The Journal of Supercomputing

, Volume 62, Issue 3, pp 1425–1450 | Cite as

Analysis of large scale traceroute datasets in Internet routing overlays by parallel computation

  • Mohammad Z. AhmadEmail author
  • Ratan Guha


The creation of a routing overlay network on the Internet requires the identification of shorter detour paths between end hosts in comparison to the default path available. These detour paths are typically the edges forming a Triangle Inequality Violation (TIV), an artifact of the Internet delay space where the sum of latencies across an intermediate hop is lesser than the direct latency between the pair of end hosts. These violations are caused mainly due to interdomain routing policies between Autonomous Systems (ASes) and AS peering through Internet eXchange Points (IXPs). Identifying detours for a global overlay network requires large amounts of computational capabilities due to the sheer number of possible paths linking source and destination ASes. In this work, we use parallel programming paradigms to exploit the massively parallel capabilities of analyzing the large network measurement datasets made available to the network research community by CAIDA. We study Internet routes traversing IXPs and measure potential TIVs created by these paths. Large scale analysis of the dataset is carried out by implementing an efficient parallel solution on the CPU and then the general purpose graphics processor unit (GPGPU) as well. Both multicore CPU and GPGPU implementations can be carried out with ease on desktop environments with readily available software. We find both parallel solutions yield high improvements in speedup (2-35x) in comparison to the serial methodologies thereby opening up the possibility of harnessing the power of parallel programming with readily available hardware. The large amount of data analyzed and studied helps draw various inferences for the networking research community in building future scalable Internet routing overlays with greater routing efficiencies.


Internet topology Internet exchange points GPGPU CUDA Data analysis Parallel computation 


  1. 1.
    Caida: Archipelago measurement infrastructure.
  2. 2.
    Nvidia corporation: Nvida cuda compute unified device architecture programming.
  3. 3.
    Packet clearing house.
  4. 4.
    Public exchange points search/list.
  5. 5.
  6. 6.
    Triangle inequality violations due to ixps, public page.
  7. 7.
    Ahmad MZ, Guha R (2010) Studying the effect of Internet exchange points on Internet link delays. In: Proceedings of the communications and networking symposium (CNS), SCS SpringSim 2010, Orlando, FL Google Scholar
  8. 8.
    Aho AV, Corasick MJ (1975) Efficient string matching: an aid to bibliographic search. Commun ACM 18:333–340. MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Augustin B, Krishnamurthy B, Willinger W (2009) IXPs: mapped? In: IMC’09: proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference. ACM, New York, pp 336–349. doi: 10.1145/1644893.1644934 CrossRefGoogle Scholar
  10. 10.
    Buluç A, Gilbert JR, Budak C (2010) Solving path problems on the gpu. Parallel Comput 36:241–253. zbMATHCrossRefGoogle Scholar
  11. 11.
    Dabek F, Cox R, Kaashoek F, Morris R (2004) Vivaldi: a decentralized network coordinate system. Comput Commun Rev 34:15–26. CrossRefGoogle Scholar
  12. 12.
    Dhamdhere A, Dovrolis C (2010) The Internet is flat: modeling the transition from a transit hierarchy to a peering mesh. In: Proceedings of the 6th international conference, Co-NEXT’10. ACM, New York, pp 21:1–21:12. Google Scholar
  13. 13.
    Gill P, Arlitt M, Li Z, Mahanti A (2008) The flattening Internet topology: natural evolution, unsightly barnacles or contrived collapse? In: PAM’08: proceedings of the 9th international conference on passive and active network measurement. Springer, Berlin, pp 1–10 CrossRefGoogle Scholar
  14. 14.
    Harish P, Narayanan PJ (2007) Accelerating large graph algorithms on the gpu using cuda. In: HiPC, pp 197–208 Google Scholar
  15. 15.
    He Y, Siganos G, Faloutsos M, Krishnamurthy S (2009) Lord of the links: a framework for discovering missing links in the Internet topology. IEEE/ACM Trans Netw 17(2):391–404. CrossRefGoogle Scholar
  16. 16.
    Huang NF, Hung HW, Lai SH, Chu YM, Tsai WY (2008) A gpu-based multiple-pattern matching algorithm for network intrusion detection systems. In: Proceedings of the 22nd international conference on advanced information networking and applications—workshops. IEEE Computer Society, Washington, DC, pp 62–67. Google Scholar
  17. 17.
    Katz GJ, Kider JT Jr (2008) All-pairs shortest-paths for large graphs on the gpu. In: Proceedings of the 23rd ACM SIGGRAPH/EUROGRAPHICS symposium on graphics hardware, GH’08. Eurographics Association, Aire-la-Ville, pp 47–55. Google Scholar
  18. 18.
    Lin CH, Tsai SY, Liu CH, Chang SC, Shyu JM (2010) Accelerating string matching using multi-threaded algorithm on gpu. In: GLOBECOM, pp 1–5 Google Scholar
  19. 19.
    Lumezanu C, Baden R, Spring N, Bhattacharjee B (2009) Triangle inequality and routing policy violations in the Internet. In: Proceedings of the 10th international conference on passive and active network measurement, PAM’09. Springer, Berlin, pp 45–54 CrossRefGoogle Scholar
  20. 20.
    Lumezanu C, Baden R, Spring N, Bhattacharjee B (2009) Triangle inequality variations in the Internet. In: Internet measurement conference, pp 177–183 Google Scholar
  21. 21.
    Lumezanu C, Levin D, Spring N (2007) Peerwise discovery and negotiation of faster paths. In: ACM Sigcomm workshop on hot topics in networking Google Scholar
  22. 22.
    Ly C, Hsu CH, Hefeeda M (2010) Improving online gaming quality using detour paths. In: ACM multimedia, pp 55–64 Google Scholar
  23. 23.
    Madhyastha HV, Isdal T, Piatek M, Dixon C, Anderson T, Krishnamurthy A, Venkataramani A (2006) iplane: an information plane for distributed services. In: OSDI’06: proceedings of the 7th symposium on operating systems design and implementation. USENIX Association, Berkeley, pp 367–380 Google Scholar
  24. 24.
    Mahadevan P, Krioukov D, Fomenkov M, Huffaker B, Dimitropoulos X, Claffy K, Vahdat A (2005) Lessons from three views of the Internet topology.
  25. 25.
    Oliveira RV, Pei D, Willinger W, Zhang B, Zhang L (2008) In search of the elusive ground truth: the Internet’s as-level connectivity structure. ACM SIGMETRICS Perform Eval Rev 36(1):217–228. CrossRefGoogle Scholar
  26. 26.
    Savage S, Collins A, Hoffman E, Snell J, Anderson T (1999) The end-to-end effects of Internet path selection. Comput Commun Rev 29(4):289–299. doi: 10.1145/316194.316233 CrossRefGoogle Scholar
  27. 27.
    Smith R, Goyal N, Ormont J, Sankaralingam K, Estan C (2009) Evaluating gpus for network packet signature matching. In: IEEE international symposium on performance analysis of systems and software, ISPASS 2009, pp 175–184 Google Scholar
  28. 28.
    Wang G, Zhang B, Ng TSE (2007) Towards network triangle inequality violation aware distributed systems. In: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, IMC’07. ACM, New York, pp 175–188. CrossRefGoogle Scholar
  29. 29.
    Zhang B, Ng TSE, Nandi A, Riedi R, Druschel P, Wang G (2006) Measurement based analysis, modeling, and synthesis of the Internet delay space. In: Proceedings of the 6th ACM SIGCOMM conference on Internet measurement, IMC’06. ACM, New York, pp 85–98. CrossRefGoogle Scholar
  30. 30.
    Zheng H, Lua EK, Pias M, Griffin TG (2005) Internet routing policies and round-trip-times. In: PAM, pp 236–250 Google Scholar
  31. 31.
    Zhu Y, Chen Y, Zhang Z, Fu X, Li D, Deng B, Li X (2010) Taming the triangle inequality violations with network coordinate system on real Internet. In: Proceedings of the re-architecting the Internet workshop, ReARCH’10. ACM, New York, pp 7:1–7:6. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of EECSUniversity of Central FloridaOrlandoUSA

Personalised recommendations