Network environment simulation method, device, equipment, medium and program product

Abstract

The invention relates to a network environment simulation method and device, equipment, a medium and a program product, and the method comprises the steps: obtaining an internal variable database of a network transmission system, and the internal variable database comprises a plurality of groups of transmission system internal variables obtained in a real network environment; predicting network loss parameters respectively corresponding to the internal variables of the plurality of groups of transmission systems; and inputting the network loss parameter to a target network simulator, and simulating a real network environment through the target network simulator. The internal variable database comprises a large amount of transmission system internal variables obtained in a real network environment, so that when the network simulator module performs network environment simulation based on the network loss quality parameters obtained through prediction, a simulated network scene is closer to the real network environment.

Description

Technical Field

[0001] The present disclosure relates to the field of computer network technology, and in particular to a network environment simulation method, apparatus, device, medium and program product. Background Art

[0002] With the continuous development of technology, network technology has been greatly developed. Users can watch videos or play games online through the Internet, which can enrich their daily lives. Users can also hold video conferences or work online through the Internet, making their work more convenient and efficient.

[0003] In order to improve the efficiency of data transmission in the network, relevant staff usually develop a series of algorithms to improve the efficiency of data transmission in the network. Related technologies usually use network simulators to simulate the real network environment and verify these algorithms in the simulated environment. However, network simulators in related technologies generally cannot simulate a network environment that is closer to the real one. Summary of the invention

[0004] The present disclosure provides a network environment simulation method, apparatus, device, medium and program product.

[0005] According to a first aspect of the present disclosure, a network environment simulation method is provided, the method comprising:

[0006] Acquire an internal variable database of the network transmission system, wherein the internal variable database includes multiple groups of transmission system internal variables obtained in a real network environment;

[0007] Predicting network loss parameters corresponding to the multiple groups of transmission system internal variables respectively;

[0008] The network loss parameters are input into a target network simulator, and a real network environment is simulated by the target network simulator.

[0009] According to a second aspect of the present disclosure, a network environment simulation device is provided, the device comprising:

[0010] An internal variable database acquisition module, used to acquire an internal variable database of a network transmission system, wherein the internal variable database includes multiple groups of transmission system internal variables obtained in a real network environment;

[0011] A network loss parameter prediction module, used to predict network loss parameters corresponding to the multiple groups of transmission system internal variables;

[0012] The environment simulation module is used to input the network loss parameters into the target network simulator and simulate the real network environment through the target network simulator.

[0013] According to a third aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0014] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above method of the present disclosure is implemented.

[0015] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements the above method of the present disclosure when executed by a processor.

[0016] The network environment simulation method, device, equipment, medium and program product provided by the embodiments of the present disclosure obtain an internal variable database of the network transmission system, which includes multiple groups of internal variables of the transmission system obtained in a real network environment; and predict the network loss parameters corresponding to the multiple groups of internal variables of the transmission system. The network loss parameters are input into the target network simulator, and the real network environment is simulated by the target network simulator. Since the internal variable database includes a large number of internal variables of the transmission system obtained in a real network environment, when the network simulator simulates the network environment based on the predicted network loss quality parameters, the simulated network scenario will be closer to the real network environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0018] Figure 1 A schematic diagram of a scenario provided for an exemplary embodiment of the present disclosure;

[0019] Figure 2 A schematic diagram of a network simulation based on a massive network scenario provided for an exemplary embodiment of the present disclosure;

[0020] Figure 3 A flowchart of a network environment simulation method provided by an exemplary embodiment of the present disclosure;

[0021] Figure 4 A schematic block diagram of functional modules of a network environment simulation device provided by an exemplary embodiment of the present disclosure;

[0022] Figure 5 A structural block diagram of an electronic device provided for an exemplary embodiment of the present disclosure;

[0023] Figure 6 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0025] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0026] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0027] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0028] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0029] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0030] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0031] As an optional but non-limiting implementation, in response to receiving an active request from the user, the method of sending a prompt message to the user may be, for example, a pop-up window, in which the prompt message may be presented in text. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0032] When performing network transmission through a network transmission system, it is often necessary to continuously optimize the network transmission system. However, the biggest problem facing the optimization of the network transmission system is how to verify the optimization algorithm in a real network environment. However, if the verification is performed in a real network environment, some optimization algorithms may degrade before verification, or affect the stability of the entire network transmission system. Therefore, the verification in a real network environment may have a greater impact on the existing real network environment.

[0033] Therefore, when it is necessary to verify the relevant optimization algorithm, it is usually verified through offline simulation. Offline simulation can be isolated from the real network environment without interfering with the real network environment. However, since the simulated environment of offline simulation is a blood draw from the real network environment, or a sampling of the real network, when simulating the real network environment through offline simulation, the relevant technology usually faces the following problems:

[0034] First, the simulated environment of offline simulation in related technologies is not representative compared with the real network environment. For example, the real network environment may contain tens of millions of different network scenarios, and the related technologies may only simulate a small part of them during simulation, such as only simulating a few thousand or 1% of the real scenarios. In this way, the environment simulated in the form of offline simulation will be quite different from the real network environment, so that the optimization effect simulated in the offline environment may not be displayed as expected in the real network.

[0035] Therefore, the number or proportion of scenarios in the offline simulated environment does not match the real network environment. In addition, the offline simulated environment cannot match the real network environment. For example, users collect some bandwidth, packet loss, and latency data in the real network environment to simulate the real network environment, but because users can only collect limited data, the network simulator cannot simulate the real network environment well, resulting in network simulator failure.

[0036] In the embodiments provided in the present disclosure, in a network transmission system, there are generally multiple variables, such as bandwidth, RTT (Round-Trip Time), packet loss or cache size, etc. These variables can be used as network loss parameters, and multiple different network loss parameters constitute a network loss parameter set. In a network transmission system, there are also indicators related to transmission performance, that is, internal variables of the transmission system, such as throughput, retransmission ratio, congestion window or number of selected confirmation segments commonly seen in TCP (Transmission Control Protocol). There will be a corresponding relationship between the network loss parameters and the internal variables of the transmission system.

[0037] In the embodiment, it is necessary to obtain network loss parameters that match the real network quality, so that the mutual mapping relationship between the network loss parameters and the internal variables of the transmission system is obtained in an offline manner, and the distribution of the internal variables of the transmission system is obtained in the real network, so that the distribution of the network loss parameters implicit in the real network can be obtained based on the offline mapping relationship. The embodiment can apply the distribution of the network loss parameters implicit in the real network to the network simulator to achieve the goal of simulating the real network quality.

[0038] It should be noted that the network simulator in the embodiment may refer to a related hardware network loss meter or a software network loss simulator, etc., and the embodiment is not limited thereto. The network transmission system in the embodiment is a system that can run a certain transmission algorithm on the client and server sides and serve a certain business. For example, the network transmission system can specifically be a streaming media transmission system, which can specifically include: a video transmission algorithm for serving a streaming media transmission business.

[0039] The transmission node in the embodiment may include a server and a client. The offline network quality database refers to a storage network loss parameter and an internal variable of the transmission system obtained under the network loss parameter. The network quality model may be a model for mapping the internal variable of the transmission system to a specific network loss parameter, obtained by performing machine learning training on the data of the offline network quality database.

[0040] In an embodiment, the network loss parameters in the network transmission system can be obtained based on the log information by obtaining the log information in the network transmission system. The network loss parameters obtained through the log information can be understood as the true performance of the network transmission system.

[0041] like Figure 1 As shown, Figure 1A scenario diagram provided for an embodiment of the present disclosure. By obtaining network loss parameters in a real network, and continuously sending the network loss parameters to a network simulator, and running a network transmission system. The network parameters can be stored in an offline network quality database, and the internal variables of the network transmission system can be recorded, and the internal variables of the transmission system can also be stored in the offline network quality database. Specifically, the following steps can be included:

[0042] Step 1: In the offline database construction module, a set of specified network losses can be selected from the network loss parameter set (for example, bandwidth change, RTT, loss, buffer size, number of parallel connections, time distribution of connection joining / leaving, etc.), which can be obtained by V i Identifier, i represents the i-th group of network loss parameters, V i =(Bandwidth i ,RTT i , loss i , buffer size i , …) and added to the network simulation tool.

[0043] Step 2: In V i The network transmission system is operated under the network loss parameters. For example, taking TCP as an example, a TCP connection is established and data transmission is performed.

[0044] Step 3: Record the internal real-time variable vector of the network transmission system, such as the congestion window, the number of segments selected for confirmation, the number of retransmissions, etc., using F j,t Indicates that, that is, at V i Internal variables of the transmission system at time t for the jth connection under network loss.

[0045] Step 4: Record V in the offline network quality database i With F j,t .

[0046] Step 5: Before different network loss parameters are fully selected (for example, no more than 100,000), continue to select new network loss parameters and execute step 1, otherwise execute step 6.

[0047] Step 6: Build V through machine learning model training i With F j,t The mapping relationship model can be specified by a F j,t To obtain the corresponding V i , referred to as network quality model. A variety of machine learning models can be used to directly achieve this goal, such as: decision tree model or deep neural network model, the embodiment is not limited thereto.

[0048] In the embodiments provided in the present disclosure, Figure 2 As shown, Figure 2A network simulation diagram based on a massive network scenario provided in an embodiment of the present disclosure is provided. The transmission system internal variables stored in the transmission system internal variable database can collect the transmission system internal variables of the network transmission system recorded by the server in the real network environment when the user group accesses the server through the Internet, and store the transmission system internal variables in the transmission system internal variable database.

[0049] In this way, each transmission system internal variable in the transmission system internal variable database can be input into the network quality model. According to the correspondence between the network loss parameters and the transmission system internal variables in the network quality model, the network loss parameters corresponding to each transmission system internal variable can be predicted through the network quality model. In this way, the network loss parameter distribution in the real network environment can be obtained.

[0050] In an embodiment, by continuously inputting the predicted network loss parameters into the network simulator, the real network environment can be continuously simulated by the network simulator. Among them, a set of network loss parameters can correspond to a real network environment. For example, when a new transmission system optimization measure is adopted for the client and the server, a plurality of different real network environments can be continuously simulated by the network simulator, so that the benefits of the transmission system optimization measure in the different simulated network environments can be obtained. Since the network environment simulated by the network simulator can be closer to the real network environment, it can be determined whether to adjust the optimization measure or put it into the real network environment based on the verification and benefits of the optimization measure in the simulated network environment.

[0051] Specifically, simulating a real network environment through a network simulator may include the following steps:

[0052] Step 1: Collect the internal variables F of the transmission system in massive real networks x,t , and stored in the internal variable database.

[0053] Step 2: Based on the above network quality model, x,t Mapped to the corresponding network loss parameters, the V corresponding to the real network is obtained i distribution.

[0054] Step 3: The network simulation tool is based on the V i The probability distribution selects a sufficient number of groups of network loss parameters (for example, more than 10,000 groups) to operate the transmission system, and collects its performance indicators to complete the performance evaluation.

[0055] Based on the above embodiments, the present disclosure also provides a network environment simulation method, such as Figure 3 As shown, the method may include the following steps:

[0056] In step S310, an internal variable database of the network transmission system is obtained.

[0057] The internal variable database includes multiple groups of transmission system internal variables obtained in a real network environment.

[0058] In the embodiment, the network transmission system in the real network environment may include thousands of multiple scenarios. In order to facilitate the network environment simulated by the network simulator to be closer to the real network environment, the embodiment collects the internal variables of the transmission system in the real environment. For example, multiple groups of transmission system internal variables in the network transmission system in a certain period of time in the historical period can be obtained, and the obtained multiple groups of transmission system internal variables are stored in the internal variable database.

[0059] In the embodiment, multiple groups of transmission system internal variables in the network transmission system can be obtained according to the records of the relevant servers. For example, the transmission system internal variables in the network transmission system can be obtained based on the log data by acquiring the log data of the relevant servers.

[0060] In step S320, network loss parameters corresponding to multiple groups of transmission system internal variables are predicted.

[0061] In an embodiment, the internal variables of the transmission system in the network transmission system usually have a corresponding relationship with the network loss parameters. A set of network loss parameters is equivalent to a network scenario. The internal variables of the transmission system can be considered as the intrinsic factors forming the network scenario and are implicit internal variables in the network transmission system.

[0062] Therefore, by obtaining the corresponding relationship between the internal variables of the transmission system and the network loss parameters, the network loss parameters corresponding to each internal variable of the transmission system in the internal variable database can be obtained.

[0063] In step S330, network loss parameters are input into a target network simulator, and a real network environment is simulated by the target network simulator.

[0064] In an embodiment, the predicted network loss parameters can be continuously input into the target network simulator, and the target network simulator can be used to simulate the real network environment. In this way, by continuously inputting the network loss parameters into the target network simulator, different real environments can be continuously simulated, or the real network environment in different time periods can be simulated.

[0065] In an embodiment, a real network environment may contain network scenarios with a certain probability, and each network scenario may correspond to a set of network loss parameters. Therefore, in a real network environment, the probability distribution of different network scenarios contained can be obtained, that is, the network loss parameter distribution in the real network scenario can be obtained. For example, in the TCP network transmission process, it may be affected by the peak or off-peak period of the user's Internet access. The network scenarios corresponding to different moments or time periods in the real network environment of the network transmission system are different, that is, the network loss parameters are different. That is, in a real network environment, the probability of network scenario A may be 10%, the probability of network scenario B may be 40%, and the probability of network scenario C may be 50%. Each scenario corresponds to one / a set of network loss parameters, so that the network loss distribution in the real network can be obtained.

[0066] Therefore, the above network loss distribution can also be input into the target simulator. The target network simulator will be closer to the real network environment, which is convenient for users to verify the benefits of new algorithms adopted by the client or server.

[0067] The network environment simulation method provided by the embodiment of the present disclosure obtains an internal variable database of the network transmission system, which includes multiple groups of internal variables of the transmission system obtained in a real network environment; and predicts the network loss parameters corresponding to the multiple groups of internal variables of the transmission system. The network loss parameters are input into the target network simulator, and the real network environment is simulated by the target network simulator. Since the internal variable database includes a large number of internal variables of the transmission system obtained in a real network environment, when the network simulator simulates the network environment based on the predicted network loss quality parameters, the simulated network scenario will be closer to the real network environment.

[0068] Based on the above embodiment, in another embodiment provided by the present disclosure, the above step S310 may further include the following steps:

[0069] In step S311, the transmission system internal variables of the network transmission system within the target time period are obtained, wherein the transmission system internal variables are obtained when the user accesses the server through the network transmission system.

[0070] In step S312, the system internal variables are stored in the target database to obtain an internal variable database.

[0071] In the embodiment, when the user group accesses the server through the network transmission system, the server will record the internal variables in the network transmission system, which can be specifically recorded in the log data of the server. By continuously obtaining the transmission system internal variables of the network transmission system within the target time period, the real network environment can be more truly reflected.

[0072] Based on the above embodiment, in another embodiment provided by the present disclosure, the above step S320 may further include the following steps:

[0073] In step S321, multiple groups of transmission system internal variables are input into the network quality model to obtain multiple groups of network loss parameters corresponding to the multiple groups of transmission system internal variables.

[0074] Among them, the network quality model is used to characterize the mapping relationship between the internal variables of the transmission system and the network loss parameters.

[0075] In an embodiment, a preset model such as a neural network model can be trained through training samples, so that the preset model can learn the corresponding relationship between the internal variables of the transmission system and the network loss parameters. Therefore, a training sample can be obtained; wherein the training sample includes a preset internal variable of the transmission system and a preset network loss parameter, and the preset internal variable of the transmission system corresponds to the preset network loss parameter. The preset model is trained through the training sample to obtain a network quality model.

[0076] A set of training samples in the embodiment may include a transmission system internal variable and a corresponding network loss parameter, by obtaining a preset network loss parameter and inputting the preset network loss parameter into a target network simulator; running a network transmission system based on the target network simulator and obtaining the preset transmission system internal variable in the network transmission system. For details, see Figure 1 And the corresponding embodiments can obtain network loss parameters and corresponding internal variables of the transmission system and store them in an offline network quality database, and then obtain training samples containing network loss parameters and corresponding internal variables of the transmission system from the offline network quality database.

[0077] Based on the above embodiment, in another embodiment provided by the present disclosure, the above step S330 may further include the following steps:

[0078] In step S331, the probability distribution of network loss parameters in the target real network environment is obtained.

[0079] In step S332, multiple groups of network loss parameters are selected through a target network simulator based on the probability distribution, and the network transmission system is operated.

[0080] In an embodiment, the target network simulator can select a sufficient number of groups of network loss parameters based on the probability distribution of network loss parameters in the real network, such as the time distribution of bandwidth changes, RTT, loss or buffer size. For example, when the number of groups of network loss parameters exceeds a preset threshold, the network transmission system can be run to simulate a network environment close to the real one. In this way, as the network loss parameters are continuously input into the target network simulator, the target network simulator can continuously simulate different simulation environments, and by obtaining the benefits of transmission data in different simulated simulation environments, the purpose of verifying the relevant optimization algorithms can be achieved. Since the simulated environment is close to the real network environment, the verification result is closer to the verification result in the real network environment.

[0081] In the case of dividing each functional module according to each function, an embodiment of the present disclosure provides a network environment simulation device, which may be a server, a terminal, or a chip applied to a server. Figure 4 A schematic block diagram of the functional modules of a network environment simulation device provided by an exemplary embodiment of the present disclosure. Figure 4 As shown, the network environment simulation device includes:

[0082] A database acquisition module 10, used to acquire an internal variable database of a network transmission system, wherein the internal variable database includes multiple groups of transmission system internal variables obtained in a real network environment;

[0083] A network loss parameter prediction module 20, used to predict network loss parameters corresponding to the multiple groups of transmission system internal variables;

[0084] The environment simulation module 30 is used to input the network loss parameters into the target network simulator, and simulate the real network environment through the target network simulator.

[0085] In another embodiment provided by the present disclosure, the database acquisition module is specifically used to:

[0086] Acquire the internal variables of the transmission system within the target time period; wherein the internal variables of the transmission system are obtained based on when the user accesses the server through the network transmission system;

[0087] The system internal variables are stored in a target database to obtain an internal variable database.

[0088] In another embodiment provided by the present disclosure, the network loss parameter prediction module is specifically used to:

[0089] Input the multiple groups of transmission system internal variables into the network quality model to obtain multiple groups of network loss parameters corresponding to the multiple groups of transmission system internal variables respectively; wherein the network quality model is used to characterize the mapping relationship between the transmission system internal variables and the network loss parameters.

[0090] In another embodiment provided by the present disclosure, the network loss parameter prediction module is specifically used to:

[0091] Acquire a training sample; wherein the training sample includes a preset transmission system internal variable and a preset network loss parameter, and the preset transmission system internal variable corresponds to the preset network loss parameter;

[0092] The preset model is trained through the training samples to obtain the network quality model.

[0093] In another embodiment provided by the present disclosure, the network loss parameter prediction module is specifically used to:

[0094] Obtaining preset network loss parameters, and inputting the preset network loss parameters into a target network simulator;

[0095] The network transmission system is run based on the target network simulator, and preset transmission system internal variables in the network transmission system are obtained.

[0096] In another embodiment provided by the present disclosure, the environment simulation module is specifically used to:

[0097] Obtain the probability distribution of network loss parameters in the target real network environment;

[0098] Based on the probability distribution, multiple groups of network loss parameters are selected through the target network simulator, and the network transmission system is operated.

[0099] The network environment simulation device provided by the embodiment of the present disclosure obtains an internal variable database of the network transmission system, which includes multiple groups of internal variables of the transmission system obtained in a real network environment; and predicts the network loss parameters corresponding to the multiple groups of internal variables of the transmission system. The network loss parameters are input into the target network simulator, and the real network environment is simulated by the target network simulator. Since the internal variable database includes a large number of internal variables of the transmission system obtained in a real network environment, when the network simulator simulates the network environment based on the predicted network loss quality parameters, the simulated network scenario will be closer to the real network environment.

[0100] An embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory for storing instructions executable by the at least one processor; wherein the at least one processor is configured to execute the instructions to implement the above method disclosed in the embodiment of the present disclosure.

[0101] Figure 5 The structure diagram of an electronic device provided by an exemplary embodiment of the present disclosure is shown in FIG. Figure 5 As shown, the electronic device 1800 includes at least one processor 1801 and a memory 1802 coupled to the processor 1801 , and the processor 1801 can execute corresponding steps in the above method disclosed in the embodiment of the present disclosure.

[0102] The processor 1801 may also be referred to as a central processing unit (CPU), which may be an integrated circuit chip having signal processing capabilities. Each step in the method disclosed in the embodiment of the present disclosure may be completed by an integrated logic circuit of hardware in the processor 1801 or by instructions in the form of software. The processor 1801 may be a general-purpose processor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present disclosure may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a memory 1802, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, or other mature storage media in the art. The processor 1801 reads the information in the memory 1802 and completes the steps of the method in combination with its hardware.

[0103] In addition, when various operations / processes according to the present disclosure are implemented by software and / or firmware, they can be transmitted from a storage medium or a network to a computer system having a dedicated hardware structure, such as Figure 6 The computer system 1900 shown is installed with the programs constituting the software. When the various programs are installed, the computer system can perform various functions, including the functions described above. Figure 6 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure.

[0104] Computer system 1900 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0105] like Figure 6 As shown, the computer system 1900 includes a computing unit 1901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1902 or a computer program loaded from a storage unit 1908 to a random access memory (RAM) 1903. In the RAM 1903, various programs and data required for the operation of the computer system 1900 can also be stored. The computing unit 1901, the ROM 1902, and the RAM 1903 are connected to each other via a bus 1904. An input / output (I / O) interface 1905 is also connected to the bus 1904.

[0106] A plurality of components in the computer system 1900 are connected to the I / O interface 1905, including: an input unit 1906, an output unit 1907, a storage unit 1908, and a communication unit 1909. The input unit 1906 may be any type of device capable of inputting information to the computer system 1900, and the input unit 1906 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 1907 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1908 may include, but is not limited to, a disk, an optical disk. The communication unit 1909 allows the computer system 1900 to exchange information / data with other devices over a network such as the Internet, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0107] The computing unit 1901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1901 performs the various methods and processes described above. For example, in some embodiments, the above methods disclosed in the embodiments of the present disclosure may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1908. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 1902 and / or communication unit 1909. In some embodiments, the computing unit 1901 may be configured to perform the above methods disclosed in the embodiments of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0108] The embodiment of the present disclosure further provides a computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the above method disclosed in the embodiment of the present disclosure.

[0109] The computer-readable storage medium in the disclosed embodiments may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specifically, the computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0110] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0111] The embodiments of the present disclosure further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor, the above method disclosed in the embodiments of the present disclosure is implemented.

[0112] In embodiments of the present disclosure, computer program codes for performing the operations of the present disclosure may be written in one or more programming languages ​​or combinations thereof, including but not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0113] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0114] The modules, components or units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the names of the modules, components or units do not, in some cases, limit the modules, components or units themselves.

[0115] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0116] The above descriptions are only some embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, a technical solution formed by replacing the above features with the technical features with similar functions disclosed in the present disclosure (but not limited to).

[0117] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A network environment simulation method, characterized in that: The method comprises: Acquire an internal variable database of the network transmission system, wherein the internal variable database includes multiple groups of transmission system internal variables obtained in a real network environment; Predicting network loss parameters corresponding to the multiple groups of transmission system internal variables respectively; The network loss parameters are input into a target network simulator, and a real network environment is simulated by the target network simulator.

2. The method according to claim 1, characterized in that The step of obtaining an internal variable database of the network transmission system includes: Acquire the internal variables of the transmission system within the target time period; wherein the internal variables of the transmission system are obtained based on when the user accesses the server through the network transmission system; The system internal variables are stored in a target database to obtain an internal variable database.

3. The method according to claim 1, characterized in that The predicting of the multiple groups of network loss parameters corresponding to the multiple groups of transmission system internal variables respectively includes: Input the multiple groups of transmission system internal variables into the network quality model to obtain multiple groups of network loss parameters corresponding to the multiple groups of transmission system internal variables respectively; wherein the network quality model is used to characterize the mapping relationship between the transmission system internal variables and the network loss parameters.

4. The method according to claim 3, characterized in that The method further comprises: Acquire a training sample; wherein the training sample includes a preset transmission system internal variable and a preset network loss parameter, and the preset transmission system internal variable corresponds to the preset network loss parameter; The preset model is trained through the training samples to obtain the network quality model.

5. The method according to claim 4, characterized in that The obtaining of training samples comprises: Obtaining preset network loss parameters, and inputting the preset network loss parameters into a target network simulator; The network transmission system is run based on the target network simulator, and preset transmission system internal variables in the network transmission system are obtained.

6. The method according to claim 1, characterized in that The simulating a real network environment by the target network simulator comprises: Obtain the probability distribution of network loss parameters in the target real network environment; Based on the probability distribution, multiple groups of network loss parameters are selected through the target network simulator, and the network transmission system is operated.

7. A network environment simulation device, characterized in that: The device comprises: A database acquisition module, used to acquire an internal variable database of a network transmission system, wherein the internal variable database includes multiple groups of transmission system internal variables obtained in a real network environment; A network loss parameter prediction module, used to predict network loss parameters corresponding to the multiple groups of transmission system internal variables; The environment simulation module is used to input the network loss parameters into the target network simulator and simulate the real network environment through the target network simulator.

8. An electronic device, characterized in that: include: at least one processor; a memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method as claimed in any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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