Internet-Draft | IPv6 Network Monitoring Deployment | July 2025 |
Pang, et al. | Expires 5 January 2026 | [Page] |
This document presents an IPv6 network end-to-end monitoring and analysis system. It describes a standardized end-to-end monitoring and analysis architecture and an indicator system, while also enabling capabilities for end-to-end monitoring data collection and integrated intelligent analysis. This solution has been verified in the operators' network.¶
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The emergence of IPv6 can be traced back to the 1990s, when the development of IPv6 was initiated by the Internet Engineering Task Force (IETF) to solve the problem of IPv4 address exhaustion. In 1998, the IPv6 protocol specification was published. As IPv6 adoption accelerating over the past years, the IPv6 protocol was elevated to be an Internet Standard status [RFC8200] in 2017.¶
In today's digital age, the deployment of IPv6 has become a core driving force for network development. With the continuous expansion of network scale and the emergence of new applications, the extensive address space, enhanced security, and improved network performance of IPv6 have made it a key element in network evolution. How to better deploy and promote IPv6 networks has become a widely concerned issue.¶
As of 2023, significant strides have been made in the global deployment of IPv6. According to the statistics from the 'Global IPv6 Development Report 2024', in 2023 the deployment of IPv6 networks significantly accelerated, breaking through the 30% mark in global coverage for the first time. Among leading countries, the IPv6 coverage rate has reached or approached 70%, and the percentage of IPv6 mobile traffic has surpassed that of IPv4.¶
[RFC9386] presents the state of IPv6 network deployment in 2022, and its Section 5 lists common challenges, such as transition mechanisms, network management and operation, performance, and customer experience. 'ETSI-GR-IPE-001' also discusses the existing gaps in IPv6-related use cases.¶
Existing IPv6 deployment monitoring approaches include (not an exhaustive list):¶
Internet Society Pulse: Curating information about levels of IPv6 adoption in countries and networks around the world.¶
Akamai IPv6 Adoption Visualization: Reviewing IPv6 adoption trends at a country or network level.¶
APNIC IPv6 Measurement: Providing an interactive map that users can click on to see the IPv6 deployment rate in a particular country.¶
Cloudflare IPv6 Adoption Trends: Offering insights into IPv6 adoption across the Internet.¶
Cisco 6lab IPv6: Displaying IPv6 prefix data.¶
Regional or National Monitoring Platforms: Examples include the NZ IPv6, the RIPE NCC IPv6 Statistics, and the USG IPv6 & DNSSEC External Service Deployment Status, among others.¶
The aforementioned tools are capable of providing effective statistics and visualization of IPv6 support levels. However, they do not adequately address the key problems that currently exist. The specific deficiencies are presented in the following five aspects.¶
Existing monitoring points are concentrated in the backbone network [RFC7707], lacking fine-grained coverage of terminals and applications.¶
It mainly relies on basic indicators such as connection availability [RFC9099] and address allocation rate, lacking a comprehensive assessment of service continuity, transmission quality, Network Element Readiness, Active IPv6 Connections, and other key metrics.¶
The monitoring data of each network domain is isolated, making it impossible to conduct correlation analysis of end-to-end traffic paths [RFC9312].¶
For instance, the IPv6 transformation in some private network applications is not thorough enough, with internal application systems yet to be upgraded. This results in secondary and tertiary links, as well as multimedia content traffic, still relying predominantly on IPv4. However, there is a lack of effective deep monitoring methods to oversee these connections.¶
Existing models find it difficult to quantify the impact of external factors such as policies and regulations, user behavior patterns, and market dynamics on the evolution of IPv6.¶
This system adheres to the following core principles.¶
Correlation analysis: Capabilities for cross-domain data integration and analysis¶
Business orientation: A standardized indicator measurement system¶
Visualized operation: Key operations supported by visual charts¶
Scalability: Full utilization of current infrastructure, support for external system docking, and compatibility with multi-vendor devices and subsystems¶
+==================================================================+ | Visualization Layer | +==================================================================+ | | | | +==================================================================+ | Intelligent Analysis Layer | +==================================================================+ | | | | +==================================================================+ | Data Collection Layer | +==================================================================+ | | | | +----------------+ +----------------+ +----------------+ +----------------+ | Home Broadband | | Mobile | | IP Bearer | | Application | | Network | | Network | | Network | | | +----------------+ +----------------+ +----------------+ +----------------+
The system architecture is divided into three layers from top to bottom (shown in Figure 1): the Data Collection Layer, the Intelligent Analysis Layer, and the Visualization Layer.¶
Based on network functions and service scenarios, the network is divided into four major professional network domains. These specifically include: Home Broadband Network, Mobile Network, IP Bearer Network, and Application.¶
For these four major professional network domains, data cleaning, transformation, and standardization are performed respectively. Based on multi-source data fusion methods, the aggregation, correlation and integration of data from each professional network are realized, forming a unified data analysis foundation. According to the hierarchical division of the network architecture, the collected data indicators are explained from three dimensions: the user side, the network side and the application side. The core collection indicators are specified as follows. For specific indicator details, refer to Section 3.2.¶
User side: network element readiness, network readiness, basic resources, network traffic, active connections.¶
Network side: network element readiness, network readiness, basic resources, network traffic, active connections.¶
Application side: network element readiness, network readiness, basic resources, network traffic, active connections. Data collection relies on the existing technical system. The specific methods are:¶
TBD.¶
The system develops a fine-grained, multi-dimensional traffic analysis model. It enables correlated analysis of monitoring data from cloud, network, edge, and end systems. This allows accurate identification of issues related to IPv6 traffic improvement.¶
End-to-end cross-system integration: Integrate end-to-end data from professional systems such as user home networks, access networks, metro networks, IDCs, and content providers (covering cloud, network, edge, and end). This enables end-to-end traffic analysis, quality localization and demarcation, and evaluation of overall IPv6 support.¶
End-to-end traffic analysis: Perform correlated analysis on traffic data from end, network, and cloud systems. It precisely attributes the causes of IPv6 traffic changes to end, network, or cloud subsystems.¶
Network traffic analysis: Support collection of IPv6/IPv4 inbound and outbound traffic at key network nodes. Analyze traffic change trends.¶
Application traffic analysis: Support collection and analysis of IPv6/IPv4 active applications on the user side and application side. Calculate IPv6 traffic data for different service applications.¶
Inter-network traffic analysis: Support analysis of IPv6/IPv4 traffic direction and application bearing information between municipal-level networks. Provide an inter-municipal traffic matrix.¶
Dynamic traffic attribution¶
Identify traffic-restricted areas. Develop multi-dimensional problem investigation plans covering the network side, user side, and application side. Investigate potential influencing factors level by level. Attribute traffic fluctuations to specific subsystems.¶
User-level Topology Reconstruction: Using user services as the link, reconstruct the end-to-end topology and diagnose latency/packet loss segment by segment (e.g., segmental quality of home terminal, access network, and application sides).¶
Segmented Quality Degradation Localization: Compare IPv4/IPv6 performance differences segment by segment to locate degraded network elements.¶
Data is statistically aggregated and presented according to scenarios, with support for manual editing of display content and dimensions.¶
Based on a standardized indicator system, conduct IPv6 support monitoring and analysis for each professional domain, breaking down monitoring metrics into specific services and network segments.¶
Readiness Indicators¶
Network Element Readiness: IPv6 Readiness of Network Equipment, End-User Devices, and Security Devices.¶
Application Readiness: IPv6 Support Rate of Website Applications and Business Systems.¶
Infrastructure Readiness: IPv6 Readiness of Fixed Internet, Mobile Internet, Private Lines, and Data Center Network (DCN) Infrastructure.¶
Network Readiness:¶
Cloud Readiness: IPv6 Readiness of Content Delivery Networks (CDNs), Cloud Services, Cloud Platforms, and DNS Servers.¶
Operational Metrics¶
IPv6 Traffic: IPv6 Traffic Share in Cross-Border, Inter-Domain, Intra-Domain, Fixed Metropolitan Area Networks (MANs), Mobile Core Networks, Internet Data Centers (IDCs), Private Lines, and Applications.¶
Active IPv6 Connections: Active IPv6 Connection Share in Fixed Metropolitan Area Networks (MANs), Mobile Core Networks, Internet Data Centers (IDCs), Private Lines, and Applications.¶
Policy Compliance Indicators.¶
Monitor and analyze data from fixed and mobile network user sides, including: IPv6 support monitoring and IPv6 traffic quality analysis. Support end-to-end data analysis at the intelligent analysis layer.¶
Through application monitoring points, monitor and analyze the IPv6 support of application systems, including: website and APP monitoring, IPv6 application access quality evaluation, and DNS resolution capability monitoring.¶
TBD.¶
When User A experiences network congestion while playing cloud-based games at home, it affects the gaming experience. To identify the cause, it is necessary to collect performance data from each network segment for quality localization. However, current independent management of network domains prevents direct data correlation. The network segments are as follows: N1 (terminal device to ONT), N2 (ONT to BRAS), and N3 (BRAS to application side).¶
+-----------------+ +--------------+ +----------------+ +--------------+ | Terminal device |--------| ONT |--------| BRAS |--------| APP | +-----------------+ +--------------+ +----------------+ +--------------+ | | | | |<--------- N1 ----------> | | | | |<--------- N2 ---------->| | | | |<--------- N3 ---------->|
The system detected end-to-end quality degradation of a certain service. After the system segmented and delimited the quality degradation, it compared the performance of N1/N2/N3 segment by segment based on its preset multi-segment monitoring model (Figure 2). It was determined that an abnormality occurred in the N3 network segment. At the same time, by correlating with CDN scheduling logs, a content source switching event was found. The original service node was a local IDC, while the monitored service node was a remote node across provinces. Therefore, it is concluded that the end-to-end quality degradation is caused by CDN remote scheduling and the bottleneck of the N3 cross-network link. Adjusting the CDN scheduling strategy can improve the quality degradation issue.¶
Home terminals and routers, as the "last kilometer" for users to access the Internet, play a crucial role in user experience with regard to their IPv6 support. The system detected that the proportion of IPv6 traffic in a demonstration community was lower than the city-wide average. By correlating with home broadband terminal data, it was found that the proportion of bridge-mode optical modems in this community was relatively high, and the proportion of old devices among the connected routers was higher than the average. Therefore, the root cause was identified as inadequate terminal support. Old routers only support IPv4/NAT mode, which forces IPv6 traffic to be downgraded. Targeted terminal replacements were carried out. Specifically, bridge-mode optical modems for community users were upgraded to router-mode ones. This has led to a significant increase in the proportion of IPv6 traffic.¶
The monitoring system must implement:¶
TBD.¶