CN114143830A

CN114143830A - Flow optimization method and device, equipment and storage medium

Info

Publication number: CN114143830A
Application number: CN202111396933.3A
Authority: CN
Inventors: 蔡安宁; 杨星; 孙翔
Original assignee: Lenovo Beijing Ltd
Current assignee: Lenovo Beijing Ltd
Priority date: 2021-11-23
Filing date: 2021-11-23
Publication date: 2022-03-04
Anticipated expiration: 2041-11-23
Also published as: CN114143830B

Abstract

The application discloses a flow optimization method, a device, equipment and a storage medium; the method is applied to a flow monitoring component in the edge computing equipment and comprises the following steps: acquiring a network access parameter of a first application of a terminal; acquiring flow information of a second application in the edge computing device, which is used for providing a service data flow for the first application, based on the network access parameter; wherein the traffic information comprises traffic information at a forwarding node of the second application at least one of: RAN, UPF, data plane; analyzing the flow information to obtain a flow congestion condition corresponding to the second application; and executing a corresponding flow optimization strategy according to the flow congestion condition.

Description

Flow optimization method and device, equipment and storage medium

Technical Field

The embodiment of the application relates to communication technology, and relates to but is not limited to a traffic optimization method, a traffic optimization device, traffic optimization equipment and a traffic optimization storage medium.

Background

Edge Computing (MEC) reduces network delay caused by network transmission and multi-level service forwarding by sinking service to the Edge of the network, and further can meet the strict requirements of services such as a fifth generation Mobile communication technology (5 generation Mobile networks, 5G) on bandwidth, delay and the like. There is no good solution for how to monitor the traffic congestion status of the edge application deployed in the edge computing device and to provide a corresponding traffic optimization strategy.

Disclosure of Invention

In view of this, the traffic optimization method, apparatus, device, and storage medium provided in the embodiments of the present application are implemented as follows:

according to an aspect of the embodiments of the present application, there is provided a traffic optimization method applied to a traffic monitoring component in an edge computing device, the method including: acquiring a network access parameter of a first application of a terminal; acquiring flow information of a second application in the edge computing device, which is used for providing a service data flow for the first application, based on the network access parameter; wherein the traffic information comprises traffic information at a forwarding node of the second application at least one of: RAN, UPF, data plane; analyzing the flow information to obtain a flow congestion condition corresponding to the second application; and executing a corresponding flow optimization strategy according to the flow congestion condition.

According to an aspect of an embodiment of the present application, there is provided a traffic optimization apparatus, deployed on an edge computing device, including: the first acquisition module is used for acquiring network access parameters of a first application of the terminal; a second obtaining module, configured to obtain, by using the network access parameter, traffic information of a second application in the edge computing device, where the second application is used to provide a service data flow for the first application; wherein the traffic information comprises traffic information at a forwarding node of the second application at least one of: RAN, UPF, data plane; the analysis module is used for analyzing the flow information to obtain the flow congestion condition corresponding to the second application; and the execution module is used for executing a corresponding flow optimization strategy according to the flow congestion condition.

According to an aspect of the embodiments of the present application, there is provided an electronic device, including a memory and a processor, the memory storing a computer program executable on the processor, and the processor implementing the method of the embodiments of the present application when executing the program.

According to an aspect of the embodiments of the present application, there is provided a computer-readable storage medium on which a computer program is stored, the computer program, when executed by a processor, implementing the method provided by the embodiments of the present application.

In the embodiment of the present application, a traffic monitoring component deployed on an edge computing device obtains a network access parameter of a first application of a terminal, obtains traffic information of each forwarding node of a second application in the edge computing device based on the network access parameter, analyzes the traffic information, obtains a traffic congestion status corresponding to the second application, and executes a corresponding traffic optimization policy according to the traffic congestion status.

It can be seen that, in the embodiment of the present application, first, the traffic monitoring component monitors the second application on the edge computing device, rather than monitoring the entire device, so that traffic optimization can be performed on the second application more specifically, and thus the second application can better serve the first application. Secondly, when acquiring the traffic information of the second application in the edge computing device, the traffic monitoring component does not only acquire the traffic information at the radio network RAN, but at least acquires the traffic information at each forwarding node including the radio network RAN, the user plane UPF, the data plane, and the like. Therefore, on one hand, the traffic congestion condition of the second application can be more accurately reflected by acquiring the traffic information of the second application at each forwarding node, so that a more reasonable optimization scheme is provided; on the other hand, the traffic information of the second application at each node is acquired, so that the traffic congestion condition of the second application at each node can be identified, and the occurrence position of the traffic congestion and the cause of the congestion can be more accurately found.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.

The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

Fig. 1 is a schematic flow chart illustrating an implementation of a traffic optimization method according to an embodiment of the present application;

fig. 2 is a schematic flow chart illustrating an implementation of a traffic optimization method according to an embodiment of the present application;

fig. 3 is a schematic flow chart illustrating an implementation of a traffic optimization method according to an embodiment of the present application;

FIG. 4 is a schematic diagram of traffic transmission of an edge computing device in the related art;

fig. 5 is a schematic diagram of traffic transmission in an edge computing device according to an embodiment of the present disclosure;

fig. 6 is a schematic flow chart of an implementation of a processing method for flow optimization according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of a flow rate optimizing device according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, specific technical solutions of the present application will be described in further detail below with reference to the accompanying drawings in the embodiments of the present application. The following examples are intended to illustrate the present application but are not intended to limit the scope of the present application.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.

In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.

It should be noted that reference to the terms "first \ second \ third" in the embodiments of the present application does not denote a particular ordering with respect to the objects, and it should be understood that "first \ second \ third" may be interchanged under certain circumstances or of a certain order, such that the embodiments of the present application described herein may be performed in an order other than that shown or described herein.

With the rapid increase of the number of devices accessing the internet, a mode of uploading data of all the devices to a cloud computing center through a network and solving the problem of computing requirements of the devices by using computing capacity of the cloud computing center is insufficient, for example, the computing speed cannot meet the real-time requirement, the user data privacy has a leakage risk, the energy consumption of the cloud computing center is large, and the like.

The edge computing is a novel computing mode for executing computing at the network edge, the edge computing equipment has the processing capacity of executing computing and data analysis, part or all of computing tasks executed by an original cloud computing model are transferred to the network edge computing equipment, the computing load of a cloud server is reduced, the pressure of network bandwidth is relieved, and the data processing efficiency in the world of everything interconnection is improved. The edge computing is not used for replacing cloud computing, but is used for supplementing cloud computing, and a better computing platform is provided for relevant technologies such as mobile computing and the Internet of things.

The embodiment of the application provides a flow optimization method, which is applied to edge computing equipment, wherein the edge computing equipment refers to electronic equipment for transferring an artificial intelligence algorithm from a cloud computing center to a network edge. The electronic device may be various types of devices with information processing capability in the implementation process, for example, the electronic device may include a personal mobile device (e.g., a smartphone and a wearable device), and may also be a terminal device (e.g., a gateway, a surveillance camera, a bank ATM), or other internet of things devices. The functions implemented by the method can be implemented by calling program code by a processor in an electronic device, and the program code can be stored in a computer storage medium.

Fig. 1 is a schematic implementation flowchart of a flow optimization method provided in an embodiment of the present application, where the method is applied to a flow monitoring component in an edge computing device, as shown in fig. 1, the method may include the following steps 101 to 104:

step 101, a traffic monitoring component obtains a network access parameter of a first application of a terminal.

In some embodiments, the Traffic monitoring component is a Traffic Engine (Traffic Engine), which, as a service component of the edge computing device, is capable of collecting Traffic information at the data forwarding node and has processing capability for the Traffic information.

In some embodiments, the network access parameters include at least one of: internet Protocol (IP) address, port, Protocol number.

In some embodiments, step 101 may be implemented by performing steps 201 to 202 in the following embodiments.

Step 102, the traffic monitoring component obtains traffic information of a second application in the edge computing device, which is used for providing a service data flow for the first application, based on the network access parameter.

Wherein the traffic information comprises traffic information at the forwarding node for the second application at least one of: a Radio Access Network (RAN), a User Port Function (UPF), and a data plane.

In the embodiment of the present application, when acquiring traffic information of the second application in the edge computing device, the traffic monitoring component does not acquire only traffic information at the wireless network RAN, but acquires at least traffic information at each forwarding node including the wireless network RAN, a user plane, and a data plane. Therefore, on one hand, the traffic congestion condition of the second application can be more accurately reflected by acquiring the traffic information of the second application at each forwarding node, so that a more reasonable optimization scheme is provided; on the other hand, the traffic information of the second application at each node of the whole transmission link is acquired, so that the traffic congestion condition of the second application at each node can be identified, and the occurrence position of the traffic congestion and the cause of the congestion can be more accurately found.

In some embodiments, the second application is an Edge application (MEC APP).

In some embodiments, the traffic information includes at least one of: uplink bandwidth, downlink bandwidth, packet loss information and network delay.

And 103, analyzing the flow information by the flow monitoring component to obtain a flow congestion state corresponding to the second application.

In some embodiments, the traffic monitoring component analyzes the traffic congestion status of the second application by periodically collecting traffic information of the second application at each forwarding node and based on the traffic information at each forwarding node. In some embodiments, the traffic congestion conditions include traffic congestion level, congestion percentage, and the like.

And 104, executing a corresponding flow optimization strategy by the flow monitoring component according to the flow congestion condition.

In some embodiments, step 104 may be implemented by performing steps 303 to 304 in the following embodiments.

In the embodiment of the application, the traffic monitoring component monitors the second application, rather than monitoring the whole terminal device, so that traffic optimization can be performed on the second application in a more targeted manner, and the second application can better serve the first application.

The traffic congestion condition at least comprises the occurrence position of the traffic congestion and the occurrence reason of the traffic congestion. Accordingly, the traffic monitoring component can perform a corresponding traffic optimization strategy in a targeted manner according to the specific occurrence position and the occurrence reason of the traffic congestion.

Fig. 2 is a schematic flow chart of an implementation flow of a traffic optimization method provided in an embodiment of the present application, where the method is applied to a traffic monitoring component in an edge computing device, as shown in fig. 2, the method may include the following steps 201 to 205:

in step 201, the traffic monitoring component receives a monitoring request initiated by a second application.

It should be noted that the monitoring request may be initiated by the second application according to an actual requirement, that is, when the second application determines that it needs a congestion condition of the monitored traffic, the monitoring request may be initiated to the traffic monitoring component.

Step 202, the traffic monitoring component analyzes the monitoring request to obtain the network access parameter of the first application carried by the monitoring request.

Here, the second application is used to provide a traffic data stream for the first application on the terminal device. For example, assuming that a first application of the terminal is an application a, the application a has a video playing function, and when the application a plays a certain video, a service request may be initiated to a second application, and a network access parameter of the application a is sent to the second application, so that the second application provides a service data stream for the first application. In some embodiments, the second application is an edge server.

After receiving the network access parameter sent by the first application, the second application can also send the network access parameter to the traffic monitoring component when the second application initiates a monitoring request to the traffic monitoring component; or, the monitoring request is sent to the traffic monitoring component together with the network access parameter, which is not limited to this.

Step 203, the traffic monitoring component acquires traffic information of a second application in the edge computing device, which is used for providing a service data flow for the first application, based on the network access parameter; wherein the traffic information comprises traffic information at the forwarding node for the second application at least one of: RAN, UPF, data plane;

and 204, analyzing the flow information by the flow monitoring component to obtain a flow congestion position corresponding to the second application.

In the embodiment of the present application, the location where the traffic congestion occurs in the second application may be at least one of the nodes (such as RAN, UPF or data plane) that forward the traffic data stream in the edge computing device.

In step 205, the traffic monitoring component executes a corresponding congestion adjustment policy according to the traffic congestion position.

In some embodiments, step 205 may be implemented by performing steps 303 to 304 in the following embodiments.

In the embodiment of the application, the traffic monitoring component determines the traffic congestion position and executes the corresponding congestion adjustment strategy based on the traffic congestion position, so that the traffic congestion can be controlled more specifically, and the traffic congestion problem can be improved better.

In some embodiments, the traffic monitoring component may also retrieve traffic information of the second application according to the traffic congestion condition to determine the traffic congestion condition of the second application based on the retrieved traffic information.

Here, the timing at which the traffic monitoring component re-acquires the traffic information of the second application is not limited. In some embodiments, the traffic monitoring component may also retrieve traffic information for the second application and re-determine the traffic congestion condition for the second application if no traffic congestion occurs for the second application. The mode of periodically acquiring the traffic information can avoid the phenomenon that the second application is not monitored due to traffic congestion within a certain time.

In other embodiments, the traffic monitoring component may further re-acquire the traffic information of the second application and re-determine the traffic congestion condition of the second application in case of traffic congestion of the second application. Therefore, on one hand, the traffic monitoring component can continue to monitor the second application to determine whether the second application continues to generate traffic congestion in the next time period, or the traffic monitoring component continues to monitor whether the second application still has traffic congestion after the adjustment policy is executed; on the other hand, if the traffic monitoring component determines again that there is no traffic congestion for the second application within a short interval, then there is no need to adjust the traffic congestion status of the second application, thereby saving the power consumption of the device.

Fig. 3 is a schematic implementation flowchart of a flow optimization method provided in this embodiment, where the method is applied to a flow monitoring component in an edge computing device, as shown in fig. 3, the method may include the following steps 301 to 304:

301, a traffic monitoring component acquires a network access parameter of a first application of a terminal;

step 302, the traffic monitoring component obtains traffic information of a second application in the edge computing device, which is used for providing a service data flow for the first application, based on the network access parameter; wherein the traffic information comprises traffic information at the forwarding node for the second application at least one of: RAN, UPF, data plane;

step 303, the traffic monitoring component obtains a data transmission indicator of the second application.

It will be appreciated that the second application is data transfer indexed in providing the traffic data stream to the first application. When the data transmission index of the second application cannot be satisfied at the forwarding node, it is indicated that traffic congestion may occur at the forwarding node.

Here, the manner of determining the data transmission index of the second application is not limited. For example, in some embodiments, the data transmission indicator is preset, and the traffic monitoring component may directly obtain the data transmission indicator of the second application.

For another example, in other embodiments, the data transmission indicator of the second application may be determined by the traffic congestion condition of the second application at the historical time, such as by performing the following steps 3031 to 3032:

step 3031, the traffic monitoring component obtains the historical traffic congestion position of the second application and the corresponding historical adjustment parameter.

Here, what the traffic monitoring component obtains is the location where the second application generates traffic congestion within the history period (i.e. the forwarding node generating traffic congestion) and the corresponding tuning parameters (i.e. the tuning policy) at said location. It is to be appreciated that when the historical period is sufficiently long, the traffic monitoring component is generally capable of obtaining corresponding adjustment parameters for the second application when traffic congestion occurs at various forwarding nodes.

Step 3032, the traffic monitoring component determines the data transmission index of the second application according to the historical traffic congestion position and the corresponding historical adjustment parameter.

Here, after determining the data transmission index of the second application, the traffic monitoring component may monitor the second application according to the received monitoring request to determine whether the second application satisfies the data transmission index at each forwarding node, and if not, may execute a corresponding adjustment policy.

In some embodiments, the traffic monitoring component may further determine, according to the historical traffic congestion position of the second application and the corresponding adjustment scheme, a more reasonable optimization scheme for the second application at each forwarding node. In this way, the traffic monitoring component can intelligently monitor and optimize traffic of the second application based on the historical optimization scheme without involvement of the second application in the case that the second application does not initiate a monitoring request.

And step 304, the traffic monitoring component executes a corresponding congestion adjustment strategy according to the traffic congestion position and the data transmission index.

In the embodiment of the present application, when executing the congestion adjustment policy, the traffic monitoring component executes the congestion adjustment policy according to the traffic congestion position and the data transmission index, that is, adjusts the congestion adjustment policy according to the actual quality requirement of the second application, so as to conveniently adjust the traffic condition of the second application to the transmission index value at one time.

In some embodiments, step 304 may be implemented by performing steps 3041 through 3043 as follows:

in step 3041, in the case that the traffic congestion position is at the RAN, the traffic monitoring component adjusts the impact parameter of the data transmission rate of the RAN according to the data transmission index.

Here, the first adjustment value of the parameter affecting the data transmission rate of the RAN may be determined; then, the data transmission rate is adjusted according to the first adjustment value. The first adjustment value may be a difference value or a target value, which is not limited in this application.

Step 3042, in case the traffic congestion location is at the UPF, the traffic monitoring component adjusts the bandwidth and/or priority occupied in the UPF by the second application according to the data transmission index.

Here, the determination may be made by determining a second adjustment value for the bandwidth occupied by the second application in the UPF and/or a third adjustment value for the priority; and then adjusting the bandwidth occupied by the edge application in the UPF according to the second adjustment value, and/or adjusting the priority of the edge application in the UPF according to the third adjustment value.

Step 3043, in case the congested location of the traffic is at the data plane, the traffic monitoring component adjusts the bandwidth and/or priority occupied by the second application in the data plane according to the data transmission index.

Here, the fourth adjustment value of the bandwidth occupied in the Data Plane and/or the fifth adjustment value of the priority can be applied by determining the edge; the bandwidth occupied by the edge application in the Data Plane is then adjusted according to the fourth adjustment value and/or the priority of the edge application in the Data Plane is adjusted according to the fifth adjustment value.

In some embodiments, after executing the corresponding traffic optimization strategy according to the traffic congestion condition, the method further includes:

the flow monitoring component sends a prompt message to the second application according to the data transmission parameters of each forwarding node, so that the second application can reduce the data transmission index according to the prompt message; and the prompting message is used for prompting that the current data transmission index of the second application cannot be met.

Here, the form of the data transmission parameter is not limited, and the data transmission parameter may be a rate, or may be a data amount forwarded in a period of time, for example.

In addition, the timing of sending the prompt message by the traffic monitoring component is not limited. For example, after executing the corresponding optimization policy on the forwarding node, the traffic monitoring component determines that the data transmission indicator is still not satisfied, and then may send a prompt message to the second application, so that the second application reduces its own data transmission indicator. Alternatively, the traffic monitoring component may send a prompt message at any time to prompt the second application to lower its own data transmission indicator.

The traffic calculated at the MEC edge mainly aims at data from the user terminal to the edge application APP (as shown in fig. 4), is accessed through the radio access network RAN in the middle, and reaches the edge computing platform MEP through the core network user plane function UPF, and is handled by the edge application MEC APP of the MEP.

Remote Network Interface Specification (RNIS) is a Service provided on an edge platform (an example of an edge computing device) and is responsible for collecting wireless Network information related to an edge application, including Cell-granular Physical Resource Block (PRB) utilization information on RAN, metric information of L2 (Bear-granular PRB utilization, Gigabit Ethernet (GBE) in a Cell, number of users borne by non-Guaranteed Bit Rate (non-gbr), GDB of a Cell, bearer Rate of non-gbr, throughput, traffic, and the like), and Bear-granular PRB utilization of a terminal, RAB information (e.g., Quality of Service (QoS) parameters), MEC APP interacts with MEP to obtain a user identity ID, and may provide traffic characteristics (e.g., user identity ID, IP address of Service, QoS address of MEP) to a radius of MEP, Port, protocol, etc.), obtaining wireless network information, the edge application determining the states of congestion, packet loss, etc. of its own traffic at RAN based on the information, and then making corresponding processing by the edge application itself, such as interacting with Bandwidth Management function (BWMS) of MEP, adjusting QoS parameters, such as GBR (guaranteed Bandwidth rate) and MBR (maximum Bandwidth rate) at RAN, or MEC APP adjusting its QoS requirements, such as reducing video code rate (video traffic) to reduce Bandwidth requirements, thereby achieving the purpose of optimizing Service quality.

Among them, flow optimization has the following problems:

(1) firstly, an edge application MEC APP is required to understand wireless network information, whether the QoS problem exists in the service flow of the edge application MEC APP is judged, meanwhile, the flow can be optimized, and the requirements for the edge application are provided: the RNIS provides resource occupancy rate information of the wireless network (e.g. utilization rate of physical resource blocks, bandwidth utilization rate of bearers, quality of signals, etc.), belongs to a low-level interface, and has no direct mapping relation with the traffic characteristic parameters (embodied as addresses, ports, protocols of applications), and the edge application MEC APP needs to be processed by a complex service flow to obtain wireless network resources corresponding to its own service flow; the processing procedure involves the professional knowledge of the mobile network;

(2) there is only a single source of information: the RNIS information only provides air interface related information of the RAN, lacks complete perception of the whole link in the transmission process, cannot identify the service quality problem caused by other nodes, cannot determine the reason of poor service experience by the edge application MEC APP, and cannot find the network location and the reason thereof causing the poor experience;

(3) similarly, the edge application of the MEC APP can only configure and control a specific network node, but cannot perform targeted regulation and control, and the regulation and control effect is limited;

(4) under the condition that a plurality of MEC APPs are deployed simultaneously, conflicting service quality requirements may exist for the edge application MEC APP as well; meanwhile, the part of feedback, analysis and optimization processing is dispersed in each edge application, so that repeated optimization processing exists, and the deployment cost of the system is increased;

(5) finally, each edge application needs to interact with multiple services (such as User Experience Identity (UEID), BWMS, etc.) or the periphery on the MEP, so as to achieve the purpose of traffic optimization, increase the complexity of the service, and increase the failure rate due to high coupling.

In order to solve the above problem, in the embodiment of the present application, a Traffic Engine (Traffic Engine) is introduced, which can be used as a service for edge calculation, collect network service quality data, and analyze the cause of the service quality to provide service quality sensing and Traffic optimization service capability to the outside (edge application MEC APP).

As shown in fig. 5, Traffic Engine monitors the information of the RNIS wireless network, the Traffic statistical information of the core network UPF or Data plane, and integrates the information of multiple nodes to analyze the following information:

(1) whether the current edge application MEC APP has the condition of service quality;

(2) the location of the network where the problem occurred.

The Traffic Engine can provide the service quality condition to the edge application MEC APP; the edge application MEC APP, acting as a consumer or user of Traffic Engine, can obtain the quality of service conditions (e.g., whether there is Traffic congestion).

The Traffic Engine can receive the service quality requirement of the edge application MEC APP; synthesizing the service quality requirements of a plurality of applications, and giving specific service quality parameters aiming at the network equipment causing the service quality, such as:

(1) modify policy (MBR, GBR, etc.) of radio Bear at RAN over RIC interface;

(2) or modifying the bandwidth, priority and the like of the MEC APP in the core network UPF;

(3) or modifying the bandwidth, priority and the like of the MEC APP in the MEP Dataplane;

(4) docking a core network control plane, and issuing QoS parameters;

(5) or the return network can not meet the required quality requirement, and the MEC APP adjusts (reduces) the service quality requirement; multiple interactions may be performed to achieve a final acceptable quality of service requirement.

The edge application MEC APP as a consumer or user of Traffic Engine can obtain the service quality status (e.g. whether congestion occurs) from the edge application MEC APP, and the Traffic Engine automatically completes the purpose of Traffic optimization.

In the embodiment of the application, (1) Traffic Engine is externally unified through an edge computing platform, converts professional wireless network information into flow bandwidth information which can be understood by edge application, and can directly provide an interface for edge application MEC APP to judge whether data flow congestion exists, so that the complexity of RNIS service use is reduced; the knowledge requirement of the edge application on the wireless network is reduced, and the development difficulty is reduced.

(2) Traffic Engine interacts with a plurality of internal services, complex analysis and processing flows are shielded, edge application MEC APP only interacts with Traffic Engine, complexity of edge application MEC APP is simplified, light weight is achieved, business is concentrated on, and redundancy of a flow optimization function in edge application is avoided.

(3) Traffic Engine as unified edge calculation Traffic optimization can provide richer and stronger capability, such as Traffic monitoring, which not only can monitor Traffic statistical information of RAN, but also can count Traffic information on a core network UPF and a Data plane, help the Traffic congestion condition of edge application MEC APP to be reflected more accurately, and provide a more reasonable optimization scheme.

(4) Traffic Engine can rely on the AI ability that the edge computing platform calculated, need not MEC APP and provide the quality of service demand that can intelligent recognition MEC APP to and the reason that the quality of service problem appears, help the edge to use more rationally, optimize the flow high-efficiently, can provide certain extended capability simultaneously, satisfy the specific demand of MEC APP.

(5) Traffic Engine provides Traffic optimization function externally in the form of MEC edge computing platform micro-service.

Fig. 6 shows a processing method for traffic optimization, which, as shown in fig. 6, includes the following steps 601 to 605:

step 601, an edge application MEC APP initiates a request (i.e. a monitoring request), a consumer serving as an MEP platform traffic optimization engine uses the service, and provides corresponding traffic characteristic parameters (i.e. network access parameters), such as an ip address, a port or a protocol number of a terminal;

step 602, after receiving the request from the MEC APP, the Traffic Engine performs corresponding monitoring according to the Traffic characteristics provided by the edge application, where the monitoring includes the Traffic of the radio access network RAN, the user Plane function UPF, and the Data Plane (i.e. an example of a forwarding node), such as the statistical information of the conditions of the uplink and downlink bandwidth of the Traffic of the characteristics, whether packet loss exists, delay, and the like (i.e. an example of Traffic information);

step 603, the Traffic Engine analyzes the statistical data collected from different network element nodes/data planes according to the Traffic monitoring;

step 604, Traffic Engine synthesizes the collected statistical data, and determines whether the edge application MEC APP has Traffic congestion (or congestion level, percentage, etc.); if so, go to step 605; otherwise, returning to step 602, and continuing to monitor the traffic congestion condition of the MEC APP.

And step 605, giving corresponding countermeasures for the traffic congestion condition of the MEC APP. Specifically, different policies are implemented for different traffic congestion locations. Such as traffic congestion occurring at the radio access network RAN, it is possible to adjust parameters (guaranteed bit rate GBR or maximum bit rate MBR, etc.) at the RAN through RIC API interface (a 1-P); if the congestion occurs in the UPF, the bandwidth of the port where the traffic is located needs to be adjusted, or the Data Plane has congestion, and accordingly, the traffic parameter of the Data Plane needs to be adjusted, so that the purpose of optimizing the traffic is finally achieved.

In some embodiments, Traffic Engine may also introduce an Artificial Intelligence (AI) capability, and may provide some relatively reasonable and comprehensive optimization schemes according to Traffic characteristics, congestion conditions, and places where edge application MEC APPs are collected in an edge computing platform MEP, so as to achieve Traffic optimization Intelligence.

In the embodiment of the present application, a concept of a Traffic Engine (Traffic Engine) is introduced first, which may exist as a micro service of an edge computing platform or in other manners, and the Traffic Engine provides a simple and easy-to-use capability of feeding back the service flow quality of an edge application MEC APP to the outside by using information provided by a network-related complex concept, such as an RNIS service. Secondly it can interact with a plurality of services inside the MEC platform, accomplishes functions such as control, analysis and optimization of marginal application MEC APP flow, does not need MEC APP to monitor and optimize the processing to data flow, has reduced the interface and the logic complexity of using. Again it may provide a similar end-to-end optimization scheme, including the entire path traversed by the edge application Data traffic, such as RAN, UPF or Data Plane, etc. Finally, the method can also intelligently identify the flow characteristics of the edge application MEC APP collected in the edge computing platform MEP by means of AI (automatic instruction) capability, learn reasonable parameters of the data flow to form experience, and perform flow optimization based on historical experience; the intelligent flow optimization is achieved by intelligently identifying the application without application participation and actively optimizing.

It should be noted that although the various steps of the methods in this application are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the shown steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.

Based on the foregoing embodiments, the present application provides a flow optimization device, which includes modules and units included in the modules, and can be implemented by a processor; of course, the implementation can also be realized through a specific logic circuit; in implementation, the processor may be a Central Processing Unit (CPU), a Microprocessor (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like.

Fig. 7 is a schematic structural diagram of a traffic optimization apparatus according to an embodiment of the present application, and as shown in fig. 7, the traffic optimization apparatus is deployed on an edge computing device, and the traffic optimization apparatus 700 includes a first obtaining module 701, a second obtaining module 702, an analyzing module 703, and an executing module 704, where:

a first obtaining module 701, configured to obtain a network access parameter of a first application of a terminal; a second obtaining module 702, configured to obtain, by using the network access parameter, traffic information of a second application in the edge computing device, where the second application is used to provide a service data flow for the first application; wherein the traffic information comprises traffic information at a forwarding node of the second application at least one of: RAN, UPF, data plane; an analysis module 703, configured to analyze the traffic information to obtain a traffic congestion status corresponding to the second application; and the executing module 704 is configured to execute a corresponding traffic optimization policy according to the traffic congestion status.

In some embodiments, the first obtaining module 701 further includes a receiving unit and a parsing unit, where the receiving unit is configured to receive the monitoring request initiated by the second application; the analysis unit is used for analyzing the monitoring request to obtain the network access parameter of the first application carried by the monitoring request; wherein the network access parameters are sent by the first application to the second application.

In some embodiments, the traffic congestion condition comprises a traffic congestion location; the executing module 704 includes an adjusting unit, configured to execute a corresponding congestion adjusting policy according to the traffic congestion position.

In some embodiments, the execution module 704 further includes a first obtaining unit, configured to obtain a data transmission indicator of the second application; and the adjusting unit is used for executing a corresponding congestion adjusting strategy according to the flow congestion position and the data transmission index.

In some embodiments, the obtaining unit further includes an obtaining subunit and a determining subunit, where the obtaining subunit is configured to obtain a historical traffic congestion location and a corresponding historical adjustment parameter of the second application; the determining subunit is configured to determine the data transmission indicator of the second application according to the historical traffic congestion position and the corresponding historical adjustment parameter.

In some embodiments, the apparatus 700 further includes a sending module, where the sending module is configured to send a prompt message to the second application according to the data transmission parameter of each forwarding node, so that the second application reduces the data transmission indicator according to the prompt message; and the prompt message is used for prompting that the current data transmission index of the second application cannot be met.

In some embodiments, the executing module 704 includes a second obtaining unit, and the second obtaining unit is configured to obtain the traffic information of the second application again according to the traffic congestion condition, so as to determine the traffic congestion condition of the second application based on the newly obtained traffic information.

The above description of the apparatus embodiments, similar to the above description of the method embodiments, has similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the apparatus of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.

It should be noted that, in the embodiment of the present application, the division of the flow optimization device shown in fig. 7 into modules is schematic, and is only one logical function division, and there may be another division manner in actual implementation. In addition, functional units in the embodiments of the present application may be integrated into one processing unit, may exist alone physically, or may be integrated into one unit by two or more units. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. Or may be implemented in a combination of software and hardware.

It should be noted that, in the embodiment of the present application, if the method described above is implemented in the form of a software functional module and sold or used as a standalone product, it may also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing an electronic device to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.

An electronic device according to an embodiment of the present application is provided, fig. 8 is a schematic diagram of a hardware entity of the electronic device according to the embodiment of the present application, and as shown in fig. 8, the electronic device 800 includes a memory 801 and a processor 802, the memory 801 stores a computer program that can be executed on the processor 802, and the processor 802 implements the steps in the method provided in the embodiment when executing the program.

It should be noted that the Memory 801 is configured to store instructions and applications executable by the processor 802, and may also buffer data (e.g., image data, audio data, voice communication data, and video communication data) to be processed or processed by each module in the processor 802 and the electronic device 800, and may be implemented by a FLASH Memory (FLASH) or a Random Access Memory (RAM).

Embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps in the methods provided in the above embodiments.

Embodiments of the present application provide a computer program product containing instructions which, when run on a computer, cause the computer to perform the steps of the method provided by the above-described method embodiments.

Here, it should be noted that: the above description of the storage medium and device embodiments is similar to the description of the method embodiments above, with similar advantageous effects as the method embodiments. For technical details not disclosed in the embodiments of the storage medium, the storage medium and the device of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.

It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" or "some embodiments" means that a particular feature, structure or characteristic described in connection with the embodiments is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" or "in some embodiments" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments. The foregoing description of the various embodiments is intended to highlight various differences between the embodiments, and the same or similar parts may be referred to each other, and for brevity, will not be described again herein.

The term "and/or" herein is merely an association relationship describing an associated object, and means that three relationships may exist, for example, object a and/or object B, may mean: the object A exists alone, the object A and the object B exist simultaneously, and the object B exists alone.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice, such as: multiple modules or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or modules may be electrical, mechanical or other.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules; can be located in one place or distributed on a plurality of network units; some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, all functional modules in the embodiments of the present application may be integrated into one processing unit, or each module may be separately regarded as one unit, or two or more modules may be integrated into one unit; the integrated module can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.

Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing an electronic device to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.

The methods disclosed in the several method embodiments provided in the present application may be combined arbitrarily without conflict to obtain new method embodiments.

Features disclosed in several of the product embodiments provided in the present application may be combined in any combination to yield new product embodiments without conflict.

The features disclosed in the several method or apparatus embodiments provided in the present application may be combined arbitrarily, without conflict, to arrive at new method embodiments or apparatus embodiments.

The above description is only for the embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for traffic optimization, the method being applied to a traffic monitoring component in an edge computing device, the method comprising:

acquiring a network access parameter of a first application of a terminal;

acquiring flow information of a second application in the edge computing device, which is used for providing a service data flow for the first application, based on the network access parameter; wherein the traffic information comprises traffic information at a forwarding node of the second application at least one of: RAN, UPF, data plane;

analyzing the flow information to obtain a flow congestion condition corresponding to the second application;

and executing a corresponding flow optimization strategy according to the flow congestion condition.

2. The method of claim 1, wherein the obtaining network access parameters of the first application of the terminal comprises:

receiving a monitoring request initiated by the second application;

analyzing the monitoring request to obtain the network access parameter of the first application carried by the monitoring request; wherein the network access parameters are sent by the first application to the second application.

3. The method of claim 1, wherein the traffic congestion status comprises a traffic congestion location, and wherein the performing a corresponding traffic optimization strategy according to the traffic congestion status further comprises:

and executing a corresponding congestion adjustment strategy according to the flow congestion position.

4. The method of claim 3, wherein the performing a corresponding congestion adjustment policy according to the traffic congestion location comprises:

acquiring a data transmission index of the second application;

and executing a corresponding congestion adjustment strategy according to the flow congestion position and the data transmission index.

5. The method of claim 4, wherein the obtaining the data transmission indicator of the second application comprises:

acquiring a historical traffic congestion position and a corresponding historical adjustment parameter of the second application;

and determining a data transmission index of the second application according to the historical traffic congestion position and the corresponding historical adjustment parameter.

6. The method of claim 1, wherein after the executing the corresponding traffic optimization policy according to the traffic congestion condition, the method further comprises:

sending a prompt message to the second application according to the data transmission parameters of the forwarding nodes, so that the second application can reduce the data transmission index according to the prompt message; and the prompt message is used for prompting that the current data transmission index of the second application cannot be met.

7. The method of claim 3, wherein the performing a corresponding traffic optimization policy according to the traffic congestion status further comprises:

and according to the traffic congestion condition, re-acquiring the traffic information of the second application, so as to determine the traffic congestion condition of the second application based on the newly acquired traffic information.

8. The method according to any of claims 1 to 7, wherein the traffic information comprises at least one of: uplink bandwidth, downlink bandwidth, packet loss information and network delay.

9. A traffic optimization apparatus deployed on an edge computing device, the traffic optimization apparatus comprising:

the first acquisition module is used for acquiring network access parameters of a first application of the terminal;

a second obtaining module, configured to obtain, by using the network access parameter, traffic information of a second application in the edge computing device, where the second application is used to provide a service data flow for the first application; wherein the traffic information comprises traffic information at a forwarding node of the second application at least one of: RAN, UPF, data plane;

the analysis module is used for analyzing the flow information to obtain the flow congestion condition corresponding to the second application;

and the execution module is used for executing a corresponding flow optimization strategy according to the flow congestion condition.

10. The apparatus of claim 9, wherein the traffic congestion condition comprises a traffic congestion location; the execution module comprises an adjusting unit used for executing a corresponding congestion adjusting strategy according to the flow congestion position.