Gateway traffic prediction method, fusing method, device, equipment and storage medium

Abstract

The present disclosure relates to the technical field of traffic prediction, and particularly provides a gateway traffic prediction method, a fuse method, a device, equipment and a storage medium. A gateway traffic prediction method comprises: obtaining a heat matrix diagram, wherein the heat matrix diagram takes an encoding value of a service information index vector of a network element as a first coordinate axis, takes a time slice as a second coordinate axis, and takes a color of a pixel point to represent a traffic value of the corresponding network element at the corresponding time slice; and predicting network element traffic based on the heat matrix diagram. A micro-service fuse method obtains network element traffic predicted according to the gateway traffic prediction method; and executes a fuse strategy according to the network element traffic. The technical solution of the present disclosure can improve the accuracy of network element traffic prediction and reduce fuse misjudgment.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of traffic prediction, and in particular to a gateway traffic prediction method, a fuse method, a device, equipment and a storage medium. BACKGROUND

[0002] In the prior art of microservice architecture, the gateway fuse technology is mainly based on adaptive fuse technology for microservice access data. According to the real-time data volume of service access, the fuse strategy is dynamically adjusted, and when the service access data volume exceeds a certain threshold, the system may automatically trigger the fuse operation to prevent service overload or collapse. The specific adaptive fuse strategy includes dynamically adjusting the fuse threshold, automatically switching the fuse state according to the data volume change, etc.

[0003] However, since the adaptive fuse is decided by the system based on real-time data, and the network element traffic monitoring data has problems such as poor real-time performance and data detection errors, it is easy to cause the occurrence of fuse misjudgment. SUMMARY

[0004] The present disclosure is proposed in view of the above problems. The present disclosure provides a gateway traffic prediction method, a fuse method, a device, equipment and a storage medium.

[0005] According to one aspect of the present disclosure, a gateway traffic prediction method is provided, the method comprising:

[0006] obtaining a heat matrix diagram, wherein the heat matrix diagram takes the encoding value of the service information index vector of the network element as the first coordinate axis, takes the time slice as the second coordinate axis, and takes the color of the pixel point to represent the traffic value of the corresponding network element at the corresponding time slice;

[0007] predicting the network element traffic based on the heat matrix diagram.

[0008] In addition, according to the gateway traffic prediction method according to one aspect of the present disclosure, obtaining a heat matrix diagram comprises:

[0009] obtaining the service information index vector of a plurality of network elements under the gateway and the traffic value of the network elements in a historical time slice sequence;

[0010] generating a heat matrix diagram based on the service information index vector of the plurality of network elements under the gateway and the traffic value of the network elements in the historical time slice sequence.

[0011] In addition, according to the gateway traffic prediction method according to one aspect of the present disclosure, the plurality of network elements under the gateway are the top M network elements in terms of traffic frequency under the gateway, wherein M is greater than or equal to 2.

[0012] In addition, according to a gateway traffic prediction method according to an aspect of the present disclosure, the service information index vector corresponds to service information including multiple information of a service type, a technology type, a capability provider, a belonging capability pool, and a geographical location.

[0013] and / or,

[0014] The service information index vector is a multi-dimensional index vector.

[0015] In addition, according to a gateway traffic prediction method according to an aspect of the present disclosure, the predicting of the gateway element traffic based on the heat matrix diagram comprises:

[0016] The predicting of the gateway element traffic based on the heat matrix diagram is performed by using a pre-trained traffic prediction network model, wherein the traffic prediction network model is obtained by training a neural network model by using a sample, and the sample includes a sample heat matrix diagram and a corresponding gateway element traffic label.

[0017] In addition, according to a gateway traffic prediction method according to an aspect of the present disclosure, the neural network model includes a convolutional neural network and a long short-term memory network.

[0018] The convolutional neural network is used to extract a feature vector of the heat matrix diagram.

[0019] The long short-term memory network is used to predict the gateway element traffic according to the feature vector based on a time sequence.

[0020] According to another aspect of the present disclosure, a microservice fusing method is provided, and the method comprises:

[0021] The gateway element traffic predicted by any of the gateway traffic prediction methods according to aspects of the present disclosure is obtained.

[0022] A fusing strategy is executed according to the gateway element traffic.

[0023] In addition, according to a microservice fusing method according to another aspect of the present disclosure, the method comprises:

[0024] The traffic proportion of a microservice application is determined according to the gateway element traffic.

[0025] The application weight of the microservice application is adjusted according to the traffic proportion of the microservice application, wherein the increase proportion of the application weight of the microservice application is positively correlated with the traffic proportion of the microservice application.

[0026] According to still another aspect of the present disclosure, a gateway traffic prediction device is provided, and the device comprises:

[0027] The acquisition module is configured to acquire a heat matrix diagram, wherein the heat matrix diagram takes an encoded value of a service information index vector of a network element as a first coordinate axis, takes a time slice as a second coordinate axis, and takes a color of a pixel point to represent a traffic value of a corresponding network element at a corresponding time slice.

[0028] The prediction module is configured to predict network element traffic based on the heat matrix diagram.

[0029] According to yet another aspect of the present disclosure, a micro-service fuse device is provided, and the device comprises:

[0030] The acquisition module is configured to acquire a heat matrix diagram, wherein the heat matrix diagram takes an encoded value of a service information index vector of a network element as a first coordinate axis, takes a time slice as a second coordinate axis, and takes a color of a pixel point to represent a traffic value of a corresponding network element at a corresponding time slice.

[0031] The prediction module is configured to predict network element traffic based on the heat matrix diagram.

[0032] The fuse module is configured to execute a fuse strategy according to the network element traffic.

[0033] According to yet another aspect of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the above-mentioned methods.

[0034] According to yet another aspect of the present disclosure, a computer-readable storage medium is provided, and the computer-readable storage medium stores a computer program / instruction, wherein the computer program / instruction is executed by a processor to implement the steps of any of the above-mentioned methods.

[0035] According to yet another aspect of the present disclosure, a computer program product is provided, and the computer program product comprises a computer program / instruction, wherein the computer program / instruction is executed by a processor to implement the steps of any of the above-mentioned methods.

[0036] As will be described in detail below, according to the gateway traffic prediction method, the fuse method, the device, the equipment, and the storage medium of the embodiments of the present disclosure, a heat matrix diagram is acquired, and network element traffic is predicted based on the heat matrix diagram, wherein the heat matrix diagram takes an encoded value of a service information index vector of a network element as a first coordinate axis, takes a time slice as a second coordinate axis, and takes a color of a pixel point to represent a traffic value of a corresponding network element at a corresponding time slice, thereby improving the accuracy of network element traffic prediction and reducing fuse misjudgment.

[0037] It is to be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further explanation of the subject technology. BRIEF DESCRIPTION OF DRAWINGS

[0038] The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which:

[0039] Figure 1 is a flow chart illustrating a gateway traffic prediction method according to an embodiment of the present disclosure.

[0040] Figure 2 is a flow chart further illustrating a generation process of a heat matrix graph in the gateway traffic prediction method according to an embodiment of the present disclosure.

[0041] Figure 3 is a schematic diagram further illustrating a heat matrix graph in the gateway traffic prediction method according to an embodiment of the present disclosure.

[0042] Figure 4 is a flow chart further illustrating a microservice fusing method according to an embodiment of the present disclosure.

[0043] Figure 5 is a block diagram illustrating a gateway traffic prediction apparatus according to an embodiment of the present disclosure.

[0044] Figure 6 is a block diagram illustrating a microservice fusing apparatus according to an embodiment of the present disclosure.

[0045] Figure 7 is a schematic diagram illustrating a computer program product according to an embodiment of the present disclosure.

[0046] Figure 8 is a hardware block diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0047] In order to make the objectives, technical solutions and advantages of the present disclosure more apparent, the following will describe example embodiments according to the present disclosure in detail with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the example embodiments described herein.

[0048] Referring to Figure 1 , a gateway traffic prediction method, the method comprising:

[0049] S101, obtaining a heat matrix graph, wherein the multi-dimensional index vector heat matrix graph takes the encoded value of a service information index vector of a network element as a first coordinate axis, takes a time slice as a second coordinate axis, and takes the color of a pixel point to represent the traffic value of the corresponding network element at the corresponding time slice.

[0050] The gateway traffic prediction method of the embodiments of the present disclosure can be applied to the traffic prediction of a gateway. For example, the traffic prediction of an apisix gateway.

[0051] In step S101 of the embodiments of the present disclosure, the first coordinate axis can be a vertical coordinate, and the corresponding second coordinate axis is a horizontal coordinate. The first coordinate axis can be a horizontal coordinate, and the corresponding second coordinate axis is a vertical coordinate.

[0052] In step S101 of the embodiments of the present disclosure, the encoding value of the service information index vector of the network element refers to the encoding value of the index vector of the service information of the network element. The index vector of the service information can be a multi-dimensional index vector, and the service information can include multiple information such as service type, technology type, capability provider, belonging capability pool, and geographical location. Taking the service information of the network element including service type, capability provider, geographical location, belonging capability pool, and technology type as an example, the index vector structure of the service information can be (service type, capability provider, geographical location, capability pool, technology type). By encoding the index vector of the service information, an encoding value is formed. The encoding value can be mapped to the corresponding index vector of the service information, and can be mapped to the corresponding service information. Cartesian product or hash encoding and other encoding methods can be used to convert the encoding of each dimension into an encoding value. The heat matrix diagram can intuitively reflect the traffic situation of different service combinations under different time slices.

[0053] In step S101 of the embodiments of the present disclosure, the time axis is divided into several time periods, each time period can be referred to as a time slice. For example, a 15-minute granularity can be used as a time slice, and the traffic value of the network element in the time slice can be used as the traffic value of the network element in the time slice.

[0054] In step S101 of the embodiments of the present disclosure, the color of the pixel point represents the traffic value of the corresponding network element in the corresponding time slice. For example, the encoding value of the index vector of the service information of the network element W is G, the traffic in the first time slice is L, and the color corresponding to the traffic L is S. Therefore, the color of the pixel point corresponding to the second coordinate axis coordinate of the first time slice as the second coordinate axis and the first coordinate axis coordinate of the encoding value G as the first coordinate axis is S.

[0055] Referring to Figure 2 In one embodiment, step S101 includes:

[0056] S201, obtaining the service information index vector of a plurality of network elements under a gateway and the traffic value of the network elements in a historical time slice sequence.

[0057] In step S201 of the embodiment of the present disclosure, the multiple network elements under the gateway refer to multiple network elements under the gateway to be predicted traffic, which can be determined according to actual needs, for example, the multiple network elements under the gateway can be all network elements under the gateway, for example, the multiple network elements under the gateway can be the top M network elements in terms of traffic frequency, and M is greater than or equal to 2, for example, M is set to 50.

[0058] In step S201 of the embodiment of the present disclosure, the traffic information index vector of the network element refers to the index vector of the traffic information of the network element.

[0059] In step S202 of the embodiment of the present disclosure, a heat matrix diagram is generated based on the traffic information index vectors of the multiple network elements under the gateway and the traffic values of the network elements in the historical time slicing sequence.

[0060] In step S202 of the embodiment of the present disclosure, a heat matrix diagram is generated based on the traffic information index vectors of the multiple network elements under the gateway and the traffic values of the network elements in the historical time slicing sequence.

[0061] Taking the gateway as the apisix gateway, the time slicing as a 15-minute granularity time slicing, and the multiple network elements under the gateway as the top 50 network elements in terms of traffic frequency as an example; the log information of the apisix gateway includes a log information timestamp, a request type, a response status code, a request source and the like, the apisix gateway collects log information, uses a plug-in elasticsearch-logger to report the collected log information to an Elasticsearch engine, and the system extracts log information from the Elasticsearch engine, extracts relevant information according to gateway traffic monitoring, and can know that the relevant information can be necessary clear, converted and feature extracted. Specifically, taking a 15-minute granularity as a time slicing, selecting the top 50 network elements in terms of traffic frequency, taking the encoding value of the index vector of the traffic information of each network element as the vertical coordinate, and taking the traffic value passed every 15 minutes as the horizontal coordinate, 96 15-minute heat matrix diagrams are generated in a day. Referring to Figure 3 , the heat matrix diagram 300 each pixel point of different colors represents the traffic passed by a network element in the 15 minutes. A two-dimensional index vector (y, x) is used to represent the pixel point in the picture, where y is the vertical coordinate and x is the horizontal coordinate.

[0062] The service (label) information can be extracted from the apisix gateway configuration, and the ordinate coding is performed using a multi-dimensional index vector. For example, the network elements can be classified according to service scenarios, capability pools, capability providers, etc. For example, service type: network service, cloud computing, big data analysis, etc. Capability provider: headquarters, provincial company, other manufacturers, etc. Geographic location: network elements are distributed in different geographic locations, such as data centers, edge computing nodes, etc. Technology type: such as 5G, 4G, IoT, etc. Belonging to the capability pool: such as planning domain, construction domain, optimization domain, maintenance domain, etc. Based on the above classification dimensions, a multi-dimensional index vector can be defined to represent each network element information. The multi-dimensional index vector can include multiple elements, and each element corresponds to a classification dimension. The multi-dimensional index vector structure can be (service type, capability provider, geographic location, capability pool, technology type).

[0063] In step S102, the network element traffic is predicted based on the multi-dimensional index vector heat matrix diagram.

[0064] In step S102 of the embodiments of the present disclosure, the network element traffic is predicted based on the heat matrix diagram. It should be understood that the network element traffic is the traffic of the network element under the gateway, which can be implemented by using a neural network model and other related technical solutions to predict the network element traffic by using pictures. For example, a pre-trained traffic prediction network model can be used to predict the network element traffic based on the multi-dimensional index vector heat matrix diagram, wherein the multi-dimensional index vector traffic prediction network model is obtained by training a neural network model using samples, and the multi-dimensional index vector samples include sample heat matrix diagrams and corresponding network element traffic labels. In order to improve the training effect, recent (for example: the past three months) data can be used as samples for training. Specifically, a series of matrix heat maps with time information can be collected as samples, and features can be extracted by using multi-modal input, multi-task learning or attention mechanism, etc. The extracted features or flattened image data are arranged in time sequence to form a feature sequence arranged in time sequence. These feature sequences are used as the input of the LSTM model, and a sliding window or other techniques are used to construct a fixed-length subsequence. The LSTM model is trained using the picture sequence with time sequence and the corresponding labels, and the weights of the model are updated by using the back propagation algorithm and the optimizer. In the embodiments of the present disclosure, other neural network models can also be used for implementation, such as other time series convolutional neural network models.

[0065] The traffic prediction network model of the embodiments of the present disclosure can use a lightweight prediction model based on the LSTM attention mechanism, and use a lightweight plugin embedding technology to embed the traffic prediction network model into the APISIX gateway, so that it can update the gateway weight information in real time, and ensure that the gateway can quickly respond to traffic changes. The pluggable lightweight prediction model makes the gateway easy to expand and maintain, and meets the changing business needs.

[0066] In an embodiment, the multi-dimensional index vector neural network model comprises a convolutional neural network and a long short-term memory network (LSTM); the multi-dimensional index vector convolutional neural network is used to extract a feature vector of a multi-dimensional index vector heat matrix graph; and the multi-dimensional index vector long short-term memory network is used to predict a traffic of a network element according to a time series-based feature vector. The disclosure embodiment considers the server performance requirement and selects an LSTM attention mechanism.

[0067] The gateway traffic prediction method of the disclosure embodiment expresses the traffic distribution of a plurality of network elements under a time slicing sequence through a heat matrix graph, can better associate the topology between the plurality of network elements, is convenient for capturing the spatial and temporal correlation of the network elements, and, in combination with a service information index vector, is helpful for capturing the correlation between the gateway traffic and the service characteristics, so that the network element traffic predicted based on the heat matrix graph is more accurate, and the accuracy of the traffic prediction is improved.

[0068] The gateway traffic prediction method of the disclosure embodiment adds a service index vector, so that the traffic prediction network model trained by using a sample heat matrix graph and a corresponding network element traffic label can better understand the service logic behind the traffic, improve the prediction ability of the model for the same type of gateway traffic, and thus make more accurate traffic prediction.

[0069] Referring to Figure 4 The disclosure embodiment further provides a microservice fusing method, and the multi-dimensional index vector method comprises:

[0070] S401, acquiring a network element traffic predicted according to any gateway traffic prediction method of the disclosure embodiment.

[0071] S402, executing a fusing strategy according to the multi-dimensional index vector network element traffic.

[0072] The fusing strategy of the disclosure embodiment can be any fusing strategy based on the network element traffic, and the disclosure embodiment does not make any limitation.

[0073] In an embodiment, the microservice fusing method further comprises: determining a traffic proportion of a microservice application according to the multi-dimensional index vector network element traffic; and adjusting an application weight of the microservice application according to the traffic proportion of the multi-dimensional index vector microservice application, wherein the increase proportion of the application weight of the microservice application is positively correlated with the traffic proportion of the microservice application.

[0074] According to the predicted network element traffic, the traffic proportion consumed by each microservice application in the next period can be predicted, and the application weight of the microservice application is adjusted according to the traffic proportion consumed by each microservice application in the next period. After adjusting the application weight of each microservice application, the gateway can be sent, and the gateway configures the upstream component to distribute the weight proportion of the microservice application. For example, the initialized weight proportion of the microservice application is 1, the application weight proportion of the microservice application with a traffic proportion of the first 20% is increased by 100%, the application weight proportion of the microservice application with a traffic proportion of the first 20%-50% is increased by 50%, and the application weight proportion of the microservice application with a traffic proportion of the last 20% is reduced by 50%, and the minimum value is 1, to ensure dynamic balance. The prediction information can be fed back to the permission system, and for a user with long-term malicious access, the account will be recovered and other restrictions will be taken.

[0075] In some cases, according to the predicted network element traffic, the malicious access of a certain user to the microservice in the next period can be predicted. If it is predicted that a certain user may maliciously access the microservice in the next period, an alarm information can be sent to the administrator of the permission system, prompting the risk level and suggesting appropriate measures.

[0076] Referring to Figure 5 The exemplary embodiments of the present disclosure also provide a gateway traffic prediction device, and the multi-dimensional index vector device comprises:

[0077] The acquisition module 501 acquires the heat matrix diagram, wherein the multi-dimensional index vector heat matrix diagram takes the encoding value of the service information index vector of the network element as the first coordinate axis, takes the time slice as the second coordinate axis, and takes the color of the pixel point to represent the traffic value of the corresponding network element in the corresponding time slice.

[0078] The prediction module 502 predicts the network element traffic based on the multi-dimensional index vector heat matrix diagram.

[0079] In one embodiment, when the acquisition module 501 acquires the heat matrix diagram, it is specifically used for: acquiring the service information index vector of a plurality of network elements under the gateway and the traffic value thereof in a historical time slice sequence; and generating the heat matrix diagram based on the service information index vector of the plurality of network elements under the multi-dimensional index vector gateway and the traffic value thereof in the historical time slice sequence.

[0080] In one embodiment, the plurality of network elements under the multi-dimensional index vector gateway are the top M network elements in terms of the number of traffic under the multi-dimensional index vector gateway, wherein M is greater than or equal to 2.

[0081] In one embodiment, the service information corresponding to the service information index vector includes multiple information such as service type, technology type, capability provider, belonging capability pool, and geographic location.

[0082] In an embodiment, the prediction module 502 is configured to predict the network element traffic based on the multi-dimensional index vector thermodynamic matrix diagram, and specifically configured to: utilize a pre-trained traffic prediction network model to predict the network element traffic based on the multi-dimensional index vector thermodynamic matrix diagram, wherein the multi-dimensional index vector traffic prediction network model is obtained by training a neural network model using samples, and the multi-dimensional index vector samples include sample thermodynamic matrix diagrams and corresponding network element traffic labels.

[0083] In an embodiment, the multi-dimensional index vector neural network model includes a convolutional neural network and a long short-term memory network; the multi-dimensional index vector convolutional neural network is configured to extract feature vectors of the multi-dimensional index vector thermodynamic matrix diagram; and the multi-dimensional index vector long short-term memory network is configured to predict the network element traffic based on time series-based feature vectors.

[0084] Referring to Figure 6 The disclosure exemplary embodiments further provide a microservice fuse device, and the multi-dimensional index vector device includes:

[0085] The acquisition module 601 is configured to acquire a thermodynamic matrix diagram, wherein the multi-dimensional index vector thermodynamic matrix diagram takes an encoded value of a service information index vector of a network element as a first coordinate axis, takes a time slice as a second coordinate axis, and takes a color of a pixel point to represent a traffic value of the corresponding network element at the corresponding time slice.

[0086] The prediction module 602 is configured to predict the network element traffic based on the multi-dimensional index vector thermodynamic matrix diagram.

[0087] The fuse module 603 is configured to execute a fuse strategy based on the multi-dimensional index vector network element traffic.

[0088] In an embodiment, the acquisition module 601 is configured to acquire the thermodynamic matrix diagram, and specifically configured to: acquire service information index vectors of a plurality of network elements under a gateway and traffic values of the service information index vectors in a historical time slice sequence; and generate the thermodynamic matrix diagram based on the service information index vectors of the plurality of network elements under the gateway and the traffic values of the service information index vectors in the historical time slice sequence.

[0089] In an embodiment, the plurality of network elements under the multi-dimensional index vector gateway are network elements with top M traffic frequencies under the multi-dimensional index vector gateway, wherein M is greater than or equal to 2.

[0090] In an embodiment, the service information corresponding to the service information index vector includes multiple information such as a service type, a technology type, a capability provider, a belonging capability pool, and a geographic location.

[0091] In an embodiment, the prediction module 602 is configured to predict the network element traffic based on the multi-dimensional index vector heat matrix graph, and specifically configured to: utilize a pre-trained traffic prediction network model to predict the network element traffic based on the multi-dimensional index vector heat matrix graph, wherein the multi-dimensional index vector traffic prediction network model is obtained by training a neural network model using samples, and the multi-dimensional index vector samples include sample heat matrix graphs and corresponding network element traffic labels.

[0092] In an embodiment, the multi-dimensional index vector neural network model includes a convolutional neural network and a long short-term memory network; the multi-dimensional index vector convolutional neural network is configured to extract feature vectors of the multi-dimensional index vector heat matrix graph; and the multi-dimensional index vector long short-term memory network is configured to predict the network element traffic based on the time series-based feature vectors.

[0093] In an embodiment, the apparatus further includes an adjustment module configured to: determine a traffic proportion of the micro-service application based on the multi-dimensional index vector network element traffic; and adjust an application weight of the micro-service application based on the traffic proportion of the micro-service application, wherein an increase proportion of the application weight of the micro-service application is positively correlated with the traffic proportion of the micro-service application.

[0094] An electronic device is also provided in an example embodiment of the present disclosure, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, and the computer program, when executed by the at least one processor, causes the electronic device to perform the method according to an embodiment of the present disclosure.

[0095] A non-transitory computer-readable storage medium storing a computer program is also provided in an example embodiment of the present disclosure, and the computer program, when executed by a processor of a computer, causes the computer to perform the method according to an embodiment of the present disclosure.

[0096] Reference is made to Figure 7 A computer program product 700 is also provided in an example embodiment of the present disclosure, including a computer program 701, and the computer program, when executed by a processor of a computer, causes the computer to perform the method according to an embodiment of the present disclosure.

[0097] Reference is made to Figure 8An example of a hardware device that can be employed in the implementation of the present disclosure will now be described with reference to FIG. 8, which is a block diagram of an electronic device 800 that can be used as a server or a client in the present disclosure, and which is an example of a hardware device that can be employed in the implementation of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices such as personal digital assistants, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0098] The electronic device 800 includes a computing unit 801 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. Various programs and data required for device operation can also be stored in the RAM 803. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0099] Various components in the electronic device 800 are connected to the I / O interface 805, including an input unit 806, an output unit 807, the storage unit 808, and a communication unit 809. The input unit 806 can be any type of device that can input information to the electronic device 800, and can receive inputted digital or character information, and generate key signal inputs related to user settings and / or function controls of the electronic device. The output unit 807 can be any type of device that can present information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 808 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0100] The computing unit 801 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 801 performs various methods and processes described above. For example, in some embodiments, the methods of the present embodiments can be implemented as a computer software program tangibly embodied in a machine readable medium, such as the storage unit 808. In some embodiments, portions or all of the computer program can be loaded and / or installed onto the electronic device 800 via the ROM 802 and / or the communication unit 809. In some embodiments, the computing unit 801 can be configured to perform the methods of the present embodiments by any other appropriate means, such as by means of firmware.

[0101] The above generally describes the basic principles of the present disclosure in connection with specific embodiments, but it should be noted that the advantages, benefits, effects and the like mentioned in the present disclosure are only examples and are not limiting, and these advantages, benefits, effects and the like cannot be considered as necessary for each embodiment of the present disclosure. In addition, the above specific details of the disclosure are only for the purpose of example and understanding, and are not limiting, and the above details do not limit the present disclosure to be necessarily implemented with the above specific details.

[0102] The block diagrams of the devices, apparatuses, equipment, systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that the connections, arrangements, configurations shown in the block diagrams must be made. As those skilled in the art will recognize, these devices, apparatuses, equipment, systems can be connected, arranged, configured in any manner. Words such as "include", "contain", "have" and the like are open-ended words that mean "including but not limited to", and can be used interchangeably. The words "or" and "and" used herein mean the word "and / or", and can be used interchangeably unless the context clearly indicates otherwise. The word "such as" used herein means the phrase "such as but not limited to", and can be used interchangeably.

[0103] In addition, as used herein, "or" used in the listing of items in "at least one of' indicates a disjunctive list, such that, for example, "at least one of A, B, or C" means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Furthermore, the word "example" is not meant to convey that a described example is preferred or better than other examples.

[0104] It is also important to note that the systems and methods of the present disclosure can be implemented in a variety of ways. The systems and methods of the present disclosure can be implemented in hardware, software, or a combination thereof. The various components of the systems and methods of the present disclosure can be implemented or provided using one or more computers, such as computer system 1000, in FIG. 10, where the various components and techniques either natively exist or are emulated by a processor, such as software, and stored in a storage medium such as a computer-readable medium, which includes non-transitory media. The software component or module can exist on the computer-readable medium or memory as a stand-alone software package, as part of a larger software package, as a portion of a computer program, or as a utility program, etc. As used in this disclosure, the term "computer-readable medium" includes one or more types of computer-readable media that store data, which is accessible by a computer, and includes both transitory and non-transitory media, both volatile and non-volatile media. The computer-readable media can also include a propagated signal.

[0105] Various changes, substitutions and alterations can be made to the technology of multi-dimensional index vectors herein without departing from the teachings of the technology defined by the appended claims. Moreover, the scope of the claims of this disclosure is not limited to the specific aspects of processing, machines, manufactures, compositions of matter, means, methods, and acts of the above multi-dimensional index vectors. Processes, machines, manufactures, compositions of matter, means, methods, or acts that perform substantially the same function or achieve substantially the same results as the corresponding aspects of multi-dimensional index vectors herein can be utilized. Accordingly, the appended claims include within their scope such processes, machines, manufactures, compositions of matter, means, methods, or acts.

[0106] The above description of the disclosed aspects is given for illustrative purposes only and is not intended to limit the scope of the disclosure. The various modifications to the aspects, as well as alternatives thereof, can be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0107] The above description has been given for illustrative purposes only and is not intended to limit the embodiments of the disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain modifications, permutations, additions, and sub-combinations thereof.

Claims

1. A gateway traffic prediction method, characterized in that, The method includes: A heat map is obtained, wherein the heat map uses the encoded value of the service information index vector of the network element as the first coordinate axis, the time slice as the second coordinate axis, and the color of the pixel represents the traffic value of the corresponding network element under the corresponding time slice. Based on the aforementioned heat map, predict network element traffic; The acquisition of the heat matrix diagram includes: Obtain the service information index vectors of multiple network elements under the gateway and their traffic values ​​in the historical time fragmentation sequence; A heat map is generated based on the service information index vectors of multiple network elements under the gateway and their traffic values ​​in the historical time sharding sequence. The prediction of network element traffic based on the heat map includes: Using a pre-trained traffic prediction network model, based on the heat map, network element traffic is predicted, wherein the traffic prediction network model is obtained by training a neural network model using samples, and the samples include sample heat map and corresponding network element traffic labels. The neural network model includes convolutional neural networks and long short-term memory networks; The convolutional neural network is used to extract the feature vector of the heat map; The Long Short-Term Memory (LSTM) network is used to predict network element traffic based on time-series-based feature vectors.

2. The method according to claim 1, characterized in that, The multiple network elements under the gateway are the M network elements with the highest number of traffic under the gateway, where M is greater than or equal to 2.

3. The method according to claim 1, characterized in that, The business information index vector corresponds to multiple pieces of information including business type, technology type, capability provider, capability pool, and geographical location. And / or, The business information index vector is a multi-dimensional index vector.

4. A microservice circuit breaker method, characterized in that, The method includes: Obtain the network element traffic predicted by the gateway traffic prediction method according to any one of claims 1 to 3; The circuit breaker policy is executed based on the network element traffic.

5. The method according to claim 4, characterized in that, The method further includes: Based on the network element traffic, determine the traffic share of microservice applications; The application weight of the microservice application is adjusted based on its traffic share, wherein the increase in the application weight of the microservice application is positively correlated with its traffic share.

6. A gateway traffic prediction device, characterized in that, The device includes: An acquisition module is used to acquire a heatmap, wherein the heatmap uses the encoded value of the service information index vector of a network element as the first coordinate axis, time slices as the second coordinate axis, and the color of a pixel to represent the traffic value of the corresponding network element in the corresponding time slice; the acquisition of the heatmap includes: acquiring the service information index vectors of multiple network elements under the gateway and their traffic values ​​in the historical time slice sequence; and generating a heatmap based on the service information index vectors of the multiple network elements under the gateway and their traffic values ​​in the historical time slice sequence. The prediction module predicts network element traffic based on the heatmap. This prediction includes: using a pre-trained traffic prediction network model to predict network element traffic based on the heatmap, wherein the traffic prediction network model is obtained by training a neural network model using samples, the samples including sample heatmaps and corresponding network element traffic labels; the neural network model includes a convolutional neural network and a long short-term memory network; the convolutional neural network is used to extract feature vectors from the heatmap; and the long short-term memory network is used to predict network element traffic based on time-series-based feature vectors.

7. A microservice circuit breaker, characterized in that, The device includes: An acquisition module is used to acquire a heatmap, wherein the heatmap uses the encoded value of the service information index vector of a network element as the first coordinate axis, time slices as the second coordinate axis, and the color of a pixel to represent the traffic value of the corresponding network element in the corresponding time slice; the acquisition of the heatmap includes: acquiring the service information index vectors of multiple network elements under the gateway and their traffic values ​​in the historical time slice sequence; and generating a heatmap based on the service information index vectors of the multiple network elements under the gateway and their traffic values ​​in the historical time slice sequence. The prediction module predicts network element traffic based on the heatmap. This prediction includes: using a pre-trained traffic prediction network model to predict network element traffic based on the heatmap, wherein the traffic prediction network model is obtained by training a neural network model using samples, the samples including sample heatmaps and corresponding network element traffic labels; the neural network model includes a convolutional neural network and a long short-term memory network; the convolutional neural network is used to extract feature vectors from the heatmap; the long short-term memory network is used to predict network element traffic based on time-series-based feature vectors. The circuit breaker module is used to execute circuit breaker policies based on the network element traffic.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1 to 5.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1 to 5.

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