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Inferring Cloud Interconnections: Validation, Geolocation, and Routing Behavior

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Passive and Active Measurement (PAM 2021)

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Abstract

Public clouds fundamentally changed the Internet landscape, centralizing traffic generation in a handful of networks. Internet performance, robustness, and public policy analyses struggle to properly reflect this centralization, largely because public collections of BGP and traceroute reveal a small portion of cloud connectivity.

This paper evaluates and improves our ability to infer cloud connectivity, bootstrapping future measurements and analyses that more accurately reflect the cloud-centric Internet. We also provide a technique for identifying the interconnections that clouds use to reach destinations around the world, allowing edge networks and enterprises to understand how clouds reach them via their public WAN. Finally, we present two techniques for geolocating the interconnections between cloud networks at the city level that can inform assessments of their resilience to link failures and help enterprises build multi-cloud applications and services.

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Notes

  1. 1.

    We observed different behavior in February, 2021 (Appendix A).

References

  1. The CAIDA AS relationships dataset. https://www.caida.org/data/as-relationships/

  2. PeeringDB. https://peeringdb.com/

  3. Routing information service (RIS). https://www.ripe.net/analyse/internet-measurements/routing-information-service-ris

  4. University of Oregon route views project. http://www.routeviews.org/routeviews/

  5. AWS global accelerator, October 2020. https://aws.amazon.com/global-accelerator

  6. Azure global network, May 2020. https://azure.microsoft.com/en-us/global-infrastructure/global-network/

  7. Azure regions, May 2020. https://azure.microsoft.com/en-us/global-infrastructure/regions/

  8. Cloud locations, May 2020. https://cloud.google.com/about/locations

  9. Global infrastructure, May 2020. https://aws.amazon.com/about-aws/global-infrastructure/

  10. Internet2 - visible network raw data access, October 2020. https://vn.net.internet2.edu/xml/Internet2/2020/10/15/14/43/show_interfaces.gz

  11. Pennnet network architecture (2020). https://upenn.app.box.com/v/RouterCoreDiagram

  12. Regions and availability zones, May 2020. https://aws.amazon.com/about-aws/global-infrastructure/regions_az/

  13. VPC network overview, May 2020. https://cloud.google.com/vpc/docs/vpc

  14. Arnold, T., et al.: (How much) does a private wan improve cloud performance? In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 79–88. IEEE (2020)

    Google Scholar 

  15. Arnold, T., et al.: Cloud provider connectivity in the flat internet. IMC (2020)

    Google Scholar 

  16. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  17. Beverly, R.: Yarrp’ing the internet: Randomized high-speed active topology discovery. In: IMC, pp. 413–420 (2016)

    Google Scholar 

  18. Chen, K., et al.: Where the sidewalk ends: extending the internet as graph using traceroutes from P2P users. In: Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies, pp. 217–228 (2009)

    Google Scholar 

  19. Dimitropoulos, X., Krioukov, D., Fomenkov, M., Huffaker, B., Hyun, Y., Claffy, K., Riley, G.: As relationships: inference and validation. ACM SIGCOMM Comput. Commun. Rev. 37(1), 29–40 (2007)

    Article  Google Scholar 

  20. d’Itri, M.: whois. https://github.com/rfc1036/whois

  21. Donnet, B., Luckie, M., Mérindol, P., Pansiot, J.J.: Revealing MPLS tunnels obscured from traceroute. ACM SIGCOMM Comput. Commun. Rev. 42(2), 87–93 (2012)

    Article  Google Scholar 

  22. Du, B., Candela, M., Huffaker, B., Snoeren, A.C., Claffy, K.: RIPE IPmap active geolocation: mechanism and performance evaluation. SIGCOMM Comput. Commun. Rev. 50(2), 3–10 (2020)

    Article  Google Scholar 

  23. Euro-IX: Ixpdb. https://ixpdb.euro-ix.net/en/

  24. Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. ACM SIGCOMM Comput. Commun. Rev. 29(4), 251–262 (1999)

    Article  Google Scholar 

  25. Gao, L.: On inferring autonomous system relationships in the internet. IEEE/ACM Trans. Netw. 9(6), 733–745 (2001)

    Article  Google Scholar 

  26. Gharaibeh, M., Shah, A., Huffaker, B., Zhang, H., Ensafi, R., Papadopoulos, C.: A look at router geolocation in public and commercial databases. In: Proceedings of the 2017 Internet Measurement Conference, pp. 463–469 (2017)

    Google Scholar 

  27. Giotsas, V., Luckie, M., Huffaker, B., Claffy, K.: Inferring complex as relationships. In: Proceedings of the 2014 Conference on Internet Measurement Conference, pp. 23–30 (2014)

    Google Scholar 

  28. Gueye, B., Ziviani, A., Crovella, M., Fdida, S.: Constraint-based geolocation of internet hosts. IEEE/ACM Trans. Netw. 14(6), 1219–1232 (2006)

    Article  Google Scholar 

  29. Haq, O., Raja, M., Dogar, F.R.: Measuring and improving the reliability of wide-area cloud paths. In: WWW, pp. 253–262 (2017)

    Google Scholar 

  30. Huffaker, B., Dhamdhere, A., Fomenkov, M., Claffy, K.C.: Toward topology dualism: improving the accuracy of AS annotations for routers. In: Krishnamurthy, A., Plattner, B. (eds.) PAM 2010. LNCS, vol. 6032, pp. 101–110. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12334-4_11

    Chapter  Google Scholar 

  31. Huffaker, B., Fomenkov, M., Claffy, K.: DRoP:DNS-based router positioning. ACM SIGCOMM Comput. Commun. Rev. 44(3), 5–13 (2014)

    Article  Google Scholar 

  32. Jin, Y., Scott, C., Dhamdhere, A., Giotsas, V., Krishnamurthy, A., Shenker, S.: Stable and practical AS relationship inference with ProbLink. In: 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2019), pp. 581–598 (2019)

    Google Scholar 

  33. Katz-Bassett, E., John, J.P., Krishnamurthy, A., Wetherall, D., Anderson, T., Chawathe, Y.: Towards IP geolocation using delay and topology measurements. In: Proceedings of the 6th ACM SIGCOMM Conference on Internet Measurement, pp. 71–84 (2006)

    Google Scholar 

  34. Luckie, M.: Scamper: a scalable and extensible packet prober for active measurement of the Internet. In: IMC (2010)

    Google Scholar 

  35. Luckie, M., Dhamdhere, A., Clark, D., Huffaker, B., Claffy, K.: Challenges in inferring internet interdomain congestion. In: IMC (2014)

    Google Scholar 

  36. Luckie, M., Dhamdhere, A., Huffaker, B., Clark, D., Claffy, K.: bdrmap: inference of borders between IP networks. In: IMC (2016)

    Google Scholar 

  37. Luckie, M., Huffaker, B., Dhamdhere, A., Giotsas, V., Claffy, K.: As relationships, customer cones, and validation. In: Proceedings of the 2013 Conference on Internet Measurement Conference, pp. 243–256 (2013)

    Google Scholar 

  38. Luckie, M., Marder, A., Fletcher, M., Huffaker, B., Claffy, K.C.: Learning to extract and use ASNs in hostnames. In: Proceedings of the 2020 Internet Measurement Conference (2020)

    Google Scholar 

  39. Mao, Z.M., Johnson, D., Rexford, J., Wang, J., Katz, R.: Scalable and accurate identification of AS-level forwarding paths. In: IEEE INFOCOM 2004, vol. 3, pp. 1605–1615. IEEE (2004)

    Google Scholar 

  40. Mao, Z.M., Rexford, J., Wang, J., Katz, R.H.: Towards an accurate AS-level traceroute tool. In: Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pp. 365–378 (2003)

    Google Scholar 

  41. Marder, A.: Sharp snapshots of the internet’s graph with HONE. Ph.D. thesis, University of Pennsylvania (2019)

    Google Scholar 

  42. Marder, A.: vrfinder: finding outbound addresses in traceroute. In: SIGMETRICS (2020)

    Google Scholar 

  43. Marder, A., Luckie, M., Dhamdhere, A., Huffaker, B., Claffy, K.C., Smith, J.M.: Pushing the boundaries with bdrmapIT: mapping router ownership at internet scale. In: IMC (2018)

    Google Scholar 

  44. Marder, A., Smith, J.M.: MAP-IT: multipass accurate passive inferences from traceroute. In: Proceedings of the 2016 Internet Measurement Conference, pp. 397–411. ACM (2016)

    Google Scholar 

  45. NCC, R.: RIPE IPMap. https://ipmap.ripe.net/

  46. Network, M.: RADb: The Internet routing registry. https://www.radb.net/

  47. Norton, W.B.: Cloud interconnections, September 2016. https://www.caida.org/workshops/wie/1612/slides/wie1612_wnorton.pdf

  48. Poese, I., Uhlig, S., Kaafar, M.A., Donnet, B., Gueye, B.: IP geolocation databases: unreliable? ACM SIGCOMM Comput. Commun. Rev. 41(2), 53–56 (2011)

    Article  Google Scholar 

  49. Saunders, B.: Who’s using amazon web services? [2020 update], January 2020. https://www.contino.io/insights/whos-using-aws

  50. Scheitle, Q., Gasser, O., Sattler, P., Carle, G.: HLOC: hints-based geolocation leveraging multiple measurement frameworks. In: 2017 Network Traffic Measurement and Analysis Conference (TMA), pp. 1–9. IEEE (2017)

    Google Scholar 

  51. Spring, N., Mahajan, R., Wetherall, D.: Measuring ISP topologies with rocketfuel. ACM SIGCOMM Comput. Commun. Rev. 32(4), 133–145 (2002)

    Article  Google Scholar 

  52. Taneja, S., Pretzer, X.: Google cloud networking in depth: understanding network service tiers, May 2019. https://cloud.google.com/blog/products/networking/google-cloud-networking-in-depth-understanding-network-service-tiers

  53. Vanaubel, Y., Luttringer, J.R., Mérindol, P., Pansiot, J.J., Donnet, B.: TNT, watch me explode: a light in the dark for revealing MPLS tunnels. In: 2019 Network Traffic Measurement and Analysis Conference (TMA), pp. 65–72. IEEE (2019)

    Google Scholar 

  54. Vanaubel, Y., Mérindol, P., Pansiot, J.J., Donnet, B.: Through the wormhole: tracking invisible MPLS tunnels. In: Proceedings of the 2017 Internet Measurement Conference, pp. 29–42. ACM (2017)

    Google Scholar 

  55. Xia, J., Gao, L.: On the evaluation of as relationship inferences [internet reachability/traffic flow applications]. In: IEEE Global Telecommunications Conference, 2004. GLOBECOM 2004, vol. 3, pp. 1373–1377. IEEE (2004)

    Google Scholar 

  56. Yeganeh, B., Durairajan, R., Rejaie, R., Willinger, W.: How cloud traffic goes hiding: a study of Amazon’s peering fabric. In: IMC, pp. 202–216 (2019)

    Google Scholar 

  57. Yeganeh, B., Durairajan, R., Rejaie, R., Willinger, W.: A first comparative characterization of multi-cloud connectivity in today’s internet. In: Sperotto, A., Dainotti, A., Stiller, B. (eds.) PAM 2020. LNCS, vol. 12048, pp. 193–210. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44081-7_12

    Chapter  Google Scholar 

  58. Zhang, B., Liu, R., Massey, D., Zhang, L.: Collecting the internet as-level topology. ACM SIGCOMM Comput. Commun. Rev. 35(1), 53–61 (2005)

    Article  Google Scholar 

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Acknowledgments

This work was supported by DARPA CA HR00112020014, NSF OAC-1724853, NSF CNS-1901517, and NSF CNS-1925729.

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Correspondence to Alexander Marder .

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A Recent GCP Traceroute Behavior

A Recent GCP Traceroute Behavior

Fig. 11.
figure 11

Unlike the traceroute in October, 2020, the traceroute from GCP Los Angeles to UPenn in February, 2021 revealed an internal GCP IP addresses (a). The first responsive hop in the traceroute from Virginia was an address on a UPenn router, but the path contained unresponsive hops until that point (b).

We conducted the traceroutes in Sect. 4.1 in October, 2020. Revisiting our examples in February, 2021, we noticed a different behavior. Many paths still do not contain any internal GCP addresses, but the paths no longer appear to start in neighboring networks. As seen in the traceroute path from GCP Los Angeles to UPenn (Fig. 11a), hop #1 is an internal GCP address followed by the interconnection with Internet2 at hop #2 [10], rather than a UPenn address. The first responsive hop in the path from our GCP Virginia VM (Fig. 11b) is the same UPenn address that we previously observed as hop #1 in Sect. 4.1, but hop #1 is now an unresponsive address. This behavior makes interpreting GCP traceroutes more intuitive, as they follow conventional traceroute semantics, but observing GCP internal addresses still appears to depend on the combination of VM region and traceroute destination.

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Marder, A., Claffy, K.C., Snoeren, A.C. (2021). Inferring Cloud Interconnections: Validation, Geolocation, and Routing Behavior. In: Hohlfeld, O., Lutu, A., Levin, D. (eds) Passive and Active Measurement. PAM 2021. Lecture Notes in Computer Science(), vol 12671. Springer, Cham. https://doi.org/10.1007/978-3-030-72582-2_14

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