, the first content distribution network and the second content distribution network may be used to cooperatively execute the download task, so as to control the bandwidth value consumed by the task to be located at the last 95% of the 95 month peak charging, and achieve the cost control effect. And the second content distribution network using the flow charging and the first content distribution network cooperate to execute the downloading task, thereby improving the flow issuing speed to a certain extent and improving the execution effect of the downloading task.
In some optional implementations of this embodiment, the performing the download task through the first content distribution network and the second content distribution network includes: subtracting the actual bandwidth value of the first content distribution network by using a second preset bandwidth threshold value to obtain a distribution bandwidth value distributed to the first content distribution network; determining a first traffic value assigned to the first content distribution network based on the allocation bandwidth value; subtracting the first flow value from the target flow value to obtain a second flow value; executing a download task through the first content distribution network according to the first traffic value; and executing the download task through the second content distribution network according to the second traffic value.
In this implementation manner, the actual bandwidth value of the first content distribution network may be subtracted from the second preset bandwidth threshold to obtain the maximum bandwidth that the download task can be distributed to the first content distribution network. The second preset bandwidth threshold may be a bandwidth value of a bandwidth charging point charged by the peak of 95 months. A corresponding first traffic value to the first content distribution network may be determined based on a maximum bandwidth that can be allocated to the first content distribution network. The specific calculation formula of the first flow value is as follows:
(10-B′)×300÷8
wherein, B 'is an actual bandwidth value of the first content distribution network, 10 is a second preset bandwidth threshold, 10-B' is an allocated bandwidth value, 300 is a duration of a traffic issuing period, 5 minutes can be converted into 300 seconds under the condition that traffic is issued every five minutes, and then 300 is substituted into the above formula for calculation, and since 1 byte should be converted into 8 bits, unit conversion can be performed by dividing 8 by the above formula.
Further, the first flow value may be subtracted from the target flow value to obtain a second flow value. Since the target traffic value is the total issued traffic value in one period, the second traffic value allocated to the second content distribution network can be obtained by subtracting the first traffic value allocated to the first content distribution network from the total issued traffic value. The download task may then be performed by the first content distribution network according to the first traffic value and by the second content distribution network according to the second traffic value. The first content distribution network and the second content distribution network are cooperated to execute the downloading task, and the execution effect of the downloading task is improved.
In some optional implementation manners of this embodiment, the second preset bandwidth threshold is calculated by: a second preset bandwidth threshold is determined based on a preset traffic budget cost.
In this implementation manner, the second preset bandwidth threshold may be calculated based on the preset traffic budget cost and the preset bandwidth consumption cost, where the preset traffic budget cost is assumed to be 10Gbps, the bandwidth consumption cost is 1Gbps/1 Gbps, and the second preset bandwidth threshold is the preset traffic budget cost divided by the bandwidth consumption cost, that is, 10 Gbps.
As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, the process 400 of the content distribution network scheduling method in this embodiment may further determine the task parameters of the historical download task related to the historical bandwidth based on the historical download task and the preset bandwidth generated within the preset time after the execution start time of the historical download task, and construct a traffic prediction model based on the correlation coefficient between the task parameters and the historical bandwidth and the task parameters, so as to realize prediction of a traffic value generated within the predicted time of the download task, and improve accuracy of traffic value prediction. Furthermore, a first preset bandwidth threshold value can be determined based on the flow budget cost, the preset input-output ratio and the number of bandwidth points, and a second preset bandwidth threshold value is determined based on the flow budget cost, so that the second preset bandwidth threshold value is used as the bandwidth charging point control cost of the first content distribution network, the first preset bandwidth threshold value is used as the scheduling basis of the content distribution network, the user experience is ensured, the balance between the cost and the user experience is realized, and the execution effect of the downloading task can be improved on the premise of controlling the cost.
With further reference to fig. 5, as an implementation of the methods shown in the above-mentioned figures, the present application provides an embodiment of a content distribution network scheduling apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied in a server.
As shown in fig. 5, the content distribution network scheduling apparatus 500 of the present embodiment includes: a task determination unit 501, a flow prediction unit 502, a numerical value determination unit 503, and a task execution unit 504.
And a task determining unit 501 configured to receive a task downloading request and determine a downloading task.
A flow prediction unit 502 configured to predict a flow value generated within a preset time period after the execution start time of the download task, according to the download task and a preset flow prediction model.
A value determining unit 503 configured to determine a target bandwidth value and a target flow value according to the preset time length and the flow value.
A task execution unit 504 configured to execute the download task through the first content distribution network and/or the second content distribution network according to the target bandwidth value, the target traffic value, and the first preset bandwidth threshold.
In this embodiment, the first content distribution network and the second content distribution network have different charging methods.
In some optional implementations of this embodiment, the content delivery network scheduling apparatus further includes: the model training unit is configured to determine task parameters of the historical downloading tasks related to historical bandwidth based on the historical downloading tasks and the historical bandwidth generated within a preset time after the execution starting time of the historical downloading tasks; determining a correlation coefficient of the task parameter and the historical bandwidth; and constructing a flow prediction model based on the correlation coefficient and the task parameter.
In some optional implementations of this embodiment, the flow prediction unit 502 is further configured to: determining a task parameter value of a downloading task; and inputting the task parameter value of the download task into a preset flow prediction model so that the preset flow prediction model outputs a flow value generated within a preset time after the execution starting moment of the download task based on the correlation coefficient corresponding to the task parameter value of the download task.
In some optional implementations of the present embodiment, the numerical value determining unit 503 is further configured to: determining a target bandwidth value based on the ratio of the flow value to the preset time length; determining a target periodicity corresponding to a preset duration; a target flow value is determined based on a ratio of the flow value to the target number of cycles.
In some optional implementations of this embodiment, the task execution unit 504 is further configured to: in response to determining that the target bandwidth value is greater than or equal to the first preset bandwidth threshold, performing the download task over the first content distribution network.
In some optional implementations of this embodiment, the task execution unit 504 is further configured to: in response to determining that the target bandwidth value is less than the first preset bandwidth threshold, performing a download task over the first content distribution network and the second content distribution network.
In some optional implementations of this embodiment, the task execution unit 504 is further configured to: subtracting the actual bandwidth value of the first content distribution network by using a second preset bandwidth threshold value to obtain a distribution bandwidth value distributed to the first content distribution network; determining a first traffic value assigned to the first content distribution network based on the allocation bandwidth value; subtracting the first flow value from the target flow value to obtain a second flow value; executing a download task through the first content distribution network according to the first traffic value; and executing the download task through the second content distribution network according to the second traffic value.
In some optional implementation manners of this embodiment, the second preset bandwidth threshold is calculated by: a second preset bandwidth threshold is determined based on a preset traffic budget cost.
In some optional implementations of this embodiment, the first preset bandwidth threshold is calculated by: obtaining a flow value to be output based on the flow budget cost and a preset input-output ratio; determining the flow value to be generated of each bandwidth point according to the ratio of the flow value to be generated to the number of the bandwidth points; and obtaining the bandwidth value to be generated of each bandwidth point based on the flow value to be generated of each bandwidth point, and determining the bandwidth value to be generated of each bandwidth point as a first preset bandwidth threshold value.
It should be understood that units 501 to 504 recited in the content distribution network scheduling apparatus 500 correspond to respective steps in the method described with reference to fig. 2, respectively. Thus, the operations and features described above for the content distribution network scheduling method are equally applicable to the apparatus 500 and the units included therein, and are not described in detail here.
According to an embodiment of the present application, a server and a readable storage medium are also provided.
Fig. 6 is a block diagram of a server executing the scheduling method of the content distribution network according to the embodiment of the present application. The server includes: one or more processors 601, memory 602, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the server. In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple servers may be connected, with each device providing portions of the necessary operations (e.g., as an array of servers, a group of blade servers, or a multi-processor system). In fig. 6, one processor 601 is taken as an example.
The memory 602 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform the content distribution network scheduling method provided herein. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform the content distribution network scheduling method provided by the present application.
The memory 602, 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, such as program instructions/modules corresponding to the content distribution network scheduling method in the embodiment of the present application (for example, the task determination unit 501, the traffic prediction unit 502, the numerical value determination unit 503, and the task execution unit 504 shown in fig. 5). The processor 601 executes various functional applications of the server and data processing by running non-transitory software programs, instructions and modules stored in the memory 602, that is, implements the content distribution network scheduling method in the above-described method embodiment.
The memory 602 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 use of a server, and the like. Further, the memory 602 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 602 optionally includes memory located remotely from the processor 601, which may be connected to a server over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The server performing the content distribution network scheduling method may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected by a bus or other means, and fig. 6 illustrates the connection by a bus as an example.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the server. The output device 604 may output corresponding information after performing data processing on the input numeric or character information received by the input device 603.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, the content distribution network scheduling method is provided, and the equipment upgrading effect can be improved.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (18)
1. A method of scheduling a content delivery network, the method comprising:
receiving a task downloading request and determining a downloading task;
according to the downloading task and a preset flow prediction model, predicting a flow value generated within a preset time after the execution starting moment of the downloading task;
determining a target bandwidth value and a target flow value according to the preset time length and the flow value;
according to the target bandwidth value, the target traffic value and a first preset bandwidth threshold value, the downloading task is executed through a first content distribution network and/or a second content distribution network, wherein the first content distribution network and the second content distribution network have different charging modes, and the method comprises the following steps: in response to determining that the target bandwidth value is greater than or equal to the first preset bandwidth threshold, performing the download task over the first content distribution network.
2. The content delivery network scheduling method of claim 1, wherein the traffic prediction model is obtained by the following training steps:
determining task parameters of the historical downloading tasks related to historical bandwidth based on the historical downloading tasks and the historical bandwidth generated within the preset time after the execution starting time of the historical downloading tasks;
determining a correlation coefficient of the task parameter and the historical bandwidth;
and constructing the flow prediction model based on the correlation coefficient and the task parameter.
3. The content distribution network scheduling method according to claim 2, wherein the predicting, according to the download task and a preset traffic prediction model, a traffic value generated within a preset time after an execution start time of the download task includes:
determining a task parameter value of the downloading task;
and inputting the task parameter value of the download task into the preset flow prediction model so that the preset flow prediction model outputs the flow value generated in the preset time after the execution starting moment of the download task based on the correlation coefficient corresponding to the task parameter value of the download task.
4. The content distribution network scheduling method of claim 1, wherein the determining a target bandwidth value and a target traffic value according to the preset duration and the traffic value comprises:
determining the target bandwidth value based on the ratio of the flow value to the preset duration;
determining a target periodicity corresponding to the preset duration;
determining the target flow value based on a ratio of the flow value to the target number of cycles.
5. The content distribution network scheduling method according to claim 1, wherein the performing the download task through the first content distribution network and/or the second content distribution network according to the target bandwidth value, the target traffic value, and the first preset bandwidth threshold comprises:
in response to determining that the target bandwidth value is less than the first preset bandwidth threshold, performing the download task over the first content distribution network and the second content distribution network.
6. The content distribution network scheduling method of claim 5, wherein the performing the download task over the first content distribution network and the second content distribution network comprises:
subtracting the actual bandwidth value of the first content distribution network by using a second preset bandwidth threshold value to obtain a distribution bandwidth value distributed to the first content distribution network;
determining a first traffic value assigned to the first content distribution network based on the distribution bandwidth value;
subtracting the first flow value from the target flow value to obtain a second flow value;
executing the download task through the first content distribution network according to the first traffic value; and executing the download task through the second content distribution network according to the second traffic value.
7. The content distribution network scheduling method according to claim 6, wherein the second preset bandwidth threshold is calculated by:
determining the second preset bandwidth threshold based on a preset traffic budget cost.
8. The content distribution network scheduling method according to any one of claims 1 to 7, wherein the first preset bandwidth threshold is calculated by:
obtaining a flow value to be output based on the flow budget cost and a preset input-output ratio;
determining the flow value to be produced of each bandwidth point according to the ratio of the flow value to be produced to the number of the bandwidth points;
and obtaining the bandwidth value to be produced of each bandwidth point based on the flow value to be produced of each bandwidth point, and determining the bandwidth value to be produced of each bandwidth point as the first preset bandwidth threshold.
9. A content distribution network scheduling apparatus, the content distribution network scheduling apparatus comprising:
the task determining unit is configured to receive a task downloading request and determine a downloading task;
a flow prediction unit configured to predict a flow value generated within a preset time period after an execution start time of the download task according to the download task and a preset flow prediction model;
a numerical value determination unit configured to determine a target bandwidth value and a target flow value according to the preset duration and the flow value;
a task execution unit configured to execute the download task through a first content distribution network and/or a second content distribution network according to the target bandwidth value, the target traffic value, and a first preset bandwidth threshold, wherein the first content distribution network and the second content distribution network have different charging manners;
the task execution unit is further configured to: in response to determining that the target bandwidth value is greater than or equal to the first preset bandwidth threshold, performing the download task over the first content distribution network.
10. The content distribution network scheduling apparatus according to claim 9, wherein the content distribution network scheduling apparatus further comprises:
the model training unit is configured to determine task parameters of historical downloading tasks related to historical bandwidths based on the historical downloading tasks and the historical bandwidths generated within the preset time after the execution starting time of the historical downloading tasks; determining a correlation coefficient of the task parameter and the historical bandwidth; and constructing the flow prediction model based on the correlation coefficient and the task parameter.
11. The content distribution network scheduling apparatus according to claim 10, wherein the traffic prediction unit is further configured to:
determining a task parameter value of the downloading task;
and inputting the task parameter value of the download task into the preset flow prediction model so that the preset flow prediction model outputs the flow value generated in the preset time after the execution starting moment of the download task based on the correlation coefficient corresponding to the task parameter value of the download task.
12. The content distribution network scheduling apparatus according to claim 9, wherein the numerical value determining unit is further configured to:
determining the target bandwidth value based on the ratio of the flow value to the preset duration;
determining a target periodicity corresponding to the preset duration;
determining the target flow value based on a ratio of the flow value to the target number of cycles.
13. The content distribution network scheduling apparatus of claim 9, wherein the task execution unit is further configured to:
in response to determining that the target bandwidth value is less than the first preset bandwidth threshold, performing the download task over the first content distribution network and the second content distribution network.
14. The content distribution network scheduling apparatus of claim 13, wherein the task execution unit is further configured to:
subtracting the actual bandwidth value of the first content distribution network by using a second preset bandwidth threshold value to obtain a distribution bandwidth value distributed to the first content distribution network;
determining a first traffic value assigned to the first content distribution network based on the distribution bandwidth value;
subtracting the first flow value from the target flow value to obtain a second flow value;
executing the download task through the first content distribution network according to the first traffic value; and executing the download task through the second content distribution network according to the second traffic value.
15. The content delivery network scheduling apparatus according to claim 14, wherein the second preset bandwidth threshold is calculated by:
determining the second preset bandwidth threshold based on a preset traffic budget cost.
16. The content distribution network scheduling apparatus according to any one of claims 9 to 15, wherein the first preset bandwidth threshold is calculated by:
obtaining a flow value to be output based on the flow budget cost and a preset input-output ratio;
determining the flow value to be produced of each bandwidth point according to the ratio of the flow value to be produced to the number of the bandwidth points;
and obtaining the bandwidth value to be produced of each bandwidth point based on the flow value to be produced of each bandwidth point, and determining the bandwidth value to be produced of each bandwidth point as the first preset bandwidth threshold.
17. A content distribution network scheduling server, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8.
18. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011018566.9A CN112165691B (en) | 2020-09-24 | 2020-09-24 | Content delivery network scheduling method, device, server and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011018566.9A CN112165691B (en) | 2020-09-24 | 2020-09-24 | Content delivery network scheduling method, device, server and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112165691A CN112165691A (en) | 2021-01-01 |
| CN112165691B true CN112165691B (en) | 2022-02-08 |
Family
ID=73862792
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011018566.9A Active CN112165691B (en) | 2020-09-24 | 2020-09-24 | Content delivery network scheduling method, device, server and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112165691B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112866985B (en) * | 2021-02-20 | 2023-06-23 | 百度在线网络技术（北京）有限公司 | Flow control method, resource downloading method, device, equipment and storage medium |
| CN113179224B (en) * | 2021-04-28 | 2022-10-28 | 北京达佳互联信息技术有限公司 | Traffic scheduling method and device for content distribution network |
| CN115333982B (en) * | 2021-05-11 | 2024-02-09 | 北京金山云网络技术有限公司 | Flow monitoring method, device, equipment and computer readable storage medium |
| CN113507419B (en) * | 2021-07-07 | 2022-11-01 | 工银科技有限公司 | Training method of traffic distribution model, traffic distribution method and device |
| CN113726691B (en) * | 2021-08-20 | 2024-04-30 | 北京字节跳动网络技术有限公司 | Bandwidth reservation method, device, equipment and storage medium |
| CN114338705B (en) * | 2021-11-24 | 2023-12-01 | 阿里巴巴（中国）有限公司 | Resource water level control method, equipment and medium for CDN node of content delivery network |
| CN114401449A (en) * | 2021-12-27 | 2022-04-26 | 中国电信股份有限公司 | Version upgrading method and device and readable storage medium |
| CN114500663B (en) * | 2021-12-28 | 2024-04-12 | 网宿科技股份有限公司 | Scheduling method, device, equipment and storage medium of content distribution network equipment |
| CN114338429B (en) * | 2021-12-30 | 2024-01-30 | 中国工商银行股份有限公司 | Network bandwidth determining method and device and electronic equipment |
| CN114979169B (en) * | 2022-05-27 | 2024-03-29 | 山东派盟网络科技有限公司 | Network resource pushing method and device, storage medium and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101681331A (en) * | 2006-09-06 | 2010-03-24 | 阿卡麦科技公司 | Hybrid content delivery network network (CDN) and equity (P2P) network |
| EP2940967A1 (en) * | 2014-04-30 | 2015-11-04 | Alcatel Lucent | Content-centric networking |
| CN107465708A (en) * | 2016-06-02 | 2017-12-12 | 腾讯科技（深圳）有限公司 | A kind of CDN bandwidth scheduling systems and method |
| CN108023734A (en) * | 2016-11-02 | 2018-05-11 | 阿里巴巴集团控股有限公司 | Bandwidth acquisition methods and device, data processing method and device, server |
| WO2020146272A1 (en) * | 2019-01-08 | 2020-07-16 | Apple Inc. | Systems and methods for control signaling of uplink transmission for multiple antenna panels |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11025456B2 (en) * | 2018-01-12 | 2021-06-01 | Apple Inc. | Time domain resource allocation for mobile communication |
| US10474450B1 (en) * | 2018-05-21 | 2019-11-12 | GM Global Technology Operations LLC | System and method to transmit queued over-the-air software updates |
| CN109818881B (en) * | 2019-03-19 | 2022-09-27 | 网宿科技股份有限公司 | CDN bandwidth scheduling method, acquisition and scheduling server and storage medium |
-
2020
- 2020-09-24 CN CN202011018566.9A patent/CN112165691B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101681331A (en) * | 2006-09-06 | 2010-03-24 | 阿卡麦科技公司 | Hybrid content delivery network network (CDN) and equity (P2P) network |
| EP2940967A1 (en) * | 2014-04-30 | 2015-11-04 | Alcatel Lucent | Content-centric networking |
| CN107465708A (en) * | 2016-06-02 | 2017-12-12 | 腾讯科技（深圳）有限公司 | A kind of CDN bandwidth scheduling systems and method |
| CN108023734A (en) * | 2016-11-02 | 2018-05-11 | 阿里巴巴集团控股有限公司 | Bandwidth acquisition methods and device, data processing method and device, server |
| WO2020146272A1 (en) * | 2019-01-08 | 2020-07-16 | Apple Inc. | Systems and methods for control signaling of uplink transmission for multiple antenna panels |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112165691A (en) | 2021-01-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112165691B (en) | Content delivery network scheduling method, device, server and medium | |
| US8719297B2 (en) | System for managing data collection processes | |
| US10289973B2 (en) | System and method for analytics-driven SLA management and insight generation in clouds | |
| CN111694646B (en) | Resource scheduling method, device, electronic equipment and computer readable storage medium | |
| CN111866085B (en) | Data storage method, system and device based on block chain | |
| CN110753131A (en) | Microservice distributed current limiting method and device, storage medium and electronic equipment | |
| CN111083058B (en) | Content distribution network service flow limiting method and electronic equipment | |
| CN110990138B (en) | Resource scheduling method, device, server and storage medium | |
| CN111970132B (en) | Control method, device and server for OTA data packet issuing flow | |
| CN104243405A (en) | Request processing method, device and system | |
| CN114095443B (en) | Method, device, electronic equipment and readable storage medium for controlling flow | |
| CN104995899A (en) | Server load management | |
| US20210360058A1 (en) | Job allocation support system and method | |
| CN112860974A (en) | Computing resource scheduling method and device, electronic equipment and storage medium | |
| CN111988817B (en) | Method and device for controlling OTA data packet issuing flow | |
| CN109428926B (en) | Method and device for scheduling task nodes | |
| CN112219191A (en) | Self-configuration of services and servers in a data center | |
| CN103713852A (en) | Method for processing information, service platform and electronic devices | |
| CN112527509B (en) | Resource allocation method and device, electronic equipment and storage medium | |
| CN110113176B (en) | Information synchronization method and device for configuration server | |
| CN111970320B (en) | Control method and device for OTA data packet issuing flow | |
| CN112866985A (en) | Flow control method, resource downloading method, device, equipment and storage medium | |
| CN113254191A (en) | Method, electronic device and computer program product for running applications | |
| CN113596920B (en) | Flow control method, device, electronic equipment and storage medium | |
| CN114265692A (en) | Service scheduling method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |