Data processing method and device and nonvolatile storage medium

Abstract

The invention discloses a data processing method and device and a nonvolatile storage medium. The method comprises the following steps: collecting a plurality of network data packets forwarded by a plurality of network nodes in a cloud platform of a private network; in the plurality of network data packets, counting the amount of data sent by each application program under the same source IP address within a preset time range; acquiring application priority information and application load information of each application program under the same source IP address, and determining an application score of the application program; and obtaining a load index of the network node at the target moment, and under the condition that the load index is greater than a preset threshold value, performing shunting processing on target traffic corresponding to the target application program under the target source IP address forwarded by the network node. According to the method and the device, the technical problems that the route for forwarding the data request of each access unit to the exclusive cloud is not reasonable enough and the efficiency is low due to the lack of an effective node load monitoring mechanism and a dynamic routing adjustment strategy are solved.

Description

Technical Field

[0001] The present application relates to the field of cloud network operations, and specifically, to a data processing method and device, and a non-volatile storage medium. Background Art

[0002] With the rapid development of cloud-network integration technology and modern industries, operators have established private networks for various industries, and built dedicated clouds on their basis to deploy various industry applications and serve all levels. By deploying various industry applications on the dedicated cloud, it can cover all aspects from production management, business process optimization to intelligent monitoring.

[0003] In the private networks of various industries, such as the private network of the education industry: kindergartens, primary schools, middle schools, educational institutions, etc. will be connected to the education-specific cloud through the access layer network, aggregation, and core network to achieve global Internet access, financial management, electronic inspection, video monitoring of physics and chemistry examinations and other application scenarios. In actual business scenarios, the load borne by the data requests forwarded to the private cloud by the above-mentioned unit nodes changes dynamically during different usage time periods. How to monitor the node load and dynamically adjust the routing strategy to make the route for forwarding data requests from each access unit to the private cloud more reasonable and efficient is an urgent problem to be solved.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The present application provides a data processing method and device, and a non-volatile storage medium, to at least solve the technical problem that the routes for forwarding data requests from each access unit to a dedicated cloud are not reasonable and efficient due to the lack of an effective node load monitoring mechanism and a dynamic routing adjustment strategy.

[0006] According to one aspect of the present application, a data processing method is provided, comprising: collecting multiple network data packets forwarded by multiple network nodes in a cloud platform of a dedicated network, wherein each network data packet includes at least: a source IP address and a target IP address; in the multiple network data packets, counting the amount of data sent by the same source IP address within a preset time range, and counting the amount of data sent by each application under the same source IP address within the preset time range; obtaining application priority information and application load information of each application under the same source IP address, and determining the application score of the application according to the application priority information and application load information of each application; obtaining the load index of the network node at a target time, and when the load index is greater than a preset threshold, performing diversion processing on the target traffic corresponding to the target application under the target source IP address forwarded by the network node, wherein the difference between the target time and the cutoff time in the preset time range is less than a first preset threshold, the target source IP address is a source IP address whose amount of data sent within the preset time range is greater than a second preset threshold, and the target application is an application whose amount of data sent within the preset time range in the target source IP address is greater than a third preset threshold and whose application score is less than a fourth preset threshold.

[0007] Optionally, the target traffic corresponding to the target application under the target source IP address forwarded by the network node is diverted, including: obtaining network topology information corresponding to the cloud platform of the dedicated network, wherein the network topology information includes the connection relationship between the network nodes and the real-time status information of the links between the network nodes, and the real-time status information includes at least: link bandwidth, delay and packet loss rate; based on the network topology information, determining multiple candidate links from the network node whose load index is greater than a preset threshold to other network nodes; for each candidate link, performing weighted summation of the link bandwidth, delay and packet loss rate therein to obtain the target length of each candidate link; obtaining the load index of other network nodes, and determining a node set consisting of nodes whose load index is less than a fifth preset threshold among the other network nodes, wherein the load index is determined at least based on the CPU usage and memory occupancy of the network node; in the node set, determining the first network node with the shortest target length, and sending the target traffic to the first network node to forward the target traffic through the first network node.

[0008] Optionally, the method also includes: if there are multiple second network nodes with the shortest target lengths in the node set, calculating the reliability index of each second network node according to the availability index of the second network node, the average time interval between two adjacent failures of the second network node, and the average time required for the second network node to resume normal operation from the occurrence of a failure, wherein the availability index is determined by the total operating time, the failure downtime, and the total operating time; among the multiple second network nodes, determining a third network node with the largest reliability index, sending the target traffic to the third network node, so as to forward the target traffic through the third network node.

[0009] Optionally, after obtaining the network topology information corresponding to the cloud platform of the dedicated network, the method also includes: based on the Telemetry protocol, encapsulating the real-time status information in a standardized format, wherein the standardized format adopts a predefined JSON or binary encoding structure; transmitting the encapsulation result to the central control server through a secure encrypted channel, wherein the secure encrypted channel adopts a secure socket layer or a transport layer security protocol; the central control server receives the encapsulation result, and presents the link bandwidth, delay and packet loss rate therein in the form of a visual curve; obtaining a performance threshold, and when any indicator in the encapsulation result exceeds the performance threshold, triggering an alarm, wherein the performance threshold is dynamically adjusted through the network service level agreement and the historical status information of the network node.

[0010] Optionally, the method further includes: sending warning information to a management object of the private network when the load indicator is less than a preset threshold and the difference between the load indicator and the preset threshold is less than a fifth preset threshold.

[0011] Optionally, obtaining application priority information and application load information for each application under the same source IP address includes: obtaining an application load model table, wherein the application load model table includes: application name information, application description information, application priority information, application address information, application port number information, application status information, and application load information; and filtering the application priority information and application load information for each application under the same source IP address in the application load model table.

[0012] Optionally, collecting multiple network data packets forwarded by multiple network nodes in the cloud platform of the private network includes: a target node in the public network connected to the cloud platform of the private network collects multiple network data packets forwarded by multiple network nodes in the cloud platform of the private network by enabling a target protocol, wherein the target protocol includes: Telemetry protocol.

[0013] According to another aspect of the present application, a data processing device is also provided, including: a collection module, which is used to collect multiple network data packets forwarded by multiple network nodes in a cloud platform of a dedicated network, wherein each network data packet includes at least: a source IP address and a target IP address; a statistical module, which is used to count the amount of data sent by the same source IP address within a preset time range in multiple network data packets, and count the amount of data sent by each application under the same source IP address within the preset time range; a determination module, which is used to obtain application priority information and application load information of each application under the same source IP address, and determine the application score of the application according to the application priority information and application load information of each application; a processing module, which is used to obtain the load index of the network node at the target time, and when the load index is greater than a preset threshold, divert the target traffic corresponding to the target application under the target source IP address forwarded by the network node, wherein the difference between the target time and the deadline in the preset time range is less than a first preset threshold, the target source IP address is a source IP address whose amount of data sent within the preset time range is greater than a second preset threshold, and the target application is an application whose amount of data sent within the preset time range in the target source IP address is greater than a third preset threshold and whose application score is less than a fourth preset threshold.

[0014] According to another aspect of the present application, a non-volatile storage medium is provided, the storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the above data processing method.

[0015] According to another aspect of the present application, an electronic device is provided, including: a memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the above data processing method is executed when the program is run.

[0016] According to yet another aspect of the present application, a computer program is provided, wherein the above data processing method is implemented when the computer program is executed by a processor.

[0017] According to another aspect of the present application, a computer program product is provided, which includes a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above data processing method is implemented.

[0018] In the present application, multiple network data packets forwarded by multiple network nodes in a cloud platform of a dedicated network are collected, wherein each network data packet includes at least: a source IP address and a target IP address; in multiple network data packets, the amount of data sent by the same source IP address within a preset time range is counted, and the amount of data sent by each application under the same source IP address within a preset time range is counted; application priority information and application load information of each application under the same source IP address are obtained, and the application score of the application is determined according to the application priority information and application load information of each application; the load index of the network node at the target time is obtained, and when the load index is greater than a preset threshold, the target application corresponding to the target source IP address forwarded by the network node is The target traffic is diverted, wherein the difference between the target time and the deadline in the preset time range is less than the first preset threshold, the target source IP address is a source IP address whose data volume sent within the preset time range is greater than the second preset threshold, and the target application is an application whose data volume sent within the preset time range in the target source IP address is greater than the third preset threshold and whose application score is less than the fourth preset threshold, thereby achieving the purpose of effectively monitoring the node load and dynamically adjusting the routing strategy, thereby realizing the technical effect of making the route for forwarding data requests from each access unit to the exclusive cloud more reasonable and efficient, and further solving the technical problem of unreasonable and inefficient routes for forwarding data requests from each access unit to the exclusive cloud due to the lack of an effective node load monitoring mechanism and a dynamic routing adjustment strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 is a flow chart of a data processing method according to an embodiment of the present application;

[0021] Figure 2 is a structural diagram of a data processing system according to an embodiment of the present application;

[0022] Figure 3 is a flow chart of another data processing method according to an embodiment of the present application;

[0023] Figure 4 is a structural diagram of a data processing device according to an embodiment of the present application;

[0024] Figure 5 It is a hardware structure block diagram of a computer terminal according to a data processing method of an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] In the related art, a single fixed forwarding node is overloaded, while some nodes are idle. Therefore, there are problems such as uneven data forwarding distribution, inability to add a monitoring system on the customer cloud, and insufficient network elasticity. In order to solve this problem, the embodiments of the present application provide a related solution, which is described in detail below.

[0028] According to an embodiment of the present application, a method embodiment of a data processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] Figure 1 is a flow chart of a data processing method according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0030] Step S102, collecting multiple network data packets forwarded by multiple network nodes in the cloud platform of the dedicated network, wherein each network data packet at least includes: a source IP address and a destination IP address.

[0031] In step S102, in the dedicated network of the cloud platform, the Telemetry module integrated in the software defined network (SDN) controller is used to collect network data packets forwarded by network nodes in real time. The data packet collection includes but is not limited to information such as the source IP address, the target IP address, the source port number, the target port number, and the number of bytes in the data packet. The Telemetry module is configured in a high-precision, low-latency data collection mode to ensure that the collected data packet information can accurately reflect the network status.

[0032] Step S104, among multiple network data packets, counting the amount of data sent by the same source IP address within a preset time range, and counting the amount of data sent by each application under the same source IP address within the preset time range.

[0033] Specifically, network data packets are classified according to the source IP address, and the total amount of data sent by each source IP address within a preset time range (for example, the last 5 minutes, 1 hour, etc.) is counted. Then, combined with the application load model table, the application under each source IP address is determined, and the amount of data sent by each application within the same preset time range is further counted. This process can accelerate query and statistics by establishing a hash table or database index.

[0034] Step S106, obtaining application priority information and application load information of each application under the same source IP address, and determining the application score of the application according to the application priority information and application load information of each application.

[0035] In step S106, the priority information and load information of each application are obtained from the application load model table. Specifically, the application priority information (AppPriority) and the application load information (AppLevel) are used to calculate the application score (AppScore) of the application. The application score can be calculated by the weighted average of the application priority and the application load, or the application priority multiplied by the application load level. The calculation result of the application score will be used for the subsequent traffic adjustment strategy.

[0036] Step S108, obtaining the load index of the network node at the target time, and when the load index is greater than the preset threshold, diverting the target traffic corresponding to the target application under the target source IP address forwarded by the network node, wherein the difference between the target time and the end time in the preset time range is less than the first preset threshold, the target source IP address is a source IP address whose data volume sent within the preset time range is greater than the second preset threshold, and the target application is an application in the target source IP address whose data volume sent within the preset time range is greater than the third preset threshold and whose application score is less than the fourth preset threshold.

[0037] In step S108, the load index of each network node is obtained at the target time (e.g., the current time point), and if the load index of any node is found to exceed the preset threshold (Threshold), the traffic diversion strategy is triggered. The triggering condition of the strategy can also be: the difference between the target time and the deadline of the preset time range is less than the first preset threshold (e.g., 1 minute), which means that the traffic analysis is the most recent data.

[0038] It should be noted that the target source IP address is a source IP address that sends data greater than the second preset threshold (e.g., 1GB) within a preset time range. The target application is an application that sends data greater than the third preset threshold (e.g., 100MB) and whose application score is less than the fourth preset threshold (e.g., lower than the average score) among the target source IP addresses. Once the target traffic is determined, instructions will be sent to the network through the SDN controller to automatically transfer traffic on high-load nodes to nodes with lower loads to achieve dynamic balancing.

[0039] According to the above steps, multiple network data packets forwarded by multiple network nodes in a cloud platform of a dedicated network are collected, wherein each network data packet at least includes: a source IP address and a target IP address; in multiple network data packets, the amount of data sent by the same source IP address within a preset time range is counted, and the amount of data sent by each application under the same source IP address within a preset time range is counted; application priority information and application load information of each application under the same source IP address are obtained, and the application score of the application is determined according to the application priority information and application load information of each application; the load index of the network node at the target time is obtained, and when the load index is greater than Under the preset threshold, the target traffic corresponding to the target application under the target source IP address forwarded by the network node is diverted, wherein the difference between the target time and the deadline in the preset time range is less than the first preset threshold, the target source IP address is the source IP address whose data volume sent within the preset time range is greater than the second preset threshold, and the target application is the application whose data volume sent within the preset time range in the target source IP address is greater than the third preset threshold and whose application score is less than the fourth preset threshold. This achieves the purpose of effectively monitoring the node load and dynamically adjusting the routing strategy, thereby realizing a more reasonable and efficient technical effect in which the route for forwarding data requests from each access unit to the exclusive cloud is more reasonable.

[0040] The following Figure 1 The steps shown are exemplary and explanatory.

[0041] According to some optional embodiments of the present application, the target traffic corresponding to the target application under the target source IP address forwarded by the network node is diverted and processed, which can be achieved through the following steps: obtaining network topology information corresponding to the cloud platform of the dedicated network, wherein the network topology information includes the connection relationship between the network nodes and the real-time status information of the links between the network nodes, and the real-time status information includes at least: link bandwidth, delay and packet loss rate; based on the network topology information, determining multiple candidate links from the network node whose load index is greater than a preset threshold to other network nodes; for each candidate link, performing weighted summation of the link bandwidth, delay and packet loss rate therein to obtain the target length of each candidate link; obtaining the load index of other network nodes, and determining a node set consisting of nodes whose load index is less than a fifth preset threshold among the other network nodes, wherein the load index is determined at least based on the CPU usage and memory occupancy of the network node; in the node set, determining the first network node with the shortest target length, and sending the target traffic to the first network node to forward the target traffic through the first network node.

[0042] In the above embodiment, first, the topology discovery function of the SDN controller is used to obtain the network topology information in the cloud platform of the private network in real time. The topology information includes the connection relationship between network nodes and the real-time status of the link, such as link bandwidth, delay and packet loss rate. The Telemetry protocol is enabled on each network node to collect node performance indicators (such as CPU usage and memory occupancy) and link status information, which are uploaded to the SDN controller in real time to update the real-time status of the network topology information. The SDN controller processes the collected network status data, establishes and maintains a dynamically updated network topology database, and records link status and node load information.

[0043] Then, the load index of each network node is continuously monitored. Once the load index of a node (such as CPU usage and memory occupancy) exceeds the preset threshold (Threshold), the node is marked as a high-load node. Starting from the high-load node, all possible paths to all other network nodes are calculated using the network topology information of the SDN controller. In this process, the shortest path algorithm, such as the Dijkstra algorithm or the Bellman-Ford algorithm, is used, while considering the real-time information of the link status. According to the calculated path, multiple candidate links from the network node with a load index greater than the preset threshold to other network nodes are determined, and the real-time status information of each candidate link is recorded, including link bandwidth, delay and packet loss rate. The weight factors of the link bandwidth, delay and packet loss rate are set, and for each candidate link, its target length is calculated, where the bandwidth, delay and packet loss rate are quantified according to its real-time status information and converted into comparable values. According to the calculated target length, the comprehensive performance of each candidate link is evaluated, and the smaller the target length, the better the comprehensive performance of the link.

[0044] Finally, based on the real-time data collected by the Telemetry protocol, the load indicators of all network nodes are obtained. Specifically, a fifth preset threshold value of a node load is set (such as CPU utilization rate less than 60%, memory occupancy rate less than 50%), and network nodes with load indicators lower than this threshold are screened out to form a set of low-load nodes. Compare the target lengths of candidate links between all low-load nodes and high-load nodes, and determine the first network node connected to the link with the shortest target length. Send a redirection instruction to the network through the SDN controller to reconfigure the target traffic (i.e., the application traffic that needs to be diverted on the high-load node) so that it is forwarded through the first network node with the shortest target length.

[0045] Through the above steps, SDN technology and Telemetry protocol are used to dynamically adjust traffic in the private network to achieve reasonable allocation and optimization of network resources. Especially when processing high-load nodes, the optimal traffic forwarding path is intelligently selected by comprehensively evaluating low-load nodes and link status.

[0046] Preferably, if there are multiple second network nodes with the shortest target lengths in the node set, the following steps may also be performed:

[0047] The reliability index of each second network node is calculated according to the availability index of the second network node, the average time interval between two adjacent failures of the second network node, and the average time required for the second network node to resume normal operation from the occurrence of the failure, wherein the availability index is determined by the total operating time, the failure downtime and the total operating time; among the multiple second network nodes, a third network node with the largest reliability index is determined, and the target traffic is sent to the third network node so as to forward the target traffic through the third network node.

[0048] In the above embodiment, the reliability index of each second network node is calculated according to the availability index of the second network node, the average time interval between two adjacent faults of the second network node, and the average time required for the second network node to resume normal operation from the occurrence of the fault. First, the availability index is calculated, and the total running time and the fault downtime are recorded, wherein the total running time is the continuous running time of each network node since the last restart, and the fault downtime is the total fault downtime of each network node in the recent period. For example, if the total running time of a network node is 8760 hours (one year) and the fault downtime is 1 hour, its availability index is: (8760-1) / 8760=0.999897. Then calculate the average value of the time interval between two adjacent faults of the second network node, and the average value of the time required for the second network node to fully resume normal operation from the occurrence of the fault, which reflects the fault recovery capability of the network node. According to the availability index, the fault interval and the fault recovery time, the reliability index of each second network node can be calculated.

[0049] Then, the reliability indicators of all second network nodes are compared, and the node with the highest reliability index is found as the third network node. This node is considered to be the most stable and reliable, and can provide high-quality data forwarding services. Once the third network node is determined, a redirection instruction is sent to the network through the SDN controller. The instruction content includes the identifier of the target traffic (such as the source IP address and port number) and the identifier of the third network node (such as the node ID or IP address). The instruction requires the target traffic to be routed to the third network node. After receiving the instruction, the SDN controller adjusts the network forwarding strategy to ensure that the target traffic is forwarded through the third network node. At the same time, the operating status and performance indicators of the third network node, such as link bandwidth, delay, packet loss rate, etc., are continuously monitored to evaluate the effect of traffic adjustment and respond to any abnormalities in a timely manner.

[0050] Through the above implementation, the most suitable node can be intelligently selected for traffic forwarding based on the reliability indicators of multiple network nodes, ensuring efficient use of network resources and smooth operation of services. This dynamic traffic adjustment mechanism based on reliability and performance is crucial to improving the performance, stability and security of the private network of the cloud platform.

[0051] In some optional embodiments of the present application, after obtaining the network topology information corresponding to the cloud platform of the dedicated network, the following steps are also included: based on the Telemetry protocol, the real-time status information is encapsulated in a standardized format, wherein the standardized format adopts a predefined JSON or binary encoding structure; the encapsulation result is transmitted to the central control server through a secure encrypted channel, wherein the secure encrypted channel adopts a secure socket layer or a transport layer security protocol; the central control server receives the encapsulation result, and presents the link bandwidth, delay and packet loss rate therein in the form of a visual curve; obtains a performance threshold, and when any indicator in the encapsulation result exceeds the performance threshold, triggers an alarm, wherein the performance threshold is dynamically adjusted through the network service level agreement and the historical status information of the network node.

[0052] In the above embodiment, a standardized data format is first designed, and JSON (JavaScript Object Notation) or binary encoding (such as Protobuf) is used as the structure of data encapsulation. Specifically, JSON is easy to read and write, suitable for human-computer interaction, while binary encoding is more compact, suitable for machine-to-machine communication, and improves transmission efficiency. At the same time, necessary fields are defined in the standardized format, including performance indicators such as link bandwidth, delay, packet loss rate, CPU usage of network nodes, memory occupancy rate, and metadata fields such as timestamp, source node ID, and target node ID to ensure the integrity and accuracy of the data. Using the Telemetry protocol, performance data is collected and encapsulated in real time and encoded in a predefined standardized format. It should be noted that data encapsulation should take into account the efficiency and security of network transmission, avoid unnecessary data redundancy, and ensure the real-time and security of data. The encapsulated data is transmitted to the central control server through a secure encrypted channel. During the transmission process, the availability of the monitoring channel and the integrity of the data transmission are ensured to ensure that the data can reach the central control server in a timely and accurate manner.

[0053] After receiving the encapsulated data, the central control server uses the corresponding parser (such as JSON parser, binary decoder) to parse the data from the standardized format and extract key performance indicators such as link bandwidth, delay, and packet loss rate. Then the parsed performance indicator data is processed, such as outlier detection and data smoothing, to improve the accuracy and reliability of the data. And using data visualization tools (such as Grafana, Kibana, etc.), link bandwidth, delay, packet loss rate and other performance indicators are presented in the form of curves, which can intuitively understand the real-time changes and historical trends of network performance.

[0054] Finally, thresholds for performance indicators such as link bandwidth, latency, and packet loss rate are set. These thresholds are dynamically adjusted according to the requirements of the network service level agreement (SLA) and the historical status information of network nodes to adapt to real-time changes in the network and business needs. A threshold adjustment model is established by analyzing the historical status information of network nodes, such as long-term peak usage, average bandwidth consumption, seasonal changes in latency, etc. The model can predict and adjust performance thresholds based on machine learning algorithms such as time series analysis and anomaly detection. Real-time monitoring data, when any performance indicator (such as link bandwidth, latency or packet loss rate) exceeds its corresponding dynamically adjusted performance threshold, the central control server should immediately trigger an alarm. The alarm can notify the operation and maintenance personnel in the form of email, SMS, or internal system messages, and abnormal links or nodes are marked with conspicuous colors or icons on the visual interface to facilitate rapid location and response to problems.

[0055] Through the above implementation, it is possible to monitor network performance in real time, transmit data through secure channels, use visualization tools to intuitively display network status, and trigger and process alarms based on dynamically adjusted performance thresholds, thereby effectively ensuring network service quality, improving fault response speed, and enhancing network stability and reliability.

[0056] As some optional embodiments of the present application, the above data processing method also includes the following steps: when the load index is less than a preset threshold and the difference between the load index and the preset threshold is less than a fifth preset threshold, sending an early warning message to the management object of the private network.

[0057] In the above embodiment, the SDN controller continuously monitors the load indicators of the network nodes, including but not limited to CPU usage, memory occupancy, disk I / O, etc. At the same time, a preset threshold (Threshold) is set for each network node as the upper limit of the normal operating range. The setting of the preset threshold needs to be optimized according to the actual operation of the network and historical data. When the load indicator of the network node is lower than the preset threshold, the difference between the load indicator and the preset threshold is further calculated. At the same time, a fifth preset threshold (Delta_Threshold) is set to determine whether the difference between the load indicator and the preset threshold is close to identify potential low load conditions. When the difference between the load indicator and the preset threshold is less than the fifth preset threshold, it is considered that the node has a risk of too low load, and the warning condition is triggered to generate warning information, including but not limited to the name of the node, the current load indicator, the preset threshold, the difference, etc. The warning information can be sent to the management object of the dedicated network in a variety of ways, such as email, SMS, system message push, etc., to ensure the timeliness and reliability of the information. After receiving the warning information, the management object can take actions based on the warning information and recommended measures, such as manually adjusting resource allocation, optimizing network policies, or implementing automated resource scheduling to avoid resource waste and improve network efficiency.

[0058] Through the above implementation, the load status of network nodes can be effectively monitored, not only warnings can be issued when overloaded, but also warnings can be sent to management objects when the load is too low and resources are not fully utilized, helping to adjust network policies in a timely manner to ensure effective resource utilization and efficient network operation. This early warning mechanism is particularly important for the dedicated network of the cloud platform, which can help operation and maintenance personnel make more reasonable decisions on resource management and allocation, reduce unnecessary resource waste, and improve the overall performance and stability of the system.

[0059] In some optional embodiments of the present application, obtaining application priority information and application load information for each application under the same source IP address can be achieved through the following steps: obtaining an application load model table, wherein the application load model table includes: application name information, application description information, application priority information, application address information, application port number information, application status information, and application load information; and filtering the application priority information and application load information for each application under the same source IP address in the application load model table.

[0060] In the above embodiment, first, an application load model table is created, which includes the following fields: application name information (AppName), application description information (AppDesc), application priority information (AppPriority), application address information (AppAddress), application port number information (AppPort), application status information (AppState), and application load information (AppLoad). These fields are used to store key information related to each application running on the cloud platform, including priority and load. Then, the basic information of each application is entered into the application load model table, and the real-time status and load information of each application is updated regularly. Data updates can be event-driven. When the Telemetry protocol detects changes in the application status or load, an update is triggered immediately, or a regular refresh method is used to automatically update the data at regular intervals (such as 5 minutes or 15 minutes).

[0061] You can use SQL query or database query function to filter out all applications with the same source IP address from the application load model table. You can use the following SQL statement:

[0062] SELECT AppName,AppPriority,AppLoad

[0063] FROM AppLoadModel

[0064] WHERE AppAddress = 'specified source IP address';

[0065] Or use more complex queries, combined with source port number information, to further refine the query results, such as:

[0066] SELECT AppName,AppPriority,AppLoad

[0067] FROM AppLoadModel

[0068] WHERE AppAddress = 'specified source IP address' AND AppPort = 'specified source port number'.

[0069] You can also analyze the node and application traffic usage and node load status based on the above model table, and derive the application score AP*AL=AppScore based on the application priority AP and the application load level AL, sort the applications, and derive the data sending source with the largest current traffic dynamic consumption.

[0070] For each filtered application, its application priority information (AppPriority) and application load information (AppLoad) are further processed and analyzed. Application priority information reflects the importance of the application in business processing, while application load information indicates the current resource consumption of the application. By comparing the priority and load of each application, it can be determined which applications may need more resource support, or which applications have a load that exceeds the resources that should be allocated according to their priority.

[0071] Through the above implementation, the information in the application load model table can be effectively used to screen, score, sort and identify applications under the same source IP address, provide a decision basis for subsequent traffic adjustment and resource optimization, and ensure efficient use of network resources and smooth operation of services. This dynamic adjustment mechanism based on application priority and load is crucial to improving the performance and stability of the private network of the cloud platform.

[0072] As other optional embodiments of the present application, collecting multiple network data packets forwarded by multiple network nodes in a cloud platform of a private network can be achieved through the following steps: a target node in a public network connected to the cloud platform of the private network collects multiple network data packets forwarded by multiple network nodes in the cloud platform of the private network by enabling a target protocol, wherein the target protocol includes: Telemetry protocol.

[0073] In the above embodiment, the Telemetry protocol client is first installed and configured on the target node. The client can communicate with the network equipment in the cloud platform of the dedicated network to collect real-time network data packet information. The collection frequency and data type of the Telemetry protocol are configured. The collection frequency is high enough to reflect the network status in real time, but the limitations of network bandwidth and device performance must also be taken into account. The data type includes source IP address, target IP address, source port number, target port number, data packet size, etc., to analyze network traffic and node load.

[0074] The target node starts to collect network data packets forwarded by multiple network nodes in the private network through the Telemetry protocol. The collection of data packets can be continuous or dynamically adjusted according to network conditions and collection requirements. The Telemetry protocol transmits the collected network data packet information to the target node in real time. Encryption technology, such as SSL / TLS, is used during the transmission process to ensure data security and privacy protection. At the same time, in order to improve the efficiency and reliability of data transmission, streaming can be used to push data packet information to the target node in real time to avoid delays and potential data loss caused by batch transmission of large amounts of data.

[0075] After receiving the network data packet information, the target node uses a dedicated data processing program to parse and format it. The parsed content includes key information such as the size of the data packet, the IP address and port number of the source and target nodes. The network data packet information is analyzed and the data packet forwarding volume of each dedicated network node is counted. This helps to evaluate the load of the node and provide a basis for subsequent traffic adjustment and resource optimization. By analyzing the data packet information, the target node can detect network anomalies and potential performance bottlenecks, providing support for the rapid location and processing of network failures.

[0076] Specifically, a traffic alarm threshold Threshold is set according to the load condition of the node. When the threshold is exceeded, the diversion operation is automatically performed, that is, the size of the data packet sent by each source IP address is analyzed through a certain algorithm, combined with the load condition of the current node, and the appropriate sending source is selected to ensure that the utilization rate of the current node is reasonable and the load of the node is normal.

[0077]

[0078] When the target node is overloaded, the appropriate solution is dynamically selected based on the relationship between data traffic and the threshold. For example, when it approaches the threshold, an early warning message is sent to the operator to prompt the operator to perform traffic control and reduce the aggregation layer load in time. Manual operation can be performed at this time. When the threshold is exceeded, the device will select data sending sources with larger traffic consumption and lower APPScore for priority diversion operations.

[0079] Through the above implementation, the target node in the public network can collect and analyze the network packet information in the cloud platform of the private network in real time by enabling the Telemetry protocol, and provide accurate data support for traffic monitoring warning and dynamic traffic adjustment. This real-time monitoring and dynamic adjustment mechanism based on the Telemetry protocol helps to improve the stability and efficiency of the network, while reducing operation and maintenance costs, and is one of the key technologies for the efficient operation of the cloud platform private network.

[0080] Figure 2 is a structural diagram of a data processing system according to an embodiment of the present application, Figure 2The system architecture in the figure mainly includes the following parts: SAAS (Software as a Service) represents various applications on the cloud platform, which users can access and use through the network. PAAS (Platform as a Service) represents the operating system and database. SAAS and PAAS provide underlying cloud resources through the customer's private cloud to support the operation of upper-layer services. The node traffic monitoring, early warning and adjustment device and SDN (Software-Defined Networking) are the core below. The node traffic monitoring, early warning and adjustment device is responsible for monitoring network traffic, analyzing node load, warning of traffic anomalies, and dynamically adjusting data forwarding strategies. SDN, as the core control layer of the network, intelligently manages network resources by receiving instructions from the monitoring and early warning device to achieve optimal distribution of traffic. Data is transmitted from SDN to the destination through the core / backbone network, and then aggregated through the aggregation layer, or data is distributed from SDN to each node. The hierarchical relationship of the entire system architecture and the interaction between components are shown.

[0081] Combine the following Figure 2 The data processing method is described, and the data processing method can also be implemented through the following steps.

[0082] Step S201, waiting for a data packet sent by a receiving node, parsing the data packet, monitoring and warning traffic usage.

[0083] Specifically, it is necessary to power on and turn on the data packet receiving device, which is the core device for receiving data (including source IP address, target IP address, source port number, target port number and number of bytes in the data packet), parsing data, monitoring and early warning.

[0084] Step S202: The node connected to the customer's private cloud starts the Telemetry protocol to monitor and record the performance indicators of network devices, such as bandwidth utilization, latency, packet loss rate, etc. The collected data is sent to the traffic monitoring and early warning device for network analysis.

[0085] Specifically, the role of enabling the Telemetry protocol on load nodes is to monitor network performance, troubleshoot and warn of faults, monitor security and detect threats, plan capacity and optimize resources, manage service quality, and provide visual monitoring and reporting. It provides comprehensive, real-time, and intelligent network management capabilities to help network administrators better manage and maintain the network.

[0086] Step S203: flow monitoring and analysis.

[0087] The analysis method of this embodiment involved in step S203 is composed of the following five steps:

[0088] S2031, receiving data collected by the node when Telemetry is enabled (including source IP address, destination IP address, source port number, destination port number, and number of bytes in the data packet).

[0089] S2032, according to the format of the sent data, parse out the source IP address, destination IP address, source port number, destination port number and number of bytes of the data packet.

[0090] S2033, summarizing the parsed data to analyze the size of the data packets sent by each source IP address Where k is the number of packets, BW n is the number of bytes of the nth packet, and sorts the packet sizes of each source IP address (IP 1 , IP 2 , ..., IP i ), introduce the application load model table, the table design is as follows: application name (AppName), application description (AppDesc), application priority (AppPriority), application address (AppAddress), application port number (AppPort), application state (AppState), application load level (AppLevel), the traffic analysis device analyzes the node and application traffic usage and node load status according to the above model table, and obtains the application score AP*AL=AppScore according to the application priority AP and the application load level AL, sorts the applications, and obtains the data sending source with the largest current traffic dynamic consumption

[0091] S2034, set a traffic alarm threshold according to the load condition of the node. When it approaches the threshold, send an early warning message to the operator to prompt the operator to perform traffic control and reduce the node load in time.

[0092] S2035, sets the traffic alarm threshold Threshold according to the load condition of the node. When the threshold is exceeded, the diversion operation is automatically performed, that is, the size of the data packet sent by each source IP address is analyzed through a certain algorithm and combined with the load condition of the current node, and the appropriate sending source is selected to ensure that the utilization rate of the current node is reasonable and the load of the node is normal.

[0093]

[0094] Specifically, when the monitored node is overloaded, the appropriate solution is dynamically selected based on the relationship between data traffic and threshold. For example, when it approaches the threshold, an early warning message is sent to the operator to prompt the operator to control the traffic and reduce the load of the aggregation layer in time. Manual operation can be performed at this time. When the threshold is exceeded, the device will select the data transmission source with large traffic consumption and low APPScore according to step S2033 for priority diversion operation. Perform selection analysis and push the data source that needs to change the forwarding route to SDN.

[0095] Step S204, adjusting the data forwarding route.

[0096] The SDN receives the instructions pushed by the traffic monitoring and alarm device, monitors the load status of multiple nodes through the SDN, and sorts the nodes according to the load status (Load 1 , Load 2 , ..., Load n ), according to the minimum load With the shortest route

[0097] Figure 3 is a flow chart of another data processing method according to an embodiment of the present application. Figure 3 As shown, the method specifically includes the following steps.

[0098] Step S301, start the data packet receiving device.

[0099] TrafficMonitoringMain.start(); / / Start the overall system framework;

[0100] TMListener.start(); / / The receiver starts to receive data packets sent from the node and other operations.

[0101] Step S302, start the data analysis device.

[0102] Node.init(); / / The node connected to the customer's private cloud starts the Telemetry protocol to monitor and record the performance indicators of network devices, such as bandwidth utilization, latency, packet loss rate, etc.

[0103] Step S303, start the flow monitoring and early warning device to analyze the flow data.

[0104] Monitor and analyze the load status of the node in each time period, and set different plans for different conditions.

[0105] The analysis method of this embodiment involved in step S303 is composed of the following five steps:

[0106] S3031, start the monitoring receiving device to receive the data collected by the node when Telemetry is turned on (including source IP address, destination IP address, source port number, destination port number and number of bytes in the data packet).

[0107] TMListener.start(); / / The receiver starts to receive data packets sent from the node and other operations.

[0108] S3032, start the data analysis device, and analyze the source IP address, destination IP address, source port number, destination port number and number of bytes of the data packet according to the format of the sent data.

[0109] TM.init(); / / Data parsing device, responsible for parsing the source IP address, destination IP address, source port number, destination port number and number of bytes of the data packet from the received data.

[0110] S3033, start the traffic analysis device, summarize the parsed data, and analyze the size of the data packets sent by each source IP address , where k is the number of packets, BW n is the number of bytes of the nth packet, and sorts the packet sizes of each source IP address (IP 1 , IP 2 , ..., IP i ), introduces the application load model table, the traffic analysis device analyzes the node and application traffic usage and node load status according to the model table, obtains the application score AP*AL=AppScore according to the application priority AP and the application load level AL, sorts the applications, and obtains the data sending source with the largest current traffic dynamic consumption

[0111] TMAnalysis.start(); / / The traffic analysis device starts to perform statistical analysis and summary on the data packets of each source IP address.

[0112] S3034, turn on the traffic alarm device, set the traffic alarm threshold according to the load condition of the node, and when it approaches the threshold, send an early warning message to the operator to prompt the operator to perform traffic control and reduce the node load in time.

[0113] TMAlarm.start(); / / The traffic alarm device is started. According to the set traffic alarm threshold, when the load is close to the alarm threshold, an early warning message is sent to the operator through email or other means to prompt the operator to perform traffic control and reduce the node load in time.

[0114] S3035, turn on the automatic diversion device, set the traffic alarm threshold according to the node load condition, and automatically perform the diversion operation when the threshold is exceeded. That is, through a certain algorithm, the size of the data packet sent by each source IP address is analyzed in combination with the current node load condition, and the appropriate sending source is selected to ensure that the current node utilization rate is reasonable and the node load is normal.

[0115]

[0116] TMShunt.start(); / / The diversion device starts, and according to the set traffic alarm threshold, when the load exceeds the alarm threshold, the automatic diversion operation is performed.

[0117] Step S304, start the diversion device and adjust the data forwarding route.

[0118] The SDN receives the instructions pushed by the traffic monitoring and alarm device, monitors the load status of multiple nodes through the SDN, and sorts the nodes according to the load status (Load 1 , Load 2 , ..., Load n ), according to the minimum load With the shortest route

[0119] TMSendInstructions.start(); / / The device for adjusting the data forwarding route is started. When the traffic alarm exceeds the threshold and the operator does not perform manual processing, the diversion device will select the appropriate data source according to a certain algorithm, generate a corresponding instruction, and adjust the forwarding route of the data source.

[0120] In summary, Figure 3 The specific steps of the node traffic monitoring, early warning and adjustment device are shown in the figure, including the data packet receiving device, the data parsing device, the traffic analysis device, the traffic early warning device and the diversion device. First, the nodes of the customer's private cloud are initialized, and the customer's private cloud includes SAAS (customer application) and PAAS (operating system, database). Then, the Telemetry protocol is enabled and the data is sent to the traffic monitoring and early warning device for analysis, including data packet reception, data parsing, traffic analysis, traffic early warning and diversion. Finally, the obtained data is input into the SDN to complete a complete node traffic monitoring and data forwarding route adjustment cycle.

[0121] Figure 4 is a structural diagram of a data processing device according to an embodiment of the present application, such as Figure 4 As shown, the device comprises:

[0122] The collection module 40 is used to collect multiple network data packets forwarded by multiple network nodes in the cloud platform of the dedicated network, wherein each network data packet at least includes: a source IP address and a destination IP address.

[0123] The statistics module 42 is used to count the amount of data sent by the same source IP address within a preset time range in multiple network data packets, and to count the amount of data sent by each application under the same source IP address within a preset time range.

[0124] The determination module 44 is used to obtain the application priority information and application load information of each application under the same source IP address, and determine the application score of the application according to the application priority information and application load information of each application.

[0125] Processing module 46 is used to obtain the load index of the network node at the target time. When the load index is greater than a preset threshold, the target traffic corresponding to the target application under the target source IP address forwarded by the network node is diverted, wherein the difference between the target time and the end time in the preset time range is less than the first preset threshold, the target source IP address is a source IP address whose data volume sent within the preset time range is greater than the second preset threshold, and the target application is an application in the target source IP address whose data volume sent within the preset time range is greater than the third preset threshold and whose application score is less than the fourth preset threshold.

[0126] Optionally, the processing module 46 is also used to perform the following steps: obtaining network topology information corresponding to the cloud platform of the dedicated network, wherein the network topology information includes the connection relationship between network nodes and the real-time status information of the links between network nodes, and the real-time status information includes at least: link bandwidth, delay and packet loss rate; based on the network topology information, determining multiple candidate links from network nodes whose load indicators are greater than a preset threshold to other network nodes; for each candidate link, performing weighted summation on the link bandwidth, delay and packet loss rate therein to obtain a target length of each candidate link; obtaining the load indicators of other network nodes, and determining a node set consisting of nodes whose load indicators are less than a fifth preset threshold among the other network nodes, wherein the load indicator is determined at least based on the CPU usage and memory occupancy of the network node; in the node set, determining the first network node with the shortest target length, and sending the target traffic to the first network node to forward the target traffic through the first network node.

[0127] Optionally, the data processing device is also used to perform the following steps: if there are multiple second network nodes with the shortest target lengths in the node set, calculate the reliability index of each second network node according to the availability index of the second network node, the average time interval between two adjacent failures of the second network node, and the average time required for the second network node to resume normal operation from the occurrence of a failure, wherein the availability index is determined by the total operating time, the failure downtime and the total operating time; among the multiple second network nodes, determine the third network node with the largest reliability index, send the target traffic to the third network node, so as to forward the target traffic through the third network node.

[0128] Optionally, the data processing device is also used to perform the following steps after obtaining the network topology information corresponding to the cloud platform of the dedicated network: based on the Telemetry protocol, encapsulate the real-time status information in a standardized format, wherein the standardized format adopts a predefined JSON or binary encoding structure; transmit the encapsulation result to the central control server through a secure encrypted channel, wherein the secure encrypted channel adopts a secure socket layer or a transport layer security protocol; the central control server receives the encapsulation result, and presents the link bandwidth, delay and packet loss rate therein in the form of a visual curve; obtains a performance threshold, and when any indicator in the encapsulation result exceeds the performance threshold, triggers an alarm, wherein the performance threshold is dynamically adjusted through the network service level agreement and the historical status information of the network node.

[0129] Optionally, the data processing device is further used to execute the following steps: when the load index is less than a preset threshold and the difference between the load index and the preset threshold is less than a fifth preset threshold, send an early warning message to the management object of the private network.

[0130] Optionally, the determination module 44 is also used to perform the following steps: obtain an application load model table, wherein the application load model table includes: application name information, application description information, application priority information, application address information, application port number information, application status information, and application load information; and filter the application priority information and application load information of each application under the same source IP address in the application load model table.

[0131] Optionally, the collection module 40 is also used to perform the following steps: the target node in the public network connected to the cloud platform of the private network collects multiple network data packets forwarded by multiple network nodes in the cloud platform of the private network by enabling the target protocol, wherein the target protocol includes: Telemetry protocol.

[0132] It should be noted that the above Figure 4The modules in the embodiment may be program modules (e.g., a set of program instructions for implementing a specific function) or hardware modules. For the latter, they may be expressed in the following forms, but are not limited thereto: the expression form of the above modules is a processor, or the functions of the above modules are implemented by a processor.

[0133] It should be noted that Figure 4 The preferred implementation of the illustrated embodiment can be found in Figure 1 The relevant description of the illustrated embodiment will not be repeated here.

[0134] Figure 5 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a data processing method. Figure 5 As shown, the computer terminal 50 may include one or more (502a, 502b, ..., 502n are used to illustrate) processors 502 (the processor 502 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 504 for storing data, and a transmission module 506 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 5 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 5 More or fewer components as shown, or with Figure 5 Different configurations shown.

[0135] It should be noted that the one or more processors 502 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 50. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0136] The memory 504 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the data processing method in the embodiment of the present application. The processor 502 executes various functional applications and data processing by running the software programs and modules stored in the memory 504, that is, realizing the above-mentioned data processing method. The memory 504 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 504 may further include a memory remotely arranged relative to the processor 502, and these remote memories may be connected to the computer terminal 50 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0137] The transmission module 506 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 50. In one example, the transmission module 506 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 506 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0138] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 50 .

[0139] It should be noted that, in some optional embodiments, the above Figure 5 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 5 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0140] It should be noted that Figure 5 The computer terminal shown is used to execute Figure 1 The data processing method shown, therefore the relevant explanations in the execution method of the above command are also applicable to the electronic device and will not be repeated here.

[0141] An embodiment of the present application further provides a non-volatile storage medium, which includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the above data processing method.

[0142] A program for a non-volatile storage medium to perform the following functions: collecting multiple network data packets forwarded by multiple network nodes in a cloud platform of a dedicated network, wherein each network data packet includes at least: a source IP address and a target IP address; among the multiple network data packets, counting the amount of data sent by the same source IP address within a preset time range, and counting the amount of data sent by each application under the same source IP address within the preset time range; obtaining application priority information and application load information of each application under the same source IP address, and determining the application score of the application according to the application priority information and application load information of each application; obtaining the load index of the network node at a target time, and when the load index is greater than a preset threshold, performing diversion processing on the target traffic corresponding to the target application under the target source IP address forwarded by the network node, wherein the difference between the target time and the cutoff time in the preset time range is less than a first preset threshold, the target source IP address is a source IP address whose amount of data sent within the preset time range is greater than a second preset threshold, and the target application is an application whose amount of data sent within the preset time range in the target source IP address is greater than a third preset threshold and whose application score is less than a fourth preset threshold.

[0143] An embodiment of the present application further provides an electronic device, including: a memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the above data processing method is executed when the program is run.

[0144] The processor is used to run a program that performs the following functions: collecting multiple network data packets forwarded by multiple network nodes in a cloud platform of a dedicated network, wherein each network data packet includes at least: a source IP address and a target IP address; among the multiple network data packets, counting the amount of data sent by the same source IP address within a preset time range, and counting the amount of data sent by each application under the same source IP address within the preset time range; obtaining application priority information and application load information of each application under the same source IP address, and determining the application score of the application according to the application priority information and application load information of each application; obtaining the load index of the network node at a target time, and when the load index is greater than a preset threshold, performing diversion processing on the target traffic corresponding to the target application under the target source IP address forwarded by the network node, wherein the difference between the target time and the cutoff time in the preset time range is less than a first preset threshold, the target source IP address is a source IP address whose amount of data sent within the preset time range is greater than a second preset threshold, and the target application is an application whose amount of data sent within the preset time range in the target source IP address is greater than a third preset threshold and whose application score is less than a fourth preset threshold.

[0145] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0146] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0147] In the above-mentioned embodiments of the present application, the collected information is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary protection measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0149] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0150] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0151] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0152] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A data processing method, characterized in that: include: Collecting multiple network data packets forwarded by multiple network nodes in a cloud platform of a dedicated network, wherein each of the network data packets at least includes: a source IP address and a destination IP address; Among the plurality of network data packets, counting the amount of data sent by the same source IP address within a preset time range, and counting the amount of data sent by each application under the same source IP address within a preset time range; Obtaining application priority information and application load information of each application under the same source IP address, and determining an application score of the application according to the application priority information and the application load information of each application; Obtain a load index of the network node at a target time, and when the load index is greater than a preset threshold, perform diversion processing on the target traffic corresponding to the target application under the target source IP address forwarded by the network node, wherein the difference between the target time and the end time in the preset time range is less than a first preset threshold, the target source IP address is a source IP address whose amount of data sent within the preset time range is greater than a second preset threshold, and the target application is an application in the target source IP address whose amount of data sent within the preset time range is greater than a third preset threshold and whose application score is less than a fourth preset threshold.

2. The method according to claim 1, characterized in that Diverting target traffic corresponding to a target application under a target source IP address forwarded by the network node, including: Acquire network topology information corresponding to the cloud platform of the dedicated network, wherein the network topology information includes connection relationships between the network nodes and real-time status information of links between the network nodes, and the real-time status information includes at least: link bandwidth, delay, and packet loss rate; Based on the network topology information, determining a plurality of candidate links from the network node whose load indicator is greater than a preset threshold to other network nodes; For each of the candidate links, weighted summing the link bandwidth, the delay and the packet loss rate therein is performed to obtain a target length of each of the candidate links; Obtaining load indicators of other network nodes, and determining a node set consisting of nodes whose load indicators are less than a fifth preset threshold value among the other network nodes, wherein the load indicator is determined at least according to a CPU usage rate and a memory occupancy rate of the network node; In the node set, a first network node with the shortest target length is determined, and the target traffic is sent to the first network node, so as to forward the target traffic through the first network node.

3. The method according to claim 2, characterized in that The method further comprises: If there are multiple second network nodes with the shortest target length in the node set, the reliability index of each second network node is calculated according to the availability index of the second network node, the average time interval between two adjacent failures of the second network node, and the average time required for the second network node to resume normal operation from the occurrence of a failure, wherein the availability index is determined by the total operating time, the failure downtime, and the total operating time; Among the plurality of the second network nodes, a third network node having the largest reliability index is determined, and the target traffic is sent to the third network node, so as to forward the target traffic through the third network node.

4. The method according to claim 2, characterized in that: After obtaining the network topology information corresponding to the cloud platform of the dedicated network, the method further includes: Based on the Telemetry protocol, the real-time status information is encapsulated in a standardized format, wherein the standardized format adopts a predefined JSON or binary encoding structure; Transmitting the encapsulation result to the central control server through a secure encrypted channel, wherein the secure encrypted channel adopts the Secure Sockets Layer or Transport Layer Security protocol; The central control server receives the encapsulation result and presents the link bandwidth, delay and packet loss rate therein in the form of a visual curve; A performance threshold is obtained, and when any indicator in the encapsulation result exceeds the performance threshold, an alarm is triggered, wherein the performance threshold is dynamically adjusted according to a network service level agreement and historical status information of network nodes.

5. The method according to claim 1, characterized in that The method further includes: sending warning information to a management object of the private network when the load indicator is less than the preset threshold and the difference between the load indicator and the preset threshold is less than a fifth preset threshold.

6. The method according to claim 1, characterized in that Obtaining application priority information and application load information of each application under the same source IP address, including: Obtaining an application load model table, wherein the application load model table includes: application name information, application description information, application priority information, application address information, application port number information, application status information, and application load information; The application priority information and application load information of each application program under the same source IP address are screened in the application load model table.

7. The method according to claim 1, characterized in that Collecting multiple network data packets forwarded by multiple network nodes in a cloud platform of a private network includes: a target node in a public network connected to the cloud platform of the private network collects multiple network data packets forwarded by multiple network nodes in the cloud platform of the private network by enabling a target protocol, wherein the target protocol includes: Telemetry protocol.

8. A data processing device, characterized in that: include: A collection module, used for collecting a plurality of network data packets forwarded by a plurality of network nodes in a cloud platform of a dedicated network, wherein each of the network data packets at least includes: a source IP address and a destination IP address; A statistics module, used to count the amount of data sent by the same source IP address within a preset time range in a plurality of the network data packets, and to count the amount of data sent by each application under the same source IP address within a preset time range; A determination module, configured to obtain application priority information and application load information of each application under the same source IP address, and determine an application score of the application according to the application priority information and the application load information of each application; A processing module is used to obtain the load index of the network node at a target time, and when the load index is greater than a preset threshold, the target traffic corresponding to the target application under the target source IP address forwarded by the network node is diverted, wherein the difference between the target time and the end time in the preset time range is less than a first preset threshold, the target source IP address is a source IP address whose data volume sent within the preset time range is greater than a second preset threshold, and the target application is an application in the target source IP address whose data volume sent within the preset time range is greater than a third preset threshold and whose application score is less than a fourth preset threshold.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the data processing method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the data processing method according to any one of claims 1 to 7 is implemented.

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