Network performance test method and device and storage medium

Abstract

The invention discloses a network performance test method and device and a storage medium, relates to the field of communication, is used for improving the efficiency of network performance test, and comprises the following steps: obtaining a topological graph of a target network; the topological graph comprises network instances and a connection relationship between the network instances; determining a first network instance and a second network instance connected with the first network instance according to the topological graph; the first network instance and the second network instance belong to a target network; the first network instance is any network instance in the target network; under the condition that the second network instance sends the flow with the preset bandwidth to the first network instance, obtaining a network performance parameter of the first network instance; determining a network performance analysis report of the target network based on the network performance parameters of the first network instance and the target large language model; the target large language model is used for predicting the network performance according to the network performance parameters and outputting an analysis report of the network performance. The method and the device are applied to a network performance test process.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular to a network performance testing method, device and storage medium. Background Art

[0002] With the popularity of cloud computing and the increasing complexity and scale of cloud environments, higher requirements are placed on network performance management. As an open source cloud platform, OpenStack has a large user base and a rich ecosystem. In order to ensure the stability of the OpenStack network, it is necessary to accurately analyze the OpenStack network.

[0003] In the related art, technicians use OpenStack management tools and plug-ins to analyze OpenStack network performance. However, manual analysis methods are difficult to cope with the surge in data volume and complexity, and cannot quickly and accurately determine network performance. Therefore, how to improve the efficiency of network performance testing is a technical problem that still needs to be solved. Summary of the invention

[0004] The present application provides a network performance testing method, device and storage medium, which can improve the efficiency of network performance testing.

[0005] In a first aspect, the present application provides a network performance testing method, comprising: obtaining a topology map of a target network; the topology map includes network instances and connection relationships between network instances; according to the topology map, determining a first network instance and a second network instance connected to the first network instance; the first network instance and the second network instance belong to the target network; the first network instance is any network instance in the target network; when the second network instance sends traffic of a preset bandwidth to the first network instance, obtaining network performance parameters of the first network instance; based on the network performance parameters of the first network instance and a target large language model, determining a network performance analysis report of the target network; the target large language model is used to predict network performance based on the network performance parameters and output an analysis report of the network performance.

[0006] In a possible implementation, a training set is obtained; the training set includes: historical network performance parameters of multiple historical periods and actual network performance analysis reports corresponding to the historical network performance parameters; the historical network performance parameters in the training set are input into an initial prediction model to determine an initial prediction result; based on the initial prediction result and the actual network performance analysis report, a loss value of the initial prediction model is determined, and if the loss value does not meet a preset condition, the model parameters of the initial prediction model are adjusted based on the loss value, and iterative training is continued based on the training set until the model convergence condition is met to obtain a target large language model.

[0007] In a possible implementation manner, an iperf tool in the second network instance is instructed to send traffic of a preset bandwidth to the first network instance; and the iperf tool is installed in the second network instance through a script.

[0008] In a possible implementation, the network quality parameters received by the first network instance are obtained through the iperf tool in the first network instance; the device status information of the first network instance is obtained through the sar command in the first network instance; and the network performance parameters of the first network instance are determined based on the network quality parameters received by the first network instance and the device status information of the first network instance.

[0009] In a possible implementation, the network quality parameter includes at least one of the following: throughput, jitter, packet loss rate, and maximum transmission unit; the device status information includes at least one of the following: CPU occupancy information, memory occupancy information, and disk input and output I / O information.

[0010] In one possible implementation, the device status information of the second network instance is obtained through the sar command in the second network instance; the network performance parameters of the first network instance and the device status information of the second network instance are input into the target large language model to obtain a network performance analysis report of the target network.

[0011] In a possible implementation manner, the network performance parameter includes at least one of the following: expected bandwidth, actual sending bandwidth, actual receiving bandwidth, jitter, packet loss rate, CPU utilization, and memory utilization.

[0012] In a second aspect, the present application provides a network performance testing device, including: an acquisition unit, used to acquire a topology map of a target network; the topology map includes network instances and connection relationships between network instances; a processing unit, used to determine a first network instance and a second network instance connected to the first network instance according to the topology map; the first network instance and the second network instance belong to the target network; the first network instance is any network instance in the target network; the processing unit is also used to instruct the acquisition unit to acquire network performance parameters of the first network instance when the second network instance sends traffic of a preset bandwidth to the first network instance; the processing unit is also used to determine a network performance analysis report of the target network based on the network performance parameters of the first network instance and a target large language model; the target large language model is used to predict network performance based on the network performance parameters and output an analysis report on the network performance.

[0013] In a possible implementation, the acquisition unit is also used to acquire a training set; the training set includes: historical network performance parameters of multiple historical periods and actual network performance analysis reports corresponding to the historical network performance parameters; the processing unit is also used to input the historical network performance parameters in the training set into the initial prediction model to determine the initial prediction results; the processing unit is also used to determine the loss value of the initial prediction model based on the initial prediction results and the actual network performance analysis report, and when the loss value does not meet the preset conditions, adjust the model parameters of the initial prediction model based on the loss value, and continue to perform iterative training based on the training set until the model convergence conditions are met to obtain the target large language model.

[0014] In a possible implementation, the processing unit is further configured to instruct an iperf tool in the second network instance to send traffic of a preset bandwidth to the first network instance; and the iperf tool is installed in the second network instance through a script.

[0015] In a possible implementation, the acquisition unit is further used to acquire network quality parameters received by the first network instance through the iperf tool in the first network instance; the acquisition unit is further used to acquire device status information of the first network instance through the sar command in the first network instance; and the processing unit is further used to determine the network performance parameters of the first network instance based on the network quality parameters received by the first network instance and the device status information of the first network instance.

[0016] In a possible implementation, the network quality parameter includes at least one of the following: throughput, jitter, packet loss rate, and maximum transmission unit; the device status information includes at least one of the following: CPU occupancy information, memory occupancy information, and disk input and output I / O information.

[0017] In a possible implementation, the acquisition unit is also used to obtain the device status information of the second network instance through the sar command in the second network instance; the processing unit is also used to input the network performance parameters of the first network instance and the device status information of the second network instance into the target large language model to obtain a network performance analysis report of the target network.

[0018] In a possible implementation manner, the network performance parameter includes at least one of the following: expected bandwidth, actual sending bandwidth, actual receiving bandwidth, jitter, packet loss rate, CPU utilization, and memory utilization.

[0019] In a third aspect, the present application provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by an electronic device of the present application, enable the electronic device to perform a network performance testing method as described in the first aspect and any possible implementation of the first aspect.

[0020] In a fourth aspect, the present application provides an electronic device, comprising: a processor and a memory; wherein the memory is used to store one or more programs, and the one or more programs include computer execution instructions. When the electronic device is running, the processor executes the computer execution instructions stored in the memory to enable the electronic device to perform the network performance testing method described in the first aspect and any possible implementation of the first aspect.

[0021] In a fifth aspect, the present application provides a computer program product comprising instructions, which, when executed on a computer, enables the electronic device of the present application to perform the network performance testing method as described in the first aspect and any possible implementation of the first aspect.

[0022] In a sixth aspect, the present application provides a chip system, which is applied to a network performance test device; the chip system includes one or more interface circuits and one or more processors. The interface circuit and the processor are interconnected through a line; the interface circuit is used to receive a signal from a memory of the network performance test device and send a signal to the processor, the signal including a computer instruction stored in the memory. When the processor executes the computer instruction, the network performance test device executes the network performance test method as described in the first aspect and any possible design thereof.

[0023] In this application, the name of the above network performance test device does not limit the device or functional unit itself. In actual implementation, these devices or functional units may appear with other names. As long as the functions of each device or functional unit are similar to those of this application, they are within the scope of the claims of this application and their equivalent technologies.

[0024] Based on the above technical solution, the present application takes into account that the topology diagram includes the connection relationship between the network instances and the network instances, and the network performance testing device determines the first network instance and the second network instance connected to the first network instance according to the topology diagram. Since the first network instance and the second network instance belong to the target network, the network performance testing device can send traffic of preset bandwidth to the first network instance through the second network instance, and obtain the network performance parameters of the first network instance at the first network instance, so that the network quality between the first network instance and the second network instance can be determined. Furthermore, based on the network quality between the first network instance and the second network instance and the target language model, the network performance analysis report of the target network can be quickly and accurately determined. Compared with the prior art, which requires relevant technical personnel to analyze the OpenStack network performance through OpenStack management tools and plug-ins to obtain a network performance analysis report, the present application does not require technical personnel to analyze the network data in sequence, which saves labor costs and improves the efficiency of network performance measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the architecture of a network performance testing system provided in an embodiment of the present application;

[0026] Figure 2 A schematic diagram of the structure of a network performance testing device provided in an embodiment of the present application;

[0027] Figure 3 A flowchart of a network performance testing method provided in an embodiment of the present application;

[0028] Figure 4 A schematic diagram of a network topology diagram provided for an embodiment of the present application;

[0029] Figure 5 A flowchart of a method for obtaining a training set provided in an embodiment of the present application;

[0030] Figure 6 A flowchart of another network performance testing method provided in an embodiment of the present application;

[0031] Figure 7 A flowchart of another network performance testing method provided in an embodiment of the present application;

[0032] Figure 8 A flowchart of another network performance testing method provided in an embodiment of the present application;

[0033] Fig. 9 A flowchart of another network performance testing method provided in an embodiment of the present application;

[0034] Fig.10 A flowchart of another network performance testing method provided in an embodiment of the present application;

[0035] Fig.11 A flowchart of another network performance testing method provided in an embodiment of the present application;

[0036] Fig.12 A schematic diagram of the structure of another network performance testing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] In this article, the character " / " generally indicates that the objects before and after are in an "or" relationship. For example, A / B can be understood as A or B.

[0039] The terms "first" and "second" in the specification and claims of this application are used to distinguish different objects rather than to describe a specific order of objects. For example, a first edge service node and a second edge service node are used to distinguish different edge service nodes rather than to describe a characteristic order of edge service nodes.

[0040] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0041] In addition, in the embodiments of the present application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplarily" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present concepts in a specific way.

[0042] With the popularity of cloud computing and the increasing complexity and scale of cloud environments, higher requirements are placed on network performance management. As an open source cloud platform, OpenStack has a large user base and a rich ecosystem. In order to ensure the stability of the OpenStack network, it is necessary to accurately analyze the OpenStack network.

[0043] In the related art, technicians use OpenStack management tools and plug-ins to analyze OpenStack network performance. However, manual analysis methods are difficult to cope with the surge in data volume and complexity, and cannot quickly and accurately determine network performance. Therefore, how to improve the efficiency of network performance testing is a technical problem that still needs to be solved.

[0044] Based on the above technical solution, the present application takes into account that the topology diagram includes the connection relationship between the network instances and the network instances, and the network performance testing device determines the first network instance and the second network instance connected to the first network instance according to the topology diagram. Since the first network instance and the second network instance belong to the target network, the network performance testing device can send traffic of preset bandwidth to the first network instance through the second network instance, and obtain the network performance parameters of the first network instance at the first network instance, so that the network quality between the first network instance and the second network instance can be determined. Furthermore, based on the network quality between the first network instance and the second network instance and the target language model, the network performance analysis report of the target network can be quickly and accurately determined. Compared with the prior art, which requires relevant technical personnel to analyze the OpenStack network performance through OpenStack management tools and plug-ins to obtain a network performance analysis report, the present application does not require technical personnel to analyze the network data in sequence, which saves labor costs and improves the efficiency of network performance measurement.

[0045] For example, Figure 1 As shown, Figure 1 This is a schematic diagram of the architecture of a network performance testing system provided in the present application. The network performance testing system 10 includes: a data acquisition device 101 and a network performance testing apparatus 102.

[0046] The data collection device 101 obtains the demand document and the network topology diagram.

[0047] The network performance testing device 102 obtains a topological map of the target network through the data acquisition device 101; the topological map includes network instances and connection relationships between network instances; according to the topological map, a first network instance and a second network instance connected to the first network instance are determined; the first network instance and the second network instance belong to the target network; the first network instance is any network instance in the target network; when the second network instance sends traffic of a preset bandwidth to the first network instance, the network performance parameters of the first network instance are obtained; based on the network performance parameters of the first network instance and the target large language model, a network performance analysis report of the target network is determined.

[0048] Optionally, the target large language model is used to predict network performance based on network performance parameters and output an analysis report of the network performance.

[0049] The target large language model may be set in the network performance testing device 102 , or may be set in a processing device connected to the network performance testing device 102 .

[0050] Optionally, the physical device of the data collection device 101 is a terminal, and the physical device of the network performance testing device 102 is a server.

[0051] Optionally, the terminal may be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connection function, or other processing device connected to a wireless modem. The wireless terminal may communicate with one or more core networks via a radio access network (RAN). The wireless terminal may be a mobile terminal, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal, or a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device that exchanges language and / or data with a radio access network, such as a mobile phone, tablet computer, laptop computer, netbook, personal digital assistant (PDA).

[0052] Optionally, the above-mentioned server can be a server in a server cluster (consisting of multiple servers), or a chip in the server, or a system on a chip in the server, or can be implemented by a virtual machine (VM) deployed on a physical machine, which is not limited in the embodiments of the present application.

[0053] The present application embodiment provides a network performance testing device for executing the network performance testing system provided in the present application embodiment. Figure 2 A schematic diagram of the structure of a network performance testing device provided in an embodiment of the present application. Figure 2 As shown, the network performance test device 200 includes at least one processor 201, a communication line 202, and at least one communication interface 204, and may also include a memory 203. The processor 201, the memory 203 and the communication interface 204 may be connected via the communication line 202.

[0054] The processor 201 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application, such as one or more digital signal processors (DSP), or one or more field programmable gate arrays (FPGA).

[0055] The communication link 202 may include a pathway for transmitting information between the above-mentioned components.

[0056] The communication interface 204 is used to communicate with other devices or communication networks, and can use any transceiver-like device, such as Ethernet, radio access network (RAN), WLAN, etc.

[0057] The memory 203 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to include or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0058] In one possible design, the memory 203 can exist independently of the processor 201, that is, the memory 203 can be a memory outside the processor 201. In this case, the memory 203 can be connected to the processor 201 through the communication line 202, and is used to store execution instructions or application code, and the processor 201 controls the execution to implement the network performance testing method provided in the following embodiment of the present application. In another possible design, the memory 203 can also be integrated with the processor 201, that is, the memory 203 can be the internal memory of the processor 201, for example, the memory 203 is a high-speed cache, which can be used to temporarily store some data and instruction information, etc.

[0059] As an implementation method, the processor 201 may include one or more CPUs, for example Figure 2 As another implementation, the network performance testing device 200 may include multiple processors, such as Figure 2 As another implementation, the network performance testing apparatus 200 may further include an output device 205 and an input device 206.

[0060] Below, combined with the attached Figure 3 The network performance testing method provided in the embodiment of the present application is described in detail. Figure 3 As shown, the network performance testing method includes S301-S304.

[0061] S301. The network performance testing device obtains a topology map of the target network.

[0062] The topology diagram includes network instances and connection relationships between network instances.

[0063] Optionally, the network instance includes a virtual server.

[0064] For example, Figure 4 As shown, the topology diagram includes: network instance 1, network instance 2, network instance 3, network instance 4, network instance 5, and network instance 6. Network instance 1 is connected to network instance 2 and network instance 6 respectively, network instance 2 is connected to network instance 1 and network instance 3 respectively, network instance 3 is connected to network instance 2 and network instance 4 respectively, network instance 4 is connected to network instance 3 and network instance 5 respectively, network instance 5 is connected to network instance 4 and network instance 6 respectively, and network instance 6 is connected to network instance 5 and network instance 1 respectively.

[0065] S302: The network performance testing device determines a first network instance and a second network instance connected to the first network instance according to the topology map.

[0066] The first network instance and the second network instance belong to the target network; the first network instance is any network instance in the target network.

[0067] It should be explained that the network performance test device determines each network instance in the topology map and the network instance connected to each network instance according to the topology map, so that the network performance test device can understand the network quality of each network instance.

[0068] S303: When the second network instance sends traffic of a preset bandwidth to the first network instance, the network performance testing device obtains network performance parameters of the first network instance.

[0069] The network performance parameters include at least one of the following: expected bandwidth, actual sending bandwidth, actual receiving bandwidth, jitter, packet loss rate, CPU utilization, and memory utilization.

[0070] In a possible implementation, the network performance testing device is connected to a first network instance in an openstack network through an SSH client to obtain network performance parameters of the first network instance.

[0071] It should be explained that the network performance testing device uses the ssh command and the IP address and user name (and optional port number and key file path) of the first network instance to establish a connection with the first network instance, and then obtain the network performance parameters of the first network instance.

[0072] S304: The network performance testing device determines a network performance analysis report of the target network based on the network performance parameters of the first network instance and the target large language model.

[0073] Among them, the target large language model is used to predict network performance according to network performance parameters and output an analysis report of the network performance.

[0074] In a possible implementation, the network performance testing device inputs the network performance parameters of the first network instance into the target large language model to obtain a network performance analysis report of the target network.

[0075] Optionally, the target language models include: chatGLM model and baichuan model.

[0076] The above scheme brings at least the following beneficial effects: In this application, it is considered that the topology diagram includes the connection relationship between network instances and network instances, and the network performance testing device determines the first network instance and the second network instance connected to the first network instance according to the topology diagram. Since the first network instance and the second network instance belong to the target network, the network performance testing device can send traffic of preset bandwidth to the first network instance through the second network instance, and obtain the network performance parameters of the first network instance at the first network instance, so that the network quality between the first network instance and the second network instance can be determined. Furthermore, based on the network quality between the first network instance and the second network instance and the target language model, the network performance analysis report of the target network can be determined quickly and accurately. Compared with the prior art, which requires relevant technicians to analyze the OpenStack network performance through OpenStack management tools and plug-ins to obtain a network performance analysis report, the present application does not require technicians to analyze the network data in sequence, which saves labor costs and improves the efficiency of network performance measurement.

[0077] Combination Figure 3 ,like Figure 5 As shown, the target large language model can be constructed by electronic devices, such as Figure 5 As shown, the target large language model is trained in the following way:

[0078] S501. The network performance testing device obtains a training set.

[0079] The training set includes: historical network performance parameters of multiple historical periods and actual network performance analysis reports corresponding to the historical network performance parameters.

[0080] Optionally, the training set also includes: equipment information, historical test data, traffic scenario data and fault scenario data. Traffic scenario data includes normal traffic, burst traffic and abnormal traffic; fault scenario data includes link jitter, packet loss and delay.

[0081] It is understandable that the training set data is the original data used by the training model. Through the training set data, the large language model learns the characteristics and rules of the data.

[0082] In one achievable manner, the target large language model can be constructed by an electronic device. The electronic device needs to train an initial prediction model based on a training set, and continuously adjust the model parameters of the initial prediction model based on the initial prediction results and the training set data to obtain the target large language model. Therefore, the electronic device needs to obtain a training set including historical network performance parameters of multiple historical periods and actual network performance analysis reports corresponding to the historical network performance parameters.

[0083] In a possible implementation, the network performance testing device writes a python script and collects network data through the python script; the network performance testing device automatically cleans the network performance data through the first script.

[0084] Optionally, the network data includes at least one of the following: bandwidth, throughput, CPU utilization, memory utilization, bandwidth utilization, latency, jitter, packet loss, network congestion, device performance, signal interference, application load and other network knowledge.

[0085] Optionally, the format of the cleaned data is

[0086]

[0087]

[0088] It should be explained that the network performance testing device trains the first script to distinguish the source of input data through user id and assistant ID, from:human is used to represent the user, Value is used to represent the user's question content, from:assistant is used to represent the target language model, and value is used to represent the target language model's reply content.

[0089] S502: The network performance testing device inputs the historical network performance parameters in the training set into the initial prediction model to determine the initial prediction result.

[0090] In one achievable method, the electronic device can input the business information in the training set into the initial prediction model and determine the initial prediction result, so that the electronic device can adjust the model parameters of the initial prediction model based on the initial prediction result and the actual network performance analysis report in the training set to obtain the target large language model.

[0091] Optionally, the initial prediction model includes baichuan2-13b-chat.

[0092] S503. The network performance testing device determines the loss value of the initial prediction model based on the initial prediction result and the actual network performance analysis report, and adjusts the model parameters of the initial prediction model based on the loss value when the loss value does not meet the preset conditions, and continues to perform iterative training based on the training set until the model convergence conditions are met to obtain the target large language model.

[0093] In one practicable manner, the electronic device adjusts the model parameters of the initial prediction model based on the initial prediction result and the actual network performance analysis report to obtain a target large language model.

[0094] Optionally, the electronic device may construct a target large language model based on a neural network.

[0095] Optionally, a neural network is a computational model that mimics the connections and information processing methods between neurons in a biological brain.

[0096] Optionally, a neural network consists of a large number of interconnected nodes (also called neurons), which are arranged in a certain hierarchy (input layer, hidden layer, output layer). Information is transmitted and processed through the connections between neurons. Each neuron receives input signals from other neurons and calculates the input through an activation function to generate output signals to be passed to subsequent neurons.

[0097] Optionally, the electronic device may construct a loss function based on multiple loss values.

[0098] Optionally, the loss function includes at least one of a mean square error function, a mean absolute error function, a cross entropy loss function, a binary cross entropy loss function, a hinge loss function, a log-likelihood loss function, and a Kullback-Leibler (KL) divergence function.

[0099] Optional, such as Figure 6 As shown, the network performance test device downloads the initial prediction model and prepares a training script. The network performance test device collects and cleans the network performance data, and then trains the initial prediction model based on the cleaned network performance data to obtain a target large language model. The network performance test device uses the target language model to answer the performance data. If the answer result does not meet the requirements, the initial prediction model is retrained based on the cleaned network performance data. If the answer result meets the requirements, the performance indicators of the OpenStack instance are collected and formatted, and the performance indicators of the OpenStack instance are analyzed using the target language model, a performance analysis report and icon are generated, and an email is sent.

[0100] It should be explained that the network performance test device uses the lora method to fine-tune the target language model. The main parameters of the training are:

[0101]

[0102] Among them, Lora training principle: LoRA uses the data corresponding to the downstream tasks and only adapts to the downstream tasks by training some newly added parameters.

[0103] It is understandable that after training the new parameters, the network performance test device uses the re-parameterization method to merge the new parameters with the old model parameters, so that it can achieve the effect of fine-tuning the entire model on the new task without increasing the inference time during inference, thus reducing the consumption of server resources.

[0104] Optionally, the network performance test device uses the trained large model fine_tune_baichuan2 to perform performance analysis on the test set data. The test set data content is as follows:

[0105]

[0106]

[0107] It should be explained that the network performance test device checks the analysis results. If the analysis results meet the requirements, the instance performance indicators of openstack are collected; if they do not meet the requirements, the training data is optimized, the learning rate and the number of learning times are adjusted, and the large model is trained until the trained large model can meet the user's needs. The target language model is manually trained. Through multiple questions and answers, the model will be exposed to questions of different difficulty levels. Based on user feedback, it will learn more complex logical relationships and decision-making strategies, further improving the accuracy of the target language model.

[0108] The above scheme brings at least the following beneficial effects: In the embodiment of the present application, the network performance testing device trains the initial prediction model. During the training process of the initial prediction model, the efficiency of obtaining the target language model can be improved by adjusting the number of training times and the learning rate of the initial prediction model.

[0109] In one possible implementation, combining Figure 3 ,like Figure 7 As shown, before the network performance test device obtains the network performance parameters of the first network instance in S303, the network performance test device instructs the second network instance to send traffic to the first network instance. The process of the network performance test device instructing the second network instance to send traffic to the first network instance can be specifically implemented by the following S701.

[0110] S701. The network performance testing device instructs the iperf tool in the second network instance to send traffic of a preset bandwidth to the first network instance.

[0111] The iperf tool is installed in the second network instance through a script.

[0112] In a possible implementation, the network performance testing device installs an iperf tool in the second network instance by running a script, and instructs the iperf tool in the second network instance to send traffic of a preset bandwidth to the first network instance.

[0113] It can be understood that the network performance testing device determines whether the iperf tool is installed in the second network instance, and if the iperf tool does not exist in the second network instance, the iperf tool is installed in the second network instance through a script.

[0114] It needs to be explained that iperf is a client / server-based network performance testing tool that can test the bandwidth quality of the Transmission Control Protocol (TCP), User Datagram Protocol (UDP) and Stream Control Transmission Protocol (SCTP), and can provide network throughput information, as well as statistical information such as jitter, packet loss rate, maximum segment and maximum transmission unit size, thereby testing network performance.

[0115] For example, Figure 8 As shown, the network performance test device is connected to the first network instance, determines whether the first network instance has the iperf tool and the sar command, and if the first network instance does not have the iperf tool and the sar command, installs the iperf tool and the sar command on the first network instance. In the case where the first network instance has the iperf tool and the sar command, starts the iperf server in the first network instance. The network performance test device obtains the second network instance connected to the first network instance, searches the second network instance, and determines whether the second network instance has the iperf tool and the sar command. If the second network instance does not have the iperf tool and the sar command, installs the iperf tool and the sar command on the second network instance. In the case where the second network instance has the iperf tool and the sar command, starts the iperf client and the sar command in the second network instance, and collects the performance index of the second network instance. The network performance test device instructs the iperf client to send traffic to the iperf client server.

[0116] The above scheme brings at least the following beneficial effects: in an embodiment of the present application, the network performance testing device instructs the iperf tool in the second network instance to send traffic of a preset bandwidth to the first network instance. In this way, the network performance of the network to which the second network instance and the first network instance belong can be determined based on the data sent by the iperf tool in the second network instance and the data received by the iperf tool in the first network instance.

[0117] In one possible implementation, combining Figure 3 ,like Fig. 9 As shown, in S303, the process in which the network performance testing device obtains the network performance parameters of the first network instance can be specifically implemented through the following S901-S903.

[0118] S901. A network performance testing apparatus obtains network quality parameters received by a first network instance through an iperf tool in a first network instance.

[0119] The network quality parameter includes at least one of the following: throughput, jitter, packet loss rate, and maximum transmission unit.

[0120] Optionally, the iperf tool includes an iperf client and an iperf server. The iperf client is used to send traffic, and the iperf server is used to receive traffic.

[0121] In one possible implementation, the network performance testing device sends a first request message to an iperf server in a first network instance, receives a first request response message from the iperf server in the first network instance, and obtains a network quality parameter received by the first network instance based on the first request response message.

[0122] Optionally, the first request information is used to request flow information, and the first request response information includes flow information.

[0123] It should be explained that after the iperf server in the first network instance receives the traffic information, it saves the traffic information in a log file. When the iperf server in the first network instance receives the first request information, it generates the first request response information based on the traffic information in the log file.

[0124] Exemplarily, after receiving the instruction information of the network performance test device, the iperf client in the second network instance sends 1M, 10M, 100M and 1G of traffic to the iperf server in the first network instance. The iperf server records the network performance parameters corresponding to the 1M, 10M, 100M and 1G of traffic respectively, and sends the network performance parameters to the network performance test device.

[0125] S902. The network performance testing apparatus obtains device status information of the first network instance through a sar command in the first network instance.

[0126] The device status information includes at least one of the following: CPU occupancy information, memory occupancy information, and disk input / output (I / O) information.

[0127] It needs to be explained that sar can collect performance data through the System Activity Data Collector (SADC). SADC is a background process that periodically collects system performance data and writes it to the system log file. This data includes CPU usage, memory usage, disk I / O, network activity, etc. SADC can collect data according to the configured time interval and number of iterations, thereby providing real-time or near real-time system performance information.

[0128] S903: The network performance testing apparatus determines a network performance parameter of the first network instance according to the network quality parameter received by the first network instance and the device status information of the first network instance.

[0129] In one possible implementation, a network performance testing device obtains network quality parameters sent by a second network instance, and determines network performance parameters of the first network instance based on the network quality parameters received by the first network instance, device status information of the first network instance, and network quality parameters sent by the second network instance.

[0130] In another possible implementation, after determining the network performance parameters of the first network instance, the network performance testing device calls an agent tool to convert the generated test case into an Excel file and sends the file by email.

[0131] It needs to be explained that agent tools combine features such as large language models, planning capabilities, memory and tool use, and can autonomously understand, plan decisions and perform complex tasks.

[0132] The above scheme brings at least the following beneficial effects: In the embodiment of the present application, the network performance test device can determine the communication quality between the first network instance and the second network instance based on the network quality parameters received by the first network instance and the network quality parameters sent by the second network instance. Furthermore, the network performance test device can accurately determine the network performance parameters of the first network instance based on the communication quality between the first network instance and the second network instance and the device status information of the first network instance.

[0133] In one possible implementation, combining Figure 3 ,like Fig.10As shown, in S304, the network performance testing apparatus determines the network performance analysis report of the target network based on the network performance parameters of the first network instance and the target large language model, which can be specifically implemented through the following S1001-S1002.

[0134] S1001. The network performance testing apparatus obtains device status information of the second network instance through a sar command in the second network instance.

[0135] In a possible implementation, the network performance testing device sends a second request message to the sar command, receives a second request response message from the sar command, and obtains device status information of the second network instance based on the second request response message.

[0136] Optionally, the second request response information is used to obtain device status information of the second network instance, and the second request response information includes the device status information of the second network instance.

[0137] It needs to be explained that the sar command is a very comprehensive system performance analysis tool under Linux and an important tool in UNIX systems. The sar command is used to collect, report and save system activity information to help users understand the performance status of the system.

[0138] S1002. The network performance testing apparatus inputs the network performance parameters of the first network instance and the device status information of the second network instance into the target large language model to obtain a network performance analysis report of the target network.

[0139] In a possible implementation, the network performance test device constructs a prompt word template, determines a first performance test indicator and a first content format for output. Based on the prompt word template and the first performance test indicator, the network performance test device obtains input information that meets the performance test requirements from the network performance parameters of the first network instance and the device status information of the second network instance, and inputs the input information into the target large language model to obtain a network performance analysis report of the target network.

[0140] Optionally, the format of the network performance analysis report is the first content format.

[0141] Exemplarily, the network performance testing device determines the bandwidth performance according to the target ratio of the actual transmission bandwidth to the theoretical bandwidth. When the target ratio is less than or equal to 0.15, the network performance testing device determines that the network condition is good; when the target ratio is greater than 0.15, the network performance testing device determines that the network condition is poor. The target large language model provides possible influencing factors as a direction for adjusting the network condition.

[0142] For example, Fig.11As shown, the network performance test device constructs a prompt word template, specifies the performance test indicators and report format, uploads the performance indicator file, and determines whether the performance indicator file is successfully uploaded. In the case of failure to upload the performance indicator file, the performance indicator file is uploaded again. In the case of successful upload of the performance indicator file, the network performance test device automatically reads the data in the performance indicator file and stores the performance data in the variable. The network performance test device analyzes the performance indicators according to the prompt word template, generates an analysis report, and stores it in the output folder. The network performance test device customizes the tool to automatically send the report email.

[0143] It needs to be explained that the network performance test device uses an agent, a custom report content tool, to save the generated charts and text into a CSV file; a custom email sending tool to set sender information, recipient information and the text to be sent; and automatically sends the performance analysis report by email.

[0144] The above scheme brings at least the following beneficial effects: In the embodiment of the present application, the network performance test device inputs the network performance parameters of the first network instance and the device status information of the second network instance into the target large language model. In this way, the network performance test device can determine the communication quality between the first network instance and the second network instance. Furthermore, the network performance test device can accurately determine the network performance parameters of the first network instance based on the communication quality between the first network instance and the second network instance and the device status information of the first network instance and the device status information of the second network instance.

[0145] The embodiment of the present application can divide the network performance test device into functional modules or functional units according to the above method example. For example, each functional module or functional unit can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules or functional units. Among them, the division of modules or units in the embodiment of the present application is schematic, which is only a logical function division. There may be other division methods in actual implementation.

[0146] For example, Fig.12 FIG. 1 is a possible structural diagram of a network performance test device according to an embodiment of the present application. The network performance test device 120 includes: an acquisition unit 1201 and a processing unit 1202 .

[0147] The acquisition unit 1201 is used to acquire a topology map of a target network; the topology map includes network instances and connection relationships between network instances; the processing unit 1202 is used to determine a first network instance and a second network instance connected to the first network instance according to the topology map; the first network instance and the second network instance belong to the target network; the first network instance is any network instance in the target network; the processing unit 1202 is also used to instruct the acquisition unit 1201 to acquire network performance parameters of the first network instance when the second network instance sends traffic of a preset bandwidth to the first network instance; the processing unit 1202 is also used to determine a network performance analysis report of the target network based on the network performance parameters of the first network instance and a target large language model; the target large language model is used to predict network performance according to the network performance parameters and output an analysis report of the network performance.

[0148] Optionally, the acquisition unit 1201 is also used to acquire a training set; the training set includes: historical network performance parameters of multiple historical periods and actual network performance analysis reports corresponding to the historical network performance parameters; the processing unit 1202 is also used to input the historical network performance parameters in the training set into the initial prediction model to determine the initial prediction results; the processing unit 1202 is also used to determine the loss value of the initial prediction model based on the initial prediction results and the actual network performance analysis report, and when the loss value does not meet the preset conditions, adjust the model parameters of the initial prediction model based on the loss value, and continue to perform iterative training based on the training set until the model convergence conditions are met to obtain the target large language model.

[0149] Optionally, the processing unit 1202 is further used to instruct the iperf tool in the second network instance to send traffic of a preset bandwidth to the first network instance; the iperf tool is installed in the second network instance through a script.

[0150] Optionally, the acquisition unit 1201 is also used to obtain network quality parameters received by the first network instance through the iperf tool in the first network instance; the acquisition unit 1201 is also used to obtain device status information of the first network instance through the sar command in the first network instance; the processing unit 1202 is also used to determine the network performance parameters of the first network instance based on the network quality parameters received by the first network instance and the device status information of the first network instance.

[0151] Optionally, the network quality parameters include at least one of the following: throughput, jitter, packet loss rate, and maximum transmission unit; the device status information includes at least one of the following: CPU usage information, memory usage information, and disk input and output I / O information.

[0152] Optionally, the acquisition unit 1201 is also used to obtain the device status information of the second network instance through the sar command in the second network instance; the processing unit 1202 is also used to input the network performance parameters of the first network instance and the device status information of the second network instance into the target large language model to obtain a network performance analysis report of the target network.

[0153] Optionally, the network performance parameter includes at least one of the following: expected bandwidth, actual sending bandwidth, actual receiving bandwidth, jitter, packet loss rate, CPU utilization, and memory utilization.

[0154] An embodiment of the present application also provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run a computer program or instruction to implement the network performance testing method in the above method embodiment.

[0155] An embodiment of the present application provides a computer program product including instructions. When the instructions are executed on a computer, the computer is enabled to execute the industrial terminal switching method in the above method embodiment.

[0156] Among them, the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, and a hard disk. Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM), registers, hard disks, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any other form of computer-readable storage medium in a suitable combination of the above, or numerical values ​​in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an Application Specific Integrated Circuit (ASIC). In embodiments of the present invention, computer-readable storage media may be any tangible media that contains or stores a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0157] Since the apparatus, device, computer-readable storage medium, and computer program product in the embodiments of the present invention can be applied to the above-mentioned method, the technical effects that can be obtained can also refer to the above-mentioned method embodiments, and the embodiments of the present application will not be repeated here.

[0158] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A network performance testing method, characterized in that: The method comprises: Acquire a topology map of the target network; the topology map includes network instances and connection relationships between the network instances; According to the topology map, determining a first network instance and a second network instance connected to the first network instance; the first network instance and the second network instance belong to the target network; the first network instance is any network instance in the target network; When the second network instance sends traffic of a preset bandwidth to the first network instance, obtaining a network performance parameter of the first network instance; Based on the network performance parameters of the first network instance and a target large language model, a network performance analysis report of the target network is determined; the target large language model is used to predict network performance according to the network performance parameters and output an analysis report of the network performance.

2. The method according to claim 1, characterized in that The method further comprises: Acquire a training set; the training set includes: historical network performance parameters of multiple historical periods and actual network performance analysis reports corresponding to the historical network performance parameters; Inputting the historical network performance parameters in the training set into an initial prediction model to determine an initial prediction result; Based on the initial prediction result and the actual network performance analysis report, the loss value of the initial prediction model is determined, and when the loss value does not meet the preset conditions, the model parameters of the initial prediction model are adjusted based on the loss value, and iterative training is continued based on the training set until the model convergence conditions are met to obtain the target large language model.

3. The method according to claim 1, characterized in that Before obtaining the network performance parameter of the first network instance, the method further includes: Instructing the iperf tool in the second network instance to send traffic of the preset bandwidth to the first network instance; the iperf tool is installed in the second network instance through a script.

4. The method according to claim 3, characterized in that The obtaining the network performance parameter of the first network instance includes: Obtaining, by using the iperf tool in the first network instance, a network quality parameter received by the first network instance; Obtaining device status information of the first network instance through the sar command in the first network instance; Determine a network performance parameter of the first network instance according to the network quality parameter received by the first network instance and the device status information of the first network instance.

5. The method according to claim 4, characterized in that The network quality parameter includes at least one of the following: throughput, jitter, packet loss rate, and maximum transmission unit; the device status information includes at least one of the following: CPU occupancy information, memory occupancy information, and disk input and output I / O information.

6. The method according to claim 1, characterized in that The determining, based on the network performance parameters of the first network instance and the target large language model, a network performance analysis report of the target network includes: Obtaining device status information of the second network instance by using the sar command in the second network instance; The network performance parameters of the first network instance and the device status information of the second network instance are input into the target large language model to obtain a network performance analysis report of the target network.

7. The method according to any one of claims 1 to 6, characterized in that: The network performance parameter includes at least one of the following: expected bandwidth, actual sending bandwidth, actual receiving bandwidth, jitter, packet loss rate, CPU utilization, and memory utilization.

8. A network performance testing device, characterized in that: include: Acquisition unit and processing unit; The acquisition unit is used to acquire a topology map of the target network; The topology diagram includes network instances and connection relationships between the network instances; The processing unit is configured to determine, according to the topology map, a first network instance and a second network instance connected to the first network instance; the first network instance and the second network instance belong to the target network; the first network instance is any network instance in the target network; The processing unit is configured to obtain a network performance parameter of the first network instance when the second network instance sends traffic of a preset bandwidth to the first network instance; The processing unit is used to determine a network performance analysis report of the target network based on the network performance parameters of the first network instance and a target large language model; the target large language model is used to predict network performance according to the network performance parameters and output an analysis report of the network performance.

9. A network performance testing device, characterized in that: include: A processor and a memory; wherein the memory is used to store computer-executable instructions, and when the network performance testing device is running, the processor executes the computer-executable instructions stored in the memory, so that the network performance testing device executes the network performance testing method described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes instructions, which, when executed by a network performance testing device, cause the computer to execute the network performance testing method according to any one of claims 1 to 6.

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