Large-scale node routing matching method and system based on application traffic

Abstract

The invention discloses a large-scale node routing matching method and system based on application traffic, and relates to the technical field of routing decision, and the method comprises the following steps: traffic analysis, node classification, routing strategy formulation, routing table generation, real-time monitoring and adjustment, and optimization and iteration. The method can improve the reliability, availability and access efficiency of the system, and can ensure that only the routing table data of the related nodes are influenced when the nodes join or leave the distributed storage cluster at any time through the consistent Hash algorithm technology, thereby improving the reliability and availability of the system, being easy to expand, and reducing the cost. According to the wireless sensor network routing method, the problem of large-scale storage can also be supported, so that the routing table is small in scale, large-scale node application can be supported, high expansibility and energy conservation are achieved, the network life cycle is optimized, in the wireless sensor network routing method, energy conservation and network life cycle optimization are achieved in the mode that a core cluster head routing mechanism and a plane routing mechanism are combined, and the routing efficiency of the wireless sensor network is improved. And the number of network isolated points is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of routing decision making, and in particular to a large-scale node routing matching method and system based on application traffic. Background Art

[0002] With the rapid development of Internet applications, network traffic has shown an explosive growth trend. The increasing demand of users for applications such as high-definition video, online games, and social media has driven the further growth of network traffic. Route matching is the core link in network communication. It determines how data packets are transmitted from the source address to the destination address. Accurate and efficient route matching is essential to ensure the reliability and performance of network communications. Traditional routing methods, such as those based on static routing tables or simple dynamic routing protocols, have limitations when facing large-scale, dynamically changing network traffic. These methods may not be able to adapt to changes in network topology in a timely manner, resulting in routing errors or communication interruptions. Therefore, there is an urgent need for a large-scale node route matching method and system based on application traffic. The following problems exist in the existing technology:

[0003] 1. Existing large-scale node routing matching methods and systems based on application traffic. Although some methods support large-scale node applications, such as Linkstate network protocols, they have poor scalability and cannot support ultra-large-scale networks. Although routing protocols such as BGP support ultra-large scale and flexible policy control, the optimal path selection may not be based on the entire network topology information, resulting in end-to-end path selection not necessarily being optimal. Summary of the invention

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A large-scale node routing matching method based on application traffic includes the following steps:

[0006] Step 1: Traffic analysis;

[0007] Step 2: Node classification;

[0008] Step 3: Routing strategy formulation;

[0009] Step 4: Generate routing table;

[0010] Step 5: Real-time monitoring and adjustment;

[0011] Step 6: Optimize and iterate.

[0012] A further improvement of the technical solution of the present invention is that: the step one specifically refers to the need to collect and analyze application traffic data in the network, which can be achieved by monitoring network equipment, using data packet analysis tools or utilizing an SDN controller. The purpose of traffic analysis is to understand the traffic patterns in the network in order to provide appropriate routing strategies for different types of traffic.

[0013] A further improvement of the technical solution of the present invention is that: step two is to divide the nodes in the network into different categories according to the results of traffic analysis, and the nodes can be divided into high-traffic nodes, medium-traffic nodes and low-traffic nodes, which helps to allocate appropriate resources and routing strategies to different types of nodes.

[0014] A further improvement of the technical solution of the present invention is that: step three is to formulate corresponding routing strategies for different types of nodes. For high-traffic nodes, a load balancing strategy can be adopted to disperse the traffic to multiple nodes to avoid single-point overload; for low-traffic nodes, the shortest path strategy can be adopted to reduce delays and resource consumption.

[0015] A further improvement of the technical solution of the present invention is that: the fourth step is to generate a routing table for each node according to the established routing strategy, and the routing table should contain the best path information to other nodes and the relevant routing strategy. This can be achieved by using a routing algorithm and an SDN controller.

[0016] A further improvement of the technical solution of the present invention is that: in step five, in a large-scale network, the traffic pattern may change over time, and it is necessary to monitor the network status in real time and adjust the routing strategy and routing table according to the actual situation, which can be achieved by using automation tools and SDN controllers.

[0017] A further improvement of the technical solution of the present invention is that step six is ​​to continuously optimize the routing strategy and routing table based on monitoring data and performance indicators, which includes adjusting node classification, reallocating resources and improving routing algorithms. Through continuous iteration and optimization, more efficient and flexible network routing matching can be achieved.

[0018] A further improvement of the technical solution of the present invention is that: the step 1 also includes effect evaluation, and the specific evaluation method is as follows:

[0019] Q1: Performance indicators: By collecting and analyzing network performance indicators such as throughput, latency, packet loss rate, and bandwidth utilization, we can intuitively understand the impact of the route matching method on network performance. These indicators can help evaluate whether the method effectively improves the overall performance of the network;

[0020] Q2: Resource utilization: Evaluating the resource utilization of nodes in the network, such as CPU, memory, and storage, can help understand whether the route matching method can reasonably allocate and utilize network resources. Higher resource utilization may mean that the method can better meet network needs, while waste of resources may lead to performance degradation.

[0021] Q3: Traffic distribution: Observe the distribution of traffic in the network to evaluate whether the routing matching method can achieve reasonable traffic distribution. Traffic should be evenly distributed on each node to avoid overloading some nodes and causing performance bottlenecks.

[0022] Q4: Fault recovery capability: evaluates the network's ability to recover from node failures or link interruptions. A good routing matching method should be able to quickly adjust routing strategies when a failure occurs to ensure network connectivity and availability.

[0023] Q5: Scalability: Evaluate the performance of the routing matching method in dealing with the growth of network scale. A good method should be able to adapt to the ever-changing network environment and support routing matching of large-scale nodes.

[0024] Q6: Ease of use and maintainability: Evaluate the deployment, configuration, and management difficulty of the route matching method. A method with good practical application effect should have good ease of use and maintainability to facilitate network administrators to perform daily maintenance and optimization;

[0025] Q7: Cost-benefit analysis: Compare the network construction and operation costs before and after the implementation of the route matching method to evaluate the cost-effectiveness of the method. A method with good practical application effect should reduce the overall cost while improving network performance.

[0026] A large-scale node routing matching system based on application traffic includes a routing controller, a routing executor and a routing gateway, wherein the output end of the routing controller is electrically connected to the input end of the routing executor, and the output end of the routing executor is electrically connected to the input end of the routing gateway.

[0027] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0028] 1. The present invention provides a large-scale node routing matching method and system based on application traffic, which can improve the reliability, availability and access efficiency of the system. Through the consistent hashing algorithm technology, it can ensure that when a node joins or leaves a distributed storage cluster at any time, it only affects the routing table data of the relevant nodes, thereby improving the reliability and availability of the system. It is easy to expand and can also support ultra-large-scale storage problems, making the routing table small in size, supporting large-scale node applications, and having high scalability, energy saving and optimization of the network life cycle. In the wireless sensor network routing method, by combining the core cluster head routing mechanism with the plane routing mechanism, energy saving and optimization of the network life cycle are achieved, and the number of network isolated points is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The present invention is a flowchart of a large-scale node routing matching method based on application traffic. DETAILED DESCRIPTION

[0030] The present invention is further described in detail below in conjunction with embodiments:

[0031] Example 1

[0032] like Figure 1 As shown, the present invention provides a large-scale node routing matching method based on application traffic, comprising the following steps:

[0033] Step 1: Traffic analysis;

[0034] Step 2: Node classification;

[0035] Step 3: Routing strategy formulation;

[0036] Step 4: Generate routing table;

[0037] Step 5: Real-time monitoring and adjustment;

[0038] Step 6: Optimize and iterate.

[0039] Example 2

[0040] like Figure 1As shown, on the basis of Example 1, the present invention provides a technical solution: preferably, the step one specifically refers to the need to collect and analyze application traffic data in the network, which can be achieved by monitoring network equipment, using data packet analysis tools or utilizing an SDN controller. The purpose of traffic analysis is to understand the traffic pattern in the network so as to provide appropriate routing strategies for different types of traffic. The step two is to divide the nodes in the network into different categories according to the results of the traffic analysis. The nodes can be divided into high-traffic nodes, medium-traffic nodes and low-traffic nodes, which helps to allocate appropriate resources and routing strategies to different types of nodes. The step three is to formulate corresponding routing strategies for different types of nodes. For high-traffic nodes, a load balancing strategy can be adopted to disperse the traffic to multiple nodes to avoid single-point overload; for low-traffic nodes, a shortest path strategy can be adopted to reduce delays and resource consumption. The step four is to generate a routing table for each node according to the formulated routing strategy. The routing table should contain the best path information to other nodes, as well as related routing strategies. This can be achieved by using routing algorithms and SDN controllers. The fifth step is that in large-scale networks, traffic patterns may change over time, and it is necessary to monitor network conditions in real time and adjust routing policies and routing tables according to actual conditions. This can be achieved by using automation tools and SDN controllers. The sixth step is to continuously optimize routing policies and routing tables based on monitoring data and performance indicators, including adjusting node classification, reallocating resources, and improving routing algorithms. Through continuous iteration and optimization, more efficient and flexible network routing matching can be achieved.

[0041] The step 1 also includes effect evaluation, and the specific evaluation method is as follows:

[0042] Q1: Performance indicators: By collecting and analyzing network performance indicators such as throughput, latency, packet loss rate, and bandwidth utilization, we can intuitively understand the impact of the route matching method on network performance. These indicators can help evaluate whether the method effectively improves the overall performance of the network;

[0043] Q2: Resource utilization: Evaluating the resource utilization of nodes in the network, such as CPU, memory, and storage, can help understand whether the route matching method can reasonably allocate and utilize network resources. Higher resource utilization may mean that the method can better meet network needs, while waste of resources may lead to performance degradation.

[0044] Q3: Traffic distribution: Observe the distribution of traffic in the network to evaluate whether the routing matching method can achieve reasonable traffic distribution. Traffic should be evenly distributed on each node to avoid overloading some nodes and causing performance bottlenecks.

[0045] Q4: Fault recovery capability: evaluates the network's ability to recover from node failures or link interruptions. A good routing matching method should be able to quickly adjust routing strategies when a failure occurs to ensure network connectivity and availability.

[0046] Q5: Scalability: Evaluate the performance of the routing matching method in dealing with the growth of network scale. A good method should be able to adapt to the ever-changing network environment and support routing matching of large-scale nodes.

[0047] Q6: Ease of use and maintainability: Evaluate the deployment, configuration, and management difficulty of the route matching method. A method with good practical application effect should have good ease of use and maintainability to facilitate network administrators to perform daily maintenance and optimization;

[0048] Q7: Cost-benefit analysis: Compare the network construction and operation costs before and after the implementation of the route matching method to evaluate the cost-effectiveness of the method. A method with good practical application effect should reduce the overall cost while improving network performance.

[0049] In this embodiment, by adopting the SDN controller, network traffic can be intelligently allocated and scheduled according to the real-time network status and application priority, ensuring the rational use and fair distribution of network bandwidth. Through the programming interface of the SDN controller, it supports custom programming and flexible configuration of network behavior, quickly adapts to the needs of different scenarios, conducts deep learning and multi-dimensional detection of network data packets, discovers and prevents network attacks and abnormal behaviors, ensures network data security and privacy protection, monitors network topology, performance and status information in real time, adjusts and optimizes network resources in real time, and improves network stability and reliability.

[0050] Example 3

[0051] like Figure 1 As shown, the present invention provides a large-scale node routing matching system based on application traffic, including a routing controller, a routing executor and a routing gateway, wherein the output end of the routing controller is electrically connected to the input end of the routing executor, and the output end of the routing executor is electrically connected to the input end of the routing gateway.

[0052] In this embodiment, the system can handle large-scale application traffic and achieve fast routing matching through efficient algorithms to ensure that data can be transmitted to the destination quickly and accurately. The system supports routing strategies based on multiple conditions, such as source address, destination address, and AS path, so that network administrators can finely control traffic according to business needs and network architecture. By adopting a distributed architecture, the system can expand as the network scale expands and adapt to the growing data processing needs. Through load balancing and fault-tolerant mechanisms, the system can maintain service stability and availability when some nodes or links fail.

[0053] The above generally describes the present invention in detail, but it is obvious to a person skilled in the art that some modifications or improvements can be made to the present invention. Therefore, modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A large-scale node routing matching method based on application traffic, characterized in that: The following steps are involved: Step 1: Traffic analysis; Step 2: Node classification; Step 3: Routing strategy formulation; Step 4: Generate routing table; Step 5: Real-time monitoring and adjustment; Step 6: Optimize and iterate.

2. According to a large-scale node routing matching method based on application traffic as described in claim 1, it is characterized by: The step 1 specifically refers to the need to collect and analyze application traffic data in the network, which can be achieved by monitoring network equipment, using data packet analysis tools or leveraging SDN controllers. The purpose of traffic analysis is to understand the traffic patterns in the network in order to provide appropriate routing strategies for different types of traffic.

3. According to the large-scale node routing matching method based on application traffic in claim 1, it is characterized by: The second step is to classify the nodes in the network into different categories according to the results of traffic analysis. The nodes can be classified into high-traffic nodes, medium-traffic nodes and low-traffic nodes, which helps to allocate appropriate resources and routing strategies to different types of nodes.

4. According to claim 1, a large-scale node routing matching method based on application traffic is characterized in that: The third step is to formulate corresponding routing strategies for different types of nodes. For high-traffic nodes, a load balancing strategy can be used to disperse the traffic to multiple nodes to avoid single-point overload; for low-traffic nodes, the shortest path strategy can be used to reduce delays and resource consumption.

5. The large-scale node routing matching method based on application traffic according to claim 1 is characterized in that: The fourth step is to generate a routing table for each node according to the established routing strategy. The routing table should contain the best path information to other nodes and the relevant routing strategy. This can be achieved by using a routing algorithm and an SDN controller.

6. The large-scale node routing matching method based on application traffic according to claim 1 is characterized in that: The fifth step is that in a large-scale network, the traffic pattern may change over time, and it is necessary to monitor the network status in real time and adjust the routing strategy and routing table according to the actual situation. This can be achieved by using automation tools and SDN controllers.

7. The large-scale node routing matching method based on application traffic according to claim 1 is characterized in that: The sixth step is to continuously optimize the routing strategy and routing table based on the monitoring data and performance indicators, which includes adjusting node classification, reallocating resources and improving routing algorithms. Through continuous iteration and optimization, more efficient and flexible network routing matching can be achieved.

8. The large-scale node routing matching method based on application traffic according to claim 1 is characterized in that: The step 1 also includes effect evaluation, and the specific evaluation method is as follows: Q1: Performance indicators: By collecting and analyzing network performance indicators such as throughput, latency, packet loss rate, and bandwidth utilization, we can intuitively understand the impact of the route matching method on network performance. These indicators can help evaluate whether the method effectively improves the overall performance of the network; Q2: Resource utilization: Evaluating the resource utilization of nodes in the network, such as CPU, memory, and storage, can help understand whether the route matching method can reasonably allocate and utilize network resources. Higher resource utilization may mean that the method can better meet network needs, while waste of resources may lead to performance degradation. Q3: Traffic distribution: Observe the distribution of traffic in the network to evaluate whether the routing matching method can achieve reasonable traffic distribution. Traffic should be evenly distributed on each node to avoid overloading some nodes and causing performance bottlenecks. Q4: Fault recovery capability: Evaluate the network's ability to recover from node failures or link interruptions. A good routing matching method should be able to quickly adjust routing strategies when a failure occurs to ensure network connectivity and availability. Q5: Scalability: Evaluate the performance of the routing matching method in dealing with the growth of network scale. A good method should be able to adapt to the ever-changing network environment and support routing matching of large-scale nodes. Q6: Usability and maintainability: Evaluate the deployment, configuration, and management difficulty of the route matching method. A method with good practical application effect should have good usability and maintainability to facilitate network administrators to perform daily maintenance and optimization. Q7: Cost-benefit analysis: Compare the network construction and operation costs before and after the implementation of the route matching method to evaluate the cost-effectiveness of the method. A method with good practical application effect should reduce the overall cost while improving network performance.

9. A large-scale node routing matching system based on application traffic, comprising a routing controller, a routing executor and a routing gateway, characterized in that: The output end of the routing controller is electrically connected to the input end of the routing executor, and the output end of the routing executor is electrically connected to the input end of the routing gateway.

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