[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

BloomTime: space-efficient stateful tracking of time-dependent network performance metrics

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Network monitoring is essential to tasks ranging from planning to troubleshooting. Unfortunately, comprehensive real-time monitoring of complex networks with large traffic volume is challenging. In particular, tracking of time-dependent metrics, such as round-trip latency or transmission rate requires maintaining state and this is hard to scale. We propose BloomTime: a network monitoring primitive in hardware that employs standard bloom filters to approximately track the times between packets. We have prototyped BloomTime on the NetFPGA platform. As a use case, we use BloomTime to monitor the mean and variance of packet inter-arrival times. We have compared BloomTime against end-host measurements and a centralized solution using classic stateful monitoring. We show that BloomTime can monitor 70 times more flows than the traditional stateful approach with approximation errors below 20%. BloomTime was validated in a realistic test environment using real traces. We show that BloomTime can monitor simultaneously 2000 flows on the NetFPGA 1G board (first generation) with 4 MB of SRAM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. Programmable Forwarding Engine.

  2. A 5-tuple flow definition computes hash functions over the (source address, destination address, source port, destination port, and protocol identifier) IP header fields.

  3. We assume that both forward and reverse paths traverse BloomTime.

  4. The hash function for outgoing packets exchanges position of IP addresses and ICMP code, while the hash function for incoming packets does not.

  5. The NetFPGA SRAM is addressable only per line. When marking just a bit corresponding to \(B_0\), our prototype needs to read the entire line and write it back. Other architectures might allow for write-only implementations.

  6. Available in: http://www.tcpdump.org/.

  7. Available in: https://sourceforge.net/projects/libnet/.

  8. Available in: http://bittwist.sourceforge.net.

  9. Available in: https://dpkt.readthedocs.io/.

References

  1. 1G, N. (2017). Quad-port gigabit nic. http://netfpga.org/site/#/systems/4netfpga-1g/applications/ . Retrieved 08 August 2017.

  2. Al-Fares, M., Radhakrishnan, S., Raghavan, B., Huang, N., & Vahdat, A. (2010). Hedera: Dynamic flow scheduling for data center networks. In Proceedings of the 7th USENIX conference on networked systems design and implementation, NSDI’10 (pp. 19–19). USENIX Association, Berkeley, CA, USA. http://dl.acm.org/citation.cfm?id=1855711.1855730.

  3. Anwer, B., Benson, T., Feamster, N., Levin, D., & Rexford, J. (2013). A slick control plane for network middleboxes. In Proceedings of the second ACM SIGCOMM workshop on hot topics in software defined networking (pp. 147–148). ACM.

  4. Bender, M. A., Farach-Colton, M., Johnson, R., Kraner, R., Kuszmaul, B. C., Medjedovic, D., et al. (2012). Don’t thrash: How to cache your hash on flash. Proceedings of the VLDB Endowment, 5(11), 1627–1637. https://doi.org/10.14778/2350229.2350275.

    Article  Google Scholar 

  5. Benson, T., Anand, A., Akella, A., & Zhang, M. (2011). Microte: Fine grained traffic engineering for data centers. In Proceedings of the seventh conference on emerging networking experiments and technologies (p. 8). ACM.

  6. Bloom, B. H. (1970). Space/time trade-offs in hash coding with allowable errors. Communications of the ACM, 13(7), 422–426.

    Article  Google Scholar 

  7. Bonomi, F., Mitzenmacher, M., Panigrah, R., Singh, S., & Varghese, G. (2006). Beyond bloom filters: From approximate membership checks to approximate state machines. In ACM SIGCOMM computer communication review (Vol. 36, pp. 315–326). ACM.

  8. Bonomi, F., Mitzenmacher, M., Panigrahy, R., Singh, S., & Varghese, G. (2006). An improved construction for counting bloom filters. In European Symposium on algorithms (pp. 684–695). Springer

  9. Borthakur, D. (2007). The hadoop distributed file system: Architecture and design. Hadoop Project Website, 11(2007), 21.

    Google Scholar 

  10. Broder, A., & Mitzenmacher, M. (2004). Network applications of bloom filters: A survey. Internet Mathematics, 1(4), 485–509.

    Article  Google Scholar 

  11. Byers, J., Considine, J., Mitzenmacher, M., & Rost, S. (2002). Informed content delivery across adaptive overlay networks. SIGCOMM Computer Communivation Review, 32(4), 47–60. https://doi.org/10.1145/964725.633031.

    Article  Google Scholar 

  12. Cai, H., Ge, P., & Wang, J. (2008) Applications of bloom filters in peer-to-peer systems: Issues and questions. In International conference on networking, architecture, and storage, NAS’08 (pp. 97–103). IEEE.

  13. Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach, D. A., Burrows, M., et al. (2008). Bigtable: A distributed storage system for structured data. ACM Transactions on Computer Systems, 26(2), 4:1–4:26. https://doi.org/10.1145/1365815.1365816.

    Article  Google Scholar 

  14. Chen, X., Feibish, S.L., Koral, Y., Rexford, J., Rottenstreich, O., Monetti, S.A., & Wang, T.Y. (2019). Fine-grained queue measurement in the data plane. In Proceedings of the 15th international conference on emerging networking experiments and technologies, CoNEXT ’19 (pp. 15–29). ACM, New York, NY, USA . https://doi.org/10.1145/3359989.3365408.

  15. Chen, Y., Farley, T., & Ye, N. (2004). Qos requirements of network applications on the internet. Information Knowledge Systems Management, 4(1), 55–76.

    Google Scholar 

  16. Chowdhury, S.R., Bari, M.F., Ahmed, R., & Boutaba, R. (2014) Payless: A low cost network monitoring framework for software defined networks. In Network operations and management symposium (NOMS) (pp. 1–9). IEEE.

  17. Curtis, A. R., Mogul, J. C., Tourrilhes, J., Yalagandula, P., Sharma, P., & Banerjee, S. (2011). Devoflow: Scaling flow management for high-performance networks. ACM SIGCOMM Computer Communication Review, 41(4), 254–265.

    Article  Google Scholar 

  18. Daniel, E.J., White, C.M., Teague, K., et al. (2003). An interarrival delay jitter model using multistructure network delay characteristics for packet networks. In The thirty-seventh asilomar conference on signals, systems and computers (Vol. 2, pp. 1738–1742). IEEE.

  19. Deri, L. (2007). High-speed dynamic packet filtering. Journal of Network and Systems Management, 15(3), 401–415.

    Article  Google Scholar 

  20. Dharmapurikar, S., Krishnamurthy, P., Sproull, T., & Lockwood, J. (2003). Deep packet inspection using parallel bloom filters. In 11th symposium on high performance interconnects, 2003. Proceedings (pp. 44–51). IEEE.

  21. Dharmapurikar, S., Krishnamurthy, P., & Taylor, D. E. (2006). Longest prefix matching using bloom filters. IEEE/ACM Transactions on Networking, 14(2), 397–409.

    Article  Google Scholar 

  22. Fan, L., Cao, P., Almeida, J., & Broder, A. Z. (2000). Summary cache: A scalable wide-area web cache sharing protocol. IEEE/ACM Transactions on Networking, 8(3), 281–293. https://doi.org/10.1109/90.851975.

    Article  Google Scholar 

  23. Feng, Wc, Shin, K. G., Kandlur, D. D., & Saha, D. (2002). The blue active queue management algorithms. IEEE/ACM Transactions on Networking, 10(4), 513–528. https://doi.org/10.1109/TNET.2002.801399.

    Article  Google Scholar 

  24. Finamore, A., Mellia, M., Meo, M., Munafo, M. M., Di Torino, P., & Rossi, D. (2011). Experiences of internet traffic monitoring with tstat. IEEE Network, 25(3), 8–14.

    Article  Google Scholar 

  25. Gangam, S., Chandrashekar, J., Cunha, Í., & Kurose, J. (2013). Estimating tcp latency approximately with passive measurements. In International conference on passive and active network measurement (pp. 83–93). Springer.

  26. Gupta, A., Harrison, R., Canini, M., Feamster, N., Rexford, J., & Willinger, W. (2018). Sonata: Query-driven streaming network telemetry. In Proceedings of the 2018 conference of the ACM special interest group on data communication, SIGCOMM ’18 (pp. 357–371). ACM, New York, NY, USA. https://doi.org/10.1145/3230543.3230555.

  27. Khandelwal, A., Agarwal, R., & Stoica, I. (2019). Confluo: Distributed monitoring and diagnosis stack for high-speed networks. In: NSDI (pp. 421–436).

  28. Li, G., Zhang, M., Liu, C., Kong, X., Chen, A., Gu, G., & Duan, H. (2019) Nethcf: Enabling line-rate and adaptive spoofed ip traffic filtering. In 2019 IEEE 27th international conference on network protocols (ICNP) (pp. 1–12). IEEE.

  29. Moshref, M., Yu, M., & Govindan, R. (2013). Resource/accuracy tradeoffs in software-defined measurement. In Proceedings of the second ACM SIGCOMM workshop on hot topics in software defined networking (pp. 73–78). ACM.

  30. Narayan, A., Cangialosi, F., Goyal, P., Narayana, S., Alizadeh, M., & Balakrishnan, H. (2017). The case for moving congestion control out of the datapath. In Proceedings of the 16th ACM workshop on hot topics in networks, HotNets-XVI (pp. 101–107). ACM, New York, NY, USA. https://doi.org/10.1145/3152434.3152438.

  31. Narayana, S., Sivaraman, A., Nathan, V., Goyal, P., Arun, V., Alizadeh, M., Jeyakumar, V., & Kim, C. (2017). Language-directed hardware design for network performance monitoring. In Proceedings of the conference of the ACM special interest group on data communication, SIGCOMM ’17 (pp. 85–98). ACM, New York, NY, USA. https://doi.org/10.1145/3098822.3098829.

  32. NetFPGA (2015) Project switch openflow netfpga. http://github.com/NetFPGA/netfpga/wiki/OpenFlowNetFPGA100. Retrieved 22 November 2015.

  33. NetFPGA (2016). Site netfpga. http://netfpga.org. Retreived 22 August 2016.

  34. Pacífico, R., Silva, P., Cunha, I., Vieira, M., Vieira, A. B., Guedes, D., et al. (2016). Medição de desempenho de rede em hardware. XXXIV Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos - SBRC, 2016

  35. Pacífico, R., Silva, P., Vieira, A.B., Vieira, M.A.M., & Miranda Nacif, J.A. (2016). Hardware modules for packet interarrival time monitoring for software defined measurements. In 41st annual IEEE conference on local computer networks - LCN 2016.

  36. Pouwelse, J. A., Garbacki, P., Wang, J., Bakker, A., Yang, J., Iosup, A., et al. (2008). Tribler: A social-based peer-to-peer system. Concurrency and Computation: Practice and Experience, 20(2), 127–138.

    Article  Google Scholar 

  37. Putze, F., Sanders, P., & Singler, J. (2010). Cache-, hash-, and space-efficient bloom filters. Journal of Experimental Algorithmics, 14, 4:4.4–4:4.18. https://doi.org/10.1145/1498698.1594230.

    Article  Google Scholar 

  38. Qazi, Z. A., Tu, C. C., Chiang, L., Miao, R., Sekar, V., & Yu, M. (2013). Simple-fying middlebox policy enforcement using sdn. ACM SIGCOMM Computer Communication Review, 43(4), 27–38.

    Article  Google Scholar 

  39. Raumer, D., Schwaighofer, L., & Carle, G. (2014). Monsamp: A distributed sdn application for qos monitoring. In 2014 federated conference on computer science and information systems (FedCSIS) (pp. 961–968). IEEE.

  40. Reynolds, P., & Vahdat, A. (2003). Efficient peer-to-peer keyword searching. In Proceedings of the ACM/IFIP/USENIX 2003 international conference on Middleware, Middleware ’03 (pp. 21–40). Springer-Verlag New York, Inc., New York, NY, USA. http://dl.acm.org/citation.cfm?id=1515915.1515918.

  41. Snoeren, A. C., Partridge, C., Sanchez, L. A., Jones, C. E., Tchakountio, F., Schwartz, B., et al. (2002). Single-packet ip traceback. IEEE/ACM Transactions on Networking (ToN), 10(6), 721–734.

    Article  Google Scholar 

  42. Sonchack, J., Aviv, A.J., Keller, E., & Smith, J.M. (2018) Turboflow: Information rich flow record generation on commodity switches. In Proceedings of the thirteenth EuroSys conference, EuroSys ’18 (pp. 11:1–11:16). ACM, New York, NY, USA. https://doi.org/10.1145/3190508.3190558.

  43. Song, H., Dharmapurikar, S., Turner, J., & Lockwood, J. (2005). Fast hash table lookup using extended bloom filter: An aid to network processing. In Proceedings of the 2005 conference on applications, technologies, architectures, and protocols for computer communications, SIGCOMM ’05 (pp. 181–192). ACM, New York, NY, USA. https://doi.org/10.1145/1080091.1080114.

  44. Soule, A., Salamatia, K., Taft, N., Emilion, R., & Papagiannaki, K. (2004). Flow classification by histograms: Or how to go on safari in the internet. In Proceedings of the joint international conference on measurement and modeling of computer systems, SIGMETRICS ’04/Performance ’04 (pp. 49–60). ACM, New York, NY, USA. https://doi.org/10.1145/1005686.1005696.

  45. SUME, N. (2015). Netfpga sume. http://netfpga.org/site/#/systems/1netfpga-sume/details/. Retreived 27 December 2015.

  46. Tang, L., Huang, Q., & Lee, P.P. (2019). A fast and compact invertible sketch for network-wide heavy flow detection. arXiv preprint arXiv:1910.10441

  47. Van Adrichem, N.L., Doerr, C., & Kuipers, F., et al. (2014). Opennetmon: Network monitoring in openflow software-defined networks. In Network operations and management symposium (NOMS) (pp. 1–8). IEEE.

  48. Yu, M., Fabrikant, A., & Rexford, J. (2009) Buffalo: Bloom filter forwarding architecture for large organizations. In Proceedings of the 5th international conference on emerging networking experiments and technologies (pp. 313–324). ACM.

  49. Yu, M., Jose, L., & Miao, R. (2013). Software defined traffic measurement with opensketch. Symposium on Networked Systems Design and Implementation - NSDI (Vol. 13, pp. 29–42).

Download references

Acknowledgements

We thank research agencies FAPEMIG, CAPES, CNPq.

Author information

Authors and Affiliations

  1. Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Racyus D. G. Pacífico, Pablo G. Silva, Marcos A. M. Vieira, Ítalo F. S. Cunha & Luiz F. M. Vieira

  2. Universidade Federal de Viçosa, Florestal, Brazil

    Racyus D. G. Pacífico, Lucas B. Silva, Gerferson R. Coelho & José A. M. Nacif

  3. Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil

    Alex B. Vieira

Authors
  1. Racyus D. G. Pacífico
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lucas B. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gerferson R. Coelho
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pablo G. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alex B. Vieira
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marcos A. M. Vieira
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ítalo F. S. Cunha
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Luiz F. M. Vieira
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. José A. M. Nacif
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Racyus D. G. Pacífico.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pacífico, R.D.G., Silva, L.B., Coelho, G.R. et al. BloomTime: space-efficient stateful tracking of time-dependent network performance metrics. Telecommun Syst 74, 201–223 (2020). https://doi.org/10.1007/s11235-020-00653-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-020-00653-1

Keywords

Search

Navigation