A traffic simulation method and device, electronic equipment and storage medium

Abstract

A traffic simulation method and device, electronic equipment and storage medium are disclosed. The specific implementation includes: determining simulation configuration parameters; determining at least one independent burst flow according to the simulation configuration parameters; superimposing each independent burst flow asynchronously to obtain simulation traffic, and issuing the simulation traffic to the intelligent calculation network. Through the unified management of the simulation configuration parameters, the accuracy of the parameter support during the generation of the simulation traffic is ensured. Through the generation of at least one independent burst flow and the asynchronous superposition of each independent burst flow, the multi-flow concurrency of the simulation traffic is realized, the accuracy of the traffic simulation is realized, and the stability of the intelligent calculation network operation is improved.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a traffic simulation method, apparatus, electronic device, and storage medium. Background Technology

[0002] As intelligent computing enters the era of million-card clusters, intelligent computing networks exhibit two core trends: deployment across availability zones at a distance of hundreds of kilometers and ultra-large-scale distributed training. Ethernet bearer protocols, represented by Remote Direct Memory Access over Converged Ethernet (RoCE), have become the mainstream implementation solution for Artificial Intelligence (AI) training due to their compatibility advantages with existing data center infrastructure.

[0003] However, existing methods typically rely on predefined traffic patterns for traffic simulation when performing AI training tasks. This results in a lack of responsiveness to network states (such as ECN labeling rate and queue depth) during the training process, making it difficult to simulate the burst traffic required by the network and leading to a disconnect between the simulated traffic and the needs of the network scenario. Summary of the Invention

[0004] This invention provides a traffic simulation method, apparatus, electronic device, and storage medium to achieve accurate traffic simulation and improve network operation stability.

[0005] According to one aspect of the present invention, a flow simulation method is provided, comprising: Determine the simulation configuration parameters, which include parameters corresponding to the characteristics of the traffic required by the intelligent computing network, and the intelligent computing network includes the network to be tested; Based on the simulation configuration parameters, at least one independent burst flow is determined, the independent burst flow including the traffic generated for each node in the intelligent computing network; The independent burst flows are asynchronously superimposed to obtain simulated traffic, which is then sent to the intelligent computing network for testing.

[0006] According to another aspect of the present invention, a flow simulation apparatus is provided, comprising: The first determining module is used to determine the simulation configuration parameters, which include parameters corresponding to the characteristics of the traffic required by the intelligent computing network, and the intelligent computing network includes the network to be tested. The second determining module is used to determine at least one independent burst flow based on the simulation configuration parameters, wherein the independent burst flow includes the traffic generated for each node in the intelligent computing network; The overlay module is used to asynchronously overlay the independent burst flows to obtain simulated traffic, and then send the simulated traffic to the intelligent computing network. The simulated traffic is used to test the intelligent computing network.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the traffic simulation method according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the flow simulation method according to any embodiment of the present invention.

[0009] The technical solution of this invention involves determining simulation configuration parameters; determining at least one independent burst flow based on the simulation configuration parameters; asynchronously superimposing each of the independent burst flows to obtain simulated traffic; and distributing the simulated traffic to the intelligent computing network. By using simulation configuration parameters, unified parameter management is achieved, ensuring the accuracy of parameter support during simulated traffic generation. By generating at least one independent burst flow and asynchronously superimposing each independent burst flow, multi-stream concurrency of simulated traffic is realized, improving the accuracy of traffic simulation and enhancing the stability of the intelligent computing network operation.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a flow simulation method provided according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of an independent burst flow determination method provided according to Embodiment 2 of the present invention; Figure 3This is a schematic diagram of the structure of a flow simulation device according to Embodiment 3 of the present invention; Figure 4 This is a block diagram of an electronic device provided according to Embodiment 4 of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Example 1 Figure 1 This is a flowchart of a traffic simulation method according to Embodiment 1 of the present invention. This embodiment is applicable to simulating traffic for intelligent computing networks. The method can be executed by a traffic simulation device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes: S110. Determine the simulation configuration parameters.

[0016] The simulated configuration parameters include parameters corresponding to the characteristics of the traffic required by the intelligent computing network, and the intelligent computing network includes the network to be tested.

[0017] In this embodiment, the simulated configuration parameters can be understood as parameters corresponding to the characteristics of the traffic required by the intelligent computing network. These simulated configuration parameters can be configured based on the characteristics of the traffic required by the intelligent computing network, and may include the period, duration, bandwidth, and transmission requirements of the traffic required by the intelligent computing network. The intelligent computing network can be understood as the network to be tested, and at least one node can be configured in the intelligent computing network.

[0018] Specifically, based on the characteristics of the traffic required by the intelligent computing network, multiple parameters related to the traffic cycle, duration, bandwidth, and transmission requirements are configured, and these configured parameters are defined as the simulation configuration parameters. Subsequently, traffic samples for testing the intelligent computing network can be generated based on these simulation configuration parameters. For example, simulation configuration parameters may include general parameters such as Δt, period, and bandwidth, as well as intervals and Remote Direct Memory Access (RDMA) protocol parameters related to the device where the intelligent computing network is located.

[0019] S120. Based on the simulation configuration parameters, determine at least one independent burst flow.

[0020] The independent burst flow includes the traffic generated for each node in the intelligent computing network.

[0021] In this embodiment, independent burst flow can be understood as flow related to the characteristics of the flow required by each node in the intelligent computing network, and independent burst flow can be understood as flow generated for each node in the intelligent computing network.

[0022] Specifically, the simulation configuration parameters are first mapped to construct a multi-dimensional configuration system, enabling unified management of the various parameters included in the simulation configuration parameters. Then, the mapped and unified managed parameters are used by the programmable core of the electronic device to generate at least one independent burst stream. This independent burst stream is then cyclically executed, alternating between sending data packets and not sending data packets.

[0023] S130. Asynchronously superimpose the independent burst flows to obtain simulated traffic, and send the simulated traffic to the intelligent computing network.

[0024] The simulated traffic is used to test the intelligent computing network.

[0025] In this embodiment, simulated traffic can be understood as traffic sent to various nodes of the intelligent computing network. Simulated traffic can be used to test the intelligent computing network. Simulated traffic can be understood as traffic characteristics and transmission requirements adapted to the training services of the intelligent computing network.

[0026] Specifically, the independent burst flows are asynchronously superimposed, for example, by starting each independent burst flow according to the time corresponding to each independent burst flow, to obtain simulated traffic.

[0027] For example, asynchronously superimposing each independent burst stream can be done by delaying the start of each independent burst stream, such as starting independent burst stream 1 at 0ms, starting independent burst stream 2 at 0.5ms, and so on.

[0028] The technical solution of this invention involves determining simulation configuration parameters; determining at least one independent burst flow based on the simulation configuration parameters; asynchronously superimposing each of the independent burst flows to obtain simulated traffic; and distributing the simulated traffic to the intelligent computing network. By using simulation configuration parameters, unified parameter management is achieved, ensuring the accuracy of parameter support during simulated traffic generation. By generating at least one independent burst flow and asynchronously superimposing each independent burst flow, multi-stream concurrency of simulated traffic is realized, improving the accuracy of traffic simulation and enhancing the stability of the intelligent computing network operation.

[0029] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0030] In one embodiment, the asynchronous superposition of the independent burst flows to obtain simulated traffic includes: For each independent burst, a startup delay time corresponding to the independent burst is determined, wherein the startup delay time is related to the time when the independent burst is generated; According to the start delay time corresponding to each independent burst flow, each independent burst flow is started sequentially to obtain simulated traffic.

[0031] In this embodiment, the startup delay time can be understood as the startup time of an independent burst stream. The startup delay time can be configured when generating an independent burst stream, and the startup delay time can be configured as the time to generate an independent burst stream.

[0032] For example, the startup delay time of independent burst flow 1 can be 0ms, the startup delay time of independent burst flow 2 can be 0.5ms, and so on. Each independent burst flow is started according to its startup delay time, achieving asynchronous superposition of the independent burst flows to obtain simulated traffic.

[0033] In one embodiment, after asynchronously superimposing the independent burst flows to obtain simulated traffic and distributing the simulated traffic to the intelligent computing network, the method further includes: Receive network traffic returned by the intelligent computing network and determine the bandwidth of the network traffic; When the traffic bandwidth is greater than or equal to the link capacity threshold, the rate at which each of the independent burst flows sends data packets within the burst segment is adjusted based on the congestion control algorithm. The link capacity threshold includes the threshold of the traffic that the intelligent computing network can transmit.

[0034] In this embodiment, network traffic can be understood as the traffic output after analog traffic is input into the intelligent computing network and processed by the intelligent computing network. Traffic bandwidth can be understood as the bandwidth of network traffic, which can be used to indicate the maximum transmission rate of network traffic. The link capacity threshold can be understood as the maximum transmission rate that the intelligent computing network can support.

[0035] Specifically, after the simulated traffic is sent to the intelligent computing network, the network processes it and then receives the network traffic returned by the network. Based on the network traffic bandwidth and the link capacity threshold supported by the intelligent computing network, the individual burst flows are updated. If the traffic bandwidth is greater than or equal to the link capacity threshold, the rate at which each independent burst flow sends data packets within the burst segment can be adjusted based on a congestion control algorithm.

[0036] In one embodiment, determining the simulation configuration parameters includes: Determine the periodic characteristic parameters, which indicate the period of the traffic required by the intelligent computing network; Determine the burst characteristic parameters, which include the duration and bandwidth of the traffic required by the intelligent computing network; Determine protocol characteristic parameters, including the transmission requirements of the intelligent computing network; The periodic characteristic parameter, the burst characteristic parameter, and the protocol characteristic parameter are determined as simulation configuration parameters.

[0037] In this embodiment, the periodic characteristic parameter can be understood as a parameter indicating the period of the traffic required by the intelligent computing network. The burst characteristic parameter can be understood as a parameter indicating the duration and bandwidth of the traffic required by the intelligent computing network. The protocol characteristic parameter can be understood as a parameter indicating the transmission requirements of the traffic required by the intelligent computing network.

[0038] For example, the periodic characteristic parameter can be the period of the traffic required by the intelligent computing network, the burst characteristic parameter can be the duration Δt and bandwidth of the traffic required by the intelligent computing network, and the protocol characteristic parameter can be the transmission requirements of the traffic required by the intelligent computing network, such as the interval related to the device where the intelligent computing network is located, the remote memory direct access technology protocol parameters, etc. The above-mentioned periodic characteristic parameter, burst characteristic parameter and protocol characteristic parameter are determined as simulation configuration parameters.

[0039] Example 2 Figure 2 This is a flowchart of an independent burst flow determination method according to Embodiment 2 of the present invention. This embodiment focuses on the independent burst flow determination method described in the above embodiment. Figure 2 As shown, the method includes: S210. Determine the simulation configuration parameters.

[0040] S220. The simulated configuration parameters are processed to obtain a parameter matrix.

[0041] The parameter matrix indicates the amplitude characteristics of the simulation configuration parameters.

[0042] In this embodiment, the parameter matrix can be understood as a matrix that indicates the characteristics of the simulated configuration parameters. The parameter matrix can be a matrix obtained by mapping the simulated configuration parameters.

[0043] Specifically, the periodic characteristic parameters, burst characteristic parameters, and protocol characteristic parameters included in the simulation configuration parameters can be mapped. The mapped results are then uniformly managed using a structured matrix to obtain a parameter matrix.

[0044] Optionally, the process of processing the simulated configuration parameters to obtain a parameter matrix includes: Determine the periodic characteristic parameters, burst characteristic parameters, and protocol characteristic parameters included in the simulation configuration parameters; The periodic feature parameter, the burst feature parameter, and the protocol feature parameter are mapped respectively to obtain a periodic interval sequence, a burst interval sequence, and a gradual interval sequence. The periodic interval sequence indicates the amplitude of the period corresponding to the periodic feature parameter, the burst interval sequence indicates the duration and bandwidth amplitude of the traffic corresponding to the burst feature parameter, and the gradual interval sequence indicates the amplitude of the traffic corresponding to the protocol feature parameter. Construct a parameter matrix formed by the periodic interval sequence, the burst interval sequence, and the gradual interval sequence.

[0045] In this embodiment, the periodic interval sequence can be understood as a sequence obtained by mapping periodic feature parameters, and the periodic interval sequence can indicate the amplitude of the period corresponding to the periodic feature parameters. The burst interval sequence can be understood as a sequence obtained by mapping burst interval sequences, and the burst interval sequence can indicate the duration and bandwidth amplitude of the traffic corresponding to burst feature parameters. The gradual interval sequence can be understood as a sequence obtained by mapping gradual interval sequences, and the gradual interval sequence can indicate the amplitude of the traffic corresponding to protocol feature parameters.

[0046] For example, periodic, burst, and protocol characteristic parameters included in the simulated configuration parameters can be mapped using specified change functions such as linear, exponential, and piecewise functions to obtain periodic interval sequences, burst interval sequences, and gradual interval sequences. For instance, a linear change function can be used to map the periodic characteristic parameters to obtain a periodic interval sequence, an exponential change function to map the burst characteristic parameters to obtain a burst interval sequence, and a piecewise change function to map the protocol characteristic parameters to obtain a gradual interval sequence. These periodic, burst, and gradual interval sequences can be used to simulate various complex traffic behaviors required by intelligent computing networks, such as periodicity, burstiness, and gradual changes.

[0047] S230. Based on the parameter matrix, generate at least one independent burst flow.

[0048] Specifically, the parameter matrix is ​​used to generate at least one independent burst stream through the programmable core of the electronic device. Each independent burst stream is then cyclical, alternating between sending data packets and not sending data packets, i.e., each independent burst stream cycles through "burst segment - idle segment".

[0049] Optionally, generating at least one independent burst flow based on the parameter matrix includes: Construct at least one empty stream; For each empty stream, based on the parameter matrix, a burst segment and an idle segment are configured for the empty stream to obtain an independent burst stream; The independent burst stream circulates in an alternating manner between burst segments and idle segments. The burst segment includes the segment in the independent burst stream that sends data packets, and the idle segment includes the segment in the independent burst stream that does not send data packets.

[0050] For example, at least one blank stream with no traffic transmission is constructed. A parameter matrix is ​​input into the programmable core of the electronic device, and the number of packets sent and received is configured for each blank stream, thereby generating independent burst streams. Here, the number of packets sent is the number of data packets sent, and the number of packets received is the number of data packets not sent. Therefore, the independent burst streams cycle in an alternating manner between burst streams corresponding to the number of packets sent and idle segments corresponding to the number of packets received; that is, in the independent burst streams, burst segments send data packets according to a specified bandwidth, and idle segments pause data packet transmission.

[0051] S240. Asynchronously superimpose the independent burst flows to obtain simulated traffic, and send the simulated traffic to the intelligent computing network.

[0052] The technical solution of this invention involves processing the simulated configuration parameters to obtain a parameter matrix; based on the parameter matrix, at least one independent burst flow is generated. The parameter matrix enables precise parameter mapping, significantly improving the flexibility and adaptability of traffic simulation. By generating at least one independent burst flow and asynchronously superimposing these independent burst flows, the system accurately adapts to the multi-stream concurrency characteristics of intelligent computing networks.

[0053] Example 3 Figure 3 This is a schematic diagram of a flow simulation device according to Embodiment 3 of the present invention. Figure 3 As shown, the device includes: The first determining module 310 is used to determine the simulation configuration parameters, which include parameters corresponding to the characteristics of the traffic required by the intelligent computing network, and the intelligent computing network includes the network to be tested. The second determining module 320 is used to determine at least one independent burst flow based on the simulation configuration parameters, wherein the independent burst flow includes the traffic generated for each node in the intelligent computing network; The overlay module 330 is used to asynchronously overlay the independent burst flows to obtain simulated traffic, and to send the simulated traffic to the intelligent computing network. The simulated traffic is used to test the intelligent computing network.

[0054] The technical solution of this invention involves: a first determining module determining simulation configuration parameters; a second determining module determining at least one independent burst flow based on the simulation configuration parameters; and an overlay module asynchronously overlaying the independent burst flows to obtain simulated traffic, which is then distributed to the intelligent computing network. Through the cooperation between these modules and the unified management of parameters via the simulation configuration parameters, the accuracy of parameter support during simulated traffic generation is ensured. By generating at least one independent burst flow and asynchronously overlaying these independent burst flows, multi-stream concurrency of simulated traffic is achieved, increasing the accuracy of traffic simulation and improving the stability of the intelligent computing network.

[0055] In one embodiment, the second determining module 320 includes: The processing unit is used to process the simulation configuration parameters to obtain a parameter matrix, wherein the parameter matrix indicates the amplitude characteristics of the simulation configuration parameters; A generation unit is used to generate at least one independent burst flow based on the parameter matrix.

[0056] In one embodiment, the processing unit is specifically used for: Determine the periodic characteristic parameters, burst characteristic parameters, and protocol characteristic parameters included in the simulation configuration parameters; The periodic feature parameter, the burst feature parameter, and the protocol feature parameter are mapped respectively to obtain a periodic interval sequence, a burst interval sequence, and a gradual interval sequence. The periodic interval sequence indicates the amplitude of the period corresponding to the periodic feature parameter, the burst interval sequence indicates the duration and bandwidth amplitude of the traffic corresponding to the burst feature parameter, and the gradual interval sequence indicates the amplitude of the traffic corresponding to the protocol feature parameter. Construct a parameter matrix formed by the periodic interval sequence, the burst interval sequence, and the gradual interval sequence.

[0057] In one embodiment, the generating unit is specifically used for: Construct at least one empty stream; For each empty stream, based on the parameter matrix, a burst segment and an idle segment are configured for the empty stream to obtain an independent burst stream; The independent burst stream circulates in an alternating manner between burst segments and idle segments. The burst segment includes the segment in the independent burst stream that sends data packets, and the idle segment includes the segment in the independent burst stream that does not send data packets.

[0058] In one embodiment, the overlay module 330 is specifically used for: For each independent burst, a startup delay time corresponding to the independent burst is determined, wherein the startup delay time is related to the time when the independent burst is generated; According to the start delay time corresponding to each independent burst flow, each independent burst flow is started sequentially to obtain simulated traffic.

[0059] In one embodiment, the flow simulation device further includes an adjustment module, specifically used for: Receive network traffic returned by the intelligent computing network and determine the bandwidth of the network traffic; When the traffic bandwidth is greater than or equal to the link capacity threshold, the rate at which each of the independent burst flows sends data packets within the burst segment is adjusted based on the congestion control algorithm. The link capacity threshold includes the threshold of the traffic that the intelligent computing network can transmit.

[0060] In one embodiment, the first determining module 310 is specifically used for: Determine the periodic characteristic parameters, which indicate the period of the traffic required by the intelligent computing network; Determine the burst characteristic parameters, which include the duration and bandwidth of the traffic required by the intelligent computing network; Determine protocol characteristic parameters, including the transmission requirements of the intelligent computing network; The periodic characteristic parameter, the burst characteristic parameter, and the protocol characteristic parameter are determined as simulation configuration parameters.

[0061] The flow simulation device provided in this embodiment of the invention can execute the flow simulation method provided in any embodiment of the invention. Through the cooperation and coordination between the modules, the flow simulation is completed, and it has the corresponding functional modules and beneficial effects of the execution method.

[0062] Example 4 According to embodiments of the present invention, the present invention also provides an electronic device and a computer-readable storage medium.

[0063] Figure 4 This is a block diagram of an electronic device according to Embodiment 4 of the present invention, which implements the flow simulation method described in the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0064] like Figure 4 As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM), communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 can also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0065] Multiple components in the electronic device are connected to the I / O interface 415, including: an input unit 416, such as a keyboard, mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a disk, optical disk, etc.; and a communication unit 419, such as a network card, modem, wireless transceiver, etc. The communication unit 419 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0066] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as flow simulation methods.

[0067] In some embodiments, the flow simulation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the flow simulation method described above may be performed. Alternatively, in other embodiments, processor 411 may be configured to perform the flow simulation method by any other suitable means (e.g., by means of firmware).

[0068] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0069] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0070] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0071] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0072] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0073] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0074] The technical solution of this invention provides a traffic simulation method, apparatus, electronic device, and storage medium. The method involves determining simulation configuration parameters; identifying at least one independent burst flow based on the simulation configuration parameters; asynchronously superimposing the independent burst flows to obtain simulated traffic; and then distributing the simulated traffic to the intelligent computing network. By using simulation configuration parameters, unified parameter management is achieved, ensuring the accuracy of parameter support during simulated traffic generation. By generating at least one independent burst flow and asynchronously superimposing them, multi-stream concurrency of simulated traffic is realized, improving the accuracy of traffic simulation and enhancing the stability of the intelligent computing network.

[0075] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0076] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A flow simulation method, characterized in that, include: Determine the simulation configuration parameters, which include parameters corresponding to the characteristics of the traffic required by the intelligent computing network, and the intelligent computing network includes the network to be tested; Based on the simulation configuration parameters, at least one independent burst flow is determined, the independent burst flow including the traffic generated for each node in the intelligent computing network; The independent burst flows are asynchronously superimposed to obtain simulated traffic, which is then sent to the intelligent computing network for testing.

2. The method according to claim 1, characterized in that, The step of determining at least one independent burst flow based on the simulation configuration parameters includes: The simulation configuration parameters are processed to obtain a parameter matrix, which indicates the amplitude characteristics of the simulation configuration parameters; Based on the parameter matrix, at least one independent burst flow is generated.

3. The method according to claim 2, characterized in that, The process of processing the simulated configuration parameters to obtain a parameter matrix includes: Determine the periodic characteristic parameters, burst characteristic parameters, and protocol characteristic parameters included in the simulation configuration parameters; The periodic feature parameter, the burst feature parameter, and the protocol feature parameter are mapped respectively to obtain a periodic interval sequence, a burst interval sequence, and a gradual interval sequence. The periodic interval sequence indicates the amplitude of the period corresponding to the periodic feature parameter, the burst interval sequence indicates the duration and bandwidth amplitude of the traffic corresponding to the burst feature parameter, and the gradual interval sequence indicates the amplitude of the traffic corresponding to the protocol feature parameter. Construct a parameter matrix formed by the periodic interval sequence, the burst interval sequence, and the gradual interval sequence.

4. The method according to claim 2, characterized in that, The generation of at least one independent burst flow based on the parameter matrix includes: Construct at least one empty stream; For each empty stream, based on the parameter matrix, a burst segment and an idle segment are configured for the empty stream to obtain an independent burst stream; The independent burst stream circulates in an alternating manner between burst segments and idle segments. The burst segment includes the segment in the independent burst stream that sends data packets, and the idle segment includes the segment in the independent burst stream that does not send data packets.

5. The method according to claim 1, characterized in that, The asynchronous superposition of the independent burst flows to obtain the simulated flow includes: For each independent burst, a startup delay time corresponding to the independent burst is determined, wherein the startup delay time is related to the time when the independent burst is generated; According to the start delay time corresponding to each independent burst flow, each independent burst flow is started sequentially to obtain simulated traffic.

6. The method according to claim 1, characterized in that, After asynchronously superimposing the independent burst flows to obtain simulated traffic and distributing the simulated traffic to the intelligent computing network, the method further includes: Receive network traffic returned by the intelligent computing network and determine the bandwidth of the network traffic; When the traffic bandwidth is greater than or equal to the link capacity threshold, the rate at which each of the independent burst flows sends data packets within the burst segment is adjusted based on the congestion control algorithm. The link capacity threshold includes the threshold of the traffic that the intelligent computing network can transmit.

7. The method according to claim 1, characterized in that, The determination of the simulation configuration parameters includes: Determine the periodic characteristic parameters, which indicate the period of the traffic required by the intelligent computing network; Determine burst characteristic parameters, including the duration and bandwidth of the traffic required by the intelligent computing network; Determine protocol characteristic parameters, including the transmission requirements of the intelligent computing network; The periodic characteristic parameter, the burst characteristic parameter, and the protocol characteristic parameter are determined as simulation configuration parameters.

8. A flow rate simulation device, characterized in that, include: The first determining module is used to determine the simulation configuration parameters, which include parameters corresponding to the characteristics of the traffic required by the intelligent computing network, and the intelligent computing network includes the network to be tested. The second determining module is used to determine at least one independent burst flow based on the simulation configuration parameters, wherein the independent burst flow includes the traffic generated for each node in the intelligent computing network; The overlay module is used to asynchronously overlay the independent burst flows to obtain simulated traffic, and then send the simulated traffic to the intelligent computing network. The simulated traffic is used to test the intelligent computing network.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the flow simulation method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the flow simulation method according to any one of claims 1-7.

Images