CN116016350A - Automatic adjustment method and device for dedicated line rate and electronic equipment - Google Patents
Automatic adjustment method and device for dedicated line rate and electronic equipment Download PDFInfo
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Abstract
The application provides an automatic adjustment method and device for a special line rate and electronic equipment, wherein the method comprises the following steps: obtaining a maximum flow prediction value of the special line service in the next time period; acquiring service data of private line service in the current time period of a user; adjusting the speed of the private line service according to the maximum flow predicted value, the service data and the service rule; and the SDN controller is utilized to issue relevant configuration of rate adjustment, so as to complete automatic adjustment of the dedicated line rate. According to the method, the maximum flow prediction value of the private line service in the next time period is predicted and obtained in advance; and automatically adjusting the configuration of the private line service in the next time period according to the maximum flow predicted value and the service data of the private line service in the current time period so as to meet the configuration requirement of the private line service in the next time period. By predicting the service use condition of the user in advance, the problem of poor user experience caused by the situation that the user network is jammed before the special line rate adjustment of the next time period is avoided.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for automatically adjusting a dedicated line rate, and an electronic device.
Background
Along with the acceleration of the digitalized transformation process of enterprises, various enterprises put forward higher requirements on timeliness and flexibility of the distribution of private line business configuration, and dynamic and instant adjustment of the private line rate also becomes a necessary function of the private line product.
In the prior art, a general client needs to submit a rate change order at a service system portal, a service system issues a related request to an SDN (Software Defined Network ) controller side through a capability platform, and then changes PE (Provider Edge) configuration through the controller to complete adjustment of a dedicated line rate.
In the prior art, however, the dedicated line rate adjustment needs to be initiated after the user logs in the service system, and the dedicated line rate is adjusted according to the current network bandwidth usage situation or future network usage plan of the user, so that the user network may have congestion before the adjustment is performed, and the user experience is affected.
Disclosure of Invention
The application provides an automatic adjustment method, an adjustment device and electronic equipment for special line rate, which are used for solving the technical problem that the user experience is poor because the service use condition of a user cannot be predicted in the prior art and the user network is congested before the next time special line rate adjustment is carried out.
In a first aspect, the present application provides a method for obtaining a maximum traffic prediction value of a private line service in a next time period; wherein the maximum traffic prediction value comprises a bandwidth value;
acquiring service data of private line service in the current time period of a user; the service data comprises a special line rate and a historical bandwidth utilization rate;
adjusting the speed of the private line service according to the maximum flow predicted value, the service data and the service rule; and the SDN controller is utilized to issue relevant configuration of rate adjustment, so as to complete automatic adjustment of the dedicated line rate.
Further, obtaining a traffic prediction value of the private line service in the next time period specifically includes:
acquiring traffic data of a private line service and a date corresponding to the traffic data;
acquiring service data in a service system; the service data comprises equipment model, bandwidth and speed;
matching the flow data original text with the service data according to rules to obtain a matching result of the flow data original text and the service data; wherein, the matching result comprises a service identifier;
training a traffic flow model according to the traffic flow data, the date and the matching result to obtain a traffic flow prediction model;
and obtaining a flow prediction value of the special line service in the next time period according to the flow prediction model.
Further, the speed of the private line service is adjusted according to the maximum flow prediction value, the service data and the service rule, and the method specifically comprises the following steps:
acquiring an access bandwidth value and a line bandwidth value in service data;
if the maximum flow prediction value is larger than the access bandwidth value and smaller than the line bandwidth value, the bandwidth value of the current special line service is configured as the maximum flow prediction value.
Further, the method adjusts the speed of the private line service according to the maximum flow predicted value, the service data and the service rule, and specifically further comprises the following steps:
if the maximum flow predicted value is larger than the access bandwidth value and larger than the line bandwidth value, configuring the bandwidth value of the current special line service as the line bandwidth value;
the number of days reaching the line bandwidth value in a period of time in the service data is obtained;
and if the number of days is greater than or equal to a first preset threshold value, adjusting the bandwidth value of the special line service to be a first preset proportion value of the line bandwidth value.
Further, the method adjusts the speed of the private line service according to the maximum flow predicted value, the service data and the service rule, and specifically further comprises the following steps:
acquiring the utilization rate of the bandwidth value of the private line service in a period of time in the service data;
and if the utilization rate is smaller than a second preset threshold value, adjusting the bandwidth value of the special line service to a second preset proportion value of the line bandwidth value.
Further, the flow data original text is matched with the service data according to the rule, so as to obtain a matching result of the flow data original text and the service data, and the method specifically further comprises the following steps:
matching the flow data original text with service data according to the equipment name, the port name and the VLAN (Virtual Local Area Network ) to obtain a service identifier;
and analyzing the flow data text to obtain the time and bandwidth of the service identification.
Further, training a traffic flow model according to the traffic flow data, the date and the matching result to obtain a traffic flow prediction model, which specifically comprises:
training a traffic flow model according to the traffic flow data, the date and the matching result to obtain an initial traffic flow prediction model;
obtaining a maximum flow prediction value of the next time period according to the initial flow prediction model;
acquiring a flow value of the same time period; wherein the same time period is the same time period as the next time period;
comparing the maximum flow predicted value of the next time period with the flow value of the same time period, and verifying the accuracy of the initial flow prediction model;
and if the accuracy is greater than or equal to a third preset threshold value, determining the initial flow prediction model as a flow prediction model.
In a second aspect, the present application provides an adjustment device comprising:
the acquisition module is used for acquiring the maximum flow prediction value of the special line service in the next time period; wherein the maximum traffic prediction value comprises a bandwidth value;
the acquisition module is also used for acquiring service data of the private line service of the current time period of the user; the service data comprises a special line rate and a historical bandwidth utilization rate;
the processing module is used for adjusting the speed of the private line service according to the maximum flow predicted value, the service data and the service rule; and the SDN controller is utilized to issue relevant configuration of rate adjustment, so as to complete automatic adjustment of the dedicated line rate.
In a third aspect, the present application provides an electronic device, including a memory, a processor, where the memory stores a computer program executable on the processor, and where the processor implements the method according to the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions for performing the method of the first aspect when executed by a processor.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the method of the first aspect.
According to the automatic adjustment method, the adjustment device and the electronic equipment for the dedicated line rate, the maximum flow prediction value of the dedicated line service in the next time period is predicted and obtained in advance; and automatically adjusting the configuration of the private line service in the next time period according to the maximum flow predicted value and the service data of the private line service in the current time period so as to meet the configuration requirement of the private line service in the next time period. By predicting the service use condition of the user in advance, the problem of poor user experience caused by the situation that the user network is jammed before the special line rate adjustment of the next time period is avoided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flow chart of a method for automatically adjusting a dedicated line rate according to an embodiment of the present application;
fig. 2 is a flow chart of another automatic adjustment method for dedicated line rate according to an embodiment of the present application;
fig. 3 is a flow chart of another automatic adjustment method for dedicated line rate according to an embodiment of the present application;
fig. 4 is a flow chart of another automatic adjustment method for dedicated line rate according to an embodiment of the present application;
Fig. 5 is a flow chart of another automatic adjustment method for dedicated line rate according to an embodiment of the present application;
fig. 6 is a flow chart of another automatic adjustment method for dedicated line rate according to an embodiment of the present application;
fig. 7 is a flowchart of a method for obtaining a flow prediction model according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an apparatus according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments of the present disclosure have been shown by way of the above drawings and will be described in more detail below. These drawings and the written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the disclosed concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
Along with the acceleration of the digitalized transformation process of enterprises, various enterprises put forward higher requirements on timeliness and flexibility of the distribution of private line business configuration, and dynamic and instant adjustment of the private line rate also becomes a necessary function of the private line product.
In the prior art, a general client needs to submit a rate change order at a service system portal, a service system issues a related request to an SDN controller side through a capability platform, and then PE configuration is changed through the controller to complete adjustment of a dedicated line rate.
In the prior art, however, the dedicated line rate adjustment needs to be initiated after the user logs in the service system, and the dedicated line rate is adjusted according to the current network bandwidth usage situation or future network usage plan of the user, so that the user network may have congestion before the adjustment is performed, and the user experience is affected.
Aiming at the problems, the embodiment of the application provides an automatic adjustment method, an adjustment device and electronic equipment for special line rate, which aim to solve the technical problem of poor user experience caused by the fact that the service use condition of a user cannot be predicted and the situation that a user network is jammed before the next time special line rate adjustment is carried out. The technical conception of the application is as follows: the maximum flow prediction value of the special line service in the next time period is predicted and obtained in advance; and automatically adjusting the configuration of the private line service in the next time period according to the maximum flow predicted value and the service data of the private line service in the current time period so as to meet the configuration requirement of the private line service in the next time period. By predicting the service use condition of the user in advance, the problem of poor user experience caused by the situation that the user network is jammed before the special line rate adjustment of the next time period is avoided.
The following describes the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of a method for automatically adjusting a dedicated line rate according to an embodiment of the present application, as shown in fig. 1, where the method includes:
101. and obtaining the maximum flow predicted value of the special line service in the next time period.
The execution body of the present embodiment may be, for example, an electronic device, or a terminal device, or a maximum flow prediction apparatus or device, or other apparatus or device that may execute the present embodiment, which is not limited thereto. In this embodiment, the execution body is described as an electronic device.
First, a private line service needs to be acquired. The special line service can be obtained from the server port or obtained from the memory; or receiving the special line service transmitted by other devices. The initial text to be detected may be a picture or the like. When a user executes a prediction operation on the service, a special line service is generated, and after the electronic equipment receives the special line service, the neural network model is utilized to predict the maximum flow prediction value of the special line service, so as to obtain the maximum flow prediction value of the special line service. Wherein the maximum traffic prediction value comprises a bandwidth value; the maximum flow prediction value has a certain quantity relation with the time and bandwidth value. The maximum traffic value of the service can affect the operation and user experience of the whole network. Therefore, the maximum flow prediction value of the private line service in the next time period is obtained to avoid the operation blockage of the network.
102. And acquiring service data of the private line service of the user in the current time period.
For example, service data of the private line service of the current time period of the user can be acquired through the service system. Wherein the service data includes a dedicated line rate and a historical bandwidth utilization. The service system adopts a full decoupling architecture, and realizes the arrangement and distribution of the service based on the strong management and control capability provided by SDN technology; the fully decoupled hierarchical SDN control architecture provides a standard standardized interface, supports the intercommunication of multiple types of devices, and HAs the cooperation of heterogeneous HA (dual-machine cluster system), so that the minute-level configuration issuing can be realized, and flexible and reliable support is provided for the adjustment of the special line rate of a user.
103. Adjusting the speed of the private line service according to the maximum flow predicted value, the service data and the service rule; and the SDN controller is utilized to issue relevant configuration of rate adjustment, so as to complete automatic adjustment of the dedicated line rate.
The method includes the steps that whether service data of private line service in the current time period need to be reconfigured is judged according to a maximum flow prediction value of the private line service in the next time period, if yes, the speed of the private line service is adjusted according to service rules; the business rule comprises a relation between a maximum flow predicted value and an access bandwidth value and a line bandwidth value of a user in a current time period; considering from the service perspective, the service rule further comprises the number of days of line bandwidth in a period of time of the user and the actual bandwidth utilization rate of the user, the service system calls the SDN controller to issue related rate adjustment configuration, generates related orders to record the operation so as to help the user to know the service adjustment condition, simultaneously serves as a certificate for changing service charging of the user, and informs the user through mail to complete the whole automatic speed regulation process.
In the embodiment of the application, the maximum flow prediction value of the private line service in the next time period is predicted and obtained in advance; and automatically adjusting the configuration of the private line service in the next time period according to the maximum flow predicted value and the service data of the private line service in the current time period so as to meet the configuration requirement of the private line service in the next time period. By predicting the service use condition of the user in advance, the problem of poor user experience caused by the situation that the user network is jammed before the special line rate adjustment of the next time period is avoided.
Fig. 2 is a flow chart of another method for automatically adjusting a dedicated line rate according to an embodiment of the present application, as shown in fig. 2, the method includes:
201. and acquiring the traffic data of the private line service and the date corresponding to the traffic data.
Illustratively, the traffic monitoring system may collect traffic conditions of the monitored private line traffic and send traffic data originals to the designated kafka server. The service system acquires the traffic data of the private line service and the date corresponding to the traffic data by subscribing the kafka server message and analyzing the traffic data text. The traffic data comprises inflow traffic, inflow bandwidth utilization, inflow average traffic, outflow bandwidth utilization and outflow average traffic information. For example, the incoming bandwidth utilization of month 3 of 2020 is 80%.
202. And acquiring service data in the service system.
The electronic device invokes the southbound interface through the cloud networking service platform to obtain service data in the service system. Wherein the service data includes a device model, a bandwidth, and a rate.
203. And matching the flow data original text with the service data according to rules to obtain a matching result of the flow data original text and the service data.
Illustratively, the traffic data primitive is matched with the service data in the service system according to the device name, the port name and the VLAN. Thereby obtaining the equipment name, the corresponding port name and the corresponding VLAN of the private line service.
204. And training the traffic flow model according to the traffic flow data, the date and the matching result to obtain a traffic flow prediction model.
Illustratively, the service system periodically transmits the matched result to the AI (Artificial Intelligence ) center by uploading the file offline through an SFTP (SSH File Transfer Protocol, secure file transfer protocol) server. And training the traffic model of the service based on the AI system by utilizing traffic data of the private line service and corresponding dates, thereby obtaining a traffic prediction model.
205. And obtaining a flow prediction value of the special line service in the next time period according to the flow prediction model.
Illustratively, after the AI center completes model training, an API (Application Programming Interface ) interface is encapsulated for the cloud networking service system to call, and the cloud networking service system calls the traffic prediction model to obtain the traffic prediction value of the private line service in the next time period. The traffic prediction function may be day-granularity.
206. And acquiring service data of the private line service of the user in the current time period.
This step may refer to step 102 in fig. 1, and will not be described again.
207. Adjusting the speed of the private line service according to the maximum flow predicted value, the service data and the service rule; and the SDN controller is utilized to issue relevant configuration of rate adjustment, so as to complete automatic adjustment of the dedicated line rate.
This step may be referred to as step 103 in fig. 1, and will not be described again.
In the embodiment of the application, the traffic model of the service is trained based on the AI system by utilizing the traffic data of the private line service and the corresponding date, so as to obtain a traffic prediction model. Predicting and acquiring a maximum flow prediction value of the private line service in the next time period in advance by utilizing a flow prediction model; and automatically adjusting the configuration of the private line service in the next time period according to the maximum flow predicted value and the service data of the private line service in the current time period so as to meet the configuration requirement of the private line service in the next time period. By predicting the service use condition of the user in advance, the problem of poor user experience caused by the situation that the user network is jammed before the special line rate adjustment of the next time period is avoided.
Fig. 3 is a flow chart of another automatic adjustment method for dedicated line rate according to an embodiment of the present application, as shown in fig. 3, the method includes:
301. and acquiring the traffic data of the private line service and the date corresponding to the traffic data.
This step may be referred to as step 201 in fig. 2, and will not be described again.
302. And acquiring service data in the service system.
This step may be referred to as step 202 in fig. 2, and will not be described again.
303. And matching the flow data original text with the service data according to rules to obtain a matching result of the flow data original text and the service data.
This step may be referred to as step 203 in fig. 2, and will not be described again.
304. And training the traffic flow model according to the traffic flow data, the date and the matching result to obtain a traffic flow prediction model.
This step may refer to step 204 in fig. 2, and will not be described again.
305. And obtaining a flow prediction value of the special line service in the next time period according to the flow prediction model.
This step may be referred to as step 205 in fig. 2, and will not be described again.
306. And acquiring an access bandwidth value and a line bandwidth value in the service data.
The electronic device invokes the southbound interface through the cloud networking service platform to obtain an access bandwidth value and a line bandwidth value in service data in the service system.
307. And judging whether the maximum flow predicted value is larger than the access bandwidth value and smaller than the line bandwidth value.
The cloud networking platform determines whether the maximum traffic prediction value of the private line service is greater than the access bandwidth value and less than the line bandwidth value by comparing the relationship between the maximum traffic prediction value and the access bandwidth value and the line bandwidth value. The user access bandwidth value is the bandwidth value actually configured on the equipment by the circuit; the line bandwidth is the bandwidth value of the local physical line of the user, and is generally larger than the access bandwidth, and is used as a reserved value for increasing the circuit rate.
308. If yes, the bandwidth value of the current private line service is configured to be the maximum flow prediction value, and the SDN controller is utilized to issue relevant configuration of rate adjustment, so that the automatic adjustment of the private line rate is completed.
When the maximum flow predicted value is larger than the access bandwidth value and smaller than the line bandwidth value, the cloud networking service system configures the bandwidth value of the current private line service as the maximum flow predicted value, the service system calls the SDN controller to issue rate adjustment related configuration, generates related order records to help the user to know the service adjustment condition, simultaneously serves as a certificate for changing the service charging of the user, and informs the client through mail, so that the intelligent speed regulation whole flow is completed.
In the embodiment of the application, the traffic model of the service is trained based on the AI system by utilizing the traffic data of the private line service and the corresponding date, so as to obtain a traffic prediction model. Predicting and acquiring a maximum flow prediction value of the private line service in the next time period in advance by utilizing a flow prediction model; and the maximum flow prediction value is larger than the access bandwidth value and smaller than the line bandwidth value, and the bandwidth value of the current special line service is configured as the maximum flow prediction value so as to meet the configuration requirement of the special line service in the next time period. By predicting the service use condition of the user in advance, the problem of poor user experience caused by the situation that the user network is jammed before the special line rate adjustment of the next time period is avoided.
Fig. 4 is a flow chart of another automatic adjustment method for dedicated line rate according to an embodiment of the present application, as shown in fig. 4, the method includes:
401. and acquiring the traffic data of the private line service and the date corresponding to the traffic data.
This step may be referred to as step 201 in fig. 2, and will not be described again.
402. And acquiring service data in the service system.
This step may be referred to as step 202 in fig. 2, and will not be described again.
403. And matching the flow data original text with the service data according to rules to obtain a matching result of the flow data original text and the service data.
This step may be referred to as step 203 in fig. 2, and will not be described again.
404. And training the traffic flow model according to the traffic flow data, the date and the matching result to obtain a traffic flow prediction model.
This step may refer to step 204 in fig. 2, and will not be described again.
405. And obtaining a flow prediction value of the special line service in the next time period according to the flow prediction model.
This step may be referred to as step 205 in fig. 2, and will not be described again.
406. And acquiring an access bandwidth value and a line bandwidth value in the service data.
This step may refer to step 306 in fig. 3, and will not be described again.
407. And judging whether the maximum flow predicted value is larger than the access bandwidth value and larger than the line bandwidth value.
The cloud networking platform determines whether the maximum traffic prediction value of the private line service is greater than the access bandwidth value and greater than the line bandwidth value by comparing the relationship between the maximum traffic prediction value and the access bandwidth value and the line bandwidth value.
408. If yes, the bandwidth value of the current special line service is configured as the line bandwidth value.
For example, when the maximum traffic prediction value is greater than the access bandwidth value and greater than the line bandwidth value, the cloud networking service system configures the bandwidth value of the current private line service as the line bandwidth value.
409. And acquiring the number of days reaching the line bandwidth value in a period of time in the service data.
The electronic device invokes the southbound interface through the cloud networking service platform to obtain the number of days that reach the line bandwidth value in a period of time in the service data in the service system. For example, the period of time is 30 days, and the number of days that the actual bandwidth of the user reaches the line bandwidth value is 20 days.
410. Judging whether the number of days is greater than or equal to a first preset threshold.
The cloud networking platform determines whether the number of days is greater than or equal to a first preset threshold by comparing the magnitude relation between the number of days and the first preset threshold. The first preset threshold may be 25 days, and it should be noted that the first preset threshold may be set according to the requirement, which is not limited herein.
411. If yes, the bandwidth value of the special line service is adjusted to be a first preset proportion value of the line bandwidth value, and the SDN controller is utilized to issue relevant configuration of rate adjustment, so that automatic adjustment of the special line rate is completed.
The cloud networking service system may be configured to configure the bandwidth value of the current private line service to a first preset proportional value of the line bandwidth value when the number of days is greater than or equal to a first preset threshold. The first preset ratio may be 80%, and it should be noted that the first preset threshold may be set according to the requirement, which is not limited herein. The service system calls the SDN controller to issue the rate adjustment related configuration, generates related order records to help the user to know the service adjustment condition, simultaneously serves as a certificate for changing the service charging of the user, informs the client through mail, and completes the whole process of automatic speed regulation.
In the embodiment of the application, the traffic model of the service is trained based on the AI system by utilizing the traffic data of the private line service and the corresponding date, so as to obtain a traffic prediction model. Predicting and acquiring a maximum flow prediction value of the private line service in the next time period in advance by utilizing a flow prediction model; the maximum flow forecast value is larger than the access bandwidth value and larger than the line bandwidth value, and the bandwidth value of the current special line service is configured as the maximum flow forecast value; days when the line bandwidth value is reached in a period of time in the service data; and when the number of days is greater than or equal to a first preset threshold value, the bandwidth value of the dedicated line service is adjusted to a first preset proportion value of the line bandwidth value, and from the service perspective, the demand of the dedicated line service in the next time period is reconfigured for the user. By predicting the service use condition of the user in advance, the network configuration is optimized for the user, and the cost of the user service is reduced.
Fig. 5 is a flow chart of another method for automatically adjusting a dedicated line rate according to an embodiment of the present application, as shown in fig. 5, the method includes:
501. and acquiring the traffic data of the private line service and the date corresponding to the traffic data.
This step may be referred to as step 201 in fig. 2, and will not be described again.
502. And acquiring service data in the service system.
This step may be referred to as step 202 in fig. 2, and will not be described again.
503. And matching the flow data original text with the service data according to rules to obtain a matching result of the flow data original text and the service data.
This step may be referred to as step 203 in fig. 2, and will not be described again.
504. And training the traffic flow model according to the traffic flow data, the date and the matching result to obtain a traffic flow prediction model.
This step may refer to step 204 in fig. 2, and will not be described again.
505. And obtaining a flow prediction value of the special line service in the next time period according to the flow prediction model.
This step may be referred to as step 205 in fig. 2, and will not be described again.
506. And acquiring the utilization rate of the bandwidth value of the special line service in the service data within a period of time.
The electronic device invokes the southbound interface through the cloud networking service platform to obtain the utilization rate of the bandwidth value of the private line service in a period of time in the service data in the service system. For example, the line bandwidth value of the private line service is 100, and the actual bandwidth of the user is 70.
507. Judging that the utilization rate is smaller than a second preset threshold value.
The cloud networking platform determines that the utilization rate is smaller than the second preset threshold value by comparing the relationship between the utilization rate and the second preset threshold value. The second preset threshold may be 80%, which needs to be described that the second preset threshold may be set according to the requirement, and is not limited herein.
508. If yes, the bandwidth value of the current private line service is configured to be a second preset proportion value, and the SDN controller is utilized to carry out the issuing of relevant configuration of rate adjustment, so that the automatic adjustment of the private line rate is completed.
The cloud networking service system may further include a second preset ratio value of the line bandwidth value to the bandwidth value of the current private line service. The second preset ratio may be 75%, which should be noted that the second preset threshold may be set according to the requirement, and is not limited herein. The service system calls the SDN controller to issue the rate adjustment related configuration, generates related order records to help the user to know the service adjustment condition, simultaneously serves as a certificate for changing the service charging of the user, informs the client through mail, and completes the whole process of automatic speed regulation.
In the embodiment of the application, the traffic model of the service is trained based on the AI system by utilizing the traffic data of the private line service and the corresponding date, so as to obtain a traffic prediction model. Predicting and acquiring a maximum flow prediction value of the private line service in the next time period in advance by utilizing a flow prediction model; the utilization rate of the bandwidth value of the private line service in a period of time in the service data is obtained; if the utilization rate is smaller than the second preset threshold value, the bandwidth value of the special line service is adjusted to a second preset proportion value of the line bandwidth value, and from the viewpoint of network configuration, network configuration is optimized, and network access configuration is performed for more users.
Fig. 6 is a flow chart of another automatic adjustment method for dedicated line rate according to an embodiment of the present application, as shown in fig. 6, the method includes:
601. and acquiring the traffic data of the private line service and the date corresponding to the traffic data.
This step may be referred to as step 201 in fig. 2, and will not be described again.
602. And acquiring service data in the service system.
This step may be referred to as step 202 in fig. 2, and will not be described again.
603. And matching the flow data original text with the service data according to the equipment name, the port name and the VLAN to obtain the service identification.
The method includes the steps of obtaining a private line device and a corresponding port according to traffic data text and service data, obtaining a VLAN of the private line service, and determining a service identifier of the private line service. The service identifier is the service circuit number of the private line service.
604. And analyzing the flow data text to obtain the time and bandwidth of the service identification.
The method includes the steps of analyzing flow data text of a private line service to obtain a bandwidth of a service identifier and time corresponding to the service.
605. And training the traffic flow model according to the traffic flow data, the date and the matching result to obtain a traffic flow prediction model.
This step may refer to step 204 in fig. 2, and will not be described again.
606. And obtaining a flow prediction value of the special line service in the next time period according to the flow prediction model.
This step may be referred to as step 205 in fig. 2, and will not be described again.
607. And acquiring service data of the private line service of the user in the current time period.
This step may refer to step 102 in fig. 1, and will not be described again.
608. Adjusting the speed of the private line service according to the maximum flow predicted value, the service data and the service rule; and the SDN controller is utilized to issue relevant configuration of rate adjustment, so as to complete automatic adjustment of the dedicated line rate.
This step may be referred to as step 103 in fig. 1, and will not be described again.
In the embodiment of the application, the special line service is determined first, and the traffic model of the service is trained based on the AI system by utilizing the traffic data and the corresponding date of the special line service, so as to obtain the traffic prediction model. Predicting and acquiring a maximum flow prediction value of the private line service in the next time period in advance by utilizing a flow prediction model; and automatically adjusting the configuration of the private line service in the next time period according to the maximum flow predicted value and the service data of the private line service in the current time period so as to meet the configuration requirement of the private line service in the next time period. By predicting the service use condition of the user in advance, the problem of poor user experience caused by the situation that the user network is jammed before the special line rate adjustment of the next time period is avoided.
Fig. 7 is a flowchart of a method for obtaining a flow prediction model according to an embodiment of the present application, as shown in fig. 7, where the method includes:
701. and training the traffic flow model according to the traffic flow data, the date and the matching result to obtain an initial traffic flow prediction model.
Illustratively, the service system periodically transmits the matched result to the AI center in a manner of uploading the file offline through the SFTP server. And training the traffic model of the service based on the AI system by utilizing traffic data of the private line service and corresponding dates, thereby obtaining an initial traffic prediction model.
702. Obtaining a maximum flow prediction value of the next time period according to the initial flow prediction model;
acquiring a flow value of the same time period; wherein the same time period is the same time period as the next time period.
Illustratively, the business system obtains the actual flow value of the user for the same time period by calling the southbound interface. Wherein the same time period is the same time period as the next time period.
703. And comparing the maximum flow predicted value of the next time period with the flow value of the same time period, and verifying the accuracy of the initial flow prediction model.
The cloud networking service platform compares the maximum flow prediction value of the next time period with the flow value of the same time period, and verifies the accuracy of the initial flow prediction model.
704. Judging whether the accuracy is larger than or equal to a third preset threshold value.
For example, the cloud networking service platform determines whether the accuracy is greater than or equal to a third preset threshold. The third preset threshold may be 95%, which should be noted that the third preset threshold may be set according to the requirement, and is not limited herein.
705. If yes, determining the initial flow prediction model as a flow prediction model.
Illustratively, when it is determined that the accuracy of the initial flow prediction model is greater than or equal to the third preset threshold. The AI center completes model training and determines the initial flow prediction model as a flow prediction model.
In the embodiment of the application, the AI center uses the service circuit number in the file as a unique identifier and the model creation granularity, trains the offline traffic model based on the service according to the traffic data transmitted by the cloud networking service system, and invokes the accuracy of the verification model through the cloud networking service platform after the model training is completed to adjust and perfect the model. After the AI center finishes model training, packaging an API interface for the cloud networking service system to call, and providing a flow prediction function with day granularity; the interface takes the service circuit number and the appointed future date as the input parameters, and the output parameters are the predicted peak flow of the special line service on the appointed date.
Fig. 8 is a schematic structural diagram of an adjusting device according to an embodiment of the present application, as shown in fig. 8, the device 800 includes:
an obtaining module 801, configured to obtain a maximum traffic prediction value of a dedicated line service in a next period; wherein the maximum traffic prediction value comprises a bandwidth value;
an obtaining module 801, configured to obtain service data of a private line service in a current time period of a user; the service data comprises a special line rate and a historical bandwidth utilization rate;
a processing module 802, configured to adjust a rate of the dedicated line service according to the maximum traffic prediction value and the service data and the service rule; and the SDN controller is utilized to issue relevant configuration of rate adjustment, so as to complete automatic adjustment of the dedicated line rate.
The obtaining module 801 is further specifically configured to:
acquiring traffic data of a private line service and a date corresponding to the traffic data;
acquiring service data in a service system; the service data comprises equipment model, bandwidth and speed;
matching the flow data original text with the service data according to rules to obtain a matching result of the flow data original text and the service data; wherein, the matching result comprises a service identifier;
training a traffic flow model according to the traffic flow data, the date and the matching result to obtain a traffic flow prediction model;
And obtaining a flow prediction value of the special line service in the next time period according to the flow prediction model.
The processing module 802 is further specifically configured to:
acquiring an access bandwidth value and a line bandwidth value in service data;
if the maximum flow prediction value is larger than the access bandwidth value and smaller than the line bandwidth value, the bandwidth value of the current special line service is configured as the maximum flow prediction value.
The processing module 802 is further specifically configured to:
if the maximum flow predicted value is larger than the access bandwidth value and larger than the line bandwidth value, configuring the bandwidth value of the current special line service as the line bandwidth value;
the number of days reaching the line bandwidth value in a period of time in the service data is obtained;
and if the number of days is greater than or equal to a first preset threshold value, adjusting the bandwidth value of the special line service to be a first preset proportion value of the line bandwidth value.
The processing module 802 is further specifically configured to:
acquiring the utilization rate of the bandwidth value of the private line service in a period of time in the service data;
and if the utilization rate is smaller than a second preset threshold value, adjusting the bandwidth value of the special line service to a second preset proportion value of the line bandwidth value.
The processing module 802 is further specifically configured to:
matching the flow data original text with the service data according to the equipment name, the port name and the VLAN to obtain a service identifier;
And analyzing the flow data text to obtain the time and bandwidth of the service identification.
The processing module 802 is further specifically configured to:
training a traffic flow model according to the traffic flow data, the date and the matching result to obtain an initial traffic flow prediction model;
obtaining a maximum flow prediction value of the next time period according to the initial flow prediction model;
acquiring a flow value of the same time period; wherein the same time period is the same time period as the next time period;
comparing the maximum flow predicted value of the next time period with the flow value of the same time period, and verifying the accuracy of the initial flow prediction model;
and if the accuracy is greater than or equal to a third preset threshold value, determining the initial flow prediction model as a flow prediction model.
The device of the embodiment may execute the technical scheme in the above method, and the specific implementation process and the technical principle are the same and are not described herein again.
Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application, as shown in fig. 9, an electronic device 900 includes: a memory 501 and a processor 902;
wherein the memory 901 is for storing computer instructions executable by the processor;
the processor 902, when executing the computer instructions, implements the steps of the methods of the embodiments described above. Reference may be made in particular to the relevant description of the embodiments of the method described above.
Alternatively, the memory 901 may be separate or integrated with the processor 902. When the memory 901 is provided separately, the detection device further includes a bus for connecting the memory 901 and the processor 902.
Embodiments of the present application also provide a non-transitory computer-readable storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform the method provided by the above embodiments.
The embodiment of the application also provides a computer program product, which comprises: a computer program stored in a readable storage medium, from which at least one processor of an electronic device can read, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any one of the embodiments described above.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. An automatic adjustment method for a dedicated line rate, comprising:
obtaining a maximum flow prediction value of the special line service in the next time period; wherein the maximum traffic prediction value comprises a bandwidth value;
acquiring service data of the private line service in the current time period of the user; wherein, the business data comprises a special line rate and a historical bandwidth utilization rate;
adjusting the speed of the private line service according to the maximum flow predicted value, the service data and the service rule; and the SDN controller is utilized to issue relevant configuration of the special line rate adjustment, so as to complete automatic adjustment of the special line rate.
2. The method of claim 1, wherein obtaining the traffic prediction value of the dedicated line service in the next period of time specifically comprises:
acquiring flow data of a private line service and a date corresponding to the flow data;
acquiring service data in a service system; wherein, the business data comprises equipment model, bandwidth and speed;
Matching the flow data original text with the service data according to rules to obtain a matching result of the flow data original text and the service data; wherein, the matching result comprises a service identifier;
training a traffic flow model according to the traffic data, the date and the matching result to obtain a traffic flow prediction model;
and obtaining the maximum flow prediction value of the special line service in the next time period according to the flow prediction model.
3. The method according to claim 2, wherein adjusting the rate of the dedicated line service according to the maximum traffic prediction value, the service data, and the service rule specifically comprises:
acquiring an access bandwidth value and a line bandwidth value in the service data;
and if the maximum flow predicted value is larger than the access bandwidth value and smaller than the line bandwidth value, configuring the bandwidth value of the current special line service as the maximum flow predicted value.
4. A method according to claim 3, wherein adjusting the rate of the dedicated line service according to the maximum traffic prediction value, the service data, and the service rule, specifically further comprises:
if the maximum flow predicted value is larger than the access bandwidth value and larger than the line bandwidth value, configuring the current bandwidth value of the special line service as the line bandwidth value;
Obtaining the number of days reaching a line bandwidth value in a period of time in the service data;
and if the number of days is greater than or equal to a first preset threshold value, adjusting the bandwidth value of the special line service to be a first preset proportion value of the line bandwidth value.
5. The method according to claim 4, wherein adjusting the rate of the dedicated line service according to the maximum traffic prediction value, the service data, and the service rule, specifically further comprises:
acquiring the utilization rate of the bandwidth value of the private line service in a period of time in the service data;
and if the utilization rate is smaller than a second preset threshold value, adjusting the bandwidth value of the special line service to a second preset proportion value of the line bandwidth value.
6. The method of claim 2, wherein matching the traffic data primitive with the service data according to a rule to obtain a matching result of the traffic data primitive with the service data, and specifically further comprises:
matching the flow data original text with the service data according to the equipment name, the port name and the VLAN to obtain the service identifier;
and analyzing the flow data text to obtain the time and the bandwidth of the service identifier.
7. The method according to claim 6, wherein training the traffic flow model according to the traffic flow data, the date and the matching result to obtain a traffic flow prediction model specifically comprises:
training a traffic flow model according to the traffic data, the date and the matching result to obtain an initial traffic flow prediction model;
obtaining a maximum flow prediction value of the next time period according to the initial flow prediction model;
acquiring a flow value of the same time period; wherein the same time period is the same time period as the next time period;
comparing the maximum flow predicted value of the next time period with the flow value of the same time period, and verifying the accuracy of the initial flow prediction model;
and if the accuracy is greater than or equal to a third preset threshold, determining the initial flow prediction model as a flow prediction model.
8. A dedicated line rate adjustment device, comprising:
the acquisition module is used for acquiring the maximum flow prediction value of the special line service in the next time period; wherein the maximum traffic prediction value comprises a bandwidth value;
the acquisition module is further used for acquiring service data of the private line service in the current time period of the user; wherein, the business data comprises a special line rate and a historical bandwidth utilization rate;
The processing module is used for adjusting the speed of the private line service according to the maximum flow predicted value, the service data and the service rule; and the SDN controller is utilized to issue relevant configuration of the special line rate adjustment, so as to complete automatic adjustment of the special line rate.
9. An electronic device comprising a memory, a processor, the memory having stored therein a computer program executable on the processor, the processor implementing the method of any of the preceding claims 1-7 when the computer program is executed.
10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-7.
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