CN111756646A

CN111756646A - Network transmission control method, network transmission control device, computer equipment and storage medium

Info

Publication number: CN111756646A
Application number: CN202010652960.1A
Authority: CN
Inventors: 刘岩; 杨文正
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2020-07-08
Filing date: 2020-07-08
Publication date: 2020-10-09
Anticipated expiration: 2040-07-08
Also published as: CN111756646B

Abstract

The application relates to a network transmission control method, a device, computer equipment and a storage medium, which relate to the technical field of network transmission, and the method comprises the following steps: acquiring a historical data set, wherein the historical data set comprises network transmission data at each historical time point; each historical time point corresponds to a periodic characteristic, and the periodic characteristic is used for indicating the time domain position of the corresponding time point in the belonged time period; acquiring periodic characteristics of a target time point; acquiring a prediction strategy characteristic corresponding to a target time point according to the periodic characteristic of the target time point, the periodic characteristics of each historical time point and a historical data set; and in the time interval corresponding to the target time point, performing network transmission control according to the prediction strategy characteristics. The scheme fully considers the periodic change of the network transmission condition, thereby improving the accuracy of the prediction of the network transmission control strategy and the accuracy of the network transmission control.

Description

Network transmission control method, network transmission control device, computer equipment and storage medium

Technical Field

The embodiment of the application relates to the technical field of network transmission, in particular to a network transmission control method, a network transmission control device, computer equipment and a storage medium.

Background

In the technical field of network communication, a server and a client communicate with each other based on a network transport protocol, for example, the transport control protocol is a transport layer communication protocol, and in order to prevent network congestion, developers propose a series of congestion control strategies, each of which has a set of parameters for realizing congestion control.

In practical applications, the data transmission quality of different networks is very different, for example, optical fiber, wireless local area network, wireless cellular network, etc., even if the same network has different network quality at different times. In the related art, the time window is artificially determined through manual experience, a plurality of (several or dozens of) candidate parameter sets are determined through experience, and strategy parameters with optimal transmission performance are found through a random search mode.

However, in the above policy optimization process, the change of the network transmission condition over time is considered less, so that the accuracy of acquiring the policy characteristics is lower, and the accuracy of controlling the network transmission is lower.

Disclosure of Invention

The embodiment of the application provides a network transmission control method, a network transmission control device, computer equipment and a storage medium, which can improve the accuracy of obtaining strategy characteristics and further improve the accuracy of network transmission control, and the technical scheme is as follows:

in one aspect, a network transmission control method is provided, where the method includes:

acquiring a historical data set, wherein the historical data set comprises network transmission data at each historical time point; each historical time point corresponds to a periodic characteristic, and the periodic characteristic is used for indicating the time domain position of the corresponding time point in the belonged time period; the network transmission data is used for indicating a policy characteristic and a network transmission performance at a corresponding time point, and the policy characteristic is used for indicating at least one of a network transmission control policy and a policy parameter of the network transmission control policy;

acquiring periodic characteristics of a target time point;

acquiring a prediction strategy characteristic corresponding to the target time point according to the periodic characteristic of the target time point, the periodic characteristics of the historical time points and the historical data set;

and in the time interval corresponding to the target time point, performing network transmission control according to the prediction strategy characteristics.

In another aspect, a network transmission control apparatus is provided, the apparatus including:

the data set acquisition module is used for acquiring a historical data set, and the historical data set comprises network transmission data at each historical time point; each historical time point corresponds to a periodic characteristic, and the periodic characteristic is used for indicating the time domain position of the corresponding time point in the belonged time period; the network transmission data is used for indicating a policy characteristic and a network transmission performance at a corresponding time point, and the policy characteristic is used for indicating at least one of a network transmission control policy and a policy parameter of the network transmission control policy;

the first characteristic acquisition module is used for acquiring the periodic characteristics of the target time point;

a strategy characteristic obtaining module, configured to obtain a prediction strategy characteristic corresponding to the target time point according to the periodic characteristic of the target time point, the periodic characteristics of the historical time points, and the historical data set;

and the transmission control module is used for carrying out network transmission control according to the prediction strategy characteristics in a time interval corresponding to the target time point.

In a possible implementation manner, the policy feature obtaining module includes:

a fitting function constructing submodule, configured to construct a fitting function based on the historical data set, where the fitting function is used to indicate a relationship between the transmission state information of each historical time point and the network transmission performance corresponding to each historical time point; the transmission state information is used for indicating the periodic characteristics of the corresponding time point and the strategy characteristics of the corresponding time point;

and the strategy feature extraction submodule is used for extracting the prediction strategy feature from the fitting function based on the periodic feature of the target time point.

In one possible implementation, the policy feature extraction sub-module is configured to,

extracting reference evaluation points from the fitting function based on the periodic characteristics of the target time points, wherein the reference evaluation points comprise strategy characteristics for maximizing the network transmission performance at the target time points;

and extracting strategy features contained in the reference evaluation points as the prediction strategy features.

In one possible implementation, the fitting function building sub-module is configured to,

extracting each reference time point from each historical time point based on the periodic characteristics of the target time point and the periodic characteristics of each historical time point;

and constructing the fitting function based on the periodic characteristics of the reference time points and the network transmission data of the reference time points.

In one possible implementation, the apparatus further includes:

a second feature obtaining module, configured to, before the data set obtaining module obtains the historical data set, obtain a periodic feature of the first time point by taking a modulus of the first time point according to a cycle length of the time cycle in response to that each historical time point is represented in the form of an absolute time difference from an initial time point; the first time point is any one of the historical time points.

In one possible implementation, the apparatus further includes:

a third feature obtaining module, configured to, before the data set obtaining module obtains the historical data set, in response to that each historical time point is represented in the form of a clock time, and the time period is a period of 24 hours, obtain a clock time of a first time point as a periodic feature of the first time point; the first time point is any one of the historical time points.

In one possible implementation, the apparatus further includes:

a merging module, configured to merge the network transmission performance of the first time point and the network transmission performance of the second time point in each historical time point in response to that the transmission state information of the first time point is the same as the transmission state information of the second time point;

the transmission state information is used for indicating the periodicity characteristic of the corresponding time point and the strategy characteristic of the corresponding time point.

In one possible implementation manner, the merging module is configured to,

modifying the network transmission performance of the time point which is earlier in time into the network transmission performance of the time point which is later in time from the first time point and the second time point;

or,

and adjusting the network transmission performance of the later time point based on the network transmission performance of the earlier time point in the first time point and the second time point.

In one possible implementation, the apparatus further includes:

and the strategy characteristic generation module is used for responding to the empty historical data set and randomly generating the prediction strategy characteristic.

In one possible implementation, the apparatus further includes:

and a correcting module, configured to correct the network transmission performance of each historical time point according to the periodic feature of the target time point and the periodic features of each historical time point before the policy feature obtaining module obtains the predicted policy feature corresponding to the target time point according to the periodic feature of the target time point, the periodic features of each historical time point, and the historical data set.

In a possible implementation manner, the transmission control module is configured to perform at least one of the following network transmission controls according to the policy feature in a time interval corresponding to the target time point: the method comprises the steps of transmission control protocol TCP control, audio and video transmission performance control, content delivery network CDN scheduling control and network bandwidth scheduling control.

In another aspect, a network transmission control system is provided, the system including: the system comprises a log monitoring server, a strategy parameter optimization server and a transmission control server;

the log monitoring server is used for recording a historical data set, and the historical data set comprises network transmission data at each historical time point; each historical time point corresponds to a periodic characteristic, and the periodic characteristic is used for indicating the time domain position of the corresponding time point in the belonged time period; the network transmission data is used for indicating a policy characteristic and a network transmission performance at a corresponding time point, and the policy characteristic is used for indicating at least one of a network transmission control policy and a policy parameter of the network transmission control policy;

the strategy parameter optimization server is used for acquiring the periodic characteristics of the target time point; acquiring a prediction strategy characteristic corresponding to the target time point according to the periodic characteristic of the target time point, the periodic characteristics of the historical time points and the historical data set;

and the transmission control server is used for carrying out network transmission control according to the prediction strategy characteristics in a time interval corresponding to the target time point.

In another aspect, a computer device is provided, the computer device comprising a processor and a memory, the memory having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by the processor to implement the network transmission control method as described above.

In another aspect, a computer readable storage medium is provided, in which at least one instruction, at least one program, a set of codes, or a set of instructions is stored, which is loaded and executed by a processor to implement the network transmission control method as described above.

In another aspect, a computer program product or computer program is provided, the computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the network transmission control method provided in the above-mentioned various alternative implementations.

The technical scheme provided by the application can comprise the following beneficial effects:

the strategy characteristics at the target time point are predicted by combining the relation of time domain positions of the historical time point and the target time point in the respective belonged time period, and the strategy parameters and the network transmission performance of the historical time points, so that the periodical change of the network transmission condition is fully considered in the process of predicting the network transmission control strategy based on the historical data, the accuracy of predicting the network transmission control strategy is improved, and the accuracy of network transmission control is improved.

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

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

Fig. 1 is a schematic structural diagram of a network transmission control system according to an exemplary embodiment of the present application;

fig. 2 is a flowchart illustrating a network transmission control method according to an exemplary embodiment of the present application;

fig. 3 is a flowchart illustrating a network transmission control method according to an exemplary embodiment of the present application;

fig. 4 is a schematic diagram of a network transmission control system provided in an exemplary embodiment of the present application;

fig. 5 is a timing diagram illustrating a network transmission control method according to an exemplary embodiment of the present application;

fig. 6 is a block diagram of a network transmission control apparatus according to an exemplary embodiment of the present application;

FIG. 7 is a block diagram illustrating the structure of a computer device in accordance with an exemplary embodiment;

FIG. 8 is a block diagram illustrating the structure of a computer device according to an example embodiment.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

The embodiment of the application provides a network transmission control method, which can predict the strategy characteristics at a target time point by combining the relation of time domain positions of historical time points and the target time point in respective belonged time periods, and strategy parameters and network transmission performance of each historical time point, thereby fully considering the periodic change of the network transmission condition in the process of predicting the network transmission control strategy based on historical data, improving the accuracy of network transmission control strategy prediction and improving the accuracy of network transmission control. For ease of understanding, the terms referred to in this application are explained below.

1) CDN (Content Delivery Network )

CDN, which refers to a content delivery network, also called a content delivery network, is used to improve the quality of service of the internet.

The CDN network comprises a content cache device, a content switch, a content router, a CDN content management system and the like:

the content caching device is a CDN network node, is positioned at a user access point, is a content providing device facing an end user, can cache static Web content and streaming media content, and realizes edge propagation and storage of the content so as to facilitate the nearby access of the user.

The content exchanger is at the user access centralized point, can balance the load of a plurality of content caching devices at a single point, and can carry out caching load balance and access control on the content.

The content router is responsible for scheduling the user's request to the appropriate device. The content router is usually implemented by a load balancing system, dynamically balances load distribution of each content cache site, selects an optimal access site for a user's request, and improves availability of a website. Content routers may route based on a variety of factors, including proximity of sites to users, availability of content, network load, device conditions, etc. The load balancing system is the core of the entire CDN. The accuracy and efficiency of load balancing directly determine the efficiency and performance of the entire CDN.

The CDN content management system is responsible for management of the entire CDN, is an optional component, and is used for content management, such as content injection and delivery, content distribution, content audit, content service, and the like.

The CDN widely adopts various cache servers, distributes the cache servers to a region or a network where user access is relatively concentrated, and when a user accesses a website, directs the user access to a cache server that works normally and is closest to the user by using a global load technique, and the cache server directly responds to the user request, thereby improving the user access response speed and hit rate.

The CDN is a supplementary method based on a TCP/IP architecture, and a data packet of the CDN is a data packet of the TCP/IP.

2) TCP (Transmission Control Protocol)

TCP is a connection-oriented, reliable, byte-stream based transport-layer communication protocol.

TCP is connection-oriented, that is, before data transmission, a client and a server (or two communicating parties) need to establish a trusted connection. After the data transmission is finished, the connection is disconnected in an agreed mode, and the two communication parties release resources.

TCP is reliable and defines a "time-out retransmission mechanism" for packets, i.e., each packet waits for a response after it is sent out. If no response is received within the specified time, the sender retransmits the data a certain number of times to ensure reliable transmission of the data.

TCP is based on byte stream, an application layer does not need to pay attention to the boundary of a data packet when data is transmitted, and the TCP can automatically buffer, group and combine the data according to the network environment when the data is transmitted.

3) BOA (Bayesian Optimization Algorithm, Bayesian Optimization)

The Bayesian optimization algorithm mainly aims at the following problems:

X^*＝arg_x∈Smaxf(x)

where S is the candidate set for x. The goal is to select an x from S such that the value of f (x) is minimal or maximal, and perhaps the specific formula shape of f (x) is unknown, but if an x is selected, the value of f (x) can be found experimentally or by observation. f (x) is a function of a black box.

Referring to fig. 1, a schematic structural diagram of a network transmission control system according to an exemplary embodiment of the present application is shown, as shown in fig. 1, the network transmission control system 100 includes a server cluster 110 and a client 120.

The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network service, cloud communication, middleware service, a domain name service, a security service, a CDN, a big data and artificial intelligence platform, and the like.

The server cluster 110 includes a transmission control server, a log monitoring server, and a policy parameter optimization server. The log monitoring server may include at least one of a client log monitoring server and a transmission control log monitoring server.

The log monitoring server comprises a log monitoring module and is mainly responsible for summarizing parameters, strategies and corresponding time corresponding to each transmission control connection and returning the parameters, the strategies and the corresponding time to the strategy parameter optimization server according to requirements.

The strategy parameter optimization server optimizes the strategy and the parameters according to the log reported by the log monitoring server, and returns the optimization result, namely the optimized strategy and the optimized parameters to the corresponding transmission control server for use.

The transmission control server is mainly responsible for responding to the request of the normal client and simultaneously performs network transmission control according to the strategy and the parameters returned by the strategy parameter optimization server. Meanwhile, the transmission control server also needs to periodically report performance data of each transmission control connection in the latest period of time to the log monitoring server so that the log monitoring server performs summarization, wherein the performance data of each transmission control connection includes bandwidth, connection time, size of a downloaded file, and the like.

The client may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, or the like, but is not limited thereto. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein.

Referring to fig. 2, which shows a flowchart of a network transmission control method provided in an exemplary embodiment of the present application, as shown in fig. 2, the method is performed by a network transmission control system, which may be the network transmission control system shown in fig. 1, and as shown in fig. 2, the network transmission control method includes the following steps:

step 210, obtaining a historical data set, wherein the historical data set comprises network transmission data at each historical time point; each historical time point corresponds to a periodic characteristic which is used for indicating the time domain position of the corresponding time point in the corresponding time period; the network transmission data is used for indicating the policy characteristics and the network transmission performance at the corresponding time point, and the policy characteristics are used for indicating at least one of the network transmission control policy and the policy parameters of the network transmission control policy.

In a possible implementation manner, the network transmission control policy is a network transmission control algorithm, and the policy parameter of the corresponding network transmission control policy is an algorithm parameter of the network transmission control algorithm.

In a possible implementation manner, after each network transmission connection completes the transmission of data, the corresponding protocol stack records the relevant data of the connection, and stores the data as a network transmission log. For example, stored in text form in the log monitoring server or client or network transmission server shown in fig. 1; screening at least one group of network transmission data for indicating the network transmission condition at the corresponding time point according to a certain time interval based on the network transmission log, and forming a historical data set by the at least one group of network transmission data.

In at least one group of network transmission data, the network transmission data at each historical time point is used for representing the network transmission condition in a time area corresponding to each historical time point, taking a time interval as one hour as an example, the network transmission data corresponding to 6:00 yesterday is used for representing the network transmission condition in the time interval of 6:00-7:00 yesterday, and 6:00 is used for representing the starting time of the time interval and also representing the time for starting using the strategy characteristic corresponding to the time interval.

Step 220, obtaining the periodic characteristics of the target time point.

In one possible implementation, the target time point refers to the current time point, or a certain time point after the current time point.

That is, the network transmission control system may obtain the prediction policy characteristics in real time, for example, the current time is 20:00, and the network transmission control system obtains the prediction policy characteristics required in the time interval to which 20:00 belongs by obtaining the periodic characteristics of 20:00 in real time, so as to guide network transmission control in the time interval to which 20:00 belongs.

Or, the periodic characteristic of a certain time point may be obtained in advance to obtain the prediction policy characteristic required in the time interval to which the time point belongs in advance, so as to guide network transmission control in the time interval to which the time point belongs, for example, the current time point is 20:00, 21:00 transmission control may be obtained according to a preset rule, and the policy characteristic required in the time interval to which 21:00 belongs may be obtained in advance.

The network transmission control method provided by the present application is described in the embodiments of the present application, taking the periodic characteristic of the target time point obtained in real time as an example.

In a possible implementation manner, the time period is preset according to actual conditions, for example, the time period may be a week, a day or an hour, or a custom arbitrary time length. Taking the time period as one day (24 hours) as an example, the periodic characteristics corresponding to each historical time point are represented as a cycle from 0 to 23, i.e. the periodic characteristics of 8 pm in the previous day and 8 pm in today are represented as being in the 20 th time domain position, i.e. 20: 00.

And step 230, acquiring the prediction strategy characteristics corresponding to the target time points according to the periodic characteristics of the target time points, the periodic characteristics of each historical time point and the historical data sets.

In a possible implementation manner, the prediction policy feature is used for network transmission control, for the same network transmission control, multiple transmission control policies may be provided, each transmission control policy may correspond to different numbers and different ranges of parameters, and the parameters corresponding to the same transmission control policy may be adjusted within a specified range.

In a possible implementation manner, the prediction policy feature corresponding to the target time point is obtained according to the periodic feature of the target time point, the periodic feature of each historical time point, and the historical data set, and may be implemented to obtain a transmission control policy corresponding to the target time point, or obtain a parameter corresponding to the target time point, or obtain a pair of transmission control policies corresponding to the target time point and a parameter corresponding to the transmission control policies at the same time.

And 240, controlling network transmission according to the characteristics of the prediction strategy in a time interval corresponding to the target time point.

In a possible implementation manner, the time interval corresponding to the target time point is a certain time period in the time period, for example, the time period is one day, the target time point is 8:00, the time interval corresponding to the target time point may be 8:00-9:00, and network control transmission is performed in the time interval according to the prediction policy characteristics.

In summary, the network transmission control method provided in the embodiment of the present application predicts the policy characteristics at the target time point by combining the relationship between the time domain positions of the historical time points and the target time point in the respective time periods, and the policy parameters and the network transmission performance of each historical time point, so that the periodic variation of the network transmission condition is fully considered in the process of predicting the network transmission control policy based on the historical data, thereby improving the accuracy of predicting the network transmission control policy and improving the accuracy of network transmission control.

In a possible implementation manner, a prediction policy feature corresponding to a target time point is obtained based on a bayesian optimization algorithm, please refer to fig. 3, which shows a flowchart of a network transmission control method provided in an exemplary embodiment of the present application, as shown in fig. 3, the method is executed by a network transmission control system, which may be the network transmission control system shown in fig. 1, as shown in fig. 3, the network transmission control method includes the following steps:

step 310, acquiring a historical data set, wherein the historical data set comprises network transmission data at each historical time point; each historical time point corresponds to a periodic characteristic, and the periodic characteristic is used for indicating the time domain position of the corresponding time point in the belonged time period; the network transmission data is used for indicating the policy characteristics and the network transmission performance at the corresponding time point, and the policy characteristics are used for indicating at least one of the network transmission control policy and the policy parameters of the network transmission control policy.

In one possible implementation, before acquiring the historical data set, in response to each historical time point being represented in the form of an absolute time difference from an initial time point, a first time point is modulo according to a period length of a time period to obtain a periodic characteristic of the first time point; the first time point is any one of the respective historical time points.

In a possible implementation, the initial time refers to an initial running time of the network transmission control system, i.e. starting to time from the beginning of the running of the network transmission control system, or the initial time is a certain fixed time point in the past, e.g. starting from 0 o' clock of a certain day in the past, e.g. UNIX time, i.e. the total number of seconds from 0 o 0s of 1 month 1 day 1 1970 of 1970 to the present time of the coordinated world, without considering leap seconds.

In one possible implementation, the network transmission data includes historical policy features at corresponding time points, and network transmission performance at corresponding time points; referring to table 1, a historical data set provided by the present application according to an exemplary embodiment is shown, including multiple sets of network transmission data:

TABLE 1

In each set of the historical strategy characteristics, the first number represents the number of the strategy used by the network transmission connection under the network transmission data, and the subsequent numbers represent the corresponding parameters in the strategy. It should be noted that the relevant data of the network transmission data shown in table 1 is only illustrative, and the application does not limit the number of the network transmission data in the historical data set and the number of the data in the policy feature corresponding to each group of the network transmission data.

In one possible implementation, in order to sense the periodic variation of the network transmission performance, the absolute time difference of the network transmission data is subjected to a modulo processing according to a period duration, wherein the period duration may be hourly, daily, weekly, or the like.

The embodiment of the present application describes, by taking a cycle duration as an example every day, a process of performing modulo processing on an absolute time difference of network transmission data according to the cycle duration, where the process is implemented as follows:

t_i-1＝(t_i-1/3600)％24

wherein, t_i-1Representing the absolute time difference, t, corresponding to the data transmitted by the network_i-1The/3600 indicates that the time unit of the absolute time difference is converted from second to hour, 24 indicates that 24 hours exist in one day, and the time after the time unit is converted into hour is expressed by taking the module of 24 hours, and the cycle time length is 24 hours. And acquiring the time modulo the absolute time difference as the periodic characteristic of the corresponding time point.

In a possible implementation manner, the network transmission performance corresponding to each group of network transmission data is used to characterize the network transmission quality of each group of network transmission connection, and taking TCP control as an example, the network transmission performance may include one of bandwidth of a TCP connection, time required for establishing a connection, a rate of downloading a file, and throughput.

In a possible implementation manner, the network transmission performance corresponding to each group of network transmission data is a characteristic transmission performance parameter in the transmission performance parameters corresponding to the multiple transmission connections in the time period corresponding to each group, and the network transmission performance is used to reflect the working effect of the policy characteristic in each time period, for example, the network transmission performance may be a median, an average, or the like in the network transmission performance corresponding to the multiple transmission connections.

It should be noted that, for the acquisition of the network transmission performance, different acquisition methods may be selected according to actual service requirements, which is not limited in the present application.

In one possible implementation, in response to each historical time point being represented in the form of a clock time and the time period being a period of 24 hours, acquiring the clock time of the first time point as a periodic characteristic of the first time point; the first time point is any one of the respective historical time points.

In one possible implementation, the clock Time is a 24-hour period, such as system Time, local Time, or UTC (Coordinated Universal Time), etc. For example, the clock time may be noted as 2020.06.22.00.00, representing 6/month, 22/0 in 2020. The clock time can embody the periodic characteristics of each historical time point, so that the clock time can be directly acquired as the periodic characteristics of the corresponding time point.

In one possible implementation manner, in response to that the transmission state information of the first time point is the same as the transmission state information of the second time point in each historical time point, merging the network transmission data of the first time point and the network transmission performance of the second time point;

That is to say, for network transmission data with consistent transmission state information at historical time points, the same policy characteristics have been used at historically and periodically the same time points, and the network transmission data at the first time point and the network transmission performance at the second time point are merged, and the process is implemented as follows:

or,

and adjusting the network transmission performance of the time point at the later time based on the network transmission performance of the time point at the earlier time in the first time point and the second time point.

In a possible implementation manner, the modification of the network transmission performance of the earlier time point of the first time point and the second time point to the network transmission performance of the later time point is implemented by replacing the network transmission performance of the earlier time point with the network transmission performance of the later time point, so that the network transmission performance between the time points with the same transmission state information is kept consistent, and the instantaneity of the network transmission performance is ensured.

In a possible implementation manner, based on the network transmission performance at the time point before, the adjusting of the network transmission performance at the time point after is embodied as adjusting the network transmission performance at the time point after by respectively giving different weights to the network transmission performance at the first time point and the network transmission performance at the second time point, or increasing a specified step length or decreasing the specified step length to the network transmission at the time point after.

And step 320, acquiring the periodic characteristics of the target time points.

In one possible implementation manner, the network transmission performance of each historical time point is modified according to the periodicity characteristic of the target time point and the periodicity characteristic of each historical time point.

In a possible implementation manner, acquiring a time difference between the periodic characteristics of the target time point and the periodic characteristics of each historical time point, and performing corresponding network transmission performance enhancement processing on the historical time points close to the periodic characteristics of the target time point; and for the historical time points which are far away from the periodic characteristics of the target time points, the corresponding network transmission performance is weakened to perform processing so as to improve the guiding effect of the network transmission data of the historical time points which are close to the periodic characteristics of the target time points on the target time points.

Step 330, constructing a fitting function based on the historical data set, wherein the fitting function is used for indicating the relationship between the transmission state information of each historical time point and the network transmission performance corresponding to each historical time point; the transmission status information is used for indicating the periodicity characteristic of the corresponding time point and the strategy characteristic of the corresponding time point.

In one possible implementation, a fitting function is constructed by combining a proxy model based on the historical dataset, the proxy model being a gaussian process model.

In one possible implementation, the ith acquired historical data set is represented as:

wherein

Indicating the network transmission data after the i-1 st update,

wherein, t_i-1Indicating the periodic characteristics corresponding to the i-1 st update,

the strategy characteristics obtained by the i-1 st update are shown,

and (3) representing the transmission performance parameters under the strategy characteristics after the (i-1) th updating. And a fitting function G (X) is constructed by combining the historical data set D with the agent model, and is used for indicating the relation between the periodic characteristics of each historical time point and the corresponding strategy characteristics thereof and the transmission performance parameters corresponding to each historical time point.

In one possible implementation, constructing a fitting function based on the historical data set by using network transmission data of part of historical time points in the historical data set is implemented as:

and constructing a fitting function based on the periodic characteristics of the reference time points and the network transmission data of the reference time points.

In one possible implementation, the reference time point is a historical time point that is within a specified time period (e.g., within 6 hours before and after) of the periodic characteristic of the target time point.

And 340, extracting the prediction strategy characteristics from the fitting function based on the periodic characteristics of the target time points.

In one possible implementation manner, a reference evaluation point is extracted from the fitting function based on the periodic characteristics of the target time point, wherein the reference evaluation point comprises a strategy characteristic which enables the network transmission performance to be maximized at the target time point;

the policy features contained in the reference evaluation points are extracted as predicted policy features.

In one possible implementation, the extraction model is used to extract the target time point based on periodic features of the target time point,extracting the prediction strategy features from the fitting function, wherein the extraction time of the extraction model is the periodic features of the target time point, and extracting the function f () to obtain the target time point t_iThe predictive policy features of (a) may be implemented as:

wherein,

i.e. the prediction strategy characteristics.

In one possible implementation, the predictive policy feature is randomly generated in response to the historical data set being empty.

For example, when the network transmission control system acquires the prediction policy feature for the first time, the client and the server do not generate network transmission yet, network transmission data is not stored in the history data set, and the history data set is empty. Referring to table 2, it shows policy characteristics corresponding to a target time point provided by the present application according to an exemplary embodiment:

TABLE 2

Start time(s)	Policy features
		1585724700	{1,0.5,0.8}

It shows that the network transmission control is carried out by using the policy characteristics {1,0.5,0.8} in the time interval from the 1585724700 th s.

And 350, performing network transmission control according to the prediction strategy characteristics in a time interval corresponding to the target time point.

In one possible implementation manner, within a time interval corresponding to the target time point, at least one of the following network transmission controls is performed according to the policy characteristics: the method comprises the steps of transmission control protocol TCP control, audio and video transmission performance control, content delivery network CDN scheduling control and network bandwidth scheduling control.

Taking TCP control as an example, in a time interval corresponding to a target time point, a congestion control policy is run according to policy characteristics to perform network transmission control.

The application provides a network transmission control, the system includes: the system comprises a log monitoring server, a strategy parameter optimization server and a transmission control server; referring to fig. 4, which shows a schematic diagram of a network transmission control system according to an exemplary embodiment of the present application, as shown in fig. 4, the network transmission control system 400 includes a log monitoring server 410, a policy parameter optimization server 420, and a transmission control server 430.

The log monitoring server 410 is configured to record a historical data set, where the historical data set includes network transmission data at each historical time point; each historical time point corresponds to a periodic characteristic, and the periodic characteristic is used for indicating the time domain position of the corresponding time point in the belonged time period; the network transmission data is used for indicating the strategy characteristics and the network transmission performance at the corresponding time point, and the strategy characteristics are used for indicating at least one item of the network transmission control strategy and the strategy parameters of the network transmission control strategy;

a policy parameter optimization server 420, configured to record a historical data set, where the historical data set includes network transmission data at each historical time point; each historical time point corresponds to a periodic characteristic, and the periodic characteristic is used for indicating the time domain position of the corresponding time point in the belonged time period; the network transmission data is used for indicating the strategy characteristics and the network transmission performance at the corresponding time point, and the strategy characteristics are used for indicating at least one item of the network transmission control strategy and the strategy parameters of the network transmission control strategy;

and the transmission control server 430 is configured to perform network transmission control according to the prediction policy characteristics in the time interval corresponding to the target time point.

In a possible implementation manner, the policy parameter optimization server obtains the prediction policy feature corresponding to the target time point according to a policy feature update period set by the policy parameter optimization server, or the policy parameter optimization server obtains the prediction policy feature corresponding to the target time point in response to the prediction policy feature obtaining request after receiving the prediction policy feature obtaining request of the transmission control server.

In a possible implementation manner, the log monitoring server includes a client log server and a transmission control end log server, where the client log server is configured to monitor network transmission connection data sent by the client, and the transmission control end log server is configured to monitor network transmission connection data sent by the transmission control server. The dotted line portion shown in fig. 4 represents prediction policy feature acquisition by the client side log server, and the solid line portion shown in fig. 4 represents prediction policy feature acquisition by the transmission control side log server.

Taking network transmission control as TCP control, a log monitoring server as a TCP log server, and a policy parameter optimization server, after receiving a predicted policy characteristic obtaining request from a transmission control server (TCP server), and in response to the predicted policy characteristic obtaining request, obtaining a predicted policy characteristic corresponding to a target time point is performed as an example, to describe the network transmission control method provided in the present application, please refer to fig. 5, which shows a timing chart of the network transmission control method provided in an exemplary embodiment of the present application, as shown in fig. 5, the method is performed by a transmission control server, a log monitoring server, and a policy parameter optimization server alternately, as shown in fig. 5, the network transmission control method includes the following steps:

step 501, the TCP server sends a prediction policy feature obtaining request to the policy parameter optimization server, and correspondingly, the policy parameter optimization server receives the prediction policy feature obtaining request sent by the TCP server.

And acquiring the time point of the TCP server sending the prediction strategy characteristic acquisition request to the strategy parameter optimization server as a target time point.

In one possible implementation manner, the TCP server sends a predicted policy feature obtaining request to the policy parameter optimization server at a first time interval, assuming that the current time is t_iThat is, the TCP server currently sends the request for obtaining the prediction strategy characteristics at the time t_iThe time when the TCP server sends the prediction strategy feature acquisition request next time is t_i+ T, where T is the first time interval.

In one possible implementation, the predicted policy feature obtaining request sent by the TCP server includes obtaining at least one of a TCP policy and a TCP parameter, and the TCP policy and the TCP parameter are used for running the congestion control policy.

Step 502, the policy parameter optimization server sends a historical data set acquisition request to the TCP server, and correspondingly, the log monitoring server receives the historical data set acquisition request sent by the policy parameter optimization server.

In one possible implementation manner, the predicted policy feature obtaining request is sent to the policy parameter optimizing server in units of first time intervals corresponding to the TCP server, and the policy parameter optimizing server requests the log monitoring server for the historical data set corresponding to the TCP server in units of the first time intervals.

After receiving a prediction strategy characteristic obtaining request sent by a TCP server, a strategy parameter optimization server sends a historical data set updating request to a log monitoring server, wherein the historical data set updating request requires the log monitoring server to update a historical data set to a state nearest to a target time point. For example, the time when the TCP server first sends the policy feature update to the policy parameter optimization server is t₀The moment when the TCP server sends the strategy characteristic update to the strategy parameter optimization server for the second time is t₁，t₁＝t₀+ T, T being a first time interval at T₀To t₁During this time, the TCP connection occurring at the TCP server will generate a corresponding TCP log, where the TCP log includes the relevant data of each TCP connection in the period, and the historical data set update request requests to obtain the network transmission data in the period from the TCP log and update the historical data set.

In step 503, the log monitoring server extracts the network transmission data in the TCP log in the latest first time interval.

In a possible case, the TCP log generated by the TCP connection occurring at the TCP server is sent to the log monitoring server in non-real time, and the TCP log is sent in units of a second time interval, which is not greater than the first time interval, to ensure the timeliness of the TCP log update, that is, the time when the TCP log is sent to the log monitoring server for the first time by the TCP server is assumed to be t₀The moment when the TCP server sends the TCP log to the log monitoring server for the second time is t₁’，t₁’＝t₀+T_r，T_rT ≦ T, so when the policy parameter optimization server sends a historical dataset update request to the log monitoring server, the log monitoring server will summarize T in response to the request₀To t₁The TCP server reports TCP logs of each TCP connection to the log monitoring server in a time period to obtain network transmission data in the period; with T as 5s, T_rFor 2s as an example, assume that the time when the TCP server first sends a TCP log to the log monitoring server and the TCP serverThe time when the strategy characteristic update is sent to the strategy parameter optimization server for the first time is 0s, the time when the strategy characteristic update is sent to the strategy parameter optimization server for the second time by the TCP server is 5s, in the period, the TCP server sends two TCP logs to the log monitoring server, wherein the two TCP logs are respectively 0-2 s and 2-4 s, at the moment, when the log monitoring server receives a historical data set update request sent by the strategy parameter optimization server at the 5s, the log monitoring server summarizes the TCP logs which can be obtained within 0-5 s, namely the 0-4 s TCP logs, in response to the request, network transmission data are extracted from the TCP logs, and the historical data set is updated; the TCP server sends a policy feature update to the policy parameter optimization server for the third time in the 10 th s, and in the period from 5s to 10s, the TCP server sends TCP logs to the log monitoring server for the third time, the TCP logs are respectively the TCP logs of 4s to 6s, 6s to 8s and 8s to 10s, at the moment, when the log monitoring server receives a history data set update request sent by the policy parameter optimization server in the 10 th s, the log monitoring server updates network transmission data extracted from the TCP logs which can be obtained in the 5s to 10s on the basis of a history data set corresponding to the policy feature acquisition request predicted for the second time, and the history data set of 0s to 10s is obtained.

In one possible scenario, assuming that the current policy feature request is the (i + 1) th request, the log monitor server responds to the request from t_iTo t_i+1Acquiring transmission performance parameters corresponding to each TCP connection and extracting absolute time difference of network transmission data from a TCP log corresponding to the TCP connection in a time period, summarizing the transmission performance parameters corresponding to each TCP connection, acquiring the network transmission performance of the historical time point, performing modulo processing on the absolute time difference of the extracted network transmission data according to cycle duration, acquiring the cycle characteristics of the historical time point, and then acquiring the network transmission data corresponding to the latest first time interval

Wherein，t_iIndicating the periodic characteristics corresponding to the ith update,

the policy characteristics obtained by the ith update are shown,

and the network transmission performance in the time interval under the strategy characteristic after the ith updating is shown.

In a possible implementation manner, the network transmission performance is throughput, and summarizing the transmission performance parameters corresponding to each TCP connection may be to obtain an average value of the throughput in the latest first time interval, or obtain a median of the throughput in the first time interval, that is, obtain, from the transmission performance parameters of all TCP connections in the latest first time interval, a characteristic transmission performance parameter that can embody a working effect of a policy characteristic in the time zone as the network transmission performance in the time zone.

In one possible implementation, the policy characteristics used by the TCP connections in the same first time interval are the same, and the TCP connection data is deleted in response to the existence of at least one TCP connection in the TCP connections in the same first time interval being different from the policy characteristics used by the other TCP connections.

That is, when a singular point exists in the same cycle, the singular point is removed to ensure the accuracy of the resultant data.

In step 504, the log monitoring server adds the network transmission data in the latest first time interval to the candidate historical data set to obtain a historical data set.

In one possible implementation manner, before the log monitoring server receives the current (i +1 st) network transmission data set acquisition request, the log monitoring server has received other network transmission data set acquisition requests, that is, before extracting the network transmission data in the TCP log in the latest first time interval, the log monitoring server has performed extraction and update on the network transmission data in the corresponding TCP in response to the other network transmission data set acquisition requests (i-th request), so as to form a candidate historical data set, which is represented as:

wherein D₁A set of candidate historical data is represented,

indicating the network transmission data after the i-1 th updating.

Adding the network transmission data in the latest first time interval into the candidate historical data set to obtain a historical transmission record D₂：

In one possible implementation, in response to the policy characteristic in the network transmission data in the recent first time interval being the same as at least one policy characteristic in the candidate historical data set, indicating that the same policy characteristic has been used previously, the past record is replaced with the network transmission performance in the network transmission data in the recent first time interval, for example,

and

same, then will

Corresponding to

Is replaced by

Thereby ensuring the instantaneity of the network transmission performance.

And adding the network transmission data in the latest first time interval into the updated candidate historical data set to obtain a historical characteristic data set.

That is, when the same policy feature has been used at the same time historically and the network transmission performance at the time historically differs from the network transmission performance at the latest first time interval, the network transmission performance at the time historically is replaced with the network transmission performance at the latest first time interval, that is, the network transmission performance between the time historically is considered to have failed, and the latest network transmission performance is taken as the standard.

Or, different weights may be set for the network transmission performance at the time historically and the network transmission performance in the latest first time interval to modify the effect of the policy feature in the latest first time interval in combination with the historical network transmission performance, wherein the weights of the network transmission performance corresponding to the historical time closer to the target time point are higher.

And 505, the log monitoring server returns the historical data set to the strategy parameter optimization server, and correspondingly, the strategy parameter optimization server receives the historical data set.

Step 506, the policy parameter optimization server obtains the periodicity characteristics of the target time points.

Step 507, the strategy parameter optimization server obtains the prediction strategy characteristics corresponding to the target time points according to the periodic characteristics of the target time points, the periodic characteristics of each historical time point and the historical data sets.

The implementation process of step 505 to step 507 may refer to relevant contents in the embodiments shown in fig. 2 and fig. 3, and will not be described herein again.

In one possible implementation, the policy parameter optimization server randomly retrieves the policy features in response to the historical data set being empty.

Step 508, the policy parameter optimization server sends the prediction policy characteristics to the TCP server, and the TCP server receives the prediction policy characteristics accordingly.

In step 509, the TCP server performs network transmission control according to the prediction policy characteristics in the time interval corresponding to the target time point.

It should be noted that, the network transmission control method when the log monitoring server is the client log server is similar to that when the log monitoring server is the transmission control server, the step of acquiring the historical data set from the transmission control server by the policy parameter optimization server is changed to acquiring the historical data set from the client log server, the process of generating the historical data set by the client log server is the same as the process of generating the historical data set by the transmission control server, and details are not repeated here.

Referring to fig. 6, which shows a block diagram of a network transmission control apparatus provided in an exemplary embodiment of the present application, as shown in fig. 6, the apparatus may be applied in a network transmission control system to perform all or part of the steps of the embodiments shown in fig. 2, fig. 3, or fig. 5, the network transmission control system may be the network transmission control system shown in fig. 1 or fig. 4, as shown in fig. 6, the apparatus includes:

a data set obtaining module 601, configured to obtain a historical data set, where the historical data set includes network transmission data at each historical time point; each historical time point corresponds to a periodic characteristic, and the periodic characteristic is used for indicating the time domain position of the corresponding time point in the belonged time period; the network transmission data is used for indicating the strategy characteristics and the network transmission performance at the corresponding time point, and the strategy characteristics are used for indicating at least one item of the network transmission control strategy and the strategy parameters of the network transmission control strategy;

a first feature obtaining module 602, configured to obtain a periodic feature of a target time point;

a policy feature obtaining module 603, configured to obtain a prediction policy feature corresponding to the target time point according to the periodic feature of the target time point, the periodic features of each historical time point, and the historical data set;

and a transmission control module 604, configured to perform network transmission control according to the prediction policy characteristics in a time interval corresponding to the target time point.

In a possible implementation manner, the policy feature obtaining module 603 includes:

the fitting function constructing submodule is used for constructing a fitting function based on the historical data set, and the fitting function is used for indicating the relationship between the transmission state information of each historical time point and the network transmission performance corresponding to each historical time point; the transmission state information is used for indicating the periodic characteristics of the corresponding time point and the strategy characteristics of the corresponding time point;

and the strategy feature extraction submodule is used for extracting the prediction strategy features from the fitting function based on the periodic features of the target time points.

In one possible implementation, the strategic feature extraction sub-module is configured to,

extracting a reference evaluation point from the fitting function based on the periodic characteristics of the target time point, wherein the reference evaluation point comprises strategy characteristics for maximizing the network transmission performance on the target time point;

In one possible implementation, the fitting function constructs a sub-module for,

In one possible implementation, the apparatus further includes:

a second feature obtaining module, configured to, before the data set obtaining module 601 obtains the historical data set, obtain a periodic feature of the first time point by taking a module of the first time point according to a cycle length of the time cycle in response to that each historical time point is represented in the form of an absolute time difference from the initial time point; the first time point is any one of the respective historical time points.

In one possible implementation, the apparatus further includes:

a third feature obtaining module, configured to, before the historical data set is obtained by the data set obtaining module 601, in response to that each historical time point is represented in the form of a clock time, and a time period is a period of 24 hours, obtain a clock time of the first time point as a periodic feature of the first time point; the first time point is any one of the respective historical time points.

In one possible implementation, the apparatus further includes:

a merging module, configured to merge the network transmission performance at the first time point and the network transmission performance at the second time point in each historical time point in response to that the transmission state information at the first time point is the same as the transmission state information at the second time point;

In one possible implementation, the merging module is configured to,

or,

In one possible implementation, the apparatus further includes:

and a correcting module, configured to correct the network transmission performance at each historical time point according to the periodic features of the target time point and the periodic features of each historical time point before the policy feature obtaining module 603 obtains the predicted policy features corresponding to the target time point according to the periodic features of the target time point, the periodic features of each historical time point, and the historical data set.

In a possible implementation manner, the transmission control module 604 is configured to perform at least one of the following network transmission controls according to the policy characteristics in a time interval corresponding to the target time point: the method comprises the steps of transmission control protocol TCP control, audio and video transmission performance control, content delivery network CDN scheduling control and network bandwidth scheduling control.

FIG. 7 is a block diagram illustrating the structure of a computer device 700 according to an example embodiment. The computer device 700 may be a client, such as a smart phone, a tablet computer, an MP3 player (Moving Picture Experts group Audio Layer III, motion video Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts group Audio Layer IV, motion video Experts compression standard Audio Layer 4), a laptop computer, or a desktop computer. Computer device 700 may also be referred to by other names such as user equipment, portable terminals, laptop terminals, desktop terminals, and the like.

Generally, the computer device 700 includes: a processor 701 and a memory 702.

The processor 701 may include one or more processing cores, such as a 4-core processor, a 7-core processor, and so on. The processor 701 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 701 may also include a main processor and a coprocessor, where the main processor is a processor for processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 701 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, the processor 701 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.

Memory 702 may include one or more computer-readable storage media, which may be non-transitory. Memory 702 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 702 is used to store at least one instruction for execution by processor 701 to implement the network transmission control method provided by the method embodiments herein.

In some embodiments, the computer device 700 may also optionally include: a peripheral interface 703 and at least one peripheral. The processor 701, the memory 702, and the peripheral interface 703 may be connected by buses or signal lines. Various peripheral devices may be connected to peripheral interface 703 via a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 704, touch screen display 705, camera 706, audio circuitry 707, positioning components 708, and power source 709.

In some embodiments, the computer device 700 also includes one or more sensors 710. The one or more sensors 710 include, but are not limited to: acceleration sensor 711, gyro sensor 712, pressure sensor 713, fingerprint sensor 714, optical sensor 715, and proximity sensor 716.

Those skilled in the art will appreciate that the configuration illustrated in FIG. 7 is not intended to be limiting of the computer device 700 and may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components may be employed.

Fig. 8 is a block diagram illustrating the structure of a computer device 800 according to an example embodiment. The computer device may be implemented as a server in the above-mentioned aspects of the present application. The computer apparatus 800 includes a Central Processing Unit (CPU) 801, a system Memory 804 including a Random Access Memory (RAM) 802 and a Read-Only Memory (ROM) 803, and a system bus 805 connecting the system Memory 804 and the CPU 801. The computer device 800 also includes a basic Input/Output system (I/O system) 806, which facilitates transfer of information between devices within the computer, and a mass storage device 807 for storing an operating system 813, application programs 814, and other program modules 815.

The basic input/output system 806 includes a display 806 for displaying information and an input device 809 such as a mouse, keyboard, etc. for user input of information. Wherein the display 806 and the input device 809 are connected to the central processing unit 801 through an input output controller 810 connected to the system bus 805. The basic input/output system 806 may also include an input/output controller 810 for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, input-output controller 810 also provides output to a display screen, a printer, or other type of output device.

The mass storage device 807 is connected to the central processing unit 801 through a mass storage controller (not shown) connected to the system bus 805. The mass storage device 807 and its associated computer-readable media provide non-volatile storage for the computer device 800. That is, the mass storage device 807 may include a computer-readable medium (not shown) such as a hard disk or Compact Disc-Only Memory (CD-ROM) drive.

Without loss of generality, the computer-readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash Memory or other solid state Memory technology, CD-ROM, Digital Versatile Disks (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage, or other magnetic storage devices. Of course, those skilled in the art will appreciate that the computer storage media is not limited to the foregoing. The system memory 804 and mass storage 807 described above may be collectively referred to as memory.

The computer device 800 may also operate as a remote computer connected to a network via a network, such as the internet, in accordance with various embodiments of the present disclosure. That is, the computer device 800 may be connected to the network 812 through the network interface unit 811 coupled to the system bus 805, or may be connected to other types of networks or remote computer systems (not shown) using the network interface unit 811.

The memory further includes at least one instruction, at least one program, a code set, or a set of instructions, which is stored in the memory, and the central processing unit 801 executes the at least one instruction, the at least one program, the code set, or the set of instructions to implement all or part of the steps in the network transmission control method according to the above embodiments.

In an exemplary embodiment, a non-transitory computer readable storage medium including instructions, such as a memory including at least one instruction, at least one program, set of codes, or set of instructions, executable by a processor to perform all or part of the steps of the method shown in any of the embodiments of fig. 2, 3, or 5 described above, is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

In an exemplary embodiment, a computer program product or computer program is provided that includes computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes all or part of the steps of the network transmission control method.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A network transmission control method, the method comprising:

acquiring periodic characteristics of a target time point;

2. The method according to claim 1, wherein the obtaining the prediction strategy characteristics corresponding to the target time points according to the periodic characteristics of the target time points, the periodic characteristics of the historical time points and the historical data sets comprises:

constructing a fitting function based on the historical data set, wherein the fitting function is used for indicating the relationship between the transmission state information of each historical time point and the network transmission performance corresponding to each historical time point; the transmission state information is used for indicating the periodic characteristics of the corresponding time point and the strategy characteristics of the corresponding time point;

and extracting the prediction strategy characteristics from the fitting function based on the periodic characteristics of the target time points.

3. The method of claim 2, wherein the extracting the prediction strategy feature from the fitting function based on the periodic feature of the target time point comprises:

4. The method of claim 2, wherein said constructing a fitting function based on said historical data set comprises:

5. The method of claim 1, wherein prior to obtaining the historical data set, further comprising:

responding to each historical time point to be represented in the form of absolute time difference with an initial time point, and taking a module of a first time point according to the period length of the time period to obtain the periodic characteristic of the first time point; the first time point is any one of the historical time points.

6. The method of claim 1, wherein prior to obtaining the historical data set, further comprising:

responding to each historical time point expressed in the form of clock time, and taking the time period with the duration of 24 hours as a period, and acquiring the clock time of a first time point as a periodic characteristic of the first time point; the first time point is any one of the historical time points.

7. The method of claim 5 or 6, further comprising:

in response to that the transmission state information of the first time point is the same as the transmission state information of a second time point in the historical time points, merging the network transmission performance of the first time point and the network transmission performance of the second time point;

8. The method of claim 7, wherein the combining the network transmission data at the first time point and the network transmission performance at the second time point comprises:

or,

9. The method of claim 1, further comprising:

randomly generating the predictive policy feature in response to the historical data set being empty.

10. The method according to claim 1, wherein before obtaining the prediction strategy characteristics corresponding to the target time point according to the periodic characteristics of the target time point, the periodic characteristics of the historical time points, and the historical data set, the method further comprises:

and correcting the network transmission performance of each historical time point according to the periodic characteristics of the target time point and the periodic characteristics of each historical time point.

11. The method according to claim 1, wherein the controlling network transmission according to the policy characteristics in the time interval corresponding to the target time point comprises:

and in the time interval corresponding to the target time point, performing at least one of the following network transmission control according to the strategy characteristics: the method comprises the steps of transmission control protocol TCP control, audio and video transmission performance control, content delivery network CDN scheduling control and network bandwidth scheduling control.

12. A network transmission control system, the system comprising: the system comprises a log monitoring server, a strategy parameter optimization server and a transmission control server;

13. A network transmission control apparatus, characterized in that the apparatus comprises:

14. A computer device comprising a processor and a memory, the memory having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by the processor to implement the network transmission control method according to any one of claims 1 to 11.

15. A computer-readable storage medium, having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement the network transmission control method according to any one of claims 1 to 11.