Skip to main content
SpringerLink
Log in

Unleashing the Potential of Data-Driven Networking

  • Conference paper
  • First Online:
Communication Systems and Networks (COMSNETS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 10340))

Included in the following conference series:

Abstract

The last few years have witnessed the coming of age of data-driven paradigm in various aspects of computing (partly) empowered by advances in distributed system research (cloud computing, MapReduce, etc.). In this paper, we observe that the benefits can flow the opposite direction: the design and management of networked systems can be improved by data-driven paradigm. To this end, we present DDN, a new design framework for network protocols based on data-driven paradigm. We argue that DDN has the potential to significantly achieve better performance through harnessing more data than one single flow. Furthermore, we systematize existing instantiations of DDN by creating a unified framework for DDN, and use the framework to shed light on the common challenges and reusable design principles. We believe that by systematizing this paradigm as a broader community, we can unleash the unharnessed potential of DDN.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    A session could be an application session (e.g., video session, web session), or a transport session (e.g., TCP session).

  2. 2.

    We use “client” to denote where a session is actually run.

References

  1. ACM SIGCOMM Workshop on QoE-Based Analysis and Management of Data Communication Networks (Internet-QoE 2016). http://conferences.sigcomm.org/sigcomm/2016/qoe.php

  2. Bringing Data-Driven SDN to the Network Edge. https://www.sdxcentral.com/articles/contributed/network-edge-bringing-data-driven-sdn-to-the-network-edge-nick-kephart/2015/03/

  3. Emulab. https://www.emulab.net/

  4. ESPN, Inc., Fact Sheet. http://espnmediazone.com/us/espn-inc-fact-sheet/

  5. Periscope. https://www.periscope.tv/

  6. Spark. http://spark.incubator.apache.org/

  7. Technical note on the CFA algorithm. https://www.cs.cmu.edu/dda_technote.pdf

  8. The Data-Driven Approach to Network Management: Innovation Delivered. http://www.research.att.com/articles/featured_stories/2010_05/201005_networkmain2_article.html

  9. Twitch.tv. https://www.twitch.tv/

  10. US Census: City and Town Totals. http://www.census.gov/popest/data/cities/totals/2015/files/SUB-EST2015_ALL.csv

  11. U.S. online video platforms in September 2012. http://www.statista.com/statistics/271607/video-platforms-in-the-us-by-number-of-video-streams/

  12. Abdallah, C.T., Byrne, R., Benites-Read, J., Dorato, P.: Delayed positive feedback can stabilize oscillatory systems. In: Proceedings of ACC (American control conference) (1993)

    Google Scholar 

  13. Agarwal, A., Bird, S., Cozowicz, M., Hoang, L., Langford, J., Lee, S., Li, J., Melamed, D., Oshri, G., Ribas, O., et al.: A multiworld testing decision service. arXiv preprint arXiv:1606.03966 (2016)

  14. Chen, F., Zhang, C., Wang, F., Liu, J.: Crowdsourced live streaming over the cloud. In: INFOCOM (2015)

    Google Scholar 

  15. Clark, D.D., Partridge, C., Ramming, J.C., Wroclawski, J.T.: A knowledge plane for the internet. In: ACM SIGCOMM 2003

    Google Scholar 

  16. Crankshaw, D., Bailis, P., Gonzalez, J.E., Li, H., Zhang, Z., Franklin, M.J., Ghodsi, A., Jordan, M.I.: The missing piece in complex analytics: low latency, scalable model management and serving with velox. In: Conference on Innovative Data Systems Research (CIDR) (2015)

    Google Scholar 

  17. Datta, A., Sen, S., Zick, Y.: Algorithmic transparency via quantitative input influence. In: Proceedings of 37th IEEE Symposium on Security and Privacy (2016)

    Google Scholar 

  18. Dong, M., Li, Q., Zarchy, D., Godfrey, P.B., Schapira, M.: PCC: re-architecting congestion control for consistent high performance. In: Proceedings of NSDI (2015)

    Google Scholar 

  19. Dudík, M., Langford, J., Li, L.: Doubly robust policy evaluation and learning. In: Proceedings of International Conference on Machine Learning (2011)

    Google Scholar 

  20. Dukkipati, N., Refice, T., Cheng, Y., Chu, J., Herbert, T., Agarwal, A., Jain, A., Sutin, N.: An argument for increasing TCP’s initial congestion window. ACM SIGCOMM CCR 40, 27–33 (2010)

    Article  Google Scholar 

  21. Floyd, S., Jacobson, V.: Random early detection gateways for congestion avoidance. IEEE/ACM Trans. Netw. 1(4), 397–413 (1993)

    Article  Google Scholar 

  22. Floyd, S., Paxson, V.: Difficulties in simulating the internet. IEEE/ACM Trans. Netw. (ToN) 9(4), 392–403 (2001)

    Article  Google Scholar 

  23. Ganjam, A., Sekar, V., Zhang, H.: In-situ quality of experience monitoring: the case for prioritizing coverage over fidelity

    Google Scholar 

  24. Ganjam, A., Siddiqi, F., Zhan, J., Stoica, I., Jiang, J., Sekar, V., Zhang, H.: C3: internet-scale control plane for video quality optimization. In: NSDI. USENIX (2015)

    Google Scholar 

  25. Halevy, A., Norvig, P., Pereira, F.: The unreasonable effectiveness of data. IEEE Intell. Syst. 24(2), 8–12 (2009)

    Article  Google Scholar 

  26. Haq, O., Dogar, F.R.: Leveraging the power of cloud for reliable wide area communication. In: ACM Workshop on Hot Topics in Networks (2015)

    Google Scholar 

  27. Huang, T.-Y., Handigol, N., Heller, B., McKeown, N., Johari, R.: Confused, timid, and unstable: picking a video streaming rate is hard. In: Proceedings of SIGCOMM IMC (2012)

    Google Scholar 

  28. Jacobson, V.: Congestion avoidance and control. ACM SIGCOMM Comput. Commun. Rev. 18, 314–329 (1988). ACM

    Article  Google Scholar 

  29. Jacobson, V., Smetters, D.K., Thornton, J.D., Plass, M.F., Briggs, N.H., Braynard, R.L.: Networking named content. In: Proceedings of CoNext (2009)

    Google Scholar 

  30. Jiang, J., Das, R., Anathanarayanan, G., Chou, P., Padmanabhan, V., Sekar, V., Dominique, E., Goliszewski, M., Kukoleca, D., Vafin, R., Zhang, H.: VIA: improving internet telephony call quality using predictive relay selection. To Appear in Proceedings of SIGCOMM (2016)

    Google Scholar 

  31. Jiang, J., Liu, X., Sekar, V., Stoica, I., Zhang, H.: EONA: Experience-Oriented Network Architecture. In: ACM HotNets (2014)

    Google Scholar 

  32. Jiang, J., Sekar, V., Milner, H., Shepherd, D., Stoica, I., Zhang, H.: CFA: a practical prediction system for video QoE optimization. In Proceedings of NSDI (2016)

    Google Scholar 

  33. Jiang, J., Sekar, V., Zhang, H.: Improving fairness, efficiency, and stability in HTTP-based adaptive streaming with festive. In: ACM CoNEXT 2012

    Google Scholar 

  34. Kandoi, R., Antikainen, M.: Denial-of-service attacks in OpenFlow SDN networks. In: 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp. 1322-1326. IEEE (2015)

    Google Scholar 

  35. Krishnan, S., Sitaraman, R.: Video stream quality impacts viewer behavior: inferring causality using quasi-experimental designs (2012)

    Google Scholar 

  36. Krishnan, S.S., Sitaraman, R.K.: Video stream quality impacts viewer behavior: inferring causality using quasi-experimental designs. IEEE/ACM Trans. Netw. 21(6), 2001–2014 (2013)

    Article  Google Scholar 

  37. Kumar, A., Jain, S., Naik, U., Raghuraman, A., Kasinadhuni, N., Zermeno, E.C., Gunn, C.S., Ai, J., Carlin, B., Amarandei-Stavila, M., et al.: BwE: flexible, hierarchical bandwidth allocation for WAN distributed computing. In: Proceedings of SIGCOMM (2015)

    Google Scholar 

  38. Li, L., Chu, W., Langford, J., Schapire, R.E.: A contextual-bandit approach to personalized news article recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 661–670. ACM (2010)

    Google Scholar 

  39. Liu, H.H., Viswanathan, R., Calder, M., Akella, A., Mahajan, R., Padhye, J., Zhang, M.: Efficiently delivering online services over integrated infrastructure. In: Proceedings of NSDI (2016)

    Google Scholar 

  40. Liu, X., Dobrian, F., Milner, H., Jiang, J., Sekar, V., Stoica, I., Zhang, H.: A case for a coordinated internet video control plane. In: ACM SIGCOMM, pp. 359–370. ACM (2012)

    Google Scholar 

  41. Lu, T., Pál, D., Pál, M.: Contextual multi-armed bandits. In: AISTATS, pp. 485–492 (2010)

    Google Scholar 

  42. Madhyastha, H.V., Isdal, T., Piatek, M., Dixon, C., Anderson, T., Krishnamurthy, A., Venkataramani, A.: iPlane: an information plane for distributed services. In: USENIX OSDI 2006

    Google Scholar 

  43. Pelsser, C., Cittadini, L., Vissicchio, S., Bush, R.: From Paris to Tokyo: on the suitability of ping to measure latency. In: Proceedings of the 2013 Conference on Internet Measurement Conference, pp. 427–432. ACM (2013)

    Google Scholar 

  44. Precup, D., Sutton, R.S., Singh, S.: Eligibility traces for off-policy policy evaluation. In: Proceedings of the Seventeenth International Conference on Machine Learning (2000)

    Google Scholar 

  45. Pu, Q., Ananthanarayanan, G., Bodik, P., Kandula, S., Akella, A., Bahl, P., Stoica, I.: Low latency geo-distributed data analytics. In: Proceedings of SIGCOMM (2015)

    Google Scholar 

  46. Rabkin, A., Arye, M., Sen, S., Pai, V.S., Freedman, M.J.: Aggregation and degradation in JetStream: streaming analytics in the wide area. In: Proceedings of NSDI (2014)

    Google Scholar 

  47. Rexford, J., Wang, J., Xiao, Z., Zhang, Y.: BGP routing stability of popular destinations. In: Proceedings of SIGCOMM IMW (2002)

    Google Scholar 

  48. Richard, J.-P.: Time-delay systems: an overview of some recent advances and open problems. Automatica 39(10), 1667–1694 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  49. Rigollet, P., Zeevi, A.: Nonparametric bandits with covariates. In: Proceedings of the Conference on Learning Theory (2010)

    Google Scholar 

  50. Saltzer, J.H., Reed, D.P., Clark, D.D.: End-to-end arguments in system design. ACM Trans. Comput. Syst. (TOCS) 2(4), 277–288 (1984)

    Article  Google Scholar 

  51. Seshan, S., Stemm, M., Katz, R.H.: SPAND: shared passive network performance discovery. In: USENIX Symposium on Internet Technologies and Systems, pp. 1–13 (1997)

    Google Scholar 

  52. Shalita, A., Karrer, B., Kabiljo, I., Sharma, A., Presta, A., Adcock, A., Kllapi, H., Stumm, M.: Social hash: an assignment framework for optimizing distributed systems operations on social networks. In: Proceedings of NSDI (2016)

    Google Scholar 

  53. Slivkins, A.: Contextual bandits with similarity information. J. Mach. Learn. Res. 15(1), 2533–2568 (2014)

    MathSciNet  MATH  Google Scholar 

  54. Sun, Y., Yin, X., Jiang, J., Sekar, V., Lin, F., Wang, N., Liu, T., Sinopoli, B.: CS2P: improving video bitrate selection and adaptation with data-driven throughput prediction. To Appear in Proceedings of SIGCOMM (2016)

    Google Scholar 

  55. Vellido, A., Martin-Guerroro, J., Lisboa, P.: Making machine learning models interpretable. In: Proceedings of the 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, pp. 163–172 (2012)

    Google Scholar 

  56. Venkataraman, S., Yang, Z., Franklin, M., Recht, B., Stoica, I.: Ernest: efficient performance prediction for large-scale advanced analytics. In: Proceedings of NSDI (2016)

    Google Scholar 

  57. Winstein, K., Balakrishnan, H.: TCP ex Machina: computer-generated congestion control. In: Proceedings of SIGCOMM (2013)

    Google Scholar 

Download references

Acknowledgments

This research is supported in part by NSF award CNS-1345305 and NSF CISE Expeditions Award CCF-1139158, DOE Award SN10040 DE-SC0012463, and DARPA XData Award FA8750-12-2-0331, and gifts from Amazon Web Services, Google, IBM, SAP, The Thomas and Stacey Siebel Foundation, Adatao, Adobe, Apple Inc., Blue Goji, Bosch, Cisco, Cray, Cloudera, Ericsson, Facebook, Fujitsu, Guavus, HP, Huawei, Intel, Microsoft, Pivotal, Samsung, Schlumberger, Splunk, State Farm, Virdata and VMware. Junchen Jiang was supported in part by Juniper Networks Fellowship.

Author information

Authors and Affiliations

  1. CMU, Pittsburgh, USA

    Junchen Jiang, Vyas Sekar & Hui Zhang

  2. UC Berkeley, Berkeley, USA

    Ion Stoica

  3. Conviva, New York City, USA

    Ion Stoica & Hui Zhang

  4. Databricks, San Francisco, USA

    Ion Stoica

Authors
  1. Junchen Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vyas Sekar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ion Stoica
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junchen Jiang .

Editor information

Editors and Affiliations

  1. King’s College London, London, United Kingdom

    Nishanth Sastry

  2. IIT Kharagpur, Kharagpur, West Bengal, India

    Sandip Chakraborty

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Jiang, J., Sekar, V., Stoica, I., Zhang, H. (2017). Unleashing the Potential of Data-Driven Networking. In: Sastry, N., Chakraborty, S. (eds) Communication Systems and Networks. COMSNETS 2017. Lecture Notes in Computer Science(), vol 10340. Springer, Cham. https://doi.org/10.1007/978-3-319-67235-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67235-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67234-2

  • Online ISBN: 978-3-319-67235-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation