Document Open Access Logo

Vantage Point Selection Algorithms for Bottleneck Capacity Estimation

Authors Vikrant Ashvinkumar, Rezaul Chowdhury , Jie Gao , Mayank Goswami , Joseph S. B. Mitchell , Valentin Polishchuk

PDF

File

Thumbnail PDF
PDF
LIPIcs.WADS.2025.6.pdf
  • Filesize: 0.9 MB
  • 19 pages

Document Identifiers

Related Versions

Subject Classification

ACM Subject Classification
  • Networks → Network algorithms
Keywords
  • Bottleneck capacity
  • Approximation algorithms
  • Instance optimality

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Abstract

Motivated by the problem of estimating bottleneck capacities on the Internet, we formulate and study the problem of vantage point selection. We are given a graph G = (V, E) whose edges E have unknown capacity values that are to be discovered. Probes from a vantage point, i.e, a vertex v ∈ V, along shortest paths from v to all other vertices, reveal bottleneck edge capacities along each path. Our goal is to select k vantage points from V that reveal the maximum number of bottleneck edge capacities. 
We consider both a non-adaptive setting where all k vantage points are selected before any bottleneck capacity is revealed, and an adaptive setting where each vantage point selection instantly reveals bottleneck capacities along all shortest paths starting from that point. In the non-adaptive setting, by considering a relaxed model where edge capacities are drawn from a random permutation (which still leaves the problem of maximizing the expected number of revealed edges NP-hard), we are able to give a 1-1/e approximate algorithm. In the adaptive setting we work with the least permissive model where edge capacities are arbitrarily fixed but unknown. We compare with the best solution for the particular input instance (i.e. by enumerating all choices of k tuples), and provide both lower bounds on instance optimal approximation algorithms and upper bounds for trees and planar graphs.

Cite As Get BibTex

Vikrant Ashvinkumar, Rezaul Chowdhury, Jie Gao, Mayank Goswami, Joseph S. B. Mitchell, and Valentin Polishchuk. Vantage Point Selection Algorithms for Bottleneck Capacity Estimation. In 19th International Symposium on Algorithms and Data Structures (WADS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 349, pp. 6:1-6:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.WADS.2025.6

Author Details

Vikrant Ashvinkumar
  • Rutgers University, Piscataway, NJ, USA
Rezaul Chowdhury
  • Stony Brook University, NY, USA
Jie Gao
  • Rutgers University, Piscataway, NJ, USA
Mayank Goswami
  • Queens College, City University of New York, NY, USA
Joseph S. B. Mitchell
  • Stony Brook University, NY, USA
Valentin Polishchuk
  • Linköping University, Sweden

Funding

  • Chowdhury, Rezaul: Partially supported by NSF (CCF-2318633).
  • Gao, Jie: Ashvinkumar and Gao are supported by NSF through IIS-2229876, DMS-2220271, DMS-2311064, CCF-2208663, CCF-2118953.
  • Goswami, Mayank: Supported by NSF grant CCF-2503086.
  • Mitchell, Joseph S. B.: Partially supported by NSF (CCF-2007275).

Acknowledgements

We thank an anonymous reviewer who caught an error in a claimed result that has now been omitted from this paper; we discuss and pose the question as the first open problem in Section 4. We also thank Srinivas Narayana for discussions on capacity discovery.

References

  1. Mark Allman and Vern Paxson. On estimating end-to-end network path properties. SIGCOMM Comput. Commun. Rev., 29(4):263-274, August 1999. URL: https://doi.org/10.1145/316188.316230.
  2. Arash Asadpour, Hamid Nazerzadeh, and Amin Saberi. Stochastic submodular maximization. In Internet and Network Economics: 4th International Workshop, WINE 2008, Shanghai, China, December 17-20, 2008. Proceedings 4, pages 477-489. Springer, 2008. URL: https://doi.org/10.1007/978-3-540-92185-1_53.
  3. RIPE Atlas. User-defined measurements. https://atlas.ripe.net/docs/getting-started/user-defined-measurements.html. Google Scholar
  4. Brice Augustin, Timur Friedman, and Renata Teixeira. Multipath tracing with paris traceroute. In 2007 Workshop on End-to-End Monitoring Techniques and Services, pages 1-8, May 2007. URL: https://doi.org/10.1109/E2EMON.2007.375313.
  5. S Banerjee and A K Agrawala. Estimating available capacity of a network connection. In Proceedings IEEE International Conference on Networks 2000 (ICON 2000). Networking Trends and Challenges in the New Millennium, pages 131-138, September 2000. Google Scholar
  6. Robert Beverly. Yarrp'ing the Internet: Randomized High-Speed active topology discovery. In Proceedings of the 2016 Internet Measurement Conference, IMC '16, pages 413-420, New York, NY, USA, November 2016. Association for Computing Machinery. URL: http://dl.acm.org/citation.cfm?id=2987479.
  7. Jan Böttger. Understanding benefits of different vantage points in today’s Internet. PhD thesis, Technische Universität Berlin, 2017. Google Scholar
  8. Valentin Burger, Matthias Hirth, Christian Schwartz, Tobias Hoßfeld, and Phuoc Tran-Gia. Increasing the coverage of vantage points in distributed active network measurements by crowdsourcing. In Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance, pages 151-161. Springer International Publishing, 2014. URL: https://doi.org/10.1007/978-3-319-05359-2_11.
  9. Rui Castro, Mark Coates, Gang Liang, Robert Nowak, and Bin Yu. Network tomography: Recent developments. SSO Schweiz. Monatsschr. Zahnheilkd., 19(3):499-517, August 2004. Google Scholar
  10. A Coates, A O Hero, III, R Nowak, and Bin Yu. Internet tomography. IEEE Signal Process. Mag., 19(3):47-65, May 2002. URL: https://doi.org/10.1109/79.998081.
  11. Benoit Donnet, Bradley Huffaker, Timur Friedman, and K C Claffy. Increasing the coverage of a cooperative internet topology discovery algorithm. In NETWORKING 2007. Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet, pages 738-748. Springer Berlin Heidelberg, 2007. Google Scholar
  12. Benoit Donnet, Philippe Raoult, Timur Friedman, and Mark Crovella. Efficient algorithms for large-scale topology discovery. In Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems, SIGMETRICS '05, pages 327-338, New York, NY, USA, June 2005. Association for Computing Machinery. URL: https://doi.org/10.1145/1064212.1064256.
  13. Greg N Federickson. Fast algorithms for shortest paths in planar graphs, with applications. SIAM J. Comput., 16(6):1004-1022, December 1987. URL: https://doi.org/10.1137/0216064.
  14. Daniel Golovin and Andreas Krause. Adaptive submodularity: Theory and applications in active learning and stochastic optimization. Journal of Artificial Intelligence Research, 42:427-486, 2011. URL: https://doi.org/10.1613/JAIR.3278.
  15. Michael T Goodrich. Planar separators and parallel polygon triangulation (preliminary version). In Proceedings of the twenty-fourth annual ACM symposium on Theory of Computing, STOC '92, pages 507-516, New York, NY, USA, July 1992. Association for Computing Machinery. URL: https://doi.org/10.1145/129712.129762.
  16. Cesar D Guerrero and Miguel A Labrador. On the applicability of available bandwidth estimation techniques and tools. Comput. Commun., 33(1):11-22, January 2010. URL: https://doi.org/10.1016/J.COMCOM.2009.08.010.
  17. K Harfoush, A Bestavros, and J Byers. Measuring bottleneck bandwidth of targeted path segments. In IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428), volume 3, pages 2079-2089 vol.3, March 2003. Google Scholar
  18. David Harvey and Joris Van Der Hoeven. Integer multiplication in time O(n log n). Annals of Mathematics, 193(2):563-617, 2021. Google Scholar
  19. Thomas Holterbach, Emile Aben, Cristel Pelsser, Randy Bush, and Laurent Vanbever. Measurement vantage point selection using a similarity metric. In Proceedings of the Applied Networking Research Workshop, ANRW '17, pages 1-3, New York, NY, USA, July 2017. Association for Computing Machinery. URL: https://doi.org/10.1145/3106328.3106334.
  20. Yuchen Huang, Michael Rabinovich, and Rami Al-Dalky. FlashRoute: Efficient traceroute on a massive scale. In Proceedings of the ACM Internet Measurement Conference, IMC '20, pages 443-455, New York, NY, USA, October 2020. Association for Computing Machinery. URL: https://doi.org/10.1145/3419394.3423619.
  21. Y. Hyun and K. Claffy. Archipelago measurement infrastructure. http://www.caida.org/projects/ark/, 2015. Google Scholar
  22. Jordan Jueckstock, Shaown Sarker, Peter Snyder, Panagiotis Papadopoulos, Matteo Varvello, Benjamin Livshits, and Alexandros Kapravelos. The blind men and the internet: Multi-Vantage point web measurements, May 2019. URL: https://arxiv.org/abs/1905.08767.
  23. Mohammad Karimi, Mario Lucic, Hamed Hassani, and Andreas Krause. Stochastic submodular maximization: The case of coverage functions. Advances in Neural Information Processing Systems, 30, 2017. Google Scholar
  24. Bo Li, Jingsha He, and Henghua Shi. Improving the efficiency of network topology discovery. In 2008 The 3rd International Conference on Grid and Pervasive Computing - Workshops, pages 189-194, May 2008. URL: https://doi.org/10.1109/GPC.WORKSHOPS.2008.34.
  25. Franziska Lichtblau. From the edge to the core : towards informed vantage point selection for Internet measurement studies, 2021. Google Scholar
  26. G L Nemhauser, M L Fisher, and L A Wolsey. An Analysis of Approximations for Maximizing Submodular Set Functions. Center for Operations Research & Econometrics, 1976. Google Scholar
  27. Mahmoud Owais. Traffic sensor location problem: Three decades of research. Expert Systems with Applications, 208:118134, 2022. URL: https://doi.org/10.1016/j.eswa.2022.118134.
  28. R Prasad, C Dovrolis, M Murray, and K Claffy. Bandwidth estimation: metrics, measurement techniques, and tools. IEEE Netw., 17(6):27-35, November 2003. URL: https://doi.org/10.1109/MNET.2003.1248658.
  29. Dixon Salcedo, Cesar D. Guerrero, and Roberto Martinez. Available bandwidth estimation tools metrics, approaches and performance. International J. Commun. Netw. Inf. Secur., December 2018. Google Scholar
  30. Yuval Shavitt and Udi Weinsberg. Quantifying the importance of vantage point distribution in internet topology mapping (extended version). IEEE Journal on Selected Areas in Communications, 29(9):1837-1847, 2011. URL: https://doi.org/10.1109/JSAC.2011.111008.
  31. Y Vardi. Network tomography: Estimating Source-Destination traffic intensities from link data. J. Am. Stat. Assoc., 91(433):365-377, March 1996. Google Scholar
  32. Kévin Vermeulen, Justin P Rohrer, Robert Beverly, Olivier Fourmaux, and Timur Friedman. Diamond-miner: Comprehensive discovery of the internet’s topology diamonds. In 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20), pages 479-493, 2020. Google Scholar
  33. Andrew Chi-Chin Yao. Probabilistic computations: Toward a unified measure of complexity. In 18th Annual Symposium on Foundations of Computer Science (SFCS 1977), pages 222-227, 1977. URL: https://doi.org/10.1109/SFCS.1977.24.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact