Fault location methods, devices, storage media and program products

Abstract

This application provides a fault location method, apparatus, storage medium, and program product, relating to the field of communication technology, which can achieve rapid fault location and improve fault location efficiency. The method includes: receiving alarm information sent by a first device, wherein the first device includes multiple ports; then, for each of the multiple ports, determining a first flow rate time series and a second flow rate time series of the port's traffic rate within a first time period; and finally, based on the first and second flow rate time series of each of the multiple ports, determining the ports in the first device that are in a fault state. The first flow rate time series characterizes the actual port's traffic rate change within the first time period, and the second flow rate time series characterizes the predicted port's traffic rate change within the first time period.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a fault location method, apparatus, storage medium, and program product. Background Technology

[0002] To meet the computing power requirements of large-scale artificial intelligence (AI) models, the intelligent computing center is continuously expanding the scale of its high-performance network clusters to provide the necessary computing resources for model training and inference in different application scenarios.

[0003] However, with the continuous expansion of network clusters and the increasing complexity of intelligent computing center systems, the operation and maintenance management of intelligent computing centers also faces many challenges. Specifically, when multiple network devices collaboratively process large-scale AI model tasks, traditional fault location methods require traversing a large number of network devices and links when a network device or link fails. This greatly increases the workload of operation and maintenance personnel. Furthermore, the diversity and complexity of faults make fault location even more difficult. Therefore, how to achieve rapid fault location in complex and large-scale intelligent computing center systems to ensure the efficient operation of intelligent computing centers and the stable progress of large-scale AI model tasks has become a critical issue that urgently needs to be addressed. Summary of the Invention

[0004] This application provides a fault location method, apparatus, storage medium, and program product, which can achieve rapid fault location and improve fault location efficiency.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] Firstly, this application provides a fault location method applied to a server, the method comprising:

[0007] The system receives alarm information sent by a first device, which includes multiple ports. Then, for each of the multiple ports, it determines the first flow rate time series and the second flow rate time series of that port within a first time period. Finally, based on the first flow rate time series and the second flow rate time series of each of the multiple ports, it determines the ports in the first device that are in a fault state.

[0008] The first flow rate time series is used to characterize the change in the actual port flow rate within the first time period, and the second flow rate time series is used to characterize the change in the predicted port flow rate within the first time period.

[0009] Based on the above technical solution, after the server receives the alarm information sent by the first device, for each of the multiple ports of the first device, it can first determine the first flow rate time series and the second flow rate time series of the port in the first time period. Then, it can determine whether the port is in a fault state by using the first flow rate time series and the second flow rate time series of the port. This can achieve rapid fault location and improve fault location efficiency.

[0010] Optionally, the above method of determining the port in the first device that is in a fault state based on the first flow rate time series and the second flow rate time series of each of the multiple ports may specifically include: for each of the multiple ports, if the similarity between the first flow rate time series and the second flow rate time series of the port is less than a similarity threshold, then the port is determined to be in a fault state.

[0011] Optionally, the first time period includes multiple sub-time periods. Based on this, the first flow rate time series of the port can be determined in the following way: determine the flow rate of the port in each sub-time period, and then determine the first flow rate time series of the port based on the flow rate of the port in each sub-time period.

[0012] Optionally, the second flow rate time series of the aforementioned port can be determined by: determining the third flow rate time series of the port's flow rate within the second time period, and then predicting the second flow rate time series of the port based on the third flow rate time series of the port.

[0013] The second time period refers to the period preceding the first time period. The third flow rate time series is used to characterize the changes in the actual port flow rate during the second time period.

[0014] Optionally, the above method may further include: if there is no port in the first device that is in a fault state, determining a second device that performs the same task as the first device, and then determining the port in the second device that is in a fault state.

[0015] Optionally, a similarity determination model can be deployed in the server. Based on this model, the similarity between the first flow rate time series and the second flow rate time series of the aforementioned port can be determined in the following way: input the first flow rate time series and the second flow rate time series of the port into the similarity determination model, and obtain the similarity between the first flow rate time series and the second flow rate time series output by the model.

[0016] Secondly, this application provides a fault location device, comprising:

[0017] The receiving unit is used to receive alarm information sent by the first device. The first device includes multiple ports.

[0018] The first determining unit is used to determine, for each of the multiple ports, a first flow rate time series and a second flow rate time series within a first time period.

[0019] The first flow rate time series is used to characterize the change in the actual port flow rate within the first time period, and the second flow rate time series is used to characterize the change in the predicted port flow rate within the first time period.

[0020] The second determining unit is used to determine the port in the first device that is in a fault state based on the first flow rate time series and the second flow rate time series of each of the multiple ports.

[0021] Optionally, the second determining unit is further configured to determine that a port is in a fault state if, for each of the multiple ports, the similarity between the first flow rate time series and the second flow rate time series of the port is less than a similarity threshold.

[0022] Optionally, the first determining unit is also used to determine the flow rate of the port in each sub-period.

[0023] Optionally, the first determining unit is further configured to determine a first flow rate time series of the port based on the flow rate of the port in each sub-period.

[0024] Optionally, the first determining unit is also used to determine a third flow rate time series of the port's flow rate within a second time period.

[0025] The second time period refers to the period preceding the first time period. The third flow rate time series is used to characterize the changes in the actual port flow rate during the second time period.

[0026] Optionally, the first determining unit is further configured to, if there is no port in the first device that is in a fault state, determine a second device that performs the same task as the first device, and determine the port in the second device that is in a fault state.

[0027] Thirdly, this application provides a fault location apparatus, which includes a processor and a communication interface. The communication interface is coupled to the processor, which is used to run computer programs or instructions to implement the fault location method as described in any one of the first aspects and any possible implementations of the first aspect.

[0028] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a terminal, cause the terminal to perform the fault location method as described in any one of the first aspects and any possible implementations of the first aspect.

[0029] Fifthly, this application provides a computer program product comprising computer instructions that, when executed on a computer, cause the computer to perform the fault location method as described in any one of the first aspects and any possible implementations of the first aspect.

[0030] It is understood that the beneficial effects that can be achieved by the second to fifth aspects provided above can be referred to the beneficial effects in any possible design of the fault location method as described in any one of the first aspects and any possible implementation of the first aspect, which will not be repeated here. Attached Figure Description

[0031] Figure 1 An architecture diagram of a fault location system provided in an embodiment of this application;

[0032] Figure 2 A flowchart illustrating a fault location method provided in an embodiment of this application;

[0033] Figure 3 An interactive flowchart of a fault location method provided in an embodiment of this application;

[0034] Figure 4 A flowchart illustrating the process of determining the traffic rate of a port within a first time period, as provided in an embodiment of this application;

[0035] Figure 5 This is a schematic diagram of the structure of a fault location device provided in an embodiment of this application;

[0036] Figure 6 This is a schematic diagram of another fault location device provided in an embodiment of this application. Detailed Implementation

[0037] The following description, in conjunction with the accompanying drawings, details a fault location method, apparatus, storage medium, and program product provided in the embodiments of this application.

[0038] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0039] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0040] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0041] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0042] In today's digital age, AI technology is leading a new wave of global technological revolution. As the core driving force in this field, AI big data models, with their powerful data processing capabilities and broad application prospects, are gradually becoming an important force driving the digital transformation of various industries.

[0043] As a critical infrastructure supporting the operation of large-scale AI models, intelligent computing centers are continuously expanding their scale and building more and more high-performance network clusters to meet the computing power requirements of these models. These network clusters can provide the necessary computing resources for model training and inference in different application scenarios, ensuring that large-scale AI models can operate efficiently and stably.

[0044] However, with the continuous expansion of network clusters and the increasing complexity of intelligent computing center systems, the operation and maintenance management of intelligent computing centers faces numerous challenges. Specifically, when multiple network devices collaboratively process large-scale AI model tasks, traditional fault location methods require traversing a large number of network devices and links when a network device or link fails. This significantly increases the workload of operation and maintenance personnel. Furthermore, the diversity and complexity of faults make fault location even more difficult. Therefore, how to achieve rapid fault location in complex and large-scale intelligent computing center systems to ensure the efficient operation of intelligent computing centers and the stable progress of large-scale AI model tasks has become a critical issue that urgently needs to be addressed.

[0045] To address the aforementioned technical problems, this application provides a fault location method. After receiving alarm information from a first device, the server can first determine the first flow rate time series and the second flow rate time series of each of the multiple ports of the first device within a first time period. Then, the server can determine whether the port is in a fault state by using the first flow rate time series and the second flow rate time series of the port. This enables rapid fault location and improves fault location efficiency.

[0046] Figure 1 An architecture diagram of a fault location system provided in an embodiment of this application is shown below. Figure 1 As shown, the system framework includes: a first device 101 and a server 102.

[0047] The first device 101 can be a server or a gateway device.

[0048] A server can be a high-performance server providing various services on the internet, a standalone physical server, a server cluster consisting of multiple physical servers, or a cloud server providing at least one of the following basic cloud computing services: cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data or artificial intelligence platforms. This application embodiment does not limit this. Of course, a server can also include other functions to provide more comprehensive and diversified services.

[0049] A gateway device can be a device that connects different networks and provides network communication and data transmission services for these networks. Gateway devices can include, but are not limited to, routers, switches, and virtual private network (VPN) devices.

[0050] This application embodiment does not limit the number of first devices 101 in the fault location system, and may include more than [the number of first devices 101]. Figure 1 More or fewer of the first devices 101.

[0051] Server 102 can be a server of the same type as the servers mentioned above, and will not be described here.

[0052] In this embodiment, if the first device 101 malfunctions during task execution, it can send an alarm message to the server 102. Accordingly, upon receiving the alarm message, the server 102 can determine, for each of the multiple ports of the first device 101, a first traffic rate time series and a second traffic rate time series within a first time period for that port. Finally, based on the first and second traffic rate time series of each of the multiple ports of the first device 101, the server 102 can determine the ports in the first device 101 that are in a faulty state.

[0053] Figure 2 This is a flowchart illustrating a fault location method provided in an embodiment of this application, as shown below. Figure 2 As shown, the method is composed of Figure 1 The server shown executes a method that includes:

[0054] S201, Receive alarm information sent by the first device.

[0055] S202, for each of the multiple ports, determine the first flow rate time series and the second flow rate time series of the port's flow rate within a first time period.

[0056] The first device includes multiple ports.

[0057] The first flow rate time series is used to characterize the actual port flow rate changes within a first time period, and the second flow rate time series is used to characterize the predicted port flow rate changes within a first time period.

[0058] The duration of the first time period is not limited in this embodiment. For example, the duration of the first time period can be 1 minute, 1 hour, 1 day, or 1 week, etc.

[0059] Specifically, after the first device malfunctions while performing a task, it can send an alarm message to the server. Accordingly, after receiving the alarm message, the server can determine the first flow rate time series and the second flow rate time series for each of the multiple ports of the first device within a first time period.

[0060] For example, assume that the first device sends the alarm information at 9:10 AM, and the duration of the first time period is 10 minutes. Based on this, after receiving the alarm information, the server can determine the first traffic rate time series and the second traffic rate time series for each port of the first device between 9:00 AM and 9:10 AM.

[0061] S203, based on the first flow rate time series and the second flow rate time series of each of the multiple ports, determine the port in the first device that is in a fault state.

[0062] Specifically, for each of the multiple ports of the first device, the server can determine the similarity between the first flow rate time series and the second flow rate time series of that port. If the similarity between the first flow rate time series and the second flow rate time series is less than the similarity threshold, then the port can be determined to be in a fault state.

[0063] This application does not limit the value of the similarity threshold. For example, the similarity threshold can be 0.6, 0.7, or 0.8, etc.

[0064] In some embodiments, a similarity determination model is deployed in the server. Based on this model, the server determines the similarity between a first traffic rate time series and a second traffic rate time series of a port. Specifically, this may include: for each of a plurality of ports, after the server determines the first traffic rate time series and the second traffic rate time series of that port, the first traffic rate time series and the second traffic rate time series can be input into the similarity determination model to obtain the similarity between the first traffic rate time series and the second traffic rate time series output by the model.

[0065] This application does not limit the type of similarity determination model. For example, the similarity determination model can be a dynamic time warping (DTW) model or an Euclidean distance model.

[0066] Based on the above technical solution, the server can determine the port in the first device that is in a faulty state by judging whether the similarity between the first traffic rate time series and the second traffic rate time series of each port in the first device is less than the similarity threshold. This enables rapid fault location, thereby ensuring the stable progress of large model tasks and improving the operating efficiency of the intelligent computing center.

[0067] In some embodiments, if there are no ports in the first device that are in a fault state, the server can determine a second device that performs the same task as the first device, and then determine the ports in the second device that are in a fault state.

[0068] Specifically, the server stores multiple tasks and the execution device corresponding to each task. Accordingly, if the first device does not have any ports in a faulty state, the server can determine a second device performing the same task as the first device from among the execution devices corresponding to each task. Then, referring to the methods described in S202-S203 above, the server can first determine the first and second traffic rate time series of each of the multiple ports of the second device, and then, based on the first and second traffic rate time series of each of the multiple ports, determine the ports in the second device that are in a faulty state.

[0069] This application does not limit the task information of each task stored in the server. For example, a task can be an AI large model task. The task information of an AI large model task may include task identifier, number of task parameters (e.g., 7 billion) or task partitioning method (e.g., data parallelism (DP), pipeline parallelism, or sequence parallelism (SP)).

[0070] In some embodiments, the first time period includes multiple sub-time periods. Based on this, the first flow rate time series of the port can be determined by: determining the flow rate of the port in each sub-time period, and then determining the first flow rate time series of the port based on the flow rate of the port in each sub-time period.

[0071] Specifically, after receiving the alarm information sent by the first device, the server can send a port rate acquisition request to the first device. This request includes the acquisition time (e.g., a first time period). Correspondingly, upon receiving the port rate acquisition request, the first device can calculate the traffic rate of each port within each sub-time period of the first time period for each of the multiple ports, and send the traffic rate of that port within each sub-time period to the server. After receiving the traffic rate of each port within each sub-time period for each of the multiple ports, the server can determine the first traffic rate time series for that port.

[0072] For example, assuming the first time period is from 9:00 to 9:02, and each sub-time period is 1 minute long, the first device includes port number one. Based on this, after receiving a port rate acquisition request, the first device can calculate the traffic rate d1 of port number one in the first sub-time period (9:00 to 9:01) and the traffic rate d2 of the same port in the second sub-time period (9:00 to 9:02), and send traffic rates d1 and d2 to the server. Correspondingly, after receiving traffic rates d1 and d2, the server can determine the first traffic rate time sequence of port number one as [d1, d2].

[0073] Using the above method, the server can determine the first traffic rate time series of the port within a first time period, which can provide a basis for identifying ports in a faulty state.

[0074] In some embodiments, the second flow rate time series of the port can be determined by: determining the third flow rate time series of the port's flow rate within a second time period, and then predicting the second flow rate time series of the port based on the third flow rate time series of the port.

[0075] The second time period is the period preceding the first time period. For example, if the first time period is from 9:00 to 9:30, then the second time period is from 8:30 to 9:00.

[0076] The third flow rate time series is used to characterize the changes in the actual port flow rate during the second time period.

[0077] Specifically, a rate prediction model is deployed on the server. Accordingly, after receiving the alarm information from the first device, the server can determine the port's third traffic rate time series within a second time period, referring to the method described above for determining the first traffic rate time series of the port. Then, the server can input the port's third traffic rate time series into the rate prediction model to obtain the port's second traffic rate time series output by the rate prediction model.

[0078] Using the above method, for each port, the server can train a rate prediction model corresponding to that port. Then, using this rate prediction model and the port's third traffic rate time series within the second time period, it can predict the port's second traffic rate time series within the first time period. This provides a basis for locating faulty ports and improves the accuracy of fault location. Furthermore, predicting future traffic rates using historical traffic rates can also support traffic simulation and global load balancing.

[0079] In some embodiments, the above-mentioned rate prediction model can be trained by: obtaining the historical traffic rate time series of the port in multiple historical time periods, using the multiple historical traffic rate time series of the port as training data, and then using the training data to train the original prediction model to obtain the rate prediction model.

[0080] This application does not limit the type of the original prediction model. For example, the original prediction model may include, but is not limited to: Transformer model, long short-term memory network (LSTM) model, autoregressive model, Prophet model, or deep state-space (Deep State) model.

[0081] The embodiments of this application do not limit the amount of training data. For example, when the number of iterations in the rate prediction model is N, the training data M = N * 10%.

[0082] Specifically, taking the Transformer model as an example, the server can obtain multiple historical traffic rate time series of the port, similar to the method described above for obtaining the first traffic rate time series, and use these as training data. Then, the server can input the training data into the Transformer model to train it, obtaining the trained Transformer model. Afterwards, the server can use online learning methods to optimize the trained Transformer model, obtaining the final rate prediction model.

[0083] In this embodiment of the application, multiple rate prediction models can be deployed in the server, with each port corresponding to a different rate prediction model. For example, if port 1 corresponds to rate prediction model A, when the server predicts the second traffic rate time series of port 1, it can input the third traffic rate time series of port 1 into rate prediction model A to obtain the second traffic rate time series output by the model.

[0084] In some embodiments, the server is connected to multiple devices, including a first device and a second device. Based on this, for each port in each device, the server can also determine the similarity between a first traffic rate time series and a second traffic rate time series within a preset period, and perform fault detection on the port if the similarity between the first traffic rate time series and the second traffic rate time series is less than a similarity threshold.

[0085] In this embodiment, the server stores port information for each port. Port information may include, but is not limited to, port number, device number to which the port belongs, and port location. For example, assuming the first device is a server, the device number to which port one of the first device belongs can be the server number (e.g., server-1), and the port number can be the server's network interface card (NIC) number (e.g., server-1-NIC number). As another example, assuming the first device is a switch, the device number to which port one of the first device belongs can be the switch number (e.g., spine layer switch number, leaf layer switch number), and the port number can be the switch's port number.

[0086] Based on the above, assuming the similarity between the first and second traffic rate time series of a port is less than a similarity threshold, the server can determine the location of the port based on its port information. Then, the server can perform fault detection on the port based on its location.

[0087] In this way, the server can periodically inspect each port of each device, and detect faults in the port in advance when the similarity between the first traffic rate time series and the second traffic rate time series of the port is less than the similarity threshold, thereby ensuring the stable operation of large model tasks.

[0088] The fault location method provided in this application will be described in detail below in conjunction with the various embodiments described above. Figure 3 An interactive flowchart of a fault location method provided in an embodiment of this application is shown below. Figure 3 As shown, the method includes:

[0089] S301, the first device sends an alarm message to the server.

[0090] S302, the server sends a port traffic rate acquisition request to the first device.

[0091] The port traffic rate acquisition request includes the acquisition time (i.e., the first time period and the second time period).

[0092] S303, the first device determines the port's traffic rate in each sub-time period of the first time period and the traffic rate in each sub-time period of the second time period based on the port traffic rate acquisition request.

[0093] Specifically, each port of the first device corresponds to multiple work queues (QPs), and each QP can transmit multiple data packets simultaneously. Based on this, such as... Figure 4 As shown, determining the port's traffic rate within the first time period can specifically include:

[0094] Step 1: For each QP, the first device obtains the rate at which the QP transmits each data packet within each sub-time period.

[0095] Specifically, the first device can use the NVIDIA Collective Communications Library (NCCL) tool to obtain the rate at which each QP transmits each data packet within each sub-time period.

[0096] Step 2: The first device uses the average rate of each data packet within each sub-time period as the QP traffic rate.

[0097] Step 3: For each sub-time period, the first device takes the average traffic rate of each QP corresponding to the port within that sub-time period as the port's traffic rate within that sub-time period.

[0098] The first device can refer to the above method to determine the traffic rate of each port in each sub-time period of the second time period.

[0099] S304, The first device sends the port's traffic rate within each sub-time period in the first time period and the second time period to the server.

[0100] S305, the server determines the first traffic rate time series of the port based on the traffic rate of the port in each sub-period within the first time period.

[0101] S306, the server determines the third traffic rate time series of the port based on the traffic rate of the port in each sub-period of the second time period.

[0102] S307, the server inputs the third flow rate time series of the port into the rate prediction model to obtain the second flow rate time series of the port predicted by the model.

[0103] S308, the server determines the similarity between the first traffic rate time series and the second traffic rate time series of the port, and determines that the port is in a fault state if the similarity is less than the similarity threshold.

[0104] Figure 5 This is a schematic diagram of the structure of a fault location device provided in an embodiment of this application, as shown below. Figure 5 As shown, the device includes:

[0105] The receiving unit 501 is used to receive alarm information sent by the first device.

[0106] The first determining unit 502 is used to determine, for each of the multiple ports, a first flow rate time series and a second flow rate time series within a first time period.

[0107] The first flow rate time series is used to characterize the change in the actual port flow rate within the first time period, and the second flow rate time series is used to characterize the change in the predicted port flow rate within the first time period.

[0108] The second determining unit 503 is used to determine the port in the first device that is in a fault state based on the first flow rate time series and the second flow rate time series of each of the multiple ports.

[0109] Optionally, the second determining unit 503 is further configured to determine that the port is in a fault state if, for each of the multiple ports, the similarity between the first flow rate time series and the second flow rate time series of the port is less than a similarity threshold.

[0110] Optionally, the first determining unit 502 is also used to determine the flow rate of the port in each sub-period.

[0111] Optionally, the first determining unit 502 is further configured to determine a first flow rate time series of the port based on the flow rate of the port in each sub-period.

[0112] Optionally, the first determining unit 502 is further configured to determine the port's flow rate within a third flow rate time series during a second time period.

[0113] The second time period refers to the period preceding the first time period. The third flow rate time series is used to characterize the changes in the actual port flow rate during the second time period.

[0114] Optionally, the first determining unit 502 is further configured to, if there is no port in the first device that is in a fault state, determine a second device that performs the same task as the first device, and determine the port in the second device that is in a fault state.

[0115] Figure 6 A schematic diagram of another possible structure of the fault location device involved in the above embodiments is shown. The fault location device includes a processor 601 and a communication interface 602. The processor 601 is used to control and manage the operation of the fault location device, and the communication interface 602 is used to support communication between the fault location device and other network entities. The fault location device may also include a memory 603 and a bus 604, the memory 603 being used to store the program code and data of the fault location device.

[0116] The memory 603 may be a memory in a fault location device, and the memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid-state drive; the memory may also include a combination of the above types of memory.

[0117] The processor 601 described above can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0118] Bus 604 can be an extended industry standard architecture (EISA) bus, etc. Bus 604 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0119] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0120] This application provides a computer program product containing instructions that, when run on a computer, cause the computer to execute the fault location method in the above method embodiments.

[0121] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the fault location method in the method flow shown in the above method embodiments.

[0122] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires; portable computer disks; hard disks; random access memory (RAM); read-only memory (ROM); erasable programmable read-only memory (EPROM); registers; hard disks; optical fibers; compact disc read-only memory (CD-ROM); optical storage devices; magnetic storage devices; or any suitable combination thereof; or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). In the embodiments of this application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0123] Embodiments of the present invention provide a computer program product containing instructions that, when executed on a computer, cause the computer to perform the fault location method described in the embodiments of this application.

[0124] Since the fault location device, computer-readable storage medium, and computer program product in the embodiments of the present invention can be applied to the above method, the technical effects obtained can also be referred to the above method embodiments. The embodiments of the present invention will not be repeated here.

[0125] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0126] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A fault location method, characterized in that, Applied to a server, the method includes: Receive alarm information sent by a first device; the first device includes multiple ports; For each of the plurality of ports, determine a first flow rate time series and a second flow rate time series for the port within a first time period; Wherein, the first flow rate time series is used to characterize the actual flow rate of the port during the first time period; the second flow rate time series is used to characterize the predicted flow rate of the port during the first time period. For each of the plurality of ports, if the similarity between the first flow rate time series and the second flow rate time series of the port is less than a similarity threshold, then the port is determined to be in a fault state.

2. The method according to claim 1, characterized in that, The first time period includes multiple sub-time periods; The first flow rate time series of the port is determined in the following way: Determine the flow rate of the port in each sub-time period; The first flow rate time series of the port is determined based on the flow rate of the port in each sub-time period.

3. The method according to claim 1, characterized in that, The second flow rate time series of the port is determined in the following way: Determine the third flow rate time series of the port's flow rate within the second time period; Wherein, the second time period is the period preceding the first time period; the third flow rate time series is used to characterize the actual flow rate of the port during the second time period; Based on the third traffic rate time series of the port, the second traffic rate time series of the port is predicted to provide support for global load balancing.

4. The method according to claim 1, characterized in that, The method further includes: If there is no port in the first device that is in a faulty state, determine a second device that performs the same task as the first device; Identify the port in the second device that is in a faulty state.

5. The method according to claim 1, characterized in that, The server is deployed with a similarity determination model, and the method further includes: The first and second flow rate time series of the port are input into the similarity determination model to obtain the similarity between the first and second flow rate time series output by the model.

6. A fault location device, characterized in that, The device includes: A receiving unit is used to receive alarm information sent by a first device; the first device includes multiple ports; The first determining unit is configured to determine, for each of the plurality of ports, a first flow rate time series and a second flow rate time series of the flow rate of the port within a first time period. Wherein, the first flow rate time series is used to characterize the actual flow rate of the port during the first time period; the second flow rate time series is used to characterize the predicted flow rate of the port during the first time period. The second determining unit is configured to determine that the port is in a fault state if the similarity between the first flow rate time series and the second flow rate time series of the port is less than a similarity threshold for each of the plurality of ports.

7. A fault location device, characterized in that, include: A processor and a communication interface; the communication interface is coupled to the processor, the processor being used to run computer programs or instructions to implement the fault location method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a computer, perform the fault location method as described in any one of claims 1-5.

9. A computer program product, characterized in that, The computer program product includes computer instructions that, when executed on a computer, cause the computer to perform the fault location method as described in any one of claims 1-5.

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