A Measurement Study of Internet Bottlenecks

1. A Measurement Study of Internet Bottlenecks Ningning Hu (CMU) Joint work with Li Erran Li (Bell Lab) Zhuoqing Morley Mao (U. Mich) Peter Steenkiste (CMU) Jia Wang (AT&T) Carnegie Mellon University

2. Motivation • Recent research progress on active probing makes it possible to locate bandwidth bottlenecks • How persistent are the Internet bottlenecks? • Important for measurement frequency • What relationship exists between bottleneck and packet loss and queuing delay? • Useful for congestion identification • Are bottlenecks shared by end users within the same prefix? • Useful for path bandwidth inference • What causes intra-AS bottlenecks? • Important for traffic engineering Carnegie Mellon University

3. Pathneck • Bottleneck • Bottleneck Link: the link with the smallest available bandwidth on a network path • Bottleneck Router: the downstream router of a bottleneck link • Pathneck • An active probing tool that can detect Internet bottleneck location effectively and efficiently • For details, please refer to • “Locating Internet Bottlenecks: Algorithms, Measurements, and Implications” [SIGCOMM’04] • Source code: www.cs.cmu.edu/~hnn/pathneck • Pathneck output used in this work • Bottleneck link location • Route Carnegie Mellon University

4. Day-1 Day-2 … Day-38 Data collection D • Probing • Source: a CMU host • Destinations: 960 diverse IP addresses • 10 continuous probings for each destination (1.5 minutes) • Repeat for 38 days (for persistence study) • Limitations • Pathneck can not cover the last hop • 960 << # of Internet paths D 960 Internet Destinations D cmu S D D D D Carnegie Mellon University

5. Outline • How persistent are the Internet bottlenecks? • Route persistence • Bottleneck persistence • What relationship is between bottleneck and packet loss and queuing delay? • Are bottlenecks shared by end users within the same prefix? • What causes intra-AS bottlenecks? Carnegie Mellon University

6. probing Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Terminology • Consider both AS-level route and location-level route probing set (persistent) Day 1 not persistent Carnegie Mellon University

7. Route Persistence • Route change is very common and must be considered for bottleneck persistence analysis • Consistent with the results from Zhang, et. al. [IMW-01] on route persistence AS level Location level Carnegie Mellon University

8. Bottleneck Persistence • Persistence of a bottleneck router • Bottleneck Persistence of a path • Max(Persist(R)) for all bottleneck router R • Two views: • End-to-end view ― per (src, dst) pair • Includes the impact of route change • Route-based view ― per route • Removes the impact of route change # of persistent probing sets R is bottleneck Persist(R) = # of persistent probing sets R appears Carnegie Mellon University

9. 1 2 2 Bottleneck Persistence 3 • Bottleneck persistence in route-based view is higher than end-to-end view • AS-level bottleneck persistence is very similar to that from location level • 20% bottlenecks have perfect persistence in end-to-end view, and 30% for route-based view Carnegie Mellon University

10. Outline • How persistent are the Internet bottlenecks? • What relationship exists between bottleneck and packet loss and queuing delay? • Are bottlenecks shared by end users within the same prefix? • What causes intra-AS bottlenecks? Carnegie Mellon University

11. Motivation • Possible congestion indication • Large queuing delay • Packet loss • Bottleneck • They do not always occur together • Packet scheduling algorithm  large queuing delay • Traffic burstiness or RED  packet loss • Small link capacity  bottleneck • Bottleneck ? link loss | large link delay Carnegie Mellon University

12. Method • Collected on the same set of 960 paths, but independent measurements • Detect bottleneck location using Pathneck • Detect loss location using Tulip • Only use the forward path results • Detect link queuing delay using Tulip • medianRTT – minRTT • [ Tulip was developed in University of Washington, SOSP’03 ] • Our analysis is based on the 382 paths for which both bottleneck location and packet loss are detected Carnegie Mellon University

13. |Dist| <= 2 60% Perfectly correlated 30% Bottleneck  Packet Loss Carnegie Mellon University

14. 3% non-bottlenecks have delay > 5ms 15% bottlenecks have delay > 5ms Bottleneck  Link Delay Carnegie Mellon University

15. There is not much sharing within common cluster We observe clear correlation with link load, while observing no clear relationship with link capacity, router CPU load, and memory usage. More Results • How persistent are the Internet bottlenecks? • What relationship exists between bottleneck and packet loss and queuing delay? • Are bottlenecks shared by end users within the same prefix? • What causes intra-AS bottlenecks? Carnegie Mellon University

16. Related Work • Persistence of Internet path properties • Zhang [IMW-01], Paxson [TR-2000], Labovitz [TON-1998, Infocom-1999] • Congestion points sharing • Katabi [TR-2001], Rubenstein [Sigmetrics-2000] • Correlation among Internet path properties • Paxson [1996] • Correlation between router and link properties • Agarwal [PAM 2004] Carnegie Mellon University

17. Conclusion • Only 20-30% Internet bottlenecks have perfect persistence • Application should be ready for bottleneck location change • Bottleneck locations have a fairly strong (60%) correlation with packet loss locations • Bottleneck and loss detections should be used together for congestion detection • End users within common cluster share bottlenecks only with a low probabilityh • End user can not assume common bottlenecks • We observe evidence of a correlation between bottleneck and link loads • Network engineers should focus on traffic load to eliminate bottlenecks Carnegie Mellon University