Data processing method, device, electronic device and medium for cluster edge node

Abstract

The present application relates to a data processing method, device, electronic device and computer-readable medium for a cluster edge node. The method includes: monitoring cluster communication port data; extracting multiple current indicators based on the port data; comparing the multiple current indicators with multiple historical indicators; when the comparison result is abnormal and meets the first strategy, storing and processing log data inside the cluster edge node; when the comparison result is abnormal and meets the second strategy, storing and processing the abnormal indicator internally. The data processing method, device, electronic device and computer-readable medium for cluster edge nodes involved in the present application can provide error correction capabilities for the operation and maintenance of edge node content servers, avoid the loss of log data, and provide more accurate abnormal alarm markings.

Description

Technical Field

[0001] The present application relates to the field of cloud computing edge, and more specifically, to a data processing method, device, electronic device and computer-readable medium for cluster edge nodes. Background Art

[0002] With the popularization of network infrastructure, the informatization and intelligentization of traditional industries are in full swing. In the process of informatization and intelligentization transformation, it is of utmost importance to obtain comprehensive data of the underlying system. To this end, a large number of edge devices need to be deployed to collect and analyze data and use these data for modeling. In the edge layer of the Internet of Things, there are a large number of discrete edge devices that need to be managed and maintained, so a lightweight and reliable operation and maintenance platform becomes very important. K3s itself only requires 60 megabytes of binary files and 200 megabytes of offline data packets. K3s has very low requirements for the operating platform and starts quickly. K3s has become the main solution for edge nodes based on its lightweight, easy deployment, and application deployment that is exactly the same as Kubernetes.

[0003] The current mainstream monitoring technology stack for Kubernetes clusters, Prometheus, uses the traditional cluster collection resource objects to pull them to the Prometheus server for aggregation and push alerts or storage. The idea of ​​K3s clusters in actual situations is that the edge devices are discretely distributed in different computer rooms, factories, and different geographical areas, which causes cluster network instability. In addition, the number of edge node devices is large and resources are scarce. When the actual operation and maintenance situation is unstable and unreliable, the method of deleting nodes and restarting is usually used, which will bring the following problems:

[0004] 1. Network jitter inside the K3s edge cluster causes log loss: Cluster network instability will seriously affect the collection and presentation of the monitoring system. Once the connection of the locally stored indicators is restored, large-scale transmission will cause serious congestion and packet loss.

[0005] 2. Log loss caused by deletion and restart after node abnormality: Frequent rapid restart of unstable and unhealthy K3s clusters will cause a large amount of duplicate and redundant collected data, and deletion and restart will cause direct loss of data within the container level. This makes it more difficult for edge clusters to locate node problems and perform operations and maintenance.

[0006] Therefore, a new data processing method, device, electronic device and computer-readable medium for cluster edge nodes are needed.

[0007] The above information disclosed in the Background section is only for enhancement of understanding of the background of the present application and therefore it may contain information that does not constitute the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0008] In view of this, the present application provides a data processing method, device, electronic device and computer-readable medium for a cluster edge node, which can provide error correction capabilities for the operation and maintenance of edge node content servers, avoid loss of log data, and provide more accurate abnormal alarm markings.

[0009] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0010] According to one aspect of the present application, a data processing method for a cluster edge node is proposed, the method comprising: monitoring cluster communication port data; extracting multiple current indicators based on the port data; comparing the multiple current indicators with multiple historical indicators; when there is an abnormality in the comparison result and a first strategy is met, storing log data internally in the cluster edge node and processing it; when there is an abnormality in the comparison result and a second strategy is met, storing the abnormal indicator internally and processing it.

[0011] In an exemplary embodiment of the present application, monitoring cluster communication port data includes: monitoring cluster kubelet tunnel port data.

[0012] In an exemplary embodiment of the present application, the multiple current indicators and the multiple historical indicators are compared, including: generating an indicator tree based on the indicators to be collected by the cluster edge nodes; generating a historical indicator tree for the multiple historical indicators; generating a current indicator tree for the multiple current indicators; and recursively comparing the tree diagram sub-elements of the current indicator tree and the historical indicator tree.

[0013] In an exemplary embodiment of the present application, comparing the multiple current indicators with the multiple historical indicators further includes: updating the tree diagram sub-elements with differences.

[0014] In an exemplary embodiment of the present application, when there is an abnormality in the comparison result and the first strategy is met, the log data is stored and processed inside the cluster edge node, including: when there is an abnormality in the comparison result and there is an abnormality in the distinction of communication indicators, the log data is stored and processed inside the cluster edge node.

[0015] In an exemplary embodiment of the present application, the cluster edge node stores and processes log data internally, including: compressing the abnormal node indicator and storing it inside the cluster edge node; after the abnormal node indicator returns to normal, outputting the abnormal node indicator to the server.

[0016] In an exemplary embodiment of the present application, when there is an abnormality in the comparison result and the second strategy is met, the abnormal indicator is internally stored and processed, including: when there is an abnormality in the comparison result and there is an abnormality in the health indicator distinction, the marked node abnormality indicator is output to the server.

[0017] According to one aspect of the present application, a data processing device for a cluster edge node is proposed, and the device includes: a monitoring module for monitoring cluster communication port data; an indicator module for extracting multiple current indicators based on the port data; a comparison module for comparing the multiple current indicators with multiple historical indicators; a first exception module for storing and processing log data inside the cluster edge node when an exception exists in the comparison result and a first policy is met; and a second exception module for internally storing and processing the abnormal indicator when an exception exists in the comparison result and a second policy is met.

[0018] According to one aspect of the present application, an electronic device is proposed, which includes: 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 method as described above.

[0019] According to one aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the program is executed by a processor, the method described above is implemented.

[0020] According to the data processing method, device, electronic device and computer-readable medium of the cluster edge node of the present application, by monitoring the cluster communication port data; extracting multiple current indicators based on the port data; comparing the multiple current indicators with multiple historical indicators; when there is an abnormality in the comparison result and the first strategy is met, the log data is stored and processed inside the cluster edge node; when there is an abnormality in the comparison result and the second strategy is met, the abnormal indicator is internally stored and processed. This method can provide error correction capabilities for the operation and maintenance of the edge node content server, avoid the loss of log data, and provide more accurate abnormal alarm markings.

[0021] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and other objects, features and advantages of the present application will become more apparent by describing in detail the exemplary embodiments thereof with reference to the accompanying drawings. The accompanying drawings described below are only some embodiments of the present application, and it is clear to a person skilled in the art that other accompanying drawings can be obtained from these accompanying drawings without creative effort.

[0023] Figure 1 The present invention is a system block diagram showing a data processing method and device for a cluster edge node according to an exemplary embodiment.

[0024] Figure 2 The present invention is a flowchart of a data processing method of a cluster edge node according to an exemplary embodiment.

[0025] Figure 3 The present invention is a flowchart of a data processing method of a cluster edge node according to another exemplary embodiment.

[0026] Figure 4 The diagram is a schematic diagram of a data processing method of a cluster edge node according to another exemplary embodiment.

[0027] Figure 5 The invention is a block diagram showing a data processing device of a cluster edge node according to an exemplary embodiment.

[0028] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment.

[0029] Figure 7 It is a block diagram of a computer-readable medium according to an exemplary embodiment. DETAILED DESCRIPTION

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this application will be comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0031] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0032] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0033] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0034] It should be understood that although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another component. Therefore, the first component discussed below can be referred to as the second component without departing from the teachings of the concepts of the present application. As used herein, the term "and / or" includes any one of the associated listed items and all combinations of one or more.

[0035] Those skilled in the art will appreciate that the drawings are merely schematic diagrams of example embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing the present application, and therefore cannot be used to limit the scope of protection of the present application.

[0036] Figure 1 The present invention is a system block diagram showing a data processing method and device for a cluster edge node according to an exemplary embodiment.

[0037] like Figure 1 As shown, the cluster system architecture 10 may include edge nodes 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the edge nodes 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0038] The edge nodes 101, 102, 103 interact with the server 105 through the network 104 to receive or send messages, etc. Various applications may be installed on the edge nodes 101, 102, 103, such as monitoring applications, data processing applications, message forwarding applications, instruction execution tools, email clients, social platform software, etc.

[0039] The edge nodes 101 , 102 , and 103 may be various electronic devices that support data transmission, including but not limited to sensors, communication processing devices, smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0040] The server 105 may be a server that provides various services, such as a cluster server that provides support for data transmitted by the edge nodes 101 , 102 , and 103 .

[0041] Edge nodes 101, 102, 103 may, for example, monitor cluster communication port data; edge nodes 101, 102, 103 may, for example, extract multiple current indicators based on the port data; edge nodes 101, 102, 103 may, for example, compare the multiple current indicators with multiple historical indicators; edge nodes 101, 102, 103 may, for example, store log data and process it inside the cluster edge node when there is an abnormality in the comparison result and the first strategy is met; edge nodes 101, 102, 103 may, for example, store and process the abnormal indicators internally when there is an abnormality in the comparison result and the second strategy is met.

[0042] It should be noted that the data processing method of the cluster edge node provided in the embodiment of the present application can be executed by the edge nodes 101, 102, 103 and the server 105. Accordingly, the data processing device of the cluster edge node can be set in the edge nodes 101, 102, 103 and the server 105.

[0043] At present, the edge cluster operation and maintenance focuses on the coordination with the central cloud, and cannot be autonomous under the interruption of the edge cluster server-agent. The monitoring means need a stable environment and will not mark and collect data in unhealthy containers. The method proposed in this application can form the agent's low-resource autonomy and reduce the redundancy of records to save key logs.

[0044] Compared with the prior art, the data processing method of the cluster edge node of the present application has the following main advantages:

[0045] 1. A low-resource-demand algorithm for calculating abnormal discriminant indicators is given to reduce the amount of calculation.

[0046] 2. Provide log storage capabilities for abnormal situation processing data.

[0047] The present disclosure proposes a method for saving limited resources and resolving abnormalities and saving node log data before the node is terminated in a node abnormality scenario involving an edge cluster system K3s.

[0048] Figure 2 The flowchart of a data processing method of a cluster edge node according to an exemplary embodiment is shown. The data processing method 20 of a cluster edge node at least includes steps S202 to S210.

[0049] like Figure 2As shown, in S202, the cluster communication port data is monitored. In cluster deployment, the cluster kubelettunnel port data is monitored. The cluster in this application can be a K3s cluster. The K3s binary file is a self-sufficient encapsulated entity that runs almost all components of the Kubernetes cluster, including the API server, scheduler, and controller. By default, each K3s installation includes the control plane, kubelet, and containerd runtime, which serve as Kubernetes workloads.

[0050] All data in the K3s cluster is stored in the local SQLite database of the Server (Master) node, and can also be stored in MySQL and etcd on demand in the deployment node. The Server node and the Agent node use tunnel communication to enhance security and improve efficiency. Even if the Agent and Server nodes are disconnected from the network, their respective businesses will not be affected.

[0051] Based on these characteristics of edge nodes in the K3s cluster, a specific data processing solution is formulated in this application in combination with the relevant characteristics.

[0052] In S204, a plurality of current indicators are extracted according to the port data. The current indicators may be set in advance in the edge node, and are indicators related to the edge node itself, and the indicator settings of each edge node may be different.

[0053] In S206, the multiple current indicators are compared with the multiple historical indicators. An indicator tree may be generated according to the indicators to be collected by the edge nodes of the cluster; a historical indicator tree of the multiple historical indicators is generated; a current indicator tree of the multiple current indicators is generated; and the tree diagram sub-elements of the current indicator tree and the historical indicator tree are recursively compared.

[0054] In S208 , when the comparison result is abnormal and satisfies the first policy, the log data is stored and processed inside the cluster edge node.

[0055] In one embodiment, when there is an abnormality in the comparison result and the communication indicator differentiation is abnormal, the cluster edge node stores the log data internally and processes it. A normal communication indicator threshold can be set. When the communication indicator in the current indicator and the historical indicator is greater than the threshold, it can be considered that there is an abnormality in the communication indicator differentiation in the current network transmission environment.

[0056] More specifically, the abnormal node indicator can be compressed and stored inside the edge node of the cluster; after the abnormal node indicator returns to normal, the abnormal node indicator is output to the server.

[0057] More specifically, when the cluster communication indicator differentiation is abnormal, a warning is sent to the node process component to start pulling up the database node inside the Agent, compressing and outputting the abnormal node log for storage; after returning to normal, the internal temporary database is closed and the node abnormal indicator is output to the server-side SQlite;

[0058] In S208, when the comparison result is abnormal and satisfies the second strategy, the abnormal indicator is internally stored and processed.

[0059] In one embodiment, when there is an abnormality in the comparison result and the health indicator differentiation is abnormal, the marked node abnormality indicator is output to the server. The normal value range and difference range of each indicator can be set. When the communication indicator in the current indicator and the historical indicator is greater than the difference range, or the current indicator itself is greater than the normal value range, it can be considered that the indicator is abnormal, that is, the health indicator is abnormal.

[0060] More specifically, when the internal health indicator differentiation of a node is abnormal, the marked node indicator is output to the server tunnel port until the node access is abnormal or restored to health; the aggregation component summarizes the results on the cluster server side.

[0061] According to the data processing method of the cluster edge node of the present application, by monitoring the cluster communication port data; extracting multiple current indicators based on the port data; comparing the multiple current indicators with multiple historical indicators; when there is an abnormality in the comparison result and the first strategy is met, the log data is stored and processed inside the cluster edge node; when there is an abnormality in the comparison result and the second strategy is met, the abnormal indicator is internally stored and processed. This method can provide error correction capabilities for the operation and maintenance of the edge node content server, avoid the loss of log data, and provide more accurate abnormal alarm markings.

[0062] It should be clearly understood that the present application describes how to form and use specific examples, but the principles of the present application are not limited to any details of these examples. On the contrary, based on the teaching of the content disclosed in the present application, these principles can be applied to many other embodiments.

[0063] Figure 3 The present invention is a flowchart of a data processing method of a cluster edge node according to another exemplary embodiment. Figure 3 The process 30 shown is for Figure 2 A detailed description of S206 “comparing the multiple current indicators with the multiple historical indicators” in the process shown.

[0064] like Figure 3As shown, in S302, an indicator tree is generated according to the indicators to be collected by the edge nodes of the cluster. A tree diagram is generated in real time according to the nodes, deployed containers and indicators to be collected, and a new tree is generated for comparison each time a new indicator is collected according to the collection cycle.

[0065] In S304, a historical indicator tree of the plurality of historical indicators is generated.

[0066] In S306, a current indicator tree of the multiple current indicators is generated.

[0067] In S308, the tree diagram sub-elements of the current indicator tree and the historical indicator tree are recursively compared. Thresholds are set and tree diagram sub-elements are recursively compared: when comparing two tree sub-leaf elements of the same type, their indicator attributes are checked, the same underlying indicator values ​​are retained, and only the indicators that have changed are updated.

[0068] In S310, the tree diagram sub-elements with differences are updated. After each update, the indicator value under the evaluation indicator attribute is updated with a ratio and a value. If it is greater than a threshold, an abnormal value is thrown.

[0069] Once a node container is lost and no data is pulled, a new node tree is generated and an abnormal value is thrown. According to the abnormal value, a request can be output to the agent process module to pull the agent container to run the SQlite service. After the abnormal value is released normally, the SQlite database will transfer the data to the server node.

[0070] Figure 4 The diagram is a schematic diagram of a data processing method of a cluster edge node according to another exemplary embodiment. Figure 4 The process 40 shown is for Figure 2 Detailed description of the process shown.

[0071] In cluster deployment, monitor the kubelet tunnel port data; generate a tree diagram in real time based on the collected data for discrimination analysis; set a discrimination threshold to identify abnormal situations;

[0072] Cluster communication indicator differentiation is abnormal: send a warning to the node process component to start pulling up the database node inside the Agent, compress and output the abnormal node log for storage; after recovery, close the internal temporary database and output the node abnormal indicator to the server-side SQlite;

[0073] The node internal health indicator differentiation is abnormal: the marked node indicator is output to the server tunnel port until the node access is abnormal or recovers;

[0074] Monitor the aggregation component summary results on the cluster server side.

[0075] Compared with the existing technology, the solution of the present application can jointly model the container indicators in the node and uniformly analyze the monitoring status; the flow processing indicators are tree graph comparisons, and a tree graph algorithm with O(n) complexity is used to achieve high performance and low occupancy; network fluctuations are predicted in advance, and container fluctuations pull up a small database in the node to save key information.

[0076] According to the solution of this application, logs can be stably collected in unstable edge cluster environments in complex and extreme scenarios; it can also provide abnormal value analysis capabilities for container-level operation and maintenance data. It can also perform big data mining services on statistical analysis logs, provide more accurate abnormal alarm tags, and conduct targeted analysis of data in the previous and next time periods.

[0077] Those skilled in the art will appreciate that all or part of the steps for implementing the above embodiments are implemented as a computer program executed by a CPU. When the computer program is executed by the CPU, the above functions defined by the above method provided in the present application are executed. The program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.

[0078] In addition, it should be noted that the above figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0079] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0080] Figure 5 FIG. 1 is a block diagram of a data processing device for a cluster edge node according to an exemplary embodiment. Figure 5 As shown, the data processing device 50 of the cluster edge node includes: a monitoring module 502 , an indicator module 504 , a comparison module 506 , a first abnormal module 508 , and a second abnormal module 510 .

[0081] The monitoring module 502 is used to monitor cluster communication port data;

[0082] The indicator module 504 is used to extract multiple current indicators according to the port data;

[0083] The comparison module 506 is used to compare the multiple current indicators with the multiple historical indicators;

[0084] The first exception module 508 is used to store and process the log data inside the cluster edge node when there is an exception in the comparison result and the first policy is met;

[0085] The second exception module 510 is used to internally store and process the abnormal indicator when the comparison result is abnormal and satisfies the second strategy.

[0086] According to the data processing device of the cluster edge node of the present application, by monitoring the cluster communication port data; extracting multiple current indicators based on the port data; comparing the multiple current indicators with multiple historical indicators; when there is an abnormality in the comparison result and the first strategy is met, the log data is stored and processed inside the cluster edge node; when there is an abnormality in the comparison result and the second strategy is met, the abnormal indicator is internally stored and processed. This method can provide error correction capabilities for the operation and maintenance of the edge node content server, avoid the loss of log data, and provide more accurate abnormal alarm markings.

[0087] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment.

[0088] Refer to the following Figure 6 The electronic device 600 according to this embodiment of the present application is described. Figure 6 The electronic device 600 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0089] like Figure 6 As shown, the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0090] The storage unit stores a program code, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps described in this specification according to various exemplary embodiments of the present application. For example, the processing unit 610 can perform the following steps: Figure 2 , Figure 3 Follow the steps shown in .

[0091] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0092] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0093] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0094] The electronic device 600 may also communicate with one or more external devices 600' (e.g., keyboards, pointing devices, Bluetooth devices, etc.) to enable a user to communicate with the electronic device 600, and / or any device (e.g., routers, modems, etc.) that the electronic device 600 can communicate with one or more other computing devices. Such communication may be performed via an input / output (I / O) interface 650. In addition, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0095] Through the above description of the implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by combining software with necessary hardware. Figure 7 As shown, the technical solution according to the implementation mode of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the implementation mode of the present application.

[0096] The software product may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0097] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0098] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0099] The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by a device, the computer-readable medium realizes the following functions: monitoring cluster communication port data; extracting multiple current indicators based on the port data; comparing the multiple current indicators with multiple historical indicators; when there is an abnormality in the comparison result and the first strategy is met, storing the log data inside the cluster edge node and processing it; when there is an abnormality in the comparison result and the second strategy is met, the abnormal indicator is internally stored and processed.

[0100] Those skilled in the art will appreciate that the above modules can be distributed in the device according to the description of the embodiment, or can be changed accordingly and only used in one or more devices different from the embodiment. The modules of the above embodiments can be combined into one module, or further divided into multiple sub-modules.

[0101] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiment of the present application.

[0102] The exemplary embodiments of the present application are specifically shown and described above. It should be understood that the present application is not limited to the detailed structures, configurations or implementations described herein; on the contrary, the present application is intended to cover various modifications and equivalent configurations included in the spirit and scope of the appended claims.

Claims

1. A data processing method for a cluster edge node, characterized in that: include: Monitor cluster communication port data; extracting a plurality of current indicators according to the port data; comparing the multiple current indicators with the multiple historical indicators; When the comparison result is abnormal and satisfies the first strategy, the log data is stored and processed inside the cluster edge node; When the comparison result is abnormal and satisfies the second strategy, the abnormal indicator is stored internally and processed; The comparing the multiple current indicators with the multiple historical indicators includes: generating an indicator tree according to the indicators to be collected by the edge nodes of the cluster; generating a historical indicator tree of the multiple historical indicators; generating a current indicator tree of the multiple current indicators; recursively comparing the tree diagram sub-elements of the current indicator tree and the historical indicator tree; updating the tree diagram sub-elements with differences; When the comparison result is abnormal and the first strategy is met, the log data is stored and processed inside the cluster edge node, including: when the comparison result is abnormal and the communication indicator distinction is abnormal, the abnormal node indicator is compressed and stored inside the cluster edge node; after the abnormal node indicator returns to normal, the abnormal node indicator is output to the server; When there is an abnormality in the comparison result and the second strategy is met, the abnormal indicator is internally stored and processed, including: when there is an abnormality in the comparison result and there is an abnormality in the health indicator distinction, the marked node abnormal indicator is output to the server.

2. The data processing method according to claim 1, characterized in that: Monitor cluster communication port data, including: Monitor cluster kubelet tunnel port data.

3. A data processing device for a cluster edge node, characterized in that: include: Monitoring module, used to monitor cluster communication port data; An indicator module, used for extracting a plurality of current indicators according to the port data; A comparison module, used for comparing the multiple current indicators with the multiple historical indicators; A first exception module, used to store and process log data inside the cluster edge node when there is an exception in the comparison result and the first policy is met; A second abnormality module is used to internally store and process the abnormal indicator when the comparison result is abnormal and satisfies the second strategy; The comparing the multiple current indicators with the multiple historical indicators includes: generating an indicator tree according to the indicators to be collected by the edge nodes of the cluster; generating a historical indicator tree of the multiple historical indicators; generating a current indicator tree of the multiple current indicators; recursively comparing the tree diagram sub-elements of the current indicator tree and the historical indicator tree; updating the tree diagram sub-elements with differences; When the comparison result is abnormal and the first strategy is met, the log data is stored and processed inside the cluster edge node, including: when the comparison result is abnormal and the communication indicator distinction is abnormal, the abnormal node indicator is compressed and stored inside the cluster edge node; after the abnormal node indicator returns to normal, the abnormal node indicator is output to the server; When there is an abnormality in the comparison result and the second strategy is met, the abnormal indicator is internally stored and processed, including: when there is an abnormality in the comparison result and there is an abnormality in the health indicator distinction, the marked node abnormal indicator is output to the server.

4. 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 method as claimed in any one of claims 1 or 2.

5. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 or 2 is implemented.

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