Intelligent global hybrid cloud routing system based on SD-WAN architecture

Abstract

The invention provides an SD-WAN architecture-based intelligent global hybrid cloud routing system, and belongs to the technical field of network routing, and the system comprises a data acquisition module which is used for compressing a to-be-transmitted data stream based on a preset compression algorithm so as to obtain a compressed data stream; the node determination module is used for acquiring network performance related data and determining a plurality of dynamic distributed nodes corresponding to the SD-WAN control layer; the node connection module is used for connecting all the distributed nodes and connecting all the distributed nodes to the SD-WAN control layer at the same time; the score determination module is used for collecting real-time network state data of all the distributed nodes, performing data analysis and determining a path score of each available path in combination with a preset user demand; the path determination module is used for determining an optimal path and a redundant path; and the path switching module is used for acquiring network state data of all the distributed nodes after the optimal path performs data transmission, and further determining a redundancy switching condition and performing redundancy path switching. And the reliability and adaptability of the network are improved.

Description

Technical Field

[0001] The present invention relates to the field of network routing technology, and in particular to an intelligent global hybrid cloud routing system based on SD-WAN architecture. Background Art

[0002] With the rapid development of cloud computing and Internet technology, enterprises and individuals have an increasing demand for global network connections. The traditional wide area network (WAN) architecture can no longer meet the growing demand for data transmission, and there are problems such as limited network bandwidth, unstable connection and complex management.

[0003] SD-WAN (Software Defined Wide Area Network) technology came into being, optimizing network performance through centralized control and intelligent path selection. However, existing SD-WAN solutions are based on static path configuration and cannot adjust paths in real time to cope with complex and changeable network conditions, resulting in low transmission efficiency under different network conditions. Existing SD-WAN solutions usually lack dynamic network performance evaluation and intelligent path selection capabilities, and cannot adjust paths in real time according to network status and user needs. Existing technologies mostly rely on manual configuration or simple rule-driven, and fail to make full use of real-time data from the network to optimize routing strategies. Therefore, there are problems such as insufficient intelligence in path selection, delayed response when network status changes, and untimely switching of redundant paths, which affect the performance and reliability of the network.

[0004] Therefore, the present invention provides an intelligent global hybrid cloud routing system based on SD-WAN architecture. Summary of the invention

[0005] The present invention provides an intelligent global hybrid cloud routing system based on the SD-WAN architecture, which overcomes the shortcomings of inflexible path selection, delayed response, and untimely redundant switching in the existing SD-WAN technology by introducing dynamic distributed nodes and intelligent path scoring mechanisms. By collecting network performance data and user needs in real time, the system can automatically optimize path selection to ensure the stability and efficiency of data transmission. The node connection and path determination modules work together to enable rapid adjustment of the path when the network status changes, thereby improving the reliability and adaptability of the network. In addition, the redundant path switching module can switch to the backup path in a timely manner, effectively avoiding network interruption and significantly improving the overall network performance and user experience.

[0006] The present invention provides an intelligent global hybrid cloud routing system based on SD-WAN architecture, comprising:

[0007] Data acquisition module: obtains the data stream to be transmitted, compresses the data stream to be transmitted based on a preset compression algorithm, and then obtains the compressed data stream;

[0008] Node determination module: obtains network performance-related data based on the preset network performance monitoring tool, and at the same time, performs demand analysis on the preset network planning requirements, and determines several dynamic distributed nodes corresponding to the SD-WAN control layer based on the demand analysis results, network performance-related data, and compressed data streams;

[0009] Node connection module: obtains the node type of each distributed node, and then connects all distributed nodes. At the same time, all distributed nodes are connected to the SD-WAN control layer;

[0010] Scoring determination module: Based on the SD-WAN control layer, it collects real-time network status data of all distributed nodes and performs data analysis, and then determines the path score of each available path based on preset user needs;

[0011] Path determination module: determines the optimal path and redundant paths based on the path score of each available path and the real-time network status data of all distributed nodes;

[0012] Path switching module: Based on the SD-WAN control layer, the optimal path is obtained for data transmission, and the network status data of all distributed nodes is obtained, and then the redundant switching conditions are determined and whether to perform redundant path switching. If it is determined that redundant path switching is to be performed, redundant path switching is performed.

[0013] Edge routing cache module: obtains the access characteristics of the data to be transmitted, and caches the user's frequently accessed data in the edge router in combination with the preset cache strategy, obtains network status data and dynamically adjusts the cache content, thereby optimizing the data transmission process.

[0014] Preferably, the node determination module includes:

[0015] Initial node determination unit: performs demand analysis on preset network planning requirements and determines a number of initial distributed nodes based on the demand analysis results;

[0016] Demand acquisition unit: performs compression analysis on the compressed data stream, determines the compression ratio, and obtains the transmission demand based on the preset compression ratio-demand database;

[0017] Node adjustment unit: perform a first adjustment on the initial distributed nodes based on the transmission demand, obtain network performance related data based on a preset network performance monitoring tool, perform a second adjustment on the initial distributed nodes after the first adjustment based on the network performance related data, and then obtain a number of dynamic distributed nodes.

[0018] Preferably, the initial node determination unit includes:

[0019] Demand determination subunit: performs demand analysis on the preset network planning requirements, and then determines several data centers and several business requirements corresponding to each data center;

[0020] Type determination subunit: determines a number of node types corresponding to each business requirement based on the type of each business requirement and a preset type-node type database;

[0021] Demand analysis subunit: performs demand analysis on each business demand corresponding to each data center, and then determines the number of nodes of each node type corresponding to each business demand, and then determines the node type corresponding to each data center and the number of nodes of each node type;

[0022] Location determination subunit: determines the location of the node corresponding to each data center based on the location of each data center;

[0023] Node determination subunit: Determines a number of nodes corresponding to each data center based on the location of the nodes corresponding to each data center, the node type corresponding to each data center, and the number of nodes of each node type, and then determines the nodes of all data centers as initial distributed nodes.

[0024] Preferably, the node adjustment unit includes:

[0025] Node evaluation subunit: obtains network performance related data based on a preset network performance monitoring tool, and evaluates each initial distributed node based on the network performance related data;

[0026] A node determination subunit: determining a first topological structure of an area within a preset range where each initial distributed node is located based on a preset routing table, searching the first topological structure of an area within a preset range where each initial distributed node is located based on a preset graph search algorithm, and then determining a number of associated nodes of each initial distributed node;

[0027] Node adjustment subunit: when the evaluation result of each initial distributed node does not meet the preset evaluation requirements, the load of each initial distributed node is dispersed based on the associated nodes of each initial distributed node.

[0028] Preferably, the node adjustment subunit includes:

[0029] Number determination block: when the evaluation result of each initial distributed node does not meet the preset evaluation requirements, the number of associated nodes used to disperse the load of each initial distributed node is determined based on the evaluation result of each initial distributed node;

[0030] Threshold determination block: obtains the distance between each initial distributed node and each associated node, and then determines the distance threshold corresponding to each initial distributed node in combination with the number of associated nodes of each initial distributed node load:

[0031]

[0032] Among them, T i is the distance threshold of the i-th initial distributed node, M i is the number of all associated nodes of the i-th initial distributed node, d ij is the distance between the i-th initial distributed node and the j-th associated node, N i is the number of associated nodes used to distribute the load of the i-th initial distributed node, L i is the load of the i-th initial distributed node, V i is the load change coefficient of the i-th initial distributed node, where δ i is the distance fluctuation coefficient of the i-th node;

[0033] Node determination block: obtain the associated nodes whose distances between each initial distributed node and each associated node are less than the distance threshold and determine them as distributed nodes;

[0034] Load spreading block: Spread the load of each initial distributed node based on the distributed nodes of each initial distributed node.

[0035] Preferably, the scoring determination module includes:

[0036] Path determination unit: determines the source node and the target node based on preset user requirements, and then determines several available paths between the source node and the target node;

[0037] Scoring determination unit: Based on the SD-WAN control layer, the real-time network status data of all distributed nodes is collected and analyzed, and then the path score of each available path is determined based on the preset user needs.

[0038] Preferably, the score determination subunit includes:

[0039] Data collection block: Use the preset network monitoring tools in the SD-WAN control layer to collect real-time network status data of all distributed nodes. The real-time network status data includes several performance indicators.

[0040] Scoring calculation block: Determines the weight of each performance indicator based on preset user requirements, and then determines the path score of each available path in combination with the preset scoring algorithm.

[0041] Preferably, the path determination module includes:

[0042] Estimation determination unit: determines the estimated path score of each available path based on the real-time network status data of all distributed nodes and a preset algorithm;

[0043] A score determination unit: determining a comprehensive path score of each available path based on the path score of each available path and the estimated path score;

[0044] Optimal determination unit: determines the available path with the highest comprehensive path score as the optimal path, and determines other available paths except the path with the highest score as candidate available paths;

[0045] The redundancy determination unit determines the candidate available paths whose comprehensive path scores are greater than a preset score threshold as redundant paths.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] By introducing dynamic distributed nodes and intelligent path scoring mechanisms, the shortcomings of inflexible path selection, delayed response, and untimely redundant switching in existing SD-WAN technologies are overcome. By collecting network performance data and user needs in real time, the system can automatically optimize path selection to ensure the stability and efficiency of data transmission. The node connection and path determination modules work together to quickly adjust the path when the network status changes, improving the reliability and adaptability of the network. In addition, the redundant path switching module can switch to the backup path in a timely manner, effectively avoiding network interruptions and significantly improving the overall network performance and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 It is a structural diagram of an intelligent global hybrid cloud routing system based on SD-WAN architecture provided by an embodiment of the present invention;

[0050] Figure 2 It is a structural diagram of a node determination module of an intelligent global hybrid cloud routing system based on the SD-WAN architecture provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Embodiment 1:

[0053] The embodiment of the present invention provides an intelligent global hybrid cloud routing system based on SD-WAN architecture, such as Figure 1 As shown, including:

[0054] Data acquisition module: obtains the data stream to be transmitted, compresses the data stream to be transmitted based on a preset compression algorithm, and then obtains the compressed data stream;

[0055] Node determination module: obtains network performance-related data based on the preset network performance monitoring tool, and at the same time, performs demand analysis on the preset network planning requirements, and determines several dynamic distributed nodes corresponding to the SD-WAN control layer based on the demand analysis results, network performance-related data, and compressed data streams;

[0056] Node connection module: obtains the node type of each distributed node, and then connects all distributed nodes. At the same time, all distributed nodes are connected to the SD-WAN control layer;

[0057] Scoring determination module: Based on the SD-WAN control layer, it collects real-time network status data of all distributed nodes and performs data analysis, and then determines the path score of each available path based on preset user needs;

[0058] Path determination module: determines the optimal path and redundant paths based on the path score of each available path and the real-time network status data of all distributed nodes;

[0059] Path switching module: Based on the SD-WAN control layer, the optimal path is obtained for data transmission, and the network status data of all distributed nodes is obtained, and then the redundant switching conditions are determined and whether to perform redundant path switching. If it is determined that redundant path switching is to be performed, redundant path switching is performed.

[0060] Edge routing cache module: obtains the access characteristics of the data to be transmitted, and caches the user's frequently accessed data in the edge router in combination with the preset cache strategy, obtains network status data and dynamically adjusts the cache content, thereby optimizing the data transmission process.

[0061] In this embodiment, the preset compression algorithm is based on H.265 / HEVC optimization: by dynamically adjusting the H.265 encoding parameters, the coding efficiency of high-motion pictures (such as product displays) and static pictures (such as anchor pictures) in the e-commerce live broadcast scene is optimized: dynamic bit rate adjustment: real-time adjustment of the bit rate according to the user bandwidth (CBR / VBR hybrid mode), scene adaptive optimization: by analyzing the live screen content, using the region of interest encoding (ROI Encoding) to improve the clarity of the product area, reduce the coding resource occupation of the background area, and implement H.265 / HEVC-based optimization in the e-commerce live broadcast scene, improve the coding efficiency and video quality, and adaptively adjust according to the user bandwidth and screen content to achieve effective compression of the data stream. Then realize data stream compression.

[0062] In this embodiment, the preset network performance monitoring tool refers to a tool or device that is pre-configured in the system and is used to monitor and analyze network performance in real time. These tools can collect performance data such as network bandwidth, latency, packet loss rate, network congestion, etc., to help the system understand the health of the network. For example, network management software (such as SolarWinds, PRTG Network Monitor) is used to monitor the bandwidth usage and latency of each node in the enterprise network in real time, and collect these data for subsequent path selection and redundant switching decisions.

[0063] In this embodiment, the SD-WAN control layer is the central management layer in the entire SD-WAN architecture, responsible for monitoring and controlling the path selection of network traffic, processing user needs, and issuing control instructions to each distributed node. It realizes intelligent routing optimization by exchanging data with each node. For example, the control layer of an SD-WAN may include a centralized SD-WAN management platform for monitoring the network status of each distributed node and dynamically selecting the optimal path based on real-time data. Common SD-WAN control platforms include Viptela, VeloCloud, etc.

[0064] In this embodiment, the node type of each distributed node refers to the role or function of the node in the network. For example, some nodes may be data center nodes, some nodes may be routers of branch offices, or may be redundant nodes for backup. For example, one distributed node may be a data center at the corporate headquarters, and another node may be a network router at a remote office. The node type may affect the priority and path selection of data transmission.

[0065] In this embodiment, all distributed nodes are connected, and at the same time, all distributed nodes are connected to the SD-WAN control layer, which means that the system connects all distributed nodes together through the network to form a unified network architecture. At the same time, these nodes also need to be connected to the SD-WAN control layer so that the control layer can monitor and manage the network status and path selection of all nodes in real time. For example, suppose a multinational enterprise has multiple data centers and branches in different regions, and distributed nodes are deployed at each location. These nodes (such as routers in data centers or network equipment in remote offices) are connected via the Internet or dedicated lines, and are connected to the control layer of SD-WAN (such as a central management platform) for network traffic management and routing selection.

[0066] In this embodiment, the optimal path refers to the data transmission path that can provide the best performance (such as bandwidth, low latency, and low packet loss rate) under a given network state. The redundant path is a backup path, which is usually enabled when the optimal path fails or performance degrades to ensure the continuity and reliability of the network connection. Assuming that the path from A to B is the main path, it has the best bandwidth and latency performance and is called the optimal path. If the path is unavailable for some reason, the system will automatically switch to the alternative path C, that is, the redundant path, to ensure that data can continue to be transmitted.

[0067] In this embodiment, the redundant switching condition refers to the condition or standard for the system to determine whether to switch to the redundant path. For example, when the delay of the path exceeds a certain threshold, or the data packet loss rate is too high, the system will consider that the redundant path needs to be enabled. If the packet loss rate of a certain path exceeds 5%, or the delay increases to more than 300ms, the system will trigger the redundant switching condition and start the backup path;

[0068] In this embodiment, redundant path switching means that when a problem occurs in the primary path or performance degrades, the system automatically switches to the redundant path to ensure the stability of the network connection and the continuity of data transmission. For example, when the bandwidth of the optimal path decreases or a failure occurs, the system automatically switches the traffic to the redundant path to ensure that the network service is not interrupted. This redundant switching is usually performed in the network architecture of the cloud service provider to improve fault tolerance.

[0069] In this embodiment, the edge routing cache module includes the following units:

[0070] Traffic detection unit: monitors the data type requested by users (such as video streams, API requests, static resources, etc.), counts the frequency of statistical access, identifies high-frequency access content, and determines data cache priority.

[0071] Cache strategy judgment unit: Based on user access behavior and network load, decide whether to cache data, use machine learning algorithms to predict traffic, improve cache hit rate, combine data deduplication and compression technology, optimize storage space, and improve cache efficiency.

[0072] Edge storage unit: Store high-frequency access data in edge routers to reduce remote server access; support P2P cache sharing, collaborative storage between multiple edge devices, and reduce bandwidth consumption. Adopt LRU (least recently used) algorithm to regularly eliminate low-frequency access data and improve cache utilization.

[0073] Intelligent scheduling unit: Through intelligent scheduling, when users request data, they will first obtain data from the local cache or P2P node, reducing the pressure of remote server access; combined with the zero packet loss mechanism, it ensures the stability of data transmission and improves the transmission quality.

[0074] Cache update and invalidation processing unit: uses TTL (time to live) control to ensure that cached data does not expire; combined with dynamic cache synchronization, it ensures that cached data is consistent with remote server data to avoid cache pollution, supports real-time data update mechanism, automatically synchronizes data with high change rates, and improves system adaptability.

[0075] The beneficial effects of the above technical solution: by introducing an intelligent global hybrid cloud routing system based on the SD-WAN architecture, the problem of static and inflexible network path selection in the prior art is solved. By dynamically acquiring network performance data and user needs, the system can intelligently evaluate and optimize the data transmission path in real time to ensure the selection of the optimal path. In addition, the redundant path switching module automatically judges and switches to the backup path when the network status changes, effectively improving the reliability and fault tolerance of the network, reducing 30%-50% of repeated traffic requests, reducing enterprise operating costs, and improving data access speed: API request response time is shortened by 50%, live video freeze rate is reduced by 40%, and network traffic distribution is optimized: P2P cache sharing is used to improve data availability, reduce server pressure, and improve system stability: combined with intelligent scheduling and zero packet loss mechanism, ensure stable and reliable data transmission. This solution significantly improves the intelligence, real-time and adaptability of path selection, and optimizes data transmission efficiency and user experience in the global hybrid cloud environment.

[0076] Embodiment 2:

[0077] The embodiment of the present invention provides an intelligent global hybrid cloud routing system based on SD-WAN architecture, a node determination module, such as Figure 2 As shown, including:

[0078] Initial node determination unit: performs demand analysis on preset network planning requirements and determines a number of initial distributed nodes based on the demand analysis results;

[0079] Demand acquisition unit: performs compression analysis on the compressed data stream, determines the compression ratio, and obtains the transmission demand based on the preset compression ratio-demand database;

[0080] Node adjustment unit: perform a first adjustment on the initial distributed nodes based on the transmission demand, obtain network performance related data based on a preset network performance monitoring tool, perform a second adjustment on the initial distributed nodes after the first adjustment based on the network performance related data, and then obtain a number of dynamic distributed nodes.

[0081] In this embodiment, the initial distributed nodes refer to network nodes determined according to preset network planning requirements and demand analysis results, which are data centers, remote branch offices, or other necessary infrastructure nodes. They are the basic units for implementing data routing and transmission in the system. For example, in the network design of a global enterprise, the initial distributed nodes may include the data center of the headquarters, branches in various regions, and important edge nodes (such as cloud access points in specific regions. These initial nodes are set in the design stage according to the network requirements of the enterprise (such as high bandwidth and large traffic), and they form the initial architecture of the network. In the early stage of network deployment, network traffic may be transmitted through these nodes until the network path and node structure are optimized through real-time monitoring and adjustment.

[0082] In this embodiment, the first adjustment of the initial distributed node based on the transmission demand includes: querying the corresponding transmission demand from the database according to the compression ratio, assuming that the compression ratio is 3, after querying the database, it may be found that a certain bandwidth range (such as 10-20Mbps), a delay of no more than 200 milliseconds and a packet loss rate of less than 0.5% are required. Evaluate the load capacity of the existing nodes: check the existing load capacity of the initial distributed node, including the node's bandwidth, processing power (such as CPU, memory and storage resources) and the current network connection status. For example, view the bandwidth utilization, CPU usage and memory occupancy of each node through a network performance monitoring tool. Determine the available resources of the node: calculate how much additional load each node can carry. For an initial distributed node, if its current bandwidth utilization is 60% and its total bandwidth is 100Mbps, there is still 40Mbps of available bandwidth. According to the bandwidth demand obtained from the database, it is compared with the available bandwidth of the node. If the required bandwidth exceeds the available bandwidth of the node, it may be necessary to consider adding new links or upgrading existing links, or transferring part of the business to other nodes with more available bandwidth. For example, if the transmission demand indicates that 30 Mbps of bandwidth is required, but the current node only has 20 Mbps of available bandwidth, you can consider shifting part of the data traffic to other nodes, or adding a new link to the node to increase the bandwidth.

[0083] Beneficial effects of the above technical solution: By introducing the initial node determination unit and the node adjustment unit, intelligent dynamic node management based on the SD-WAN architecture is realized. First, the system determines the initial distributed nodes according to the preset network planning requirements analysis to provide an infrastructure for network construction. Then, by adjusting the position and configuration of the initial nodes based on the real-time data obtained by the network performance monitoring tool and combining the compressed data stream, the network performance is optimized to generate distributed nodes. Compared with the existing technology, this intelligent dynamic adjustment mechanism can respond to changes in network load in real time, ensure the stability, flexibility and efficiency of the network, greatly improve the reliability of the system and user experience, and meet the ever-changing network needs.

[0084] Embodiment 3:

[0085] An embodiment of the present invention provides an intelligent global hybrid cloud routing system based on an SD-WAN architecture, an initial node determination unit, including:

[0086] Demand determination subunit: performs demand analysis on the preset network planning requirements, and then determines several data centers and several business requirements corresponding to each data center;

[0087] Type determination subunit: determines a number of node types corresponding to each business requirement based on the type of each business requirement and a preset type-node type database;

[0088] Demand analysis subunit: performs demand analysis on each business demand corresponding to each data center, and then determines the number of nodes of each node type corresponding to each business demand, and then determines the node type corresponding to each data center and the number of nodes of each node type;

[0089] Location determination subunit: determines the location of the node corresponding to each data center based on the location of each data center;

[0090] Node determination subunit: Determines a number of nodes corresponding to each data center based on the location of the nodes corresponding to each data center, the node type corresponding to each data center, and the number of nodes of each node type, and then determines the nodes of all data centers as initial distributed nodes.

[0091] In this embodiment, business requirements refer to the network functions, performance or services required by an enterprise or organization in the network architecture design according to the actual application scenarios and goals. For example, a department may need a high-bandwidth, low-latency network connection to support key tasks such as video conferencing or big data analysis. For example, the business requirements of a financial institution may include fast real-time data transmission and highly secure data encryption. The business requirements of another e-commerce platform may include high availability and large-scale concurrent user access support.

[0092] In this embodiment, a data center refers to a physical facility used to centrally store, manage, and process large amounts of data and run various network application services. It usually includes infrastructure such as servers, storage devices, network hardware, and power supply and cooling systems to support the daily operations of an enterprise. For example, Amazon's AWS service or Google's Google Cloud data center, which provide cloud computing services and data storage for users around the world.

[0093] In this embodiment, the type of business demand refers to the way of classifying business demands according to different business scenarios or tasks. Each type of business demand may involve specific technical requirements, performance indicators or network configuration requirements. The type of business demand usually affects the configuration, node type and number of data centers. For example, the types of business demands include: high bandwidth demand: for large data transmission or high-resolution video streaming services, low latency demand: such as real-time voice communication, online games and other delay-sensitive applications, high security demand: such as financial services, medical data transmission and other applications that require high encryption and protection, high availability demand: such as key applications that need to ensure 24 / 7 operation, requiring no single point of failure and ensuring continuous service.

[0094] The beneficial effects of the above technical solution: Through the intelligent global hybrid cloud routing system based on the SD-WAN architecture, an accurate and dynamic node determination mechanism is provided. The system conducts a comprehensive demand analysis based on the preset network planning requirements, and intelligently determines the node type, quantity and specific location required for each data center in combination with the business needs and node types of each data center. This method is more flexible than traditional static configuration, and can dynamically adjust the node layout according to actual needs, optimize the network architecture, and improve the network performance, reliability and scalability of the system through precise node selection and configuration, ensuring efficient and stable data transmission in the global hybrid cloud environment, and effectively supporting the business needs and technological development of enterprises.

[0095] Embodiment 4:

[0096] An embodiment of the present invention provides an intelligent global hybrid cloud routing system based on an SD-WAN architecture, a node adjustment unit, including:

[0097] Node evaluation subunit: obtains network performance related data based on a preset network performance monitoring tool, and evaluates each initial distributed node based on the network performance related data;

[0098] A node determination subunit: determining a first topological structure of an area within a preset range where each initial distributed node is located based on a preset routing table, searching the first topological structure of an area within a preset range where each initial distributed node is located based on a preset graph search algorithm, and then determining a number of associated nodes of each initial distributed node;

[0099] Node adjustment subunit: when the evaluation result of each initial distributed node does not meet the preset evaluation requirements, the load of each initial distributed node is dispersed based on the associated nodes of each initial distributed node.

[0100] In this embodiment, the preset range of each initial distributed node refers to a specific area or network range where each initial distributed node is located. This range is pre-set and is used to evaluate the performance of the node or perform a topology search. This range is determined based on geographic location, network connectivity, bandwidth limitations or other network design factors. For example, suppose that in a global enterprise network, a distributed node is located in Asia. The preset range of the node may be a network area covering part of Asia, including multiple access points, routers and other infrastructure. During the evaluation, only the network topology within the range where the node is located will be considered, excluding other parts of the world.

[0101] In this embodiment, the first topology refers to a preliminary network topology formed within a preset range according to the network design and the connection mode of the nodes. This topology describes the connection relationship between the nodes, including the arrangement of the nodes, the connection path, and the network performance of each node. For example, in a regional network of a large enterprise, there may be multiple distributed nodes (such as routers in multiple branches). These nodes are connected to each other through dedicated lines or virtual private networks (VPNs) to form a local network topology. This local topology can be used as a "first topology" for subsequent performance evaluation and adjustment.

[0102] In this embodiment, the preset evaluation requirements refer to some standards or thresholds set according to the network performance monitoring tool during the node evaluation process. These requirements usually cover the performance indicators of the network (such as bandwidth, delay, packet loss rate, etc.), and are used to determine whether a node meets the performance requirements of the system. If the node does not meet the preset requirements, the corresponding load adjustment or node reconfiguration will be triggered. For example, the preset evaluation requirements of a distributed node may include: delay less than 100ms, bandwidth greater than 1Gbps, and packet loss rate less than 0.1%. If the evaluation result of a node does not meet these requirements (for example, the delay is too high or the packet loss rate is too large), the system will take measures to adjust the load of the node or replace it with other nodes.

[0103] The beneficial effects of the above technical solution are as follows: network performance is optimized by dynamically adjusting distributed nodes, network performance data is acquired and analyzed in real time through the node evaluation subunit, and the performance of each initial distributed node is evaluated. When a node does not meet the preset evaluation requirements, the node adjustment subunit distributes the load based on its associated nodes to ensure network load balance and stability. In addition, the system accurately determines the topological structure and associated nodes between nodes through routing tables and graph search algorithms, thereby improving the flexibility and scalability of the network. This intelligent and automated dynamic adjustment mechanism effectively avoids the network performance bottleneck that may be caused by traditional static node configuration and improves the overall efficiency and reliability of the system.

[0104] Embodiment 5:

[0105] An embodiment of the present invention provides an intelligent global hybrid cloud routing system based on an SD-WAN architecture, a node adjustment subunit, including:

[0106] Number determination block: when the evaluation result of each initial distributed node does not meet the preset evaluation requirements, the number of associated nodes used to disperse the load of each initial distributed node is determined based on the evaluation result of each initial distributed node;

[0107] Threshold determination block: obtains the distance between each initial distributed node and each associated node, and then determines the distance threshold corresponding to each initial distributed node in combination with the number of associated nodes of each initial distributed node load:

[0108]

[0109]

[0110] Among them, T i is the distance threshold of the i-th initial distributed node, M i is the number of all associated nodes of the i-th initial distributed node, d ij is the distance between the i-th initial distributed node and the j-th associated node, N i is the number of associated nodes used to distribute the load of the i-th initial distributed node, L i is the load of the i-th initial distributed node, V i is the load change coefficient of the i-th initial distributed node, where δ i is the distance fluctuation coefficient of the i-th node;

[0111] Node determination block: obtain the associated nodes whose distances between each initial distributed node and each associated node are less than the distance threshold and determine them as distributed nodes;

[0112] Load spreading block: Spread the load of each initial distributed node based on the distributed nodes of each initial distributed node.

[0113] In this embodiment, the number of associated nodes is calculated by the number determination block based on the situation that the evaluation result of each initial distributed node does not meet the preset evaluation requirements, and the number of associated nodes used to distribute the load is obtained. This number is usually determined by a certain algorithm or rule based on the evaluation results of the initial distributed nodes with heavy load or poor performance. For example, if the evaluation result shows that the load of the node exceeds a certain percentage (such as 80%), n associated nodes may be required to distribute the load, and the value of n may be dynamically adjusted according to the severity of the load and the capacity of the system.

[0114] In this embodiment, the distance fluctuation coefficient is an indicator used to measure the change in the distance between a node and its associated nodes. It reflects the connection stability of a node and the flexibility of network topology changes. A higher distance fluctuation coefficient means that the distance between the node and its associated nodes changes greatly, which may lead to unstable network connection; while a lower distance fluctuation coefficient indicates that the connection distance between nodes is relatively stable.

[0115] The beneficial effects of the above technical solution are as follows: through the number determination block and the threshold determination block, the system intelligently calculates the number of associated nodes required for load dispersion according to the performance evaluation results of each initial distributed node and the distance between the nodes, and sets a reasonable distance threshold, effectively selecting distributed nodes that are close to the initial node and can bear the load, reducing network delay and bandwidth pressure. At the same time, the load dispersion block can ensure that the load is balanced among the nodes, improving the stability and performance of the network. Compared with the traditional static load distribution method, the present invention has higher flexibility and adaptability, can be dynamically optimized according to the real-time network conditions, and improves the efficiency and reliability of the system.

[0116] Embodiment 6:

[0117] The embodiment of the present invention provides an intelligent global hybrid cloud routing system based on the SD-WAN architecture, a scoring determination module, including:

[0118] Path determination unit: determines the source node and the target node based on preset user requirements, and then determines several available paths between the source node and the target node;

[0119] Scoring determination unit: Based on the SD-WAN control layer, the real-time network status data of all distributed nodes is collected and analyzed, and then the path score of each available path is determined based on the preset user needs.

[0120] In this embodiment, the source node and the target node are determined based on the preset user needs, and then several available paths between the source node and the target node are determined as follows: First, the preset user needs need to be analyzed in depth. The user needs may include multiple aspects, such as service type (file transfer, video conferencing, database access, etc.), service quality requirements (acceptable delay range, required bandwidth, packet loss rate threshold, etc.), source location and target location information, etc. Service mapping: According to the service type, the node where the service is located is determined as the target node. For example, if the user needs to access a file storage service, the system will find the distributed node where the server storing the service is located and use it as the target node. This may be done through a service discovery protocol (such as DNS query, service registration center, etc.) or a preconfigured service mapping table. Source node determination: The source node is usually the network node to which the user device that initiates the service request is connected. For an enterprise network, the source node can be an access device of a branch office; for a user access network, the source node can be an access point to which the user's computer, mobile device, or IoT device is connected. For example, for a video conference request initiated by a mobile user, the source node can be the wireless network access point to which the user's mobile phone is connected. Determine several available paths between the source node and the target node: Topology information query: Network topology map usage: Use the network topology map stored in the SD-WAN control layer, which contains all distributed nodes and the connection relationships between them. By querying the topology map, you can find all possible combinations of connection links and intermediate nodes from the source node to the target node. These combinations constitute available paths. Routing table reference: View the routing table, which contains reachability information between network nodes. For each source node and target node pair, the routing table indicates which next-hop nodes are reachable, so that possible paths can be constructed. For example, starting from the source node, according to the next-hop information in the routing table, gradually extend to find multiple paths to the target node.

[0121] The beneficial effects of the above technical solution are: the scoring determination module is used to dynamically optimize the network path. Through the path determination unit, the system intelligently determines multiple available paths between the source node and the target node according to the preset user needs, and provides a variety of network options. The scoring determination unit assigns a path score to each path based on real-time network status data and the specific needs of the user to ensure the selection of the optimal network path. This dynamic scoring and path selection mechanism is more flexible than traditional static routing, and can provide more efficient and stable connections in complex and changing network environments, thereby improving network performance and user experience.

[0122] Embodiment 7:

[0123] An embodiment of the present invention provides an intelligent global hybrid cloud routing system based on an SD-WAN architecture, wherein a scoring determination subunit comprises:

[0124] Data collection block: Use the preset network monitoring tools in the SD-WAN control layer to collect real-time network status data of all distributed nodes. The real-time network status data includes several performance indicators.

[0125] Scoring calculation block: Determines the weight of each performance indicator based on preset user requirements, and then determines the path score of each available path in combination with the preset scoring algorithm.

[0126] In this embodiment, performance indicators are various data or standards for measuring network performance, which are generally used to evaluate network quality, stability, and efficiency. These indicators may include, but are not limited to, bandwidth, latency, packet loss rate, response time, throughput, etc. By monitoring and analyzing these indicators, the system can better understand the current network status and make appropriate adjustments. Example: For example, in an SD-WAN network, common performance indicators may include: Bandwidth: The maximum rate at which network data is transmitted, such as 100Mbps or 1Gbps. Latency: The data transmission delay from the source node to the destination node, such as 50 milliseconds. Packet loss rate: The proportion of packets lost during data transmission, such as a 1% packet loss rate. These performance indicators help to judge the quality of the network path, which in turn affects path selection.

[0127] In this embodiment, determining the weight of each performance indicator based on preset user needs means assigning different importance weights to different network performance indicators according to the specific needs of the user and the application scenario. Different applications or services are more dependent on certain performance indicators, so they need to be given higher weights in the scoring process. The weights are set according to specific business needs. For example, some applications may have extremely high requirements for latency and lower requirements for bandwidth. Assuming that the core business of an enterprise is video conferencing, latency (low latency is critical) may be more important than bandwidth (transmission rate). Based on this requirement, the system may assign a higher weight to latency (such as 0.7) and a lower weight to bandwidth (such as 0.3). In this way, when scoring the path, the impact of latency will be greater than bandwidth, so a path with lower latency will be selected.

[0128] In this embodiment, the path score of each available path is determined in combination with a preset scoring algorithm by using a weighted average formula to calculate the path score. Assuming that W = [w1, w2, w3, ..., wn] is the weight vector of each performance indicator, and P = [p1, p2, p3, ..., pn] is the normalized performance indicator vector, the path score Score can be calculated by the formula Score = w1*p1+w2*p2+w3*p3+...+wn*pn.

[0129] The beneficial effects of the above technical solution are: determining the sub-unit optimization network path selection through scoring, collecting the network status data of distributed nodes in real time at the SD-WAN control layer through the data acquisition block, including multiple performance indicators (such as delay, bandwidth, packet loss rate, etc.), to provide a data basis for path evaluation, and the scoring calculation block dynamically adjusts the weight of each performance indicator according to the specific needs of the user, and calculates the score of each path in combination with the preset scoring algorithm. It can intelligently select the optimal path according to the real-time network status and user needs, improve the flexibility and stability of the network, avoid the limitations of traditional static routing, and significantly improve network performance and user experience.

[0130] Embodiment 8:

[0131] The embodiment of the present invention provides an intelligent global hybrid cloud routing system based on the SD-WAN architecture, a path determination module, including:

[0132] Estimation determination unit: determines the estimated path score of each available path based on the real-time network status data of all distributed nodes and a preset algorithm;

[0133] A score determination unit: determining a comprehensive path score of each available path based on the path score of each available path and the estimated path score;

[0134] Optimal determination unit: determines the available path with the highest comprehensive path score as the optimal path, and determines other available paths except the path with the highest score as candidate available paths;

[0135] The redundancy determination unit determines the candidate available paths whose comprehensive path scores are greater than a preset score threshold as redundant paths.

[0136] In this embodiment, the estimated path score is a preliminary score for each available path based on real-time network status data and a preset algorithm. This score predicts the potential performance of each path by analyzing various performance indicators of the current network (such as delay, bandwidth, packet loss rate, etc.). The purpose of the estimated path score is to provide a preliminary assessment in the early stages of the path selection process to help further decision-making. Assume that there are three available paths in the network, path A, path B, and path C. Through the real-time collected network status data, the system evaluates that path A has a lower delay, path B has a larger bandwidth, and path C has a higher packet loss rate. Through the preset algorithm, the system generates an estimated path score for each path. For example, the estimated path score of path A may be 85 points, path B may be 90 points, and path C may be 60 points. This score reflects the preliminary quality assessment of each path.

[0137] In this embodiment, the preset scoring threshold refers to a minimum scoring standard set by the system during the path selection process. This threshold is used to determine which candidate paths are good enough in performance to be used as redundant paths. Paths with path scores lower than this threshold will be excluded and not selected as redundant paths. For example, assume that the preset scoring threshold of the system is 70 points. When the calculated path score is lower than 70 points, the path will be considered to have insufficient performance and cannot be used as a redundant path. For example, the comprehensive path score of path A is 80 points, and the comprehensive path score of path B is 60 points. Then the score of path B is lower than 70 points, so it will not be determined as a redundant path, while path A meets the requirements of a redundant path.

[0138] The beneficial effects of the above technical solution are as follows: the network path selection is optimized through the path determination module. The system generates an estimated path score for each available path based on the real-time network status data and the preset algorithm through the estimation determination unit, and calculates the comprehensive path score of each path in combination with the score determination unit. The optimal determination unit selects the path with the highest score as the optimal path, and other paths as candidate paths. The redundant determination unit determines the redundant path according to the preset score threshold to ensure the stability and reliability of the network connection. Compared with the traditional static path selection method, the present invention can dynamically select the optimal path according to the real-time network status and provide redundant paths for possible network interruptions, thereby improving network performance, fault tolerance and user experience.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent global hybrid cloud routing system based on SD-WAN architecture, characterized in that: include: Data acquisition module: obtains the data stream to be transmitted, compresses the data stream to be transmitted based on a preset compression algorithm, and then obtains the compressed data stream; Node determination module: obtains network performance-related data based on the preset network performance monitoring tool, and at the same time, performs demand analysis on the preset network planning requirements, and determines several dynamic distributed nodes corresponding to the SD-WAN control layer based on the demand analysis results, network performance-related data, and compressed data streams; Node connection module: obtains the node type of each distributed node, and then connects all distributed nodes. At the same time, all distributed nodes are connected to the SD-WAN control layer; Scoring determination module: Based on the SD-WAN control layer, it collects real-time network status data of all distributed nodes and performs data analysis, and then determines the path score of each available path based on preset user needs; Path determination module: determines the optimal path and redundant paths based on the path score of each available path and the real-time network status data of all distributed nodes; Path switching module: Based on the SD-WAN control layer, it obtains the network status data of all distributed nodes after the optimal path for data transmission, and then determines the redundant switching conditions and determines whether to perform redundant path switching. If it is determined that redundant path switching is to be performed, redundant path switching is performed; Edge routing cache module: obtains the access characteristics of the data to be transmitted, and caches the user's high-frequency access data in the edge router in combination with the preset cache strategy, obtains network status data and dynamically adjusts the cache content, thereby optimizing the data transmission process.

2. According to claim 1, an intelligent global hybrid cloud routing system based on SD-WAN architecture is characterized in that: The node determination module includes: Initial node determination unit: performs demand analysis on preset network planning requirements and determines a number of initial distributed nodes based on the demand analysis results; Demand acquisition unit: performs compression analysis on the compressed data stream, determines the compression ratio, and obtains the transmission demand based on the preset compression ratio-demand database; Node adjustment unit: perform a first adjustment on the initial distributed nodes based on the transmission demand, obtain network performance related data based on a preset network performance monitoring tool, perform a second adjustment on the initial distributed nodes after the first adjustment based on the network performance related data, and then obtain a number of dynamic distributed nodes.

3. The intelligent global hybrid cloud routing system based on SD-WAN architecture according to claim 2, characterized in that: The initial node determination unit includes: Demand determination subunit: performs demand analysis on the preset network planning requirements, and then determines several data centers and several business requirements corresponding to each data center; Type determination subunit: determines a number of node types corresponding to each business requirement based on the type of each business requirement and a preset type-node type database; Demand analysis subunit: performs demand analysis on each business demand corresponding to each data center, and then determines the number of nodes of each node type corresponding to each business demand, and then determines the node type corresponding to each data center and the number of nodes of each node type; Location determination subunit: determines the location of the node corresponding to each data center based on the location of each data center; Node determination subunit: Determines a number of nodes corresponding to each data center based on the location of the nodes corresponding to each data center, the node type corresponding to each data center, and the number of nodes of each node type, and then determines the nodes of all data centers as initial distributed nodes.

4. The intelligent global hybrid cloud routing system based on SD-WAN architecture according to claim 2, characterized in that: Node adjustment unit, including: Node evaluation subunit: obtains network performance related data based on a preset network performance monitoring tool, and evaluates each initial distributed node based on the network performance related data; A node determination subunit: determining a first topological structure of an area within a preset range where each initial distributed node is located based on a preset routing table, searching the first topological structure of an area within a preset range where each initial distributed node is located based on a preset graph search algorithm, and then determining a number of associated nodes of each initial distributed node; Node adjustment subunit: when the evaluation result of each initial distributed node does not meet the preset evaluation requirements, the load of each initial distributed node is dispersed based on the associated nodes of each initial distributed node.

5. The intelligent global hybrid cloud routing system based on SD-WAN architecture according to claim 4, characterized in that: Node adjustment subunit, including: Number determination block: when the evaluation result of each initial distributed node does not meet the preset evaluation requirements, the number of associated nodes used to disperse the load of each initial distributed node is determined based on the evaluation result of each initial distributed node; Threshold determination block: obtains the distance between each initial distributed node and each associated node, and then determines the distance threshold corresponding to each initial distributed node in combination with the number of associated nodes of each initial distributed node load: Among them, T i is the distance threshold of the i-th initial distributed node, M i is the number of all associated nodes of the i-th initial distributed node, d ij is the distance between the i-th initial distributed node and the j-th associated node, N i is the number of associated nodes used to distribute the load of the i-th initial distributed node, L i is the load of the i-th initial distributed node, V i is the load change coefficient of the i-th initial distributed node, where δ i is the distance fluctuation coefficient of the i-th node; Node determination block: obtain the associated nodes whose distances between each initial distributed node and each associated node are less than the distance threshold and determine them as distributed nodes; Load spreading block: Spread the load of each initial distributed node based on the distributed nodes of each initial distributed node.

6. The intelligent global hybrid cloud routing system based on SD-WAN architecture according to claim 1, characterized in that: Rating determination module, including: Path determination unit: determines the source node and the target node based on preset user requirements, and then determines several available paths between the source node and the target node; Scoring determination unit: Based on the SD-WAN control layer, the real-time network status data of all distributed nodes is collected and analyzed, and then the path score of each available path is determined based on the preset user needs.

7. The intelligent global hybrid cloud routing system based on SD-WAN architecture according to claim 6, characterized in that: Rating determination units include: Data collection block: Use the preset network monitoring tools in the SD-WAN control layer to collect real-time network status data of all distributed nodes. The real-time network status data includes several performance indicators. Scoring calculation block: Determines the weight of each performance indicator based on preset user requirements, and then determines the path score of each available path in combination with the preset scoring algorithm.

8. The intelligent global hybrid cloud routing system based on SD-WAN architecture according to claim 6, characterized in that: Path determination module, including: Estimation determination unit: determines the estimated path score of each available path based on the real-time network status data of all distributed nodes and a preset algorithm; A score determination unit: determining a comprehensive path score of each available path based on the path score of each available path and the estimated path score; Optimal determination unit: determines the available path with the highest comprehensive path score as the optimal path, and determines other available paths except the path with the highest score as candidate available paths; The redundancy determination unit determines the candidate available paths whose comprehensive path scores are greater than a preset score threshold as redundant paths.

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