CN114143830B - Flow optimization method and device, equipment and storage medium - Google Patents
Flow optimization method and device, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a flow optimization method and device, equipment and a storage medium; wherein the method is applied to a traffic monitoring component in an edge computing device, the method comprising: acquiring network access parameters of a first application of a terminal; acquiring flow information of a second application used for providing service data flow for the first application in the edge computing equipment based on the network access parameters; wherein the traffic information comprises traffic information of the second application at a forwarding node of 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
Technical Field
The embodiment of the application relates to a communication technology, and relates to, but is not limited to, a flow optimization method, a flow optimization device, flow optimization equipment and a storage medium.
Background
The edge computation (Mobile Edge Computing, MEC) reduces network delay caused by network transmission and multi-stage service forwarding by sinking the service to the network edge, so that the severe requirements of services such as the fifth generation mobile communication technology (5thgeneration mobile networks,5G) on bandwidth, delay and the like can be met. How to monitor traffic congestion conditions of edge applications deployed in edge computing devices and give corresponding traffic optimization strategies does not have a good solution.
Disclosure of Invention
In view of this, the flow optimization method, apparatus, device and storage medium provided in the embodiments of the present application are implemented as follows:
according to one aspect of the embodiments of the present application, there is provided a flow optimization method applied to a flow monitoring component in an edge computing device, the method including: acquiring network access parameters of a first application of a terminal; acquiring flow information of a second application used for providing service data flow for the first application in the edge computing equipment based on the network access parameters; wherein the traffic information comprises traffic information of the second application at a forwarding node of 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 one aspect of the embodiments 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; the second acquisition module is used for acquiring the network access parameters and acquiring flow information of a second application used for providing service data flow for the first application in the edge computing equipment; wherein the traffic information comprises traffic information of the second application at a forwarding node of 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 one 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, 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 having stored thereon a computer program which, when executed by a processor, implements the method provided by the embodiments of the present application.
In the embodiment of the application, a flow monitoring component deployed on an edge computing device acquires network access parameters of a first application of a terminal, acquires flow information of a second application at each forwarding node in the edge computing device based on the network access parameters, analyzes the flow information to obtain a flow congestion condition corresponding to the second application, and executes a corresponding flow optimization strategy according to the flow congestion condition.
It can be seen that in the embodiment of the present application, first, the flow monitoring component monitors the second application on the edge computing device, instead of monitoring the entire device, so that the second application can be more specifically optimized for flow, so that the second application can better serve the first application. Secondly, when acquiring traffic information of the second application in the edge computing device, the traffic monitoring component does not only acquire traffic information at the radio network RAN, but at least acquires traffic information at each forwarding node including the radio network RAN, the user plane UPF, and the data plane. On the one hand, by acquiring the flow information of the second application at each forwarding node, the flow congestion condition of the second application can be reflected more accurately, and therefore a more reasonable optimization scheme is provided; on the other hand, by acquiring the flow information of the second application at each node, the flow congestion condition of the second application at each node can be identified, so that the occurrence position and the congestion occurrence reason of the flow 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 application and, together with the description, serve to explain the technical aspects of the application. It is apparent that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they 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 order of actual execution may be changed according to actual situations.
Fig. 1 is a schematic implementation flow chart of a flow optimization method provided in an embodiment of the present application;
fig. 2 is a schematic implementation flow chart of a flow optimization method according to an embodiment of the present application;
fig. 3 is a schematic implementation flow chart of a flow optimization method according to an embodiment of the present application;
FIG. 4 is a flow transmission schematic diagram of an edge computing device in the related art;
fig. 5 is a schematic flow transmission diagram in an edge computing device according to an embodiment of the present application;
fig. 6 is a schematic implementation flow chart of a flow optimization processing method provided in an embodiment of the present application;
fig. 7 is a schematic structural diagram of a flow optimization device according to an embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the embodiments of the present application to be more apparent, the 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 illustrative of 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 present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
It should be noted that the term "first\second\third" in reference to the embodiments of the present application does not represent a particular ordering for objects, it being understood that the "first\second\third" may be interchanged in a particular order or precedence where allowed, to enable the embodiments of the present application described herein to be implemented in an order other than that illustrated or described herein.
With the proliferation of the number of devices accessing to the internet, data of all devices are uploaded to a cloud computing center through a network, and the computing capability of the cloud computing center is utilized to solve the problem of computing requirements of the devices, so that the problems that the computing speed cannot meet the real-time requirement, the privacy of user data has leakage risk, the energy consumption of the cloud computing center is high and the like exist.
For this, edge computing is a new computing method for performing computing on the edge of the network, and the edge computing device has the processing capability of performing computing and data analysis, so that part or all of computing tasks performed by the original cloud computing model are migrated to the network edge computing device, the computing load of the cloud server is reduced, the pressure of network bandwidth is relieved, and the processing efficiency of data in the internetworking age is improved. The edge calculation is not used for replacing cloud calculation, but is used for supplementing cloud calculation, and a better calculation platform is provided for related technologies such as mobile calculation, the Internet of things and the like.
The embodiment of the application provides a flow optimization method, which is applied to edge computing equipment, and the edge computing equipment refers to electronic equipment for transferring an artificial intelligent 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 mobile device (such as a smart phone and a wearable device) owned by a person, or may be a terminal device (such as a gateway, a surveillance camera, a bank ATM), or other internet of things devices, etc. The functions performed by the method may be performed by a processor in an electronic device, which may of course be stored in a computer storage medium, as will be seen, comprising at least a processor and a storage medium.
Fig. 1 is a schematic flow chart of an implementation of a flow optimization method according to 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, and the method may include the following steps 101 to 104:
in step 101, the flow monitoring component obtains network access parameters of a first application of the terminal.
In some embodiments, the Traffic monitoring component is a Traffic Engine (Traffic Engine) that is a service component of the edge computing device that is capable of collecting Traffic information at the data forwarding node and has processing capabilities for the Traffic information.
In some embodiments, the network access parameters include at least one of: internet protocol (Internet Protocol, IP) address, port, protocol number.
In some embodiments, step 101 may be implemented by performing steps 201 through 202 in the following embodiments.
In step 102, the traffic monitoring component obtains traffic information of a second application in the edge computing device for providing a service data flow for the first application based on the network access parameters.
Wherein the traffic information comprises traffic information for a second application at a forwarding node of at least one of: a radio access network (Radio Access Network, RAN), a user plane (User Port Function, UPF), a data plane.
In this embodiment of the present application, when the traffic monitoring component obtains the traffic information of the second application in the edge computing device, the traffic monitoring component does not only obtain the traffic information at the radio network RAN, but at least obtains the traffic information at each forwarding node including the radio network RAN, the user plane, and the data plane. On the one hand, by acquiring the flow information of the second application at each forwarding node, the flow congestion condition of the second application can be reflected more accurately, and therefore a more reasonable optimization scheme is provided; on the other hand, by acquiring the traffic information of the second application at each node of the whole transmission link, the traffic congestion condition of the second application at each node can be identified, so that the occurrence position and the congestion occurrence reason of the traffic congestion can be more accurately found.
In some embodiments, the second application is an edge application (Mobile Edge Computing application, MEC APP).
In some embodiments, the flow information includes at least one of: upstream bandwidth, downstream bandwidth, packet loss information, and network delay.
And 103, analyzing the flow information by the flow monitoring component to obtain the flow congestion condition corresponding to the second application.
In some embodiments, the traffic monitoring component analyzes traffic congestion conditions 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 and congestion percentage, among others.
And 104, the flow monitoring component executes a corresponding flow optimization strategy according to the flow congestion condition.
In some embodiments, step 104 may be implemented by performing steps 303 through 304 in the following embodiments.
In the embodiment of the application, the flow monitoring component monitors the second application instead of the whole terminal device, so that the flow of the second application can be optimized more pertinently, and the second application can serve the first application better.
The traffic congestion status at least comprises the occurrence position of traffic congestion and the occurrence reason of the traffic congestion. Accordingly, the flow monitoring component can pertinently execute corresponding flow optimization strategies according to specific occurrence positions and occurrence reasons of the flow congestion.
Fig. 2 is a schematic flow chart of an implementation of a flow optimization method according to 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. 2, and the method may include the following steps 201 to 205:
in step 201, the traffic monitoring component receives a monitoring request initiated by the second application.
It should be noted that, the monitoring request may be initiated by the second application according to actual requirements, that is, when the second application determines that the second application needs to be monitored for traffic congestion, the monitoring request may be initiated to the traffic monitoring component.
In step 202, the flow 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 service data flow for the first application on the terminal device. For example, assuming that a first application of the terminal is application a, the application a has a video playing function, 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 itself 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 parameters sent by the first application, the second application can also send the network access parameters to the flow monitoring assembly when the second application initiates a monitoring request to the flow monitoring assembly; or, the monitoring request is sent to the flow monitoring component together with the network access parameters, which is not limited.
Step 203, the flow monitoring component obtains flow information of a second application for providing a service data flow for the first application in the edge computing device based on the network access parameter; wherein the traffic information comprises traffic information for a second application at a forwarding node of at least one of: RAN, UPF, data plane;
in step 204, the flow monitoring component analyzes the flow information to obtain a flow congestion location corresponding to the second application.
In the embodiment of the application, the location where the traffic congestion occurs by the second application may be at least one of the nodes (such as RAN, UPF or data plane) forwarding the service data flows in the edge computing device.
In step 205, the traffic monitoring component executes a corresponding congestion adjustment policy according to the traffic congestion location.
In some embodiments, step 205 may be implemented by performing steps 303 through 304 in the following embodiments.
In the embodiment of the application, the flow monitoring component determines the flow congestion position and executes the corresponding congestion adjustment strategy based on the flow congestion position, so that the flow congestion can be regulated and controlled more pertinently, and the flow congestion problem can be better improved.
In some embodiments, the traffic monitoring component may further re-acquire 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 re-acquired traffic information.
Here, the timing at which the flow monitoring component re-acquires the flow information of the second application is not limited. In some embodiments, the traffic monitoring component may also re-acquire traffic information of the second application and re-determine traffic congestion conditions of the second application in the event that traffic congestion does not occur for the second application. The method for periodically re-acquiring the flow information can avoid the phenomenon that the second application is not monitored due to the fact that the flow congestion occurs in a certain time.
In other embodiments, the flow monitoring component may further re-acquire the flow information of the second application and re-determine the flow congestion status of the second application in case the second application is congested. In this way, on the 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 a subsequent period of time, or, after executing the adjustment policy, the traffic monitoring component continues to monitor whether the second application has traffic congestion; on the other hand, if the flow monitoring component re-determines that the second application has no flow congestion in a shorter interval, the flow congestion condition of the second application does not need to be adjusted, so that the power consumption of the device can be saved.
Fig. 3 is a schematic flow chart of an implementation of a flow optimization method according to 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. 3, and the method may include the following steps 301 to 304:
step 301, a flow monitoring component acquires network access parameters of a first application of a terminal;
step 302, a flow monitoring component obtains flow information of a second application used for providing a service data flow for a first application in an edge computing device based on network access parameters; wherein the traffic information comprises traffic information for a second application at a forwarding node of at least one of: RAN, UPF, data plane;
in step 303, the traffic monitoring component obtains the data transmission index of the second application.
It will be appreciated that the second application is data transfer indexed when providing a 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 index is preset, and the traffic monitoring component may directly obtain the data transmission index of the second application.
As another example, in other embodiments, the data transmission indicator of the second application may be determined by the traffic congestion status of the second application at the historical moment, such as by performing steps 3031 to 3032 as follows:
in step 3031, the traffic monitoring component obtains a historical traffic congestion location and a corresponding historical adjustment parameter for the second application.
Here, the traffic monitoring component obtains a location where the second application generates traffic congestion during the history period (i.e., the forwarding node generating the traffic congestion) and a corresponding adjustment parameter (i.e., the adjustment policy) at the location. It will be appreciated that when the history period is sufficiently long, the traffic monitoring component is generally able to obtain the corresponding adjustment parameters for the second application when traffic congestion occurs at each of the different forwarding nodes.
In step 3032, the traffic monitoring component determines a data transmission indicator of the second application according to the historical traffic congestion location and the corresponding historical adjustment parameter.
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, so as to determine whether the data transmission index is satisfied at each forwarding node, and if not, may execute a corresponding adjustment policy.
In some embodiments, the flow monitoring component may further determine a more reasonable optimization scheme for the second application at each forwarding node according to the historical flow congestion location of the second application and the corresponding adjustment scheme. In this way, the flow monitoring component can intelligently monitor and optimize the flow of the second application based on the history optimization scheme without the participation of the second application under the condition that the second application does not initiate the monitoring request.
In step 304, the traffic monitoring component executes a corresponding congestion adjustment policy according to the traffic congestion location and the data transmission index.
In the embodiment of the application, when the congestion adjustment policy is executed, the flow monitoring component is executed according to the congestion position of the flow and the data transmission index, that is, correspondingly adjusted according to the actual quality requirement of the second application, so that the flow condition of the second application is conveniently adjusted 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 where the traffic congestion location is at the RAN, the traffic monitoring component adjusts an influencing parameter of the data transmission rate of the RAN according to the data transmission index.
Here, the first adjustment value of the influencing parameter of the data transmission rate of the RAN may be determined; and then adjusting the data transmission rate 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.
In step 3042, in the case where the traffic congestion location is at the UPF, the traffic monitoring component adjusts the bandwidth and/or priority occupied by the second application in the UPF according to the data transmission index.
Here, the second adjustment value of the bandwidth occupied in the UPF and/or the third adjustment value of the priority may be determined by the second application; the bandwidth occupied by the edge application in the UPF is then adjusted according to the second adjustment value, and/or the priority of the edge application in the UPF is adjusted according to the third adjustment value.
In step 3043, in case the traffic congestion location 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 may 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 policy according to the traffic congestion condition, further comprising:
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; 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 for example, the data transmission parameter may be a rate, or may be an amount of data forwarded in a period of time.
In addition, the timing of sending the prompt message by the flow monitoring component is not limited. For example, after executing the corresponding optimization policy at the forwarding node, the traffic monitoring component may send a prompt message to the second application to cause the second application to reduce its own data transmission index, if it is determined that the data transmission index is still not satisfied. Alternatively, the traffic monitoring component may send a prompt message at any time to prompt the second application to reduce its own data transmission index.
The flow of the MEC edge calculation is mainly aimed at the data from the user terminal to the edge application APP (as shown in figure 4), the middle is accessed through the radio access network RAN, reaches the edge calculation platform MEP through the core network user plane function UPF, and is processed by the edge application MEC APP of the MEP.
The remote network program interface specification (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 edge applications, including Physical resource blocks (Physical RBs, PRBs) utilization information of Cell granularity on RAN, metric information of L2 (PRB utilization of bearer granularity, gigabit Ethernet (GBE) in Cell, non-guaranteed bit rate (non-Guaranteed Bit Rate, non-GBR) of users carried by the edge application, GDB of Cell, non-GBR of carrier loss rate, throughput, traffic, etc.), and PRB utilization of Bear granularity of a terminal, RAB information (such as quality of service (Quality of Service, qoS) parameters of a carrier), the mecapp first interacts with the MEP to obtain a user identity ID, can provide traffic characteristics (such as user identity ID, IP address, address of service, port, protocol, etc.) to the RANIS of the MEP, obtain wireless network information, the edge application determines congestion of traffic at the RAN, packet loss rate of the edge application itself, and then performs a corresponding adjustment of the bandwidth (e.g. bandwidth) of the edge application, such as bandwidth, bandwidth of the end-station, bandwidth, etc. can be greatly reduced, and bandwidth (e.g. bandwidth, bandwidth is greatly adjusted accordingly, bandwidth is reduced, such as by optimizing the bandwidth requirements of the bandwidth, and bandwidth of the edge application (e.g. bandwidth, and bandwidth is greatly reduced).
Among these, flow optimization has several problems:
(1) Firstly, the edge application MEC APP is required to understand wireless network information and judge whether the self service flow has QoS problem or not, and meanwhile, the flow can be optimized, which provides the requirements for the edge application: the RNIS provides resource occupancy information (such as utilization rate of physical resource blocks, bandwidth utilization rate of load, quality of signal, etc.) of the wireless network, belongs to a low-level interface, has no direct mapping relation with traffic characteristic parameters (embodied as addresses, ports and protocols of applications), and can acquire wireless network resources corresponding to own service flows only by complex service flow processing of edge application MEC APP; the processing involves expertise of the mobile network;
(2) Only a single source of information: the RNIS information only provides the air interface related information of the RAN, the whole link in the transmission process lacks complete perception, the service quality problem caused by other nodes cannot be identified, the edge application MEC APP cannot determine the reason of poor service experience, and the network position and the reason of poor service experience cannot be found;
(3) Likewise, the edge application MEC APP can only configure and control specific network nodes, but cannot make targeted regulation and control, and the regulation and control effect is limited;
(4) In the case of simultaneous deployment of multiple MEC APPs, there may also be conflicting quality of service requirements for edge applications MEC APPs; meanwhile, the feedback, analysis and optimization are dispersed in each edge application, so that repeated optimization is caused, and the deployment cost of the system is increased;
(5) Finally, each edge application needs to interact with a plurality of services (such as user side identification (User Experience identity, UEID), BWMS and the like) or the periphery on the MEP, so that the purpose of flow optimization can be achieved, the complexity of the service is increased, the coupling degree is high, and the failure rate is increased.
To solve the above problem, the embodiments of the present application introduce a Traffic Engine (Traffic Engine), which can be used as an edge computing service, collect network quality of service data, and analyze the cause of the quality of service to provide service capability for quality of service sensing and Traffic optimization to the outside (edge application MEC APP).
As shown in fig. 5, traffic Engine periodically collects and monitors the Traffic statistics information of the RNIS wireless network information, the core network UPF or the Data plane, synthesizes the information of a plurality of nodes, and analyzes the following information:
(1) Whether the current edge application MEC APP has the service quality or not;
(2) Network location where problems occur.
Traffic engines may provide quality of service conditions to edge applications mecapp; the edge application MEC APP is a consumer or user of Traffic Engine from which the quality of service condition (e.g., whether Traffic congestion exists) can be obtained.
The Traffic Engine can receive the service quality requirement of the edge application MEC APP; integrating the quality of service requirements of multiple applications, giving specific quality of service parameters for the network device that causes the quality of service, such as:
(1) Modifying the polarization (MBR, GBR, etc.) of the radio Bear at the RAN through the RIC interface;
(2) Or modifying the bandwidth, priority and the like of the MEC APP in the UPF;
(3) Or modifying the bandwidth, priority and the like of the MEC APP in the MEP Dataplane;
(4) Interfacing with a control plane of a core network, and issuing QoS parameters;
(5) Or the return network cannot 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 is used as a consumer or user of Traffic Engine, can obtain the service quality status (such as congestion or not) from the consumer or user, and simultaneously the Traffic Engine automatically completes the purpose of flow optimization.
In the embodiment of the application, (1) Traffic Engine is unified to the outside through the edge computing platform, professional wireless network information is converted into Traffic bandwidth information which can be understood by edge application, an interface can be directly provided for the edge application MEC APP to judge whether data flow congestion exists or not, and the complexity of using RNIS service is reduced; the knowledge requirement of the edge application on the wireless network is reduced, and the development difficulty is reduced.
(2) The Traffic Engine interacts with a plurality of internal services, complex analysis and processing flows are shielded, the edge application MEC APP interacts with the Traffic Engine only, the complexity of the edge application MEC APP is simplified, the complexity of the edge application MEC APP is lighter, the business is focused, and meanwhile redundancy of a flow optimization function in the edge application is avoided.
(3) Traffic Engine is used as unified edge computing flow optimization, so that richer and powerful capabilities such as flow monitoring can be provided, flow statistical information of the RAN can be monitored, flow information on a core network UPF and a Data plane can be counted, more accurate response of flow congestion conditions of an edge application MEC APP can be facilitated, and a more reasonable optimization scheme is provided.
(4) Traffic Engine can rely on AI ability that edge computing platform calculated, need not MEC APP and provides the business quality demand that can intelligent identification MEC APP to and the reason that appears business quality problem, help edge application more rationally, optimize the flow with high efficiency, can provide certain expansion ability simultaneously, satisfy MEC APP specific demand.
(5) Traffic Engine provides flow optimization functions externally in the form of MEC edge computing platform micro-services.
Fig. 6 shows a flow optimization processing method, as shown in fig. 6, including the following steps 601 to 605:
step 601, an edge application MEC APP initiates a request (i.e. a monitoring request), and a consumer serving as a MEP platform flow optimization engine uses the service and provides corresponding flow characteristic parameters (i.e. network access parameters), such as an ip address, a port or protocol number of a terminal, and the like;
step 602,Traffic Engine, after receiving the request from the MEC APP, performs corresponding monitoring according to the traffic characteristics provided by the edge application, where the traffic includes 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 traffic uplink and downlink bandwidth of the characteristics, whether there is packet loss, delay, and other statistics information (i.e., an example of traffic information);
Step 603,Traffic Engine, analyzing the statistical data collected from different network element nodes/data planes according to the traffic monitoring;
step 604,Traffic Engine, integrating the collected statistical data, and determining whether traffic congestion (or congestion level, percentage, etc.) exists in the edge application MEC APP; if so, step 605 is performed; otherwise, returning to the execution step 602, the flow congestion status of the MEC APP is continuously monitored.
Step 605, a corresponding countermeasure is given against the traffic congestion condition of the MEC APP. Specifically, different policies are enforced for different traffic congestion occurrences. Such as traffic congestion occurring at the radio access network RAN, it is possible to adjust parameters at the RAN (guaranteed bit rate GBR or maximum bit rate MBR, etc.) through RIC API interfaces (A1-P); if congestion occurs in UPF, bandwidth of the port where the flow is located needs to be adjusted, or congestion exists in the Data Plane, correspondingly, flow parameters of the Data Plane also need to be adjusted, and finally, the purpose of optimizing the flow is achieved.
In some embodiments, traffic Engine may also introduce artificial intelligence (Artificial Intelligence, AI) capability, and may provide some relatively reasonable and comprehensive optimization schemes according to the flow characteristics, congestion conditions and occurrence places of the edge application MEC APP collected in the edge computing platform MEP, so as to achieve flow optimization and intellectualization.
In the embodiment of the present application, firstly, a concept of a Traffic Engine (Traffic Engine) is introduced, which may exist as a micro service of an edge computing platform or in other manners, and the Traffic Engine provides information provided by a complex concept related to a network, such as an RNIS service, to the outside, so as to provide a simple and easy-to-use capability of edge application MEC APP service flow quality feedback. And secondly, the system can interact with a plurality of services in the MEC platform to complete the functions of monitoring, analyzing, optimizing and the like of the edge application MEC APP flow, does not need to monitor and optimize the data flow by the MEC APP, and reduces the interface and logic complexity of the application. Again it may provide a similar end-to-end optimization scheme including the entire path that edge application Data traffic traverses, such as RAN, UPF or Data Plane, etc. Finally, by means of the AI capability, the flow characteristics of the edge application MEC APP collected in the edge computing platform MEP can be intelligently identified, reasonable parameter formation experience of the data flow is learned, and flow optimization is performed based on historical experience; through intelligent flow identification application, the intelligent flow optimization is achieved without participation of the application and active optimization.
It should be noted that although the steps of the methods in the present application are depicted in the accompanying drawings in a particular order, this does not require or imply that the steps must be performed in that particular order, or that all illustrated steps be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
Based on the foregoing embodiments, the embodiments of the present application provide a flow optimization apparatus, where the apparatus includes each module included, and each unit included in each module may be implemented by a processor; of course, the method can also be realized by a specific logic circuit; in an 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 flow optimization device according to an embodiment of the present application, as shown in fig. 7, where the flow optimization device is deployed on an edge computing device, and the flow optimization device 700 includes a first acquisition module 701, a second acquisition module 702, an analysis module 703, and an execution 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, according to the network access parameter, flow 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 of the second application at a forwarding node of at least one of: RAN, UPF, data plane; an analysis module 703, configured to analyze the flow information to obtain a flow 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 an parsing unit, where the receiving unit is configured to receive a monitoring request initiated by the second application; the analyzing 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 parameter is sent by the first application to the second application.
In some embodiments, the traffic congestion condition includes a traffic congestion location; the execution module 704 includes an adjustment unit, configured to execute a corresponding congestion adjustment policy according to the traffic congestion location.
In some embodiments, the executing 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 traffic 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; and the determining subunit is used for determining the data transmission index 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, configured to send a hint message to the second application according to the data transmission parameters of each forwarding node, so that the second application reduces the data transmission index according to the hint message; 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, configured to re-obtain, according to the traffic congestion status, traffic information of the second application, so as to determine the traffic congestion status of the second application based on the traffic information newly obtained from the second application.
The description of the apparatus embodiments above is similar to that of the method embodiments above, with similar advantageous effects as the method embodiments. For technical details not disclosed in the device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.
It should be noted that, in the embodiment of the present application, the division of the modules by the flow optimization device shown in fig. 7 is schematic, which is merely a logic function division, and other division manners may be adopted in actual implementation. In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units. Or in a combination of software and hardware.
It should be noted that, in the embodiment of the present application, if the method is implemented in the form of a software functional module, and sold or used as a separate product, the method 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 essentially or part contributing to the related art, and the computer software product may be stored in a storage medium, including 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 U-disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
An embodiment of the present application provides an electronic device, fig. 8 is a schematic diagram of hardware entities of the electronic device according to the embodiment of the present application, as shown in fig. 8, where the electronic device 800 includes a memory 801 and a processor 802, where the memory 801 stores a computer program that can be run on the processor 802, and the processor 802 implements steps in the method provided in the foregoing 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 be cached in the processor 802 and data (e.g., image data, audio data, voice communication data, and video communication data) to be processed or already processed by each module in the electronic device 800, and may be implemented by a FLASH memory (FLASH) or a random access memory (Random Access Memory, RAM).
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method provided in the above embodiment.
The present application provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the steps of the method provided by the method embodiments described above.
It should be noted here that: the description of the storage medium and apparatus embodiments above is similar to that of the method embodiments described above, with similar benefits as the method embodiments. For technical details not disclosed in the storage medium, storage medium and device embodiments of the present application, please refer to the description of the method embodiments 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 embodiment 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 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 various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application. The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments. The foregoing description of various embodiments is intended to highlight differences between the various embodiments, which may be the same or similar to each other by reference, and is not repeated herein for the sake of brevity.
The term "and/or" is herein merely an association relation describing associated objects, meaning that there may be three relations, e.g. object a and/or object B, may represent: there are three cases where object a alone exists, object a and object B together, and object B alone exists.
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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in this 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 the division of the modules is merely a logical function division, and other divisions may be implemented 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 performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or modules, whether electrically, mechanically, or otherwise.
The modules described above as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules; can be located in one place or distributed to a plurality of network units; some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated in one processing unit, or each module may be separately used as one unit, or two or more modules may be integrated in one unit; the integrated modules may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read Only Memory (ROM), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the integrated units described above may be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or part contributing to the related art, and the computer software product may be stored in a storage medium, including 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 removable storage device, a ROM, a magnetic disk, or an optical disk.
The methods disclosed in the several method embodiments provided in the present application may be arbitrarily combined without collision to obtain a new method embodiment.
The features disclosed in the several product embodiments provided in the present application may be combined arbitrarily without conflict to obtain new product embodiments.
The features disclosed in the several method or apparatus embodiments provided in the present application may be arbitrarily combined without conflict to obtain new method embodiments or apparatus embodiments.
The foregoing is merely an embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A method of traffic optimization, the method being applied to a traffic monitoring component in an edge computing device, the method comprising:
acquiring network access parameters of a first application of a terminal;
acquiring flow information of a second application used for providing service data flow for the first application in the edge computing equipment based on the network access parameters; wherein the traffic information comprises traffic information of the second application at a forwarding node: RAN, UPF, data plane;
analyzing the flow information to obtain a flow congestion condition corresponding to the second application;
executing a corresponding flow optimization strategy according to the flow congestion condition;
wherein the traffic congestion condition includes a traffic congestion location, and correspondingly, the executing a corresponding traffic optimization policy according to the traffic congestion condition further includes:
And executing a corresponding congestion adjustment strategy according to the traffic congestion position.
2. The method according to 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 parameter is sent by the first application to the second application.
3. The method of claim 1, wherein said executing a respective congestion adjustment policy based on said traffic congestion location comprises:
acquiring a data transmission index of the second application;
and executing a corresponding congestion adjustment strategy according to the traffic congestion position and the data transmission index.
4. A method according to claim 3, wherein said obtaining a data transmission indicator of said second application comprises:
acquiring a historical traffic congestion position and a corresponding historical adjustment parameter of the second application;
and determining the data transmission index of the second application according to the historical traffic congestion position and the corresponding historical adjustment parameter.
5. The method of claim 1, wherein after said executing a respective traffic optimization strategy in accordance with said traffic congestion condition, the method further comprises:
sending 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; the prompt message is used for prompting that the current data transmission index of the second application cannot be met.
6. The method of claim 1, wherein said executing a corresponding traffic optimization strategy based on said traffic congestion condition further comprises:
and re-acquiring the flow information of the second application according to the flow congestion condition so as to determine the flow congestion condition of the second application based on the flow information newly acquired from the second application.
7. The method according to any one of claims 1 to 6, wherein the traffic information comprises at least one of: upstream bandwidth, downstream bandwidth, packet loss information, and network delay.
8. 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;
the second acquisition module is used for acquiring the network access parameters and acquiring flow information of a second application used for providing service data flow for the first application in the edge computing equipment; wherein the traffic information comprises traffic information of the second application at a forwarding node: 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;
the execution module is used for executing a corresponding flow optimization strategy according to the flow congestion condition;
the execution module comprises an adjusting unit and is used for executing a corresponding flow optimization strategy according to the flow congestion condition.
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