Link fault delimiting method and device based on relative entropy, electronic equipment and medium

Abstract

The embodiment of the invention discloses a link fault delimiting method and device based on relative entropy, electronic equipment and a medium. The method comprises the following steps: acquiring first link data in a normal time period and second link data in an abnormal time period, and respectively determining own time consumption of each piece of data in the first link data and the second link data; grouping each piece of data in the first link data according to the request name, and determining a time-consuming baseline of each group; performing preliminary detection on second link data according to the own time-consuming baseline, and dividing each fragment data in the second link data into abnormal fragment data and normal fragment data; and determining relative entropy of the abnormal fragment data and the normal fragment data in each dimension, and determining a link fault position according to the relative entropy. By adopting the technical scheme of the embodiment of the invention, the fault range is effectively reduced while the resource consumption is reduced, and precious troubleshooting time is won for operation and maintenance personnel.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of data processing, and in particular, to a link fault delimitation method, apparatus, electronic device, and medium based on relative entropy. Background Art

[0002] Link data is an event sequence consisting of a series of related operations recorded in a distributed system. Link data helps developers and operation and maintenance personnel understand how requests propagate in the system and identify the performance and health status of each component.

[0003] After a fault occurs, analyzing link data can help operation and maintenance personnel quickly locate the fault location and shorten the fault troubleshooting time. However, distributed systems usually have a large number of microservices, and the amount of call chain data is very large. Storing and processing these data requires high computing and storage capabilities, while fault analysis requires analyzing link data and outputting the fault location within a very short time, which poses a great challenge to the analysis of link data. Link data has dozens or even hundreds of dimensions. Existing link data analysis methods mainly include anomaly detection and fault delimitation for individual dimensions such as transaction codes and services, which can narrow the fault range to a certain extent, but there is still much room for improvement.

[0004] Therefore, how to effectively narrow the fault range of link data is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0005] Embodiments of the present invention provide a link fault delimitation method, apparatus, electronic device, and medium based on relative entropy, so as to effectively narrow the fault range while reducing resource consumption and gain valuable troubleshooting time for operation and maintenance personnel.

[0006] In a first aspect, embodiments of the present invention provide a link fault delimitation method based on relative entropy, including:

[0007] Obtain first link data in a normal period and second link data in an abnormal period, and respectively determine the self-consumption time of each segment data in the first link data and the second link data;

[0008] Group each segment data in the first link data by request name, and determine the self-consumption time baseline of each group;

[0009] Perform preliminary detection on the second link data according to the self-consumption time baseline, and divide each segment data in the second link data into abnormal segment data and normal segment data;

[0010] Determine the relative entropy of the abnormal segment data and the normal segment data in each dimension, and determine the link fault location according to the relative entropy.

[0011] In a second aspect, an embodiment of the present invention further provides a link fault localization device based on relative entropy, including:

[0012] A link data processing module, configured to obtain first link data during normal periods and second link data during abnormal periods, and respectively determine the self-consumption time of each segment data in the first link data and the second link data;

[0013] A self-consumption time baseline determination module, configured to group each segment data in the first link data by request name, and determine the self-consumption time baseline of each group;

[0014] A preliminary abnormal position determination module, configured to perform a preliminary detection on the second link data according to the self-consumption time baseline, and divide each segment data in the second link data into abnormal segment data and normal segment data;

[0015] An abnormal position determination module, configured to determine the relative entropy of the abnormal segment data and the normal segment data in each dimension, and determine the link fault position according to the relative entropy.

[0016] In a third aspect, an embodiment of the present invention further provides an electronic device, which includes:

[0017] One or more processors;

[0018] A storage device, configured to store one or more programs;

[0019] When the one or more programs are executed by the one or more processors, the one or more processors implement the link fault localization method based on relative entropy according to any embodiment of the present invention.

[0020] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the link fault localization method based on relative entropy according to any embodiment of the present invention.

[0021] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the link fault localization method based on relative entropy according to any embodiment of the present invention.

[0022] The embodiment of the present invention provides a method, device, electronic device and storage medium for link fault delimitation based on relative entropy, by determining the self-time baseline of each group of fragment data in the first link data; performing preliminary detection on the second link data based on the self-time baseline, and dividing each fragment data in the second link data into abnormal fragment data and normal fragment data; determining the relative entropy of the abnormal fragment data and the normal fragment data in each dimension, and determining the link fault location based on the relative entropy. The technical solution of the embodiment of the present invention is adopted, and the fragment data of the abnormal time period is detected based on the self-time baseline of the normal time period, and the abnormal fragment data is preliminarily determined; then the relative entropy on each dimension is determined to determine the fault location of the abnormal fragment data; while reducing resource consumption, the fault range is effectively reduced, and precious troubleshooting time is gained for operation and maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts. In the drawings:

[0024] Figure 1 is a flow chart of a link fault demarcation method based on relative entropy provided in an embodiment of the present invention;

[0025] Figure 2 is a flow chart of a link fault demarcation method based on relative entropy provided in an embodiment of the present invention;

[0026] Figure 3 It is a schematic diagram of a process for preliminarily determining a link data abnormality location provided in an embodiment of the present invention;

[0027] Figure 4 It is a schematic diagram of a process of determining a link data position based on relative entropy provided in an embodiment of the present invention;

[0028] Figure 5 is a structural schematic diagram of a link fault demarcation device based on relative entropy provided in an embodiment of the present invention;

[0029] Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. In addition, it should be noted that for the convenience of description, only the parts related to the present invention rather than all the structures are shown in the drawings.

[0031] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations (or steps) can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but it can also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0032] Among them, the acquisition, storage, use, and processing of data in the technical solution of this application all comply with the relevant regulations of national laws and regulations. It should be noted that in the embodiments of this application, some industry-existing solutions such as certain software, components, or models may be mentioned. They should be considered exemplary, and their purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0033] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here.

[0034] Embodiment 1

[0035] Figure 1 It is a flowchart of a link fault localization method based on relative entropy provided in an embodiment of the present invention. This embodiment is applicable to the situation of link fault localization based on relative entropy. The method of this embodiment can be executed by a link fault localization device based on relative entropy, and the device can be implemented in a hardware and / or software manner. The device can be configured in a server for link fault localization based on relative entropy. The method specifically includes the following steps:

[0036] S110. Obtain the first link data in the normal period and the second link data in the abnormal period, and respectively determine the self-consumption time of each segment data in the first link data and the second link data.

[0037] Among them, link data can refer to the processing information recorded within the scope of a single request, including data such as service calls and processing durations. Link data is a collection of a complete request lifecycle, spanning multiple services or components. For example, in a distributed call scenario, the client initiates a request, which first reaches the load balancer, then passes through the authentication service, billing service, and requests resources, and finally returns the result. The data in this process can be recorded by link data. After a failure occurs, analyzing the link data can help the operation and maintenance personnel quickly locate the failure location and shorten the failure troubleshooting time.

[0038] A link data is composed of multiple fragment data. The fragment data represents a single operation or the execution of an event in the link data. Each fragment data corresponds to a specific work unit, which records the time from start to end, the specific service where the event occurs, the status of the operation (such as success or failure), and related metadata. Among them, the operation status includes success or failure; the metadata includes but is not limited to error information, tags, and logs.

[0039] The self-consumption time can refer to the time when the fragment data executes the task operation; a fragment data includes a start time and an end time, and the self-consumption time of each fragment data can be determined through the start time and the end time.

[0040] In the embodiments of the present invention, the first link data in the normal period and the second link data in the abnormal period are obtained, and the self-consumption time of each fragment data in the first link data and the second link data is respectively determined through the start time and the end time included in each fragment data.

[0041] S120. Group each fragment data in the first link data by the request name, and determine the self-consumption time baseline of each group.

[0042] Among them, the self-consumption time baseline can refer to the upper limit range of the self-consumption time of each group of fragment data under normal circumstances. Different request names often correspond to different types, and the self-consumption time of the fragment data varies greatly and is not at the same level; the self-consumption time deviation of the fragment data with the same request name is relatively small. Therefore, grouping calculation is required. In the embodiments of the present invention, each fragment data in the first link data is grouped by the request name to determine the self-consumption time baseline of each group of fragment data.

[0043] S130. Perform a preliminary detection on the second link data according to the self-consumption time baseline, and divide each fragment data in the second link data into abnormal fragment data and normal fragment data.

[0044] After determining the baseline of the self-consumption time of each group of segment data in the normal period, the second-link data in the abnormal period is detected based on the baseline of the self-consumption time to determine the normal segment data and the abnormal segment data in the second-link data. The abnormal segment data and the normal segment data in the embodiments of the present invention are the segment data in the second-link data in the abnormal period.

[0045] In an alternative solution of the embodiments of the present invention, the second-link data can be grouped by request name, and the segment data in each group is detected based on the non-self-consumption time baseline of each group to determine whether it is abnormal segment data. For example, the baseline of the self-consumption time of the segment data in group A of the first-link data is B, and if the self-consumption time of the segment data C in the second-link data that belongs to group A exceeds B, it can be determined that the segment data C in the second-link data is abnormal segment data; otherwise, it is normal segment data.

[0046] In another alternative solution of the embodiments of the present invention, the request names of the segment data in the second-link data can also be directly corresponded to the request names in the first-link data, and whether each segment data in the second-link data is abnormal segment data is determined based on the baseline of the self-consumption time corresponding to the request name. For example, the baseline of the self-consumption time of the segment data with the request name A is B, and the baseline of the self-consumption time of the segment data C with the same request name A in the second-link data is D. If D is greater than B, it can be determined that the segment data C in the second-link data is abnormal segment data; otherwise, it is normal segment data.

[0047] In the embodiments of the present invention, the segment data in the second-link data in the abnormal period is preliminarily detected through the baseline of the self-consumption time of the first-link data in the normal period to determine the abnormal segment data. Optionally, the baseline of the self-consumption time is dynamically calculated and can change with factors such as the self-consumption time and the request name; compared with setting the threshold of the self-consumption time of the segment data in the prior art, the detection accuracy rate of the abnormal segment data is higher.

[0048] S140. Determine the relative entropy of the abnormal segment data and the normal segment data in each dimension, and determine the link failure position based on the relative entropy.

[0049] Among them, after determining the abnormal segment data, the position of the abnormal segment data also needs to be determined; in the embodiments of the present invention, by determining the relative entropy of the abnormal segment data and the normal segment data in the second-link data in each dimension, the dimension with the largest relative entropy value is determined, and thus the link failure position can be determined.

[0050] The embodiment of the present invention provides a link fault delimitation method based on relative entropy, which obtains the first link data of the normal period and the second link data of the abnormal period, and determines the self-time consumption of each fragment data in the first link data and the second link data respectively; groups each fragment data in the first link data according to the request name, and determines the self-time consumption baseline of each group; performs a preliminary detection on the second link data according to the self-time consumption baseline, and divides each fragment data in the second link data into abnormal fragment data and normal fragment data; determines the relative entropy of the abnormal fragment data and the normal fragment data in each dimension, and determines the link fault location according to the relative entropy. The technical solution of the embodiment of the present invention is adopted, and the fragment data of the abnormal period is detected based on the self-time consumption baseline of the normal period, and the abnormal fragment data is preliminarily determined; then the relative entropy in each dimension is determined to determine the fault location of the abnormal fragment data; while reducing resource consumption, the fault range is effectively reduced, and precious troubleshooting time is gained for operation and maintenance personnel.

[0051] Embodiment 2

[0052] Figure 2 The flowchart of a link fault delimitation method based on relative entropy provided in an embodiment of the present invention. The embodiment of the present invention further optimizes the above embodiment on the basis of the above embodiment, and the embodiment of the present invention can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the link fault demarcation method based on relative entropy provided in the embodiment of the present invention may include the following steps:

[0053] S210: Acquire first link data in a normal period and second link data in an abnormal period, and respectively determine the time consumption of each fragment data in the first link data and the second link data.

[0054] S220: Group each fragment data in the first link data according to the request name, and determine the time-consuming baseline of each group.

[0055] S230 , performing preliminary detection on the second link data according to the self-time-consuming baseline, and dividing each segment data in the second link data into abnormal segment data and normal segment data.

[0056] Among them, see Figure 3 After the first link data in the normal period is grouped and its own time-consuming baseline is determined, a preliminary detection is performed on the fragment data in the second link data according to the own time-consuming baseline to determine whether it is abnormal fragment data.

[0057] As an optional but non-limiting implementation, the preliminary detection of the second link data based on the self-consumption baseline, and the division of each piece of data in the second link data into abnormal piece data and normal piece data, includes but is not limited to steps A1 - A3:

[0058] Step A1: Determine the self-consumption baseline corresponding to the current piece of data according to the request name of the current piece of data in the second link data.

[0059] Step A2: If the self-consumption of the current piece of data is greater than the self-consumption baseline corresponding to the current piece of data, determine that the current piece of data is abnormal piece data; otherwise, the current piece of data is normal piece data.

[0060] Step A3: Traverse each piece of data in the second link data in sequence to divide each piece of data in the second link data into abnormal piece data and normal piece data.

[0061] Taking the preliminary detection based on the self-consumption baseline corresponding to the request name of each piece of data in the second link data as an example, determine the request name of the first piece of data in the second link data, correspond the request name to the request name in the first link data, and determine the self-consumption baseline of the corresponding request name. Based on the self-consumption baseline and the self-consumption of the first piece of data, determine whether the first piece of data is abnormal data. And so on, traverse all the pieces of data in the second link data to divide each piece of data in the second link data into abnormal piece data and normal piece data.

[0062] As an optional but non-limiting implementation, the division of each piece of data in the second link data into abnormal piece data further includes:

[0063] If the current piece of data has an abnormal mark, divide the current piece of data into abnormal piece data.

[0064] In an optional solution of the embodiment of the present invention, if there is an abnormal mark in the current piece of data in the second link data, it can be directly determined that the current piece of data is abnormal piece data. If not marked, it is necessary to determine whether it is abnormal piece data according to the self-consumption baseline.

[0065] S240: Determine the distribution of abnormal piece data and normal piece data in each dimension, and determine the relative entropy in each dimension.

[0066] Among them, divide the abnormal piece data and normal piece data in the second link data in terms of dimensions, and determine the relative entropy in each dimension.

[0067] S250. Determine the target relative entropy greater than the preset relative entropy threshold, and determine the link failure location based on the target relative entropy.

[0068] Among them, after determining the relative entropy in each dimension, compare the relative entropy in each dimension with the preset relative entropy threshold to determine the target relative entropy greater than the preset relative entropy threshold, so as to determine the corresponding dimension, and thus determine the link failure location. The dimension may refer to the attribute field of the segment data, including but not limited to IP and the peer IP, etc.

[0069] As an optional but non-limiting implementation manner, the determining the target relative entropy greater than the preset relative entropy threshold and determining the link failure location based on the target relative entropy includes but is not limited to steps B1 - B2:

[0070] Step B1: Determine the target relative entropy greater than the preset relative entropy threshold from the relative entropy in each dimension, and determine the target dimension corresponding to the target relative entropy.

[0071] Step B2: Determine the distribution of the abnormal segment data in the target dimension, and determine the link failure location based on the distribution.

[0072] Among them, see Figure 4 , determine the relative entropy in each dimension, compare the relative entropy with the preset relative entropy threshold, determine the target relative entropy greater than the preset relative entropy threshold, so as to determine the target dimension corresponding to the target relative entropy. After determining the target dimension, determine the link failure location according to the distribution of the abnormal segment data in the target dimension.

[0073] As an optional but non-limiting implementation manner, the determining the distribution of the abnormal segment data in the target dimension and determining the link failure location based on the distribution includes but is not limited to steps C1 - C2:

[0074] Step C1: Determine the distribution of the abnormal segment data in the sub-dimensions of the target dimension.

[0075] Step C2: Determine the target sub-dimension where the distribution aggregates according to the distribution, and determine the link failure location according to the position of the target sub-dimension.

[0076] Among them, determine the distribution of the abnormal segment data on the sub-dimensions of the target dimension to determine the link failure location. For example, taking the host IP-A address dimension as an example, determine the distribution of the abnormal segment data and the normal segment data in the second link data on the host IP-A address, and determine the relative entropy; and if the relative entropy is greater than the preset relative entropy threshold, it can be determined that the link failure location is on the host IP-A address dimension. At this time, the link failure location can be determined. If there are still sub-dimensions in the host IP-A address dimension at this time, the specific link failure location can be determined according to the distribution of the abnormal segment data on the sub-dimensions. For example, taking the hosts of the host IP-A address including host 1, host 2, and host 3 as an example, the specific link failure location can be determined by determining the distribution of the abnormal segment data on the 3 hosts. For example, if 6 abnormal segment data are distributed in host 1, 1 abnormal segment data is distributed in host 2, and 3 abnormal segment data are distributed in host 3 among 10 abnormal segment data, it can be determined that the link failure location is located in host 1.

[0077] In the embodiments of the present invention, a link failure delimitation method based on relative entropy is provided. On the basis of determining abnormal segment data according to its own time-consuming baseline, by determining the relative entropy of the abnormal segment data and the normal segment data in the second link data in each dimension, the link failure location is determined. Through its own time-consuming baseline and the relative entropy in each dimension, the embodiments of the present invention effectively narrow the failure range while reducing resource consumption, and strive for valuable troubleshooting time for operation and maintenance personnel.

[0078] Embodiment III

[0079] Figure 5 It is a schematic structural diagram of a link failure delimitation device based on relative entropy provided in the embodiments of the present invention. The technical solution of this embodiment is applicable to the situation of link failure delimitation based on relative entropy. The device can be implemented by software and / or hardware, and is generally integrated in any electronic device with network communication functions. The electronic device includes but is not limited to: devices such as servers, computers, and personal digital assistants. As Figure 5 shown, the link failure delimitation device based on relative entropy provided in this embodiment may include: a link data processing module 510, an own time-consuming baseline determination module 520, an abnormal location preliminary determination module 530, and an abnormal location determination module 540;

[0080] The link data processing module 510 is configured to obtain the first link data in the normal period and the second link data in the abnormal period, and respectively determine the own time-consuming of each segment data in the first link data and the second link data;

[0081] The self - time - consumption baseline determination module 520 is used to group each piece of fragment data in the first link data by the request name and determine the self - time - consumption baseline of each group;

[0082] The preliminary abnormal position determination module 530 is used to preliminarily detect the second link data according to the self - time - consumption baseline and divide each piece of fragment data in the second link data into abnormal fragment data and normal fragment data;

[0083] The abnormal position determination module 540 is used to determine the relative entropy of the abnormal fragment data and the normal fragment data in each dimension and determine the link failure position according to the relative entropy.

[0084] Based on the above - mentioned embodiments, optionally, the preliminary abnormal position determination module specifically is used for:

[0085] Determine the self - time - consumption baseline corresponding to the current fragment data according to the request name of the current fragment data in the second link data;

[0086] If the self - time - consumption of the current fragment data is greater than the self - time - consumption baseline corresponding to the current fragment data, determine that the current fragment data is abnormal fragment data; otherwise, the current fragment data is normal fragment data;

[0087] Traverse each piece of fragment data in the second link data in sequence to divide each piece of fragment data in the second link data into abnormal fragment data and normal fragment data.

[0088] Based on the above - mentioned embodiments, optionally, the preliminary abnormal position determination module is further specifically used for:

[0089] If the current fragment data has an abnormal mark, divide the current fragment data into abnormal fragment data.

[0090] Based on the above - mentioned embodiments, optionally, the abnormal position determination module specifically is used for:

[0091] Determine the distribution of the abnormal fragment data and the normal fragment data in each dimension and determine the relative entropy in each dimension;

[0092] Determine the target relative entropy greater than the preset relative entropy threshold and determine the link failure position according to the target relative entropy.

[0093] Based on the above - mentioned embodiments, optionally, the abnormal position determination module is further specifically used for:

[0094] Determine the target relative entropy greater than the preset relative entropy threshold from the relative entropies in each dimension and determine the target dimension corresponding to the target relative entropy;

[0095] Determine the distribution of the abnormal segment data in the target dimension, and determine the link failure location according to the distribution situation.

[0096] Based on the above embodiments, optionally, the abnormal location determination module is further specifically configured to:

[0097] Determine the distribution of the abnormal segment data in the sub-dimension of the target dimension;

[0098] Determine the target sub-dimension where the distribution aggregates according to the distribution situation, and determine the link failure location according to the position of the target sub-dimension.

[0099] The link failure delimitation device based on relative entropy provided in the embodiments of the present invention can execute the link failure delimitation method based on relative entropy provided in any of the above embodiments of the present invention, and has the corresponding functions and beneficial effects for executing the link failure delimitation method based on relative entropy. For the detailed process, refer to the related operations of the link failure delimitation method based on relative entropy in the foregoing embodiments.

[0100] Embodiment 4

[0101] Figure 6 It is a schematic structural diagram of an electronic device provided in an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0102] As Figure 6 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0103] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0104] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the relative entropy-based link failure delimitation method.

[0105] In some embodiments, the relative entropy-based link failure delimitation method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the relative entropy-based link failure delimitation method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the relative entropy-based link failure delimitation method by any other suitable means (e.g., by means of firmware).

[0106] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), system-on-chip systems (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0107] A computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on a remote machine or server.

[0108] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0109] In order to provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0110] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0111] A computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0112] Embodiment 5

[0113] The embodiment of the present invention also provides a computer program product, including a computer program which, when executed by a processor, implements the link fault delimitation method based on relative entropy provided in any embodiment of the present application.

[0114] In the process of implementing the computer program product, computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or can be connected to an external computer (e.g., connected through the Internet using an Internet service provider).

[0115] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitations are imposed herein.

[0116] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub - combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A link fault demarcation method based on relative entropy, characterized in that: The method comprises: Acquire the first link data in a normal period and the second link data in an abnormal period, and respectively determine the time consumption of each fragment data in the first link data and the second link data; Group each fragment data in the first link data according to the request name, and determine the time-consuming baseline of each group; Performing a preliminary detection on the second link data according to the self-time-consuming baseline, and dividing each segment data in the second link data into abnormal segment data and normal segment data; Determine the relative entropy of the abnormal segment data and the normal segment data in each dimension, and determine the link fault location according to the relative entropy.

2. The method according to claim 1, characterized in that The performing preliminary detection on the second link data according to the own time-consuming baseline and dividing each segment data in the second link data into abnormal segment data and normal segment data includes: Determine, according to the request name of the current segment data in the second link data, a time-consuming baseline corresponding to the current segment data; If the self-consuming time of the current segment data is greater than the self-consuming time baseline corresponding to the current segment data, the current segment data is determined to be abnormal segment data; otherwise, the current segment data is normal segment data; Each fragment data in the second link data is traversed in sequence to divide each fragment data in the second link data into abnormal fragment data and normal fragment data.

3. The method according to claim 1, characterized in that The step of dividing each fragment data in the second link data into abnormal fragment data further includes: If there is an abnormal mark in the current segment data, the current segment data is divided into abnormal segment data.

4. The method according to claim 1, characterized in that: The determining the relative entropy of the abnormal segment data and the normal segment data in each dimension, and determining the link fault location according to the relative entropy, includes: Determine the distribution of abnormal fragment data and normal fragment data in each dimension, and determine the relative entropy in each dimension; A target relative entropy greater than a preset relative entropy threshold is determined, and a link fault location is determined based on the target relative entropy.

5. The method according to claim 4, characterized in that The determining of a target relative entropy greater than a preset relative entropy threshold, and determining a link fault location according to the target relative entropy, includes: Determine a target relative entropy greater than a preset relative entropy threshold from the relative entropies on each dimension, and determine a target dimension corresponding to the target relative entropy; The distribution of the abnormal segment data in the target dimension is determined, and the link fault location is determined based on the distribution.

6. The method according to claim 5, characterized in that The determining the distribution of the abnormal segment data in the target dimension, and determining the link fault location according to the distribution, includes: Determine the distribution of abnormal fragment data in the sub-dimensions of the target dimension; A target sub-dimension of distribution aggregation is determined according to the distribution situation, and a link fault location is determined according to the location of the target sub-dimension.

7. A link fault demarcation device based on relative entropy, characterized in that: The device comprises: A link data processing module, used to obtain the first link data in a normal period and the second link data in an abnormal period, and respectively determine the time consumption of each fragment data in the first link data and the second link data; A self-time-consuming baseline determination module, used to group each fragment data in the first link data according to the request name, and determine the self-time-consuming baseline of each group; An abnormal position preliminary determination module is used to perform preliminary detection on the second link data according to the self-time-consuming baseline, and divide each segment data in the second link data into abnormal segment data and normal segment data; The abnormal position determination module is used to determine the relative entropy of the abnormal segment data and the normal segment data in each dimension, and determine the link fault position according to the relative entropy.

8. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the link fault demarcation method based on relative entropy as described in any one of claims 1-6.

9. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to perform the link fault demarcation method based on relative entropy as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the link fault demarcation method based on relative entropy according to any one of claims 1 to 6.

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