CN114513409B - ECN threshold configuration method and device - Google Patents
ECN threshold configuration method and device Download PDFInfo
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- CN114513409B CN114513409B CN202210007205.7A CN202210007205A CN114513409B CN 114513409 B CN114513409 B CN 114513409B CN 202210007205 A CN202210007205 A CN 202210007205A CN 114513409 B CN114513409 B CN 114513409B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
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Abstract
The present application relates to the field of network communications technologies, and in particular, to a method and an apparatus for ECN threshold configuration. The method comprises the following steps: obtaining a target scene parameter of a target outlet port in a current flow forwarding scene, wherein the scene parameter of one outlet port is a parameter related to flow forwarded through the outlet port; judging whether a target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration; if the target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration, the ECN threshold of the target output port is configured based on the target ECN configuration.
Description
Technical Field
The present application relates to the field of network communications technologies, and in particular, to a method and an apparatus for ECN threshold configuration.
Background
With the increase of high-concurrency and low-delay traffic, network congestion easily occurs at the output ports of network devices (such as switches and routers). Network congestion refers to a phenomenon that when a network device receives a much larger flow through an ingress port than a flow sent through an egress port, a large amount of messages are retained (buffered) in an egress queue (corresponding to the egress port), which affects network performance such as transmission delay and throughput of the messages.
Currently, static display congestion notification (English: explicit Congestion Notification, abbreviated: ECN) configurations are commonly employed to control network congestion. Specifically, the network device judges whether the queue is congested according to the ECN threshold configured statically, if congestion occurs, adds ECN identification to the message in the queue, sends the message to the destination device, and after receiving the message with ECN identification, the destination device sends a congestion notification message (English: congestion Notification Packet, abbreviated: CNP) to the source device, and notifies the source device to reduce the traffic sending rate so as to achieve the purpose of controlling network congestion.
However, static ECN configuration cannot adapt to dynamically changing traffic scenarios, resulting in poor network performance in each traffic scenario.
Disclosure of Invention
The application provides an ECN threshold configuration method and device, which are used for solving the problem that the static ECN configuration in the prior art cannot adapt to dynamically-changed traffic scenes, so that the network performance under each traffic scene is poor.
In a first aspect, the present application provides a method for configuring an ECN threshold, where the method includes:
obtaining a target scene parameter of a target outlet port in a current flow forwarding scene, wherein the scene parameter of one outlet port is a parameter related to flow forwarded through the outlet port;
judging whether a target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration;
if the target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration, configuring the ECN threshold of the target output port based on the target ECN configuration;
the ECN configuration corresponding to each scene parameter in the association relation between the preset scene parameter and the ECN configuration is ECN configuration when the scene parameter in each common scene and the network performance value in the common scene obtained through testing in the built test environment are optimal, and/or ECN configuration when the network performance value of the outlet port in the current scene is optimal is obtained by respectively simulating the scene parameter of each outlet port based on a trained network simulator.
Optionally, the method further comprises:
if the association relation between the preset scene parameters and the ECN configuration is judged to not have the target ECN configuration matched with the target scene parameters, inputting the target scene parameters into the trained network simulator to obtain the corresponding ECN configuration;
and configuring the ECN threshold of the target output port based on the ECN configuration.
Optionally, the method further comprises:
and adding the corresponding relation between the scene parameters in the common scene and the ECN configuration when the network performance value in the common scene obtained by testing in the built test environment is optimal to each common traffic forwarding scene into the preset association relation between the scene parameters and the ECN configuration.
Optionally, the method further comprises:
and training the network simulator based on the scene parameters of each common scene and ECN configuration when the network performance value in the common scene is optimal, so as to obtain the trained network simulator.
Optionally, based on the scene parameters of each common scene and the ECN configuration when the network performance value in the common scene is optimal, the step of training the network simulator to obtain the trained network simulator includes:
inputting scene parameters of each common scene into a network simulator to obtain ECN configuration corresponding to each common scene respectively;
judging whether the fitting degree of ECN configuration corresponding to each common parameter and ECN configuration corresponding to each common parameter when the network performance value is optimal meets the preset condition or not;
and if the preset conditions are not met, adjusting parameters of the network simulator until fitting degrees of ECN configuration corresponding to each common parameter and ECN configuration corresponding to each common parameter when the network performance value is optimal meet the preset conditions, and determining that the training of the network simulator is completed.
In a second aspect, the present application provides an ECN threshold configuration apparatus, the apparatus comprising:
the system comprises an acquisition unit, a forwarding unit and a forwarding unit, wherein the acquisition unit is used for acquiring a target scene parameter of a target outlet port in a current flow forwarding scene, wherein the scene parameter of one outlet port is a parameter related to flow forwarded through the outlet port;
the judging unit is used for judging whether a target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration;
the configuration unit is used for configuring the ECN threshold of the target output port based on the target ECN configuration if the judging unit judges that the target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration;
the ECN configuration corresponding to each scene parameter in the association relation between the preset scene parameter and the ECN configuration is ECN configuration when the scene parameter in each common scene and the network performance value in the common scene obtained through testing in the built test environment are optimal, and/or ECN configuration when the network performance value of the outlet port in the current scene is optimal is obtained by respectively simulating the scene parameter of each outlet port based on a trained network simulator.
Optionally, the apparatus further comprises an analog unit:
if the judging unit judges that the target ECN configuration matched with the target scene parameter does not exist in the association relation between the preset scene parameter and the ECN configuration, the simulation unit is used for inputting the target scene parameter into the trained network simulator to obtain the corresponding ECN configuration;
the configuration unit is further configured to configure an ECN threshold of the target egress port based on the ECN configuration.
Optionally, the apparatus further comprises:
the testing unit is used for adding the corresponding relation between the scene parameters in the common scene and the ECN configuration when the network performance value in the common scene obtained by testing in the built testing environment is optimal to the preset association relation between the scene parameters and the ECN configuration aiming at each common traffic forwarding scene.
Optionally, the apparatus further comprises:
the training unit is used for training the network simulator based on the scene parameters of each common scene and ECN configuration when the network performance value in the common scene is optimal, and obtaining the trained network simulator.
Optionally, based on scene parameters of each common scene and ECN configuration when the network performance value in the common scene is optimal, training the network simulator, so as to obtain a trained network simulator, where the training unit is specifically configured to:
inputting scene parameters of each common scene into a network simulator to obtain ECN configuration corresponding to each common scene respectively;
judging whether the fitting degree of ECN configuration corresponding to each common parameter and ECN configuration corresponding to each common parameter when the network performance value is optimal meets the preset condition or not;
and if the preset conditions are not met, adjusting parameters of the network simulator until fitting degrees of ECN configuration corresponding to each common parameter and ECN configuration corresponding to each common parameter when the network performance value is optimal meet the preset conditions, and determining that the training of the network simulator is completed.
In a third aspect, an embodiment of the present application provides an ECN threshold configuration apparatus, where the ECN threshold configuration apparatus includes:
a memory for storing program instructions;
a processor for invoking program instructions stored in said memory, performing the steps of the method according to any of the first aspects above in accordance with the obtained program instructions.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the steps of the method according to any one of the first aspects.
As can be seen from the above, in the ECN threshold configuration method provided by the embodiments of the present application, a target scenario parameter of a target egress port in a current traffic forwarding scenario is obtained, where a scenario parameter of an egress port is a parameter related to traffic forwarded through the egress port; judging whether a target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration; if the target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration, configuring the ECN threshold of the target output port based on the target ECN configuration; the ECN configuration corresponding to each scene parameter in the association relation between the preset scene parameter and the ECN configuration is ECN configuration when the scene parameter in each common scene and the network performance value in the common scene obtained through testing in the built test environment are optimal, and/or ECN configuration when the network performance value of the outlet port in the current scene is optimal is obtained by respectively simulating the scene parameter of each outlet port based on a trained network simulator.
By adopting the ECN threshold configuration method provided by the embodiment of the application, the change of the network flow model can be perceived in real time, the optimal ECN waterline can be automatically regulated, the network congestion control is realized, and the performance indexes such as network throughput and the like are improved. Meanwhile, through the trained network simulator, recommended ECN configuration corresponding to scene parameters of a plurality of common scenes is obtained in advance, so that ECN configuration issuing can be rapidly and efficiently carried out on network equipment output ports of the common scenes, and network congestion control based on the ECN configuration is realized.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will briefly describe the drawings required to be used in the embodiments of the present application or the description in the prior art, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings of the embodiments of the present application for a person having ordinary skill in the art.
Fig. 1 is a detailed flowchart of an ECN threshold configuration method according to an embodiment of the present application;
FIG. 2 is a diagram of a reinforcement learning model framework of a network simulator according to an embodiment of the present application;
fig. 3 is a schematic process diagram of an ECN threshold configuration method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an ECN threshold configuration device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of another ECN threshold configuration apparatus according to an embodiment of the present application.
Detailed Description
The terminology used in the embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to any or all possible combinations including one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used in embodiments of the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the application. Depending on the context, furthermore, the word "if" used may be interpreted as "at … …" or "at … …" or "in response to a determination".
Referring to fig. 1, a detailed flowchart of an ECN threshold configuration method according to an embodiment of the present application is shown, where the method includes the following steps:
step 100: and obtaining a target scene parameter of a target outlet port in the current flow forwarding scene, wherein the scene parameter of one outlet port is a parameter related to the flow forwarded through the outlet port.
In the embodiment of the application, the method can be applied to network equipment which performs congestion control based on an ECN threshold, such as a switch, a router and the like, and can also be applied to control equipment which is independent of the network equipment, such as a server and the like.
A so-called network device comprises at least one egress port. The administrator may specify the egress ports for which congestion control is to be performed based on the ECN threshold, or perform congestion control on all egress ports based on the ECN threshold by default. Here, the egress ports that need to perform congestion control based on the ECN threshold are all referred to as target egress ports.
Here, the scenario parameter corresponding to the output port indicates the flow related parameter of the port in a certain scenario, and in the embodiment of the present application, the parameter of the flow to be forwarded by the target output port is specified. In the following description, unless otherwise specified, traffic refers to traffic forwarded through a target egress port.
Specifically, the scene parameters may include: the number of traffic source ports (ingress ports), the average bandwidth of the traffic source ports, the number of flows, the proportion of different types of messages, etc. Here, it should be noted that forwarding traffic through the same egress port may come from one or more ingress ports.
Step 110: judging whether a target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration.
It should be noted that one ECN configuration may include at least the following three parameters: an ECN threshold upper limit, an ECN threshold lower limit, and an ECN marking probability (maximum marking probability).
In the embodiment of the application, the ECN configuration corresponding to each scene parameter in the association relation between the preset scene parameter and the ECN configuration is the ECN configuration when the scene parameter in each common scene and the network performance value in the common scene obtained by testing in the built test environment are optimal, and/or the ECN configuration when the network performance value of the outlet port in the current scene is optimal is obtained by respectively simulating the scene parameter of each outlet port based on the trained network simulator.
Specifically, for each common traffic forwarding scenario, adding a corresponding relation between a scenario parameter in the common scenario and the ECN configuration when the network performance value in the common scenario obtained through testing in the built testing environment is optimal into the preset association relation between the scenario parameter and the ECN configuration.
For example, a test environment is first set up in a laboratory, a plurality of common scenes are widely tested, corresponding recommended ECN configurations are obtained according to test data (scene parameters) of each common scene, and the scene parameters and the recommended ECN configurations are stored in a flow model file. Specifically, a test environment is built in a laboratory, and a plurality of common scenes are widely tested. Common scenarios herein mainly refer to incast (number of ingress ports), average bandwidth of each ingress port, roce number of flows, and Write/Read/Send ratio, etc. Each scene corresponds to a different scene parameter, and through adjusting the ECN threshold setting of the switch, the threshold and latency performance indexes of the system can be calculated, and the ECN threshold configuration which enables the network performance index to be optimal is found, namely the ECN configuration recommended by the required scene. The ECN configurations are stored in a flow model file, current scene parameters are judged in an actual environment, and if a matched scene can be found in the flow model file, the recommended ECN configuration is directly used.
Further, after the ECN configuration with the optimal network performance index corresponding to each common scene is obtained based on the scene parameter test corresponding to each test scene in the test environment, the network simulator is trained based on the scene parameters of each common scene and the ECN configuration with the optimal network performance value in the common scene, and the trained network simulator is obtained.
Specifically, in the embodiment of the present application, when training the network simulator based on the scene parameters of each common scene and the ECN configuration when the network performance value in the common scene is optimal, and obtaining the trained network simulator, a preferred implementation manner is as follows: inputting scene parameters of each common scene into a network simulator to obtain ECN configuration corresponding to each common scene respectively; judging whether the fitting degree of ECN configuration corresponding to each common parameter and ECN configuration corresponding to each common parameter when the network performance value is optimal meets the preset condition or not; and if the preset conditions are not met, adjusting parameters of the network simulator until fitting degrees of ECN configuration corresponding to each common parameter and ECN configuration corresponding to each common parameter when the network performance value is optimal meet the preset conditions, and determining that the training of the network simulator is completed.
That is, the scene parameters are input into the network simulator, the network simulator performs network simulation based on the input scene parameters to obtain an ECN configuration, whether the fitting degree of the ECN configuration meets the preset requirement when the network performance value corresponding to the scene parameters is optimal is judged, and if the fitting degree meets the preset requirement and the continuous n times of test results are met, the network simulator training is determined to be completed. If the test results are not met, adjusting all parameters of the network simulator, inputting scene parameters into the network simulator again, executing the subsequent flow until all the test results are met for n times, and determining that the training of the network simulator is completed.
In the embodiment of the present application, taking the network simulator as NS-3 as an example, for example, scene parameters of many common scenes, that is, test conditions (incast (number of ingress ports), average bandwidth of each ingress port, roce stream number, and Write/Read/Send ratio, etc.), are obtained in a test environment set up in a laboratory, and for these test conditions, a Config file is set on NS-3 and a program is executed correspondingly. And comparing the performance index obtained by the NS-3 with the performance index when the network performance is optimal, and adjusting DCQCN, CNP, PFC algorithm parameters and the like so that the performance index and the network performance can be well fitted.
Exemplary, referring to FIG. 2, a network simulator reinforcement learning model framework is shown in accordance with an embodiment of the present application. The frame includes "environment" that may be a scene parameter in a specific scene, such as average bandwidth of each ingress port, number of streams, and proportion of different types of messages; the "state" may be the counted number of messages added with ECN marks, throughput of dequeues, dequeue depth, current ECN configuration, etc.; the action is the configuration of the issued ECN; "rewards" are added values of network performance.
Wherein, the Value (Value) function of the reinforcement learning model can be used to represent the network performance expected to be achieved under different ECN configurations in different scenes.
Further, after using the NS-3 network simulator with the adjusted parameters, more test scenarios may be designed, the network simulator may be run and the resulting recommended ECN configuration recorded, and these scenarios may be saved in the flow model file along with the recommended ECN configuration.
Step 120: if the target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration, the ECN threshold of the target output port is configured based on the target ECN configuration.
In the embodiment of the application, if a target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration, the target ECN configuration is acquired, and the ECN threshold of the target outlet port is configured based on the target ECN configuration.
Specifically, the target ECN configuration includes an ECN threshold upper limit, an ECN threshold lower limit and an ECN marking probability, and then the parameters are configured on the target egress port, and when the dequeue depth is lower than the ECN threshold lower limit, the network device does not add an ECN marking to the message in the dequeue; when the dequeue depth is higher than the ECN threshold upper limit, adding ECN marks for all messages in the dequeue; messages in the dequeue are randomly marked according to a linear probability (between 0 and a maximum marking probability) determined based on the dequeue depth when the dequeue depth is between the lower ECN threshold limit and the upper ECN threshold limit.
Furthermore, in an embodiment of the present application, the ECN threshold adjustment method may further include the following steps: if the association relation between the preset scene parameters and the ECN configuration is judged to not have the target ECN configuration matched with the target scene parameters, inputting the target scene parameters into the trained network simulator to obtain the corresponding ECN configuration; and configuring the ECN threshold of the target output port based on the ECN configuration.
That is, if any scene parameter in the configuration file is missed by the scene parameter in the current scene, which indicates that the current scene does not belong to the tested common scene, the scene parameter in the current scene is input into the trained network simulator, the recommended ECN configuration is obtained through the network simulator, and the ECN threshold configuration is performed on the target output port by adopting the ECN configuration. The specific ECN threshold configuration process is not described herein.
The overall process of the ECN threshold configuration method provided by the embodiment of the present application is described in detail below with reference to a specific application scenario. For example, referring to fig. 3, a process schematic diagram of an ECN threshold configuration method provided by the embodiment of the present application is shown, a laboratory builds a test environment, first, tests a common scene in the test environment to obtain a correlation between scene parameters of each common scene and its corresponding recommended ECN configuration, and stores the correlation in a flow model file; then, calibrating the NS-3 model parameters by adopting the incidence relation between the scene parameters of each common scene and the corresponding recommended ECN configuration obtained by testing, simulating the scene parameters of each multi-scene by adopting the calibrated parameters to obtain the corresponding recommended ECN configuration, and storing the incidence relation between each scene and the corresponding recommended ECN configuration into a flow model file; then, scene parameters (such as queue depth, bandwidth throughput, equipment configuration, roce flow statistics and the like) of a target scene are acquired in real time, whether the target ECN configuration matched with the scene parameters exists in a flow model file is judged based on the scene parameters, if the target ECN configuration exists, the target ECN configuration is issued to a target outlet port of the network equipment, if the target ECN configuration does not exist, network simulation is performed based on the acquired scene parameters and the calibrated NS-3 model, the corresponding ECN configuration is obtained, and the ECN configuration is issued to the target outlet port of the network equipment.
Referring to fig. 4, an exemplary structure diagram of an ENC threshold configuration apparatus according to an embodiment of the present application is shown, where the apparatus includes:
an obtaining unit 40, configured to obtain a target scenario parameter of a target egress port in a current traffic forwarding scenario, where a scenario parameter of an egress port is a parameter related to traffic forwarded through the egress port;
a judging unit 41, configured to judge whether a target ECN configuration matching the target scene parameter exists in an association relationship between a preset scene parameter and the ECN configuration;
a configuration unit 42, configured to configure an ECN threshold of the target output port based on the target ECN configuration if the judging unit determines that there is a target ECN configuration matching the target scene parameter in the association relationship between the preset scene parameter and the ECN configuration;
the ECN configuration corresponding to each scene parameter in the association relation between the preset scene parameter and the ECN configuration is ECN configuration when the scene parameter in each common scene and the network performance value in the common scene obtained through testing in the built test environment are optimal, and/or ECN configuration when the network performance value of the outlet port in the current scene is optimal is obtained by respectively simulating the scene parameter of each outlet port based on a trained network simulator.
Optionally, the apparatus further comprises an analog unit:
if the judging unit 41 judges that the association relationship between the preset scene parameter and the ECN configuration does not have the target ECN configuration matched with the target scene parameter, the simulating unit is configured to input the target scene parameter into the trained network simulator to obtain a corresponding ECN configuration;
the configuration unit 42 is further configured to configure the ECN threshold of the target egress port based on the ECN configuration.
Optionally, the apparatus further comprises:
the testing unit is used for adding the corresponding relation between the scene parameters in the common scene and the ECN configuration when the network performance value in the common scene obtained by testing in the built testing environment is optimal to the preset association relation between the scene parameters and the ECN configuration aiming at each common traffic forwarding scene.
Optionally, the apparatus further comprises:
the training unit is used for training the network simulator based on the scene parameters of each common scene and ECN configuration when the network performance value in the common scene is optimal, and obtaining the trained network simulator.
Optionally, based on scene parameters of each common scene and ECN configuration when the network performance value in the common scene is optimal, training the network simulator, so as to obtain a trained network simulator, where the training unit is specifically configured to:
inputting scene parameters of each common scene into a network simulator to obtain ECN configuration corresponding to each common scene respectively;
judging whether the fitting degree of ECN configuration corresponding to each common parameter and ECN configuration corresponding to each common parameter when the network performance value is optimal meets the preset condition or not;
and if the preset conditions are not met, adjusting parameters of the network simulator until fitting degrees of ECN configuration corresponding to each common parameter and ECN configuration corresponding to each common parameter when the network performance value is optimal meet the preset conditions, and determining that the training of the network simulator is completed.
The above units may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), or one or more microprocessors (digital singnal processor, abbreviated as DSP), or one or more field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), or the like. For another example, when a unit is implemented in the form of a processing element scheduler code, the processing element may be a general purpose processor, such as a central processing unit (Central Processing Unit, CPU) or other processor that may invoke the program code. For another example, the units may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Further, in the ECN threshold configuration device provided by the embodiment of the present application, as for a hardware layer, a hardware architecture schematic diagram of the ECN threshold configuration device may be shown in fig. 5, and the ECN threshold configuration device may include: a memory 50 and a processor 51,
memory 50 is used to store program instructions; the processor 51 calls the program instructions stored in the memory 50 and executes the above-described method embodiments according to the obtained program instructions. The specific implementation manner and the technical effect are similar, and are not repeated here.
Optionally, the present application further provides an ECN threshold configuration device, including at least one processing element (or chip) configured to perform the above method embodiments.
Alternatively, the application also provides a program product, such as a computer-readable storage medium, having stored thereon computer-executable instructions for causing a computer to perform the above-described method embodiments.
Here, a machine-readable storage medium may be any electronic, magnetic, optical, or other physical storage device that may contain or store information, such as executable instructions, data, or the like. For example, a machine-readable storage medium may be: RAM (Radom Access Memory, random access memory), volatile memory, non-volatile memory, flash memory, a storage drive (e.g., hard drive), a solid state drive, any type of storage disk (e.g., optical disk, dvd, etc.), or a similar storage medium, or a combination thereof.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer, which may be in the form of a personal computer, laptop computer, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the application may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Moreover, these computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather to enable any modification, equivalent replacement, improvement or the like to be made within the spirit and principles of the application.
Claims (8)
1. An ECN threshold configuration method, comprising:
obtaining a target scene parameter of a target outlet port in a current flow forwarding scene, wherein the scene parameter of one outlet port is a parameter related to flow forwarded through the outlet port;
judging whether a target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration;
if the target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration, configuring the ECN threshold of the target output port based on the target ECN configuration;
the ECN configuration corresponding to each scene parameter in the association relation between the preset scene parameter and the ECN configuration is ECN configuration when the scene parameter in each common scene and the network performance value in the common scene obtained through testing in the built test environment are optimal, and/or ECN configuration when the network performance value of the outlet port in the current scene is optimal is obtained by respectively simulating the scene parameter of each outlet port based on a trained network simulator;
if the association relation between the preset scene parameters and the ECN configuration is judged to not have the target ECN configuration matched with the target scene parameters, inputting the target scene parameters into the trained network simulator to obtain the corresponding ECN configuration;
and configuring the ECN threshold of the target output port based on the ECN configuration.
2. The method of claim 1, wherein the method further comprises:
and adding the corresponding relation between the scene parameters in the common scene and the ECN configuration when the network performance value in the common scene obtained by testing in the built test environment is optimal to each common traffic forwarding scene into the preset association relation between the scene parameters and the ECN configuration.
3. The method of claim 2, wherein the method further comprises:
and training the network simulator based on the scene parameters of each common scene and ECN configuration when the network performance value in the common scene is optimal, so as to obtain the trained network simulator.
4. The method of claim 3, wherein training the network simulator based on the scene parameters of each common scene and the ECN configuration when the network performance value in the common scene is optimal, the step of obtaining a trained network simulator comprising:
inputting scene parameters of each common scene into a network simulator to obtain ECN configuration corresponding to each common scene respectively;
judging whether the fitting degree of ECN configuration corresponding to each common parameter and ECN configuration corresponding to each common parameter when the network performance value is optimal meets the preset condition or not;
and if the preset conditions are not met, adjusting parameters of the network simulator until fitting degrees of ECN configuration corresponding to each common parameter and ECN configuration corresponding to each common parameter when the network performance value is optimal meet the preset conditions, and determining that the training of the network simulator is completed.
5. An ECN threshold configuration apparatus, the apparatus comprising:
the system comprises an acquisition unit, a forwarding unit and a forwarding unit, wherein the acquisition unit is used for acquiring a target scene parameter of a target outlet port in a current flow forwarding scene, wherein the scene parameter of one outlet port is a parameter related to flow forwarded through the outlet port;
the judging unit is used for judging whether a target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration;
the configuration unit is used for configuring the ECN threshold of the target output port based on the target ECN configuration if the judging unit judges that the target ECN configuration matched with the target scene parameter exists in the association relation between the preset scene parameter and the ECN configuration;
the ECN configuration corresponding to each scene parameter in the association relation between the preset scene parameter and the ECN configuration is ECN configuration when the scene parameter in each common scene and the network performance value in the common scene obtained through testing in the built test environment are optimal, and/or ECN configuration when the network performance value of the outlet port in the current scene is optimal is obtained by respectively simulating the scene parameter of each outlet port based on a trained network simulator;
the apparatus further comprises an analog unit:
if the judging unit judges that the target ECN configuration matched with the target scene parameter does not exist in the association relation between the preset scene parameter and the ECN configuration, the simulation unit is used for inputting the target scene parameter into the trained network simulator to obtain the corresponding ECN configuration;
the configuration unit is further configured to configure an ECN threshold of the target egress port based on the ECN configuration.
6. The apparatus of claim 5, wherein the apparatus further comprises:
the testing unit is used for adding the corresponding relation between the scene parameters in the common scene and the ECN configuration when the network performance value in the common scene obtained by testing in the built testing environment is optimal to the preset association relation between the scene parameters and the ECN configuration aiming at each common traffic forwarding scene.
7. The apparatus of claim 6, wherein the apparatus further comprises:
the training unit is used for training the network simulator based on the scene parameters of each common scene and ECN configuration when the network performance value in the common scene is optimal, and obtaining the trained network simulator.
8. The apparatus of claim 7, wherein the training unit is specifically configured to, when training the network simulator based on the scene parameters of each common scene and the ECN configuration when the network performance value in the common scene is optimal to obtain the trained network simulator:
inputting scene parameters of each common scene into a network simulator to obtain ECN configuration corresponding to each common scene respectively;
judging whether the fitting degree of ECN configuration corresponding to each common parameter and ECN configuration corresponding to each common parameter when the network performance value is optimal meets the preset condition or not;
and if the preset conditions are not met, adjusting parameters of the network simulator until fitting degrees of ECN configuration corresponding to each common parameter and ECN configuration corresponding to each common parameter when the network performance value is optimal meet the preset conditions, and determining that the training of the network simulator is completed.
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| WO2021052374A1 (en) * | 2019-09-16 | 2021-03-25 | 华为技术有限公司 | Network congestion control method, node, system and storage medium |
| CN112532409A (en) * | 2019-09-17 | 2021-03-19 | 华为技术有限公司 | Network parameter configuration method and device, computer equipment and storage medium |
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