CN117997829A

CN117997829A - QoS parameter-based MPLS network layer route optimization method and system

Info

Publication number: CN117997829A
Application number: CN202410216082.7A
Authority: CN
Inventors: 汪春晖; 杨鹏举; 谢宝癸; 兰涛
Original assignee: Hunan Leading Wisdom Telecommunication and Technology Co Ltd
Current assignee: Hunan Leading Wisdom Telecommunication and Technology Co Ltd
Priority date: 2024-02-27
Filing date: 2024-02-27
Publication date: 2024-05-07

Abstract

The application relates to a method and a system for optimizing an MPLS network layer route based on QoS parameters. The method comprises the following steps: a QoS probe is deployed on each routing node in the MPLS network layer respectively and is used for monitoring QoS parameters among the routing nodes; the QoS parameters obtained by real-time monitoring are regularly packaged and sent to a central QoS analysis engine through a QoS probe; and acquiring the data packet containing the optimal routing path information from the routing decision support system, and updating a routing table according to the routing index in the data packet and the QoS parameter in the central QoS analysis engine. In the existing MPLS network architecture, the method effectively integrates QoS parameters to realize dynamic and self-adaptive network route optimization so as to improve the data transmission performance and the network service quality.

Description

QoS parameter-based MPLS network layer route optimization method and system

Technical Field

The present application relates to the field of computer networks and data communications technologies, and in particular, to a method and a system for optimizing an MPLS network layer route based on QoS parameters.

Background

In the prior art, multiprotocol label switching (MPLS) has been widely used in complex enterprise and service provider networks as a data carrier technology that provides an efficient forwarding mechanism between the data link layer and the network layer. MPLS optimizes management and transmission of network traffic by introducing a short label, an identifier, into each packet to achieve fast and flexible path selection. This label driven mechanism has significant advantages in processing speed and flexibility over traditional destination-based IP routing approaches.

However, while MPLS can improve network efficiency through Label switched routing (Label SWITCHED PATHS, LSPS), it is not itself directly related to management and optimization of quality of service (Quality of Service, qoS). MPLS suffers from significant drawbacks in integrating and optimizing QoS parameters. These deficiencies are mainly manifested in the following aspects:

QoS implementations of conventional MPLS networks rely primarily on Class of Service (CoS) labels that are marked in the MPLS header to indicate the processing priority of different types of traffic. The CoS model of a conventional MPLS network is too coarse in ensuring the quality of service of the data flow. The CoS mechanism is typically based on static priority labels only, and cannot take full advantage of fine-grained QoS parameters, delay, bandwidth allocation, jitter, and packet loss. This deficiency makes it difficult for the network to make accurate resource allocation and priority decisions in the face of high variability and multi-service requirements. That is, this approach is not always effective in guaranteeing quality of service under dynamic network conditions, especially under bandwidth starvation and network congestion conditions.

The existing MPLS technology has limited capabilities in terms of dynamic traffic engineering. While MPLS provides a degree of traffic management functionality, it is deficient in fast response network congestion, varying bandwidth requirements, and real-time traffic pattern adjustment. Such inflexible traffic management policies often result in reduced quality of service and inefficient use of resources in real-world large-scale network applications.

QoS implementation in conventional MPLS networks relies on cumbersome manual configuration, lacking the necessary automation and intelligence mechanisms. The manual intervention mode is time-consuming and labor-consuming, and is difficult to accurately adapt to rapid changes of network states in time, and flexibility and expandability are lacking, particularly when the manual intervention mode faces large-scale and complex network environments. Such static configuration often requires a lot of network engineering resources in actual operation, and it is difficult to quickly adapt to dynamic changes in network states. In today's data centers and cloud computing environments, there is an increasing demand for automation and intelligent management of networks, and the shortcomings of conventional MPLS networks are particularly pronounced in this regard.

Due to the lack of efficient QoS integration policies, conventional MPLS networks perform poorly in handling high-end service guarantees and complex application requirements. The limitations of the prior art are particularly apparent in situations where high availability, low latency communications, and high bandwidth applications are required.

Disclosure of Invention

Based on this, it is necessary to provide a method and a system for optimizing MPLS network layer routes based on QoS parameters in order to achieve comprehensive and efficient QoS integration and route optimization.

A method for optimizing MPLS network layer routing based on QoS parameters, the method comprising:

a QoS probe is deployed on each routing node in the MPLS network layer respectively and is used for monitoring QoS parameters among the routing nodes; the QoS parameters include: link delay, link packet loss rate, and link jitter;

The QoS parameters obtained by real-time monitoring are regularly packaged and sent to a central QoS analysis engine through a QoS probe;

And acquiring the data packet containing the optimal routing path information from the routing decision support system, and updating a routing table according to the routing index in the data packet and the QoS parameter in the central QoS analysis engine.

An MPLS network layer route optimization system based on QoS parameters, the system comprising:

The QoS parameter monitoring module is used for respectively deploying a QoS probe on each routing node in the MPLS network layer and monitoring QoS parameters among the routing nodes; the QoS parameters include: link delay, link packet loss rate, and link jitter;

the QoS parameter uploading module is used for periodically packaging and sending the QoS parameters obtained by real-time monitoring to the central QoS analysis engine through the QoS probe;

And the route updating module is used for acquiring the data packet containing the optimal route path information from the route decision support system and updating the route table according to the route index in the data packet and the QoS parameter in the central QoS analysis engine.

According to the QoS parameter-based MPLS network layer route optimization method and system, the QoS probe is directly connected to the data transmission port of the routing node, so that the characteristics of the passing data packet, including the size of the data packet, arrival and transmission time stamps, source/destination IP addresses and the like, are monitored in real time, and the hardware sensor has high-speed processing capability and can process a large number of data packets without introducing significant delay; the QoS probe periodically transmits the summarized monitoring data to the central QoS analysis engine, and the engine deeply analyzes the data, so that an important basis is provided for routing decision-making and network performance optimization, and the comprehensive and precise monitoring method enables a network manager to deeply understand the network performance and accordingly make effective management and optimization decisions. In summary, the invention significantly improves the performance and reliability of the MPLS network in a high dynamic environment by integrating advanced real-time QoS monitoring, link jitter analysis, and dynamic routing table update mechanisms. The system is particularly suitable for application scenes with high data transmission requirements and frequent change of network states, such as a cloud data center, a high-frequency transaction system and a large-scale online service platform, and effectively ensures the network communication quality and stability under the scenes.

Drawings

Fig. 1 is a flow chart of a method for optimizing an MPLS network layer route based on QoS parameters in one embodiment;

FIG. 2 is a flow chart of an implementation of link jitter measurement in one embodiment;

FIG. 3 is an internal block diagram of a computer device in one embodiment.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

In one embodiment, as shown in fig. 1, there is provided an MPLS network layer route optimization method based on QoS parameters, including the steps of:

step 102, a QoS probe is deployed on each routing node in the MPLS network layer, for monitoring QoS parameters between routing nodes.

Among them, multiprotocol label switching (MPLS) is a data transmission mechanism that exists in the OSI model (Open Systems Interconnection model) between the second layer (link layer) and the third layer (network layer). MPLS allows efficient forwarding of packets by inserting short path labels in the packets. These labels indicate the forwarding paths of the data packets in the network, resulting in faster packet processing speed, while reducing the processing burden on the network nodes (routers).

Quality of service (QoS) is an important aspect of network engineering, involving various techniques and policies for guaranteeing the performance of data transmission over a network. QoS parameters include, but are not limited to, bandwidth, link delay, link jitter, and link packet loss rate. In high quality demanding network environments, enterprise-level networks and service provider networks, qoS guarantees are critical to maintaining network performance and user satisfaction.

QoS probes qos_probe_a to qos_probe_n deployed on each routing Node node_a to node_n combine a hardware sensor and a network management protocol, so as to implement careful monitoring of link delays latency_a_b to latency_a_n, bandwidth Utilization bw_advertisement_a_b to bw_advertisement_a_n, link Packet Loss rates packet_loss_a_b to packet_loss_a_n, and link Jitter jitter_a_b to jitter_a_n.

Meanwhile, software embedded in the QoS probe retrieves key performance indicators from a Management Information Base (MIB) of the router using standard network management protocols SNMP and NetFlow, and transmits in a standardized format. The application of these protocols ensures accurate collection and efficient transmission of performance data. The probe accurately calculates and records the round trip time RTT of the data packet on the link by analyzing the arrival time stamp of the data packet, thereby accurately measuring the actual propagation delay of the link.

And 104, periodically packaging and sending the QoS parameters obtained by real-time monitoring to a central QoS analysis engine through a QoS probe.

The QoS probes periodically aggregate the monitoring data and send it to the central QoS analysis engine via a secure network layer encryption protocol such as IPSec. Here, the data will be further analyzed to identify long-term trends and patterns present in the network. The central engine also establishes the optimization strategy of the whole network according to the analysis results, and issues relevant configuration update to the probes of each node, so as to ensure that the performance of the whole MPLS network is continuously optimized. By the centralized and distributed combined measurement and management method, a network operator can obtain a global view angle and timely adjust and optimize the network.

And (3) formulating a full-network optimization strategy: based on the analysis results of the Central QoS Analysis Engine (CQAE), the network operator or automation system will formulate an optimization strategy for the whole network. These policies may include adjusting QoS policies, improving traffic management, and reconfiguring network device settings, among others.

Route update optimization: the key part in the optimization strategy is route update optimization. Based on the analysis results and recommendations obtained from CQAE, the routing update module dynamically adjusts the routing table to reflect the most current optimal path selection. This may involve adding new route records, updating existing records, or deleting routes that are no longer valid.

The collection and analysis of data is the first step of the optimization flow, which provides basis for the formulation of the whole network optimization strategy. The implementation of the optimization strategy then typically involves updating the routing table, which is the execution of the strategy. Thus, route update optimization is an operation performed after the formulation of the full network optimization policy, which is a key step in translating the policy into actual network behavior.

Throughout the process, data analysis and policy formulation provide guidance for route updates, which are an implementation of policies. This procedure ensures that the MPLS network is continuously adapted to changing network conditions and maintains optimal performance. By this method of combining centralized and distributed, global optimization and fine tuning of network performance can be achieved.

In the framework of the present application CQAE, based on the collected QoS data, one or more of the following configurations may be adjusted to optimize network performance. Updates of these configurations will be issued to individual nodes in the MPLS network through network management protocol SNMP (Simple Network Management Protocol) or through Control plane interface (Control PLANE INTERFACE). The purpose of these operations is to ensure that the performance of the network matches the traffic demands and to be able to adapt to changes in the network conditions.

QoS policy parameters: these parameters define what the different types of traffic should be handled, including rules for priority queues, bandwidth reservation, and traffic shaping.

Routing weights and costs: in cost-based routing decisions, each link may be assigned a cost reflecting its latency, bandwidth, or other performance metrics. The weights and costs affect the path selection of the routing algorithm.

Link state information: link rate, link utilization, and link quality index, which are used in dynamic routing protocols.

ACLs (Access control List): ACLs are used for network security to control which types of traffic can enter or leave the network.

Queue management configuration: a Random Early Detection (RED) parameter for controlling a packet drop policy when the network is congested.

Switch and router port settings: including port rate, duplex settings, etc.

MPLS specific parameters: label distribution, label switched path and tunnel configuration.

And 106, acquiring the data packet containing the optimal routing path information from the routing decision support system, and updating the routing table according to the routing index in the data packet and the QoS parameter in the central QoS analysis engine.

In network routing decisions, so-called "optimal paths" are often evaluated and selected among a number of potential routes. At a given point in time, there are multiple possible paths for the routing decision support system to evaluate, each path having a set of routing metrics associated with it, such as bandwidth, delay, packet loss rate, and other QoS parameters. The process herein generally involves the steps of:

And (3) path collection: the routing decision support system gathers and lists all routing paths, each of which has a particular set of routing metrics.

Performance evaluation: the system performs performance assessment for each path based on these routing metrics and real-time QoS parameters in the central QoS analysis engine. This involves calculating a composite performance score or rank for each path.

And (3) path selection: of all the evaluated paths, the system will select a highest scoring or best ranking path as the final target routing path. This path is considered to be the most suitable path in the current network state.

Updating a routing table: once the optimal path is selected, the routing table update module will perform the actual update operation, ensuring that network traffic is directed through this path.

Thus, although the term "optimal path" is used in the singular, it involves selecting a most appropriate path from a plurality of alternative paths in the actual routing decision process. This selection is based on a complex decision process that takes into account various performance metrics and QoS parameters.

In the present application, the routing decision support system (NDSS) and the Central QoS analysis engine (Central QoS ANALYSIS ENGINE, CQAE) perform complementary roles, together supporting the dynamic routing decision process of the network. The following are the associations between them:

Data sharing and interaction: NDSS relies on the real-time QoS parameters provided by CQAE to make decisions. CQAE is responsible for collecting and analyzing network performance data such as delay, bandwidth utilization, packet loss rate, etc., and then forwarding these data to the NDSS. The NDSS uses these data to calculate the optimal path and generates the data packets needed to update the routing table.

Hierarchical decision architecture: CQAE can be considered as the "brain" of the network analysis, focusing on data analysis and performance assessment, while NDSS acts as a decision executor to formulate and execute routing policies based on the analysis results of CQAE.

Function specialization: CQAE handle data analysis and interpretation works specifically related to QoS, providing deep network insight. Instead, NDSS focuses on applying these insights to optimize network performance by selecting the optimal path.

Cooperative work: the two work cooperatively, CQAE continuously analyzes and provides key performance indicators, while the NDSS selects the optimal path according to the indicators and the network policy and updates the routing table.

Automated updating of policies: the policy update mechanism built in the NDSS can automatically respond CQAE to performance evaluations without human intervention. When the network traffic mode changes or new service requirements appear, the NDSS can automatically adjust the routing policy, and the automation greatly improves the response speed and the operation efficiency of the network.

In the present application, the data packet containing the optimal Routing path information received by the Routing Update Module (RUM) is generally in an Open Shortest path first (Open Shortest test PATH FIRST, OSPF) and border gateway protocol (Border Gateway Protocol, BGP) format, and carries key Routing indicators, such as link delay (LINK LATENCY), bandwidth (Bandwidth), and other performance indicators (Performance Metrics). These metrics are extracted directly from the data plane (DATA PLANE) of the network, reflecting the instant status of the routing device at a particular time.

In contrast, the QoS parameters collected by the central QoS analysis engine from the QoS probes include fine-grained performance data. These data are collected by the probes at the control and management planes of the network, providing a macroscopic view across the network, and are deeply analyzed to reflect the global state of network performance. These parameters are subject to timing analysis and trend prediction to support more long-term and strategic network optimization decisions.

Between RUMs and CQAE, there is a mechanism for information exchange and co-optimization. The RUM uses the real-time and historical QoS analysis provided by CQAE for adaptive thresholding and optimal path computation. CQAE employ complex event processing and machine learning algorithms to interpret QoS metrics and predict the evolution of network conditions.

Thus, while the routing metrics obtained by the RUM and the QoS parameters collected by CQAE are similar in nature, they differ essentially in the point of data acquisition, processing granularity, depth of analysis, and end-use purposes. This difference is a key component of the advanced network monitoring and decision framework employed in the present invention, ensuring that MPLS networks can be implemented in dynamically changing network environments

It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.

In one embodiment, the link delay is calculated as follows:

Where Latency represents the link delay, RTT represents the round trip time of the packet between routing nodes, processingDelay represents the time required for processing the packet inside the routing node.

In one embodiment, the link packet loss rate is calculated as follows:

This formula is used to calculate the packet loss rate over a period of time. Wherein PacketLossRate denotes a link packet loss rate, totalSentPackets denotes a total number of packets transmitted, and TotalReceivedPackets denotes a number of packets successfully received. The formula determines the proportion of lost packets by comparing the number of packets sent and received.

The monitoring of the packet loss rate depends on a comparison of the transmitted and received packet sequences. The QoS probe tracks the sequence number of the data packet, and identifies and records the packet loss event in the routing process, so that the packet loss rate is accurately calculated.

In one embodiment, the link jitter is calculated as follows:

Jitter′(t)＝α·Jitter(t)+(1-α)·Jitter′(t-1)；

Wherein Jitter '(t) represents the smoothed link Jitter value of the current measurement window, jitter (t) represents the initial link Jitter value of the current measurement window, jitter' (t-1) represents the smoothed link Jitter value of the last measurement window, and α represents a smoothing factor, the value of which is between 0 and 1, determining the weight of the new measurement value in the average.

Embodiments of link jitter measurement ensure consistency and accuracy of packet time stamps by accurate time synchronization in QoS probes qos_probe_a through qos_probe_n of each routing Node node_a through node_n. The embedded high precision hardware clock of the probe synchronizes to a global positioning system (Global Positioning System, GPS) and other high precision time sources, generating a time stamp for the arriving data packet. The probe collects the time stamp differences between successive data packets and uses these time differences to calculate a preliminary jitter indicator, the calculation of which does not take each data packet into account separately, but rather is determined by analyzing the long-term pattern of arrival times of the data packets.

In QoS probes, the calculation of link jitter is typically based on analyzing the variance of the arrival time of the data packets. Jitter is generally defined as the variance of the Inter-arrival time interval (Inter-ARRIVAL TIME, IAT).

With advanced signal processing techniques, the QoS probe applies a smoothing algorithm to give higher weight to the new jitter values over the calculated time differences, gradually reducing its impact on the old jitter values, resulting in a smoother jitter time sequence. The built-in software of the probe continuously calibrates and adjusts the parameters of the algorithm to adapt to the change of network conditions, ensures the accuracy and the response speed of jitter calculation, and realizes the real-time jitter calculation in a high-speed network. That is, the QoS probe internal algorithm automatically adjusts the value of α according to the actual performance of the network, so as to provide accurate jitter information in case of sudden jitter change and network stability.

In one embodiment, the initial link jitter value is calculated as follows:

where Jitter _i represents the initial link Jitter value of the i-th measurement window, n represents the total number of packets considered, IAT _j represents the inter-arrival time between consecutive packets, Representing the average of these arrival time intervals.

In one embodiment, the method further comprises: and the link jitter threshold is adaptively adjusted according to the QoS parameters through the QoS probe and is used for triggering a link jitter relief mechanism.

When the jitter value monitored by the probe exceeds the network performance reference, the probe automatically triggers an internal alarm and starts a preset jitter relief strategy. These strategies include dynamically adjusting the depth of the transmit queue, implementing traffic shaping measures, or adjusting the priority of the packets in the transmit queue. This automated response mechanism quickly mitigates jitter, reducing impact on real-time applications. The jitter mitigation strategy is specifically as follows:

Adjustment of the transmission queue depth: depth adjustment of the transmit queues typically involves changes in the cache management policy to accommodate different traffic load conditions. Instead of simply dropping packets, it is possible to dynamically adjust the size of the queue, thereby reducing or increasing the number of packets that the queue can buffer, thereby controlling the transmission delay. In real-time applications, the adjustment of queue depth may be optimized by applying the formulas in the queue theory, while minimizing delay and avoiding buffer overflow, also minimizing packet loss.

Specific implementation of traffic shaping: traffic shaping is a network traffic management technique that smoothes network traffic by controlling the rate at which data is sent, thereby reducing congestion and ensuring network quality of service. Specific implementations may include Token Bucket (Token Bucket), leaky Bucket (leak Bucket) algorithms, or priority queuing. Traffic shaping techniques can ensure efficient utilization of network bandwidth and provide the necessary bandwidth guarantee for high priority traffic. .

Determining priority: the priority of packets is typically determined based on quality of service (it is required that packets may be marked with different priorities depending on their type of service (e.g., voice, video, background data, etc.), when the network is congested, routers or switches may decide which packets to send first and which may be delayed based on the priority.

The innovation of the present application is that these measures are adaptively triggered by the QoS probes. Unlike traditional static configuration or manual adjustment, the present application describes a dynamic mechanism that intelligently enforces these policies based on real-time monitored QoS parameters, particularly link jitter data. For example, dynamic adjustment of the transmit queue depth may utilize a machine learning algorithm to predict traffic patterns and optimize the queue depth setting accordingly. Traffic shaping measures may be combined with real-time traffic analysis to optimize bandwidth allocation rather than relying on preset policies. The priority determination may use real-time data and network state predictions rather than just static based service types.

The improvement of automation and intelligence enables the network to respond to performance fluctuation more flexibly and efficiently, and ensures the service quality of key business, especially under variable network conditions.

In addition, the probe employs the law of large numbers and the central limit theorem in probability theory to ensure the accuracy and robustness of jitter calculation in the case of a large number of data packets. These theories ensure that as the number of samples increases, the calculated jitter values tend to true network jitter levels. The probe also incorporates an adaptive threshold technique that defines a dynamic threshold based on network performance metrics to trigger jitter mitigation measures.

As shown in fig. 2, a flow chart of an implementation of link jitter measurement is provided.

In one embodiment, adaptively adjusting the link jitter threshold according to the QoS parameters includes:

according to the link jitter value in the QoS parameter, the link jitter threshold is self-adaptively adjusted, and the calculation formula is as follows:

Threshold_adaptive(t)＝β·STD(Jitter′)；

Where Jitter 'represents the smoothed link Jitter value, STD (Jitter') represents the standard deviation of the smoothed link Jitter value, β represents the adaptive adjustment factor, and Threshold _adaptive (t) represents the link Jitter Threshold of the current measurement window.

In this formula, standard deviation (STD) calculation of Jitter value Jitter' on the right of the equal sign typically requires a set of data points representing a series of Jitter values before the current time window t. The standard deviation is a statistic for measuring the variation or dispersion of the jitter values. In practical applications this usually means that the jitter values in the N windows before the current point in time t are gathered for analysis. The choice of this time series depends on how sensitive the jitter threshold is expected to reflect the historical changes in network jitter. Selecting fewer data points will make the threshold more sensitive to recent changes, while more data points will make the threshold smoother, reflecting long-term network performance trends.

Therefore, the standard deviation is calculated based on the jitter values in a series of previous windows, so that the threshold can be dynamically adjusted according to the historical fluctuation of the jitter values. This approach allows the calculation of jitter thresholds to be based not only on current jitter conditions, but also on recent jitter history, thus providing a time-based context for the thresholds. This adaptive threshold adjustment mechanism is to dynamically manage and optimize network performance, especially under varying network conditions.

In one embodiment, updating the routing table based on the routing index in the data packet and the QoS parameters in the central QoS analysis engine includes:

evaluating the routing path according to the routing index in the data packet and the QoS parameter in the central QoS analysis engine;

Wherein Score _route represents an evaluation Score of the current routing path, latency represents a link delay of the current routing path, bandwidth represents a Bandwidth of the current routing path, α represents a weight of the link delay in routing path selection, β represents a weight of the Bandwidth in routing path selection, f (QoS _parameters) is a complex function for converting a plurality of QoS parameters into a single Score, and γ represents a weight of the QoS parameters in routing path selection;

and obtaining a target route path according to the evaluation score and updating the route table.

In the present application, the advanced algorithm of routing involves a comprehensive weighting function that takes into account not only conventional network metrics such as delay and bandwidth, but also incorporates complex QoS parameters. These QoS parameters include, but are not limited to, throughput, packet loss rate, jitter, and error rate of the link. The following is a detailed technical description:

Taking delay, bandwidth utilization, and packet loss rate as examples, each parameter may be assigned a weight: w _L corresponds to delay, w _BU corresponds to bandwidth utilization, and w _PLR corresponds to packet loss. A composite network performance score (Composite Network Performance Score, CNPS) may then be calculated using the following formula:

representing the inverse of the delay, taking the inverse makes the case of low delay a higher fractional contribution because lower delays are better. BU is a direct measure of bandwidth utilization, representing the utilization of the network. /(I) And processing the packet loss rate so that the score contributes more under the condition of low packet loss rate. The weights w _Lvw_BU, and w _PLR may be set based on the specific needs and optimization objectives of the network operator. This calculation method may be adapted according to network policy or Service Level Agreement (SLA) requirements.

1. Fusion of complex QoS parameters:

the invention adopts multidimensional QoS analysis, comprehensively considers various network performance indexes. These metrics are obtained from the real-time QoS monitoring systems of sections 5.1 and 5.2 and are analyzed by depth data to obtain an accurate representation of network status.

2. Application of the weighting algorithm:

The routing function of the present invention utilizes a weighting Algorithm (ADVANCED WEIGHTED Algorithm) in which conventional metrics such as delay and bandwidth are combined with new QoS parameters to form a composite scoring mechanism.

3. Real-time network state mapping:

The routing decision support system dynamically adjusts the routing decision through a real-time network state mapping technology. NDSS considers fine-grained network metrics including data delay, bandwidth, and QoS-based monitoring to ensure real-time and accuracy of routing decisions.

4. Intelligent traffic management:

In addition, the invention also includes intelligent traffic management in routing, which uses advanced queuing theory and traffic engineering techniques to optimize the processing and transmission of data packets based on real-time QoS parameters.

In one embodiment, the routing update module performs a complex series of validation algorithms including routing information protocol (Routing Information Protocol, RIP) consistency check, loop dependency (CYCLIC DEPENDENCY) detection, and control plane stability (Control Plane Stability) analysis prior to routing table updates. These steps ensure that the introduction of a new path does not result in Routing Loops (Routing Loops) and address resolution protocol (Address Resolution Protocol, ARP) storms. The speed of route convergence is a key indicator for measuring route efficiency. Estimated by the following formula:

Where ConvergenceTime denotes the time required for route convergence, numberOfRouteUpdates denotes the number of updates of the routing table in a given time, and UpdateTime denotes the total time required for these updates.

Once verification is complete, the route update module performs an atomic update operation. This involves modifying the internal forwarding information base (Forwarding Information Base, FIB) and routing information base (Routing Information Base, RIB). Each update involves insert, delete and modify operations that are transmitted to the physical device through the standard interfaces of the network device (network management protocol SNMP and command line interface CLI).

During the update process, each change generates a system log event and is sent to the network operations center (Network Operations Center, NOC) via a remote monitoring and management Protocol (Remote Monitoring AND MANAGEMENT Protocol, RMM). These logs provide valuable data for future route optimization and network failure diagnosis.

In addition, the route update module is integrated with a network performance monitoring (Network Performance Monitoring, NPM) tool to track the impact of updates on network traffic in real time. By analyzing the data provided by the Network traffic manager (Network TRAFFIC MANAGER, NTM), the module can evaluate the effect of the new routing rules and quickly roll back changes if necessary.

The whole route table updating process is carried out under a strict network security protocol, so that the data integrity and the network security are ensured. This complex and detailed route update procedure ensures that the MPLS network can quickly and accurately respond in a dynamically changing network environment, providing efficient and reliable data transmission services.

The present proposal presents significant technical advantages over the prior art in several key respects. For the dynamic routing table update mechanism mentioned in the third paragraph, the present invention has the following technical advantages over the closest prior art:

1. Highly dynamic route optimization: the route updating mechanism adopted by the invention is based on real-time and accurate network performance data, and can respond to network state change more quickly and accurately. This highly dynamic optimization approach is superior to traditional routing adjustment methods based on preset policies or intermittent data analysis.

2. Intelligent and adaptive routing decisions: by introducing advanced routing algorithms and adaptive decision logic, the invention can intelligently select the optimal path according to real-time network conditions. This exhibits greater efficiency and reliability than the prior art when dealing with complex network topologies and highly variable traffic patterns.

3. Enhanced network stability and robustness: the route updating mechanism of the invention not only pays attention to performance optimization, but also pays attention to stability and robustness of the network. Through fine path verification and atomic updating operation, the problems of routing loops, routing concussion and the like are avoided, and the overall stability of the network is improved.

4. Automated and simplified network management: in the prior art, the invention reduces the dependence on network administrators and realizes higher degree of automation. Automated routing table updates alleviate the workload of network management while reducing the risk of human error.

5. Real-time performance monitoring and feedback mechanism: the invention provides a real-time performance monitoring and quick feedback mechanism, allows a network administrator to know the effect of route adjustment in real time, and quickly adjusts or rolls back when necessary, thereby ensuring higher operation flexibility.

6. Overall network optimization perspective: the invention comprehensively considers a plurality of QoS parameters such as delay, packet loss rate, link jitter and the like, and provides a more comprehensive network optimization view angle than the prior art.

In one embodiment, an MPLS network layer route optimization system based on QoS parameters is provided, comprising:

For specific limitations of the MPLS network layer route optimization system based on QoS parameters, reference may be made to the above limitation of the MPLS network layer route optimization method based on QoS parameters, which is not described herein. The modules in the MPLS network layer route optimization system based on QoS parameters may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data packet characteristic data transmitted between routing nodes. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a method of MPLS network layer route optimization based on QoS parameters.

It will be appreciated by those skilled in the art that the structure shown in FIG. 3 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

In an embodiment a computer device is provided comprising a memory storing a computer program and a processor implementing the steps of the method of the above embodiments when the computer program is executed.

In one embodiment, a computer readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method of the above embodiments.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

The technical features of the above embodiments may be arbitrarily combined, and all combinations of the technical features of the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.

Claims

1. A method for optimizing an MPLS network layer route based on QoS parameters, the method comprising:

2. The method of claim 1, wherein the link delay is calculated as follows:

3. The method of claim 1, wherein the link packet loss rate is calculated as follows:

wherein PacketLossRate denotes a link packet loss rate, totalSentPackets denotes a total number of packets transmitted, and TotalReceivedPackets denotes a number of packets successfully received.

4. The method of claim 1, wherein the link jitter is calculated as follows:

Jitter′(t)＝α·Jitter(t)+(1-α)·Jitter′(t-1)；

Wherein Jitter '(t) represents a link Jitter value after smoothing of a current measurement window, jitter (t) represents an initial link Jitter value of the current measurement window, jitter' (t-1) represents a link Jitter value after smoothing of a previous measurement window, and α represents a smoothing factor, the value of which is between 0 and 1.

5. The method of claim 4, wherein the initial link jitter value is calculated as follows:

6. The method according to claim 1, wherein the method further comprises: and the link jitter threshold is adaptively adjusted according to the QoS parameters through the QoS probe and is used for triggering a link jitter relief mechanism.

7. The method of claim 6, wherein adaptively adjusting the link jitter threshold based on the QoS parameters comprises:

Threshold_adaptive(t)＝β·STD(Jitter′)；

8. The method of claim 1, wherein updating the routing table based on the routing index in the data packet and the QoS parameters in the central QoS analysis engine comprises:

9. The method according to claim 1, wherein the method further comprises: before the routing table is updated, the routing information protocol consistency check, the loop dependency detection and the control plane stability analysis are performed.

10. An MPLS network layer route optimization system based on QoS parameters, the system comprising: