1. Active Measurements on the AT&T IP Backbone Len Ciavattone, Al Morton, Gomathi Ramachandran AT&T Labs

2. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 2 Colleagues on This Project Nicole Kowalski Ron Kulper George Holubec Shashi Pulakurti

3. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 3 Measurements for Large Networks Must be:  Easily understood  Estimate or assess customer performance  Useful for alarming and associated actions  Not likely to generate false positives  As close as possible to real-time notification  Part of the traditional fault/passive management system

4. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 4 Traditional Measurements Fault  Triggered by hard failures (link, card, router, etc)  Near real-time alarms Passive  Element level monitoring  Traffic, drops, device health, card performance monitored  Performance alarming possible per interface Where can traditional measurements be added to?  Path level performance information  Delay and delay variation measurements  Indication of customer degradation (except hard failures)

5. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 5 Active Measurements Active measurements introduce synthetic traffic into the network  Advantages:  Traffic flow follows a sampled customer path  Delay, delay variation and sampled loss directly measurable  Possible to estimate customer impact of element level degradation  Well designed sampling methodology will allow sound estimation of levels of degradation seen  Can be used to give customers a sense of network behavior (e.g. AT&T’s Network Status Site http://www.att.com/ipnetwork)  Disadvantages  Need to introduce traffic into the network  Based on sampling, not customer traffic

6. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 6 Practical Considerations From a practical standpoint, what limits the measurements?  Amount of data generated  Desire to use a standard/unmodified UNIX kernel  Expense of bigger and more powerful servers  Cost of deployment of new servers in COs.  Difficulty of acquiring appropriate GPS feed

7. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 7 Measurement Design Poisson Sequence  15 minute duration  = 0.3 pkts/sec  Type UDP  278 bytes total  packet loss threshold is a min of 3 s Periodic Sequence  1 minute duration  Random Start Time  20 ms spacing  Type UDP, IPv4  60 bytes total  packet loss threshold is a min of 3 s 24 hours... 15 minutes Presented at the IETF 50 IPPM meeting by Al Morton

8. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 8 Sampling and Event Detection Poisson Sequence  All 15 minutes tested with average inter-arrival time of 3.33s  Assume 10 s congestion events (minimum length)  If  Probability of Detection by one or more packets

9. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 9 Sampling and Event Detection Periodic sequence  1-min test in a 15-min test cycle (2 if considering RT processes)  Assume 10s congestion events (minimum length), assume 1 event per test cycle  Consider that only recurring events are actionable: Average Number of cycles to detection (one-way) = 1/0.0777 = 13 test cycles The Poisson Probe sequence detects accurately, the Periodic Probe sequence is used to characterize recurring events

10. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 10 Metrics Round Trip (RT) Loss RT Delay (std dev, 95th percentile, min, mean) Inter-Packet Delay Variation (IPDV) and DV jitter Out of sequence events (non-reversing sequence definition -- up for consideration in the IETF IPPM) Approximate one-way loss Degraded seconds or minutes Loss pattern (number of consecutive losses) Distributions of delay variations Traceroutes performed at the beginning of each test 85 Metrics kept indefinitely

11. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 11 TxRcvPlayout Time spent in: Transit Rcv Buffer 1 2 3 4 tt 1 2 3 4  Inter packet arrival time, longer than send interval IPDV is a measure of transfer delay variation. For Packet n, IPDV(n) = Delay(n) - Delay(n- 1) If the nominal transfer time is  =10msec, and packet 2 is delayed in transit for an additional 5 msec, then two IPDV values will be affected. IPDV(2) = 15 - 10 = 5 msec IPDV(3) = 10 - 15 = -5 msec IPDV(4) = 10 - 10 = 0 msec IPDV Definition and Example

12. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 12 IP Packet Sequence SrcDstPlayout Time spent in: Transit Rcv Buffer 1 2 3 4 1 2 3  Tolerance on R2 arrival with 2 Packet Buffer tt Arriving Packets are compared with the “next expected” RefNum. Packet 2 arrives Out-of-Sequence, since Packet 3 has arrived and the “next expected” packet in Packet 4. Packet 2 is Offset by 1 packet, or Late by the arrival time of Packet 2 - Packet 3 =  t

13. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 13 Common Problems Detected Route Changes Card degradation Low-level fiber errors Effects of Maintenance (Card swaps etc)

14. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 14 Examples of Detection Bit errors that cause low-level (~0.03%) loss can be detected accurately using this method and can be fixed before customers feel the impact  Typically in such cases the degradation is subtle enough that traditional IP alarms do not show the problem clearly  Customers aren’t complaining….yet  In the case shown, no customer complaints were made and the problem was fixed proactively

15. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 15 Increasing Bit Errors Single packet loss per Periodic test Two packet losses per Periodic test More occasional Loss was seen with the Poisson Probe Sequence Fiber span taken out of service

16. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 16 Detection of Route Changes RT Delay Time 1:00 Periodic Sequence 1:15 6 9 1:071:09

17. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 17 Poisson Probe Route change detection

18. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 18 Periodic probe (same incident)

19. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 19 The “Blenders” First shown by Steve Casner et al in the NANOG 22 conference (May 20-22, 2001, “A Fine-Grained View of High Performance Networking”, http://www.nanog.org/mtg-0105/agenda.html) Seem to be properties of route loops Rare events, but interesting as they may shed light on some properties of route convergence

20. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 20 Simple Blender 88 packets arrive within 64 ms 79 OOS packets, 9 in sequence 7 sequence discontinuities. Zero Loss Delay and IPDV actually describe this event best

21. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 21 Simple Blender Magnified

22. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 22 Blender 2 Scattered loss throughout 250 packets in event, 10 separate sequence discontinuities Delay of first packet 6s

23. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 23 Blender 2

24. L. Ciavattone, A. Morton, G. Ramachandran AT&T Labs Page 24 Summary Active measurements: Can provide a view of customer performance Can be used to alert maintenance personnel proactively Can provide insight into network behavior Can be used to improve planned maintenance