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The Cooperative Association for Internet Data Analysis > projects : ipv6_evolution
IPv6 Evolution
This page summarizes our exploration of the evolution of IPv6, supported by NSF grant CNS-1111449, "NeTS-IPv6: Exploring the evolution of IPv6: topology, performance, and traffic".

Principal Investigators

Goals of the project

The high-level goal of this project is to measure the evolution of IPv6 in three dimensions: topology, traffic, and performance. Despite significant publicity in industry related to IPv4 address run out and the long term benefits of transition to IPv6, multi-year IPv6 traffic volumes are commonly reported to be less than 0.1% of traffic. We seek to uncover characteristics of current IPv6 deployment that can be used to infer how to advance IPv6 deployment -- be it rooted in technical capability or policy development.

Our approach focuses on acquisition and multi-dimensional analysis of the following properties of the IPv6 Internet extracted from relevant data (listed below) across time.

  • Structure and dynamics of the IP and AS-level topology graphs
  • Deployment, usage, and performance of transition technologies
  • Workload characteristics and trends: who is sourcing what types of IPv6 traffic
  • Relationship of topology, geography, and network type to deployment usage and performance
  • Impact of middleboxes and protocol functions (path MTU discovery, ECN, SACK) on reachability and performance

Data collection

Data typeDescriptionSource
Interdomain trafficIPv6 traffic workloads on a US major backbone can be used to determine the extent of global IPv6 usage.Major US backbone
Interdomain topologyBGP data from Routeviews and RIPE can be used to construct AS-level Internet topology graphsRouteviews, RIPE
Interdomain topologyTraceroute data from Ark can be used to infer IP- and AS-level connectivity and performanceCAIDA's Ark infrastructure
TCP behaviourSpecial tests run on Ark systems can be used to measure the performance of TCP and infer the presence of middleboxes interfering with TCP features such as ECN and SACK, as well as IP features such as Path MTU discovery.CAIDA's Ark infrastructure
AS relationshipsAS relationship data derived from analysis of IPv4 BGP routes can be used to study relationship structures in the IPv6 Internet.CAIDA's AS Rank system
AS classificationClassifications of ASes by their type (transit provider, enterprise customer, content/access/hosting provider) can be used to study trends of IPv6 penetration by network types.[Dhamdhere et al 2012]
DNS namesDNS data from Ark can be used to infer cases where IPv4 and IPv6 refer to the same system or interface, aiding the study of relative dual-stack performanceCAIDA's Ark infrastructure
Edge measurementsSpecially constructed javascript embedded in webpages can be used to infer IPv6 capability and performance at the edge of the network.[Aben2011jul]
Edge measurementsSpecially constructed honeypot infrastructure can be used to opportunistically used to infer IPv6 capability and performance at the edge of the network, and can gain insights into any growth in IPv6 capabilities of malware, scam hosting, and botnets.[Beverly2012]
IP GeolocationFreely available data from MaxMind can resolve the locations of 6to4 and Teredo IPv6 prefixes to cities, and provides country-level granularity for the reminder.MaxMind GeoLiteCityv6-beta
Dual-stacked web sitesFreely available data from Alexa on the names of popular web sites can be used to automatically identify dual-stacked web sites and provide a set of targets for which we can measure and compare dual-stack performance.Alexa Top 1M sites

Project Deliverables

We will disseminate -- through publications at conferences, journals and network operator venues such as NANOG -- insights obtained from the measurements and data.

The schedule of work below shows categorized tasks to be completed in association with this project.

Infrastructure Support
Task 1Create project web pages and start regular updates
Task 2Evaluate IPv6 capability at Ark hosting sites
Performance of Dual-Stack and Transition Technology
Task 3Enable IPFW (software firewall) capability at Ark hosting sites to enable TBIT-style measurements
Task 4Develop and deploy methodology to automatically test the TCP capabilities of IPv6 webservers over time
Task 5Use TBIT-style tests to track the evolution of middlebox impediments in the public IPv6 Internet
Task 6Develop and deploy tools to track the dual-stack performance of webservers over time and identify more optimal routes where possible
Task 7Monitor 6to4 and Teredo relay placement relative to Ark hosting sites
Task 8Study client tunnels and other transition technologies to identify impact on performance and adoption of IPv6
IP-level Topology
Task 9Implement system to automatically update our IPv6 probing target list
Task 10Implement new IPv6 topology measurement algorithms to improve accuracy and coverage
Task 11Research methods to infer IPv6 router aliases
Task 12Evaluate the applicability of IPv4 inferred AS relationships to observed and inferred IPv6 AS-level paths
Task 13Upgrade DNS hostname-IP database to support IPv6
BGP-level Topology
Task 14Build tools to enable tracking of the evolution of IPv6 AS core and AS rankings
Task 15Analyze periodic snapshots of BGP data to investigate how the structure of the IPv6 topology evolves over time by AS classification
Task 16Analyze BGP data to investigate economic evolution of the IPv6 Internet and contrast with the current economic evolution of the IPv4 Internet
Edge Measurement
Task 17Develop and deploy JavaScript-based edge measurement at diverse third-party web sites in research, industry, enterprise, and government environments
Task 18Develop and deploy honeypot-based infrastructure to monitor and characterize security-related IPv6 behavior
Task 19Develop and deploy tools to extract IPv6 data from packet traces, including data from DNS root servers
Task 20Investigate the accuracy of publicly available IPv6 geolocation data
Task 21Correlate illuminated edge with known policy efforts to spur IPv6 deployment
Correlation with Socioeconomic Parameters
Task 22Correlate IPv6 deployment by transit providers with AS type (e.g. dial-up, DSL, cable, enterprise)
Task 23Build tools to automate tracking of IPv6 prefixes from the time they are allocated by an RIR, correlating data with observable characteristics of prefix owner
Task 24Build tools to identify IPv4 prefixes that have been allocated but not observably used on the public Internet in years, and visualize changes after IPv4 exhaustion

Prior Related Work by the Investigators

  Last Modified: Fri Mar-23-2012 16:22:38 PDT
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