IPv4 Address Allocation Trends

1. IPv4 Address Allocation Trends J. Scott Marcus Chief Technology Officer (CTO) May 22, 2001

2. IPv4 Address Allocation Trends • AS number exhaustion - the RIRs recognize the need for forecasting • IPv4 allocation and usage trends • The McFadden/Holmes Report

3. Exponential Growth of Autonomous System (AS) numbers Source: Scott Marcus, Genuity

4. Exponential vs Quadratic (Bates Data)

5. Chicken Little was Wrong! • This is far simpler to remedy than IPv4 address exhaustion, because • the solution need not impact end systems (hosts); • the solution need not impact DNS; and • the solution need not impact routers unless they speak BGP-4. • Any solution is complicated by the need for backward compatibility and phased migration. • Time until exhaustion is nonetheless sufficient to architect, design, implement and deploy solutions. • Cisco and Juniper are reportedly well into implementation.

6. Route Table Bloat - a Different Problem BGP Table Growth since 1989

7. The RIRs Recognize the Need for Forecasting • Continuing need to further refine projections. • Need for forward-looking proactive forecasting on a regular basis not only for AS numbers, but also for route table entries and IPv4/IPv6 addresses. • Forecasting needs to incorporate allocation data from all three RIRs (APNIC, ARIN, RIPE NCC). • Forecasting needs to be institutionalized by the RIRs themselves, with data readily available to independent researchers.

8. The Team • Assembled by ARIN • Frank Solensky Gotham Networks • kc claffy CAIDA • Scott Marcus Genuity • Active contributions and support by APNIC and RIPE NCC

9. Goals (and Non-Goals) for RIR Team • Formally and regularly: • Gather address deployment data from RIRs • Perform statistical analysis • Make allocation data available for independent analysis • Non-goals: • ‘Blue sky’ theorizing • Estimating NAT utilization (for now)

10. Annual IPv4 Allocations

11. Cumulative IPv4 Allocations

12. IPv4 Address Allocations: Linear Fit

13. Address Allocations: Logistic Fit

14. A Closer Look - Logistic Curve

15. Addresses Seen by Telstra (AS1221)

16. Addresses in Use

17. Routing Table Address Growth

18. McFadden/Holmes/Mylotte Projection Purported Worst Case, Most Likely and Best Cases

19. Rate of Depletion • Annual growth rate • McFadden/Holmes assumes low/medium/high growth of 30%/50%/80% respectively. • RIR data shows annual growth of about 3%, with a negative second derivative • Geoff Huston’s data shows annual growth of roughly 7% • Order of magnitude discrepancy! • The trend does NOT appear to be exponential overall.

20. Marketing Analysis • Numerous statistics, no attribution • No traceability of assumptions • No verifiability of source data

21. Lessons • The RIR team will continue to focus on conservative analysis and extrapolation of verifiable quantitative data. • Debate is a healthy thing - “Let a thousand flowers bloom.” • We will make RIR source data available to independent researchers. • Policymakers in the RIRs, ASO, IETF and elsewhere should benefit from diverse inputs and should reach their own conclusions.

22. Acknowledgments • Frank Solensky, Gotham Networks • kc claffy, CAIDA • Geoff Huston, Telstra • Cathy Murphy, ARIN • Paul Wilson, APNIC • Axel Pawlik and Mirjam Kuehne, RIPE NCC • Tony Bates and Phillip Smith, Cisco • Mark Kosters, Verisign • Christian Huitema, Microsoft