skip to main content
10.1145/3386367.3431302acmconferencesArticle/Chapter ViewAbstractPublication PagesconextConference Proceedingsconference-collections
short-paper

ZipLine: in-network compression at line speed

Published:24 November 2020Publication History

ABSTRACT

Network appliances continue to offer novel opportunities to offload processing from computing nodes directly into the data plane. One popular concern of network operators and their customers is to move data increasingly faster. A common technique to increase data throughput is to compress it before its transmission. However, this requires compression of the data---a time and energy demanding preprocessing phase---and decompression upon reception---a similarly resource consuming operation. Moreover, if multiple nodes transfer similar data chunks across the network hop (e.g., a given pair of switches), each node effectively wastes resources by executing similar steps. This paper proposes ZipLine, an approach to design and implement (de)compression at line speed leveraging the Tofino hardware platform which is programmable using the P416 language. We report on lessons learned while building the system and show throughput, latency and compression measurements on synthetic and real-world traces, showcasing the benefits and trade-offs of our design.

Skip Supplemental Material Section

Supplemental Material

3386367.3431302.mp4

mp4

74.5 MB

References

  1. 2020. Aruba Compression on SD-WAN. https://www.arubanetworks.com/assets/ds/DS_SD-WAN.pdfGoogle ScholarGoogle Scholar
  2. 2020. Brocade 6520. https://www.dataswitchworks.com/datasheets/switches/6520-switch-ds.pdfGoogle ScholarGoogle Scholar
  3. 2020. XipLink. http://www.xiplink.com/media/XipLink-XA-Appliances-Overview.pdfGoogle ScholarGoogle Scholar
  4. Barefoot Networks. 2020. Tofino product brief page. https://barefootnetworks.com/products/brief-tofino/Google ScholarGoogle Scholar
  5. Barefoot Networks and P4.org Applications Working Group. 2018. Barefoot Deep Insight Monitoring System Enabled by DataPlane Telemetry. https://p4.org/assets/P4WS_2018/9_Daniel_Alvarez_DI.pdfGoogle ScholarGoogle Scholar
  6. Theophilus A. Benson. 2019. In-Network Compute: Considered Armed and Dangerous. In Proceedings of the Workshop on Hot Topics in Operating Systems (Bertinoro, Italy) (HotOS '19). 216--224. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. P. Benácek, V. Pu, and H. Kubátová. 2016. P4-to-VHDL: Automatic Generation of 100 Gbps Packet Parsers. In 2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). 148--155. Google ScholarGoogle ScholarCross RefCross Ref
  8. Richard E. Blahut. 2003. Algebraic Codes for Data Transmission. Cambridge University Press. Google ScholarGoogle ScholarCross RefCross Ref
  9. Pat Bosshart, Dan Daly, Glen Gibb, Martin Izzard, Nick McKeown, Jennifer Rexford, Cole Schlesinger, Dan Talayco, Amin Vahdat, George Varghese, et al. 2014. P4: Programming protocol-independent packet processors. ACM SIGCOMM Computer Communication Review 44, 3 (2014), 87--95.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Xiaoqi Chen. 2020. Implementing AES Encryption on Programmable Switches via Scrambled Lookup Tables. In ACM SIGCOMM 2020 Workshop on Secure Programmable Network Infrastructure (Virtual Event) (SPIN '20). Association for Computing Machinery, New York, NY, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. H. T. Dang, P. Bressana, H. Wang, K. S. Lee, N. Zilberman, H. Weatherspoon, M. Canini, F. Pedone, and R. Soulé. 2020. P4xos: Consensus as a Network Service. IEEE/ACM Transactions on Networking (2020), 1--13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. L. Peter Deutsch. 1996. DEFLATE Compressed Data Format Specification version 1.3. RFC 1951. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Edgecore networks. 2019. Wedge100BF-32X/65X Switch. https://www.edgecore.com/_upload/images/Wedge100BF-32X_65X_DS_R05_20191210.pdfGoogle ScholarGoogle Scholar
  14. Lucas Freire, Miguel Neves, Lucas Leal, Kirill Levchenko, Alberto Schaeffer-Filho, and Marinho Barcellos. 2018. Uncovering Bugs in P4 Programs with Assertion-Based Verification. In Proceedings of the Symposium on SDN Research (Los Angeles, CA, USA) (SOSR '18). Article 4, 7 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Christian Göttel, Lars Nielsen, Niloofar Yazdani, Pascal Felber, Daniel E. Lucani, and Valerio Schiavoni. 2020. Hermes: Enabling Energy-Efficient IoT Networks with Generalized Deduplication. In Proceedings of the 14th ACM International Conference on Distributed and Event-Based Systems (Montreal, Quebec, Canada) (DEBS '20). Association for Computing Machinery, New York, NY, USA, 133--136. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. David Hancock and Jacobus van der Merwe. 2016. HyPer4: Using P4 to Virtualize the Programmable Data Plane. In Proceedings of the 12th International on Conference on Emerging Networking EXperiments and Technologies (Irvine, California, USA) (CoNEXT '16). Association for Computing Machinery, New York, NY, USA, 35--49. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Theo Jepsen, Masoud Moshref, Antonio Carzaniga, Nate Foster, and Robert Soulé. 2018. Life in the Fast Lane: A Line-Rate Linear Road. In Proceedings of the Symposium on SDN Research (Los Angeles, CA, USA) (SOSR '18). Article 10, 7 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Alberto Lerner, Rana Hussein, and Philippe Cudré-Mauroux. 2019. The Case for Network Accelerated Query Processing. In 9th Biennial Conference on Innovative Data Systems Research (CIDR'19).Google ScholarGoogle Scholar
  19. Craig Mustard, Fabian Ruffy, Anny Gakhokidze, Ivan Beschastnikh, and Alexandra Fedorova. 2019. Jumpgate: In-Network Processing as a Service for Data Analytics. In 11th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 19). USENIX Association, Renton, WA.Google ScholarGoogle Scholar
  20. Miguel Neves, Lucas Freire, Alberto Schaeffer-Filho, and Marinho Barcellos. 2018. Verification of P4 Programs in Feasible Time Using Assertions. In Proceedings of the 14th International Conference on Emerging Networking EXperiments and Technologies (Heraklion, Greece) (CoNEXT '18). Association for Computing Machinery, New York, NY, USA, 73--85. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. L. Nielsen, R. Vestergaard, N. Yazdani, P. Talasila, D. E. Lucani, and M. Sipos. 2019. Alexandria: A Proof-of-Concept Implementation and Evaluation of Generalised Data Deduplication. In IEEE Globecom Workshops (GC Wkshps). 1--6. Google ScholarGoogle ScholarCross RefCross Ref
  22. Jithin Thomas Petr Lapukhov. 2016. Using INT to Build a Real-time Network Monitoring System Scale. In P4 Workshop.Google ScholarGoogle Scholar
  23. Dan R. K. Ports and Jacob Nelson. 2019. When Should The Network Be The Computer?. In Proceedings of the Workshop on Hot Topics in Operating Systems (Bertinoro, Italy) (HotOS '19). Association for Computing Machinery, New York, NY, USA, 209--215. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Yi Qiao, Xiao Kong, Menghao Zhang, Yu Zhou, Mingwei Xu, and Jun Bi. 2020. Towards In-Network Acceleration of Erasure Coding. In Proceedings of the Symposium on SDN Research (San Jose, CA, USA) (SOSR '20). Association for Computing Machinery, New York, NY, USA, 41--47. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Irving S Reed and Gustave Solomon. 1960. Polynomial codes over certain finite fields. Journal of the society for industrial and applied mathematics 8, 2 (1960), 300--304.Google ScholarGoogle ScholarCross RefCross Ref
  26. E. Sakic, N. Deric, E. Goshi, and W. Kellerer. 2019. P4BFT: Hardware-Accelerated Byzantine-Resilient Network Control Plane. In 2019 IEEE Global Communications Conference (GLOBECOM). 1--7. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Davide Sanvito, Giuseppe Siracusano, and Roberto Bifulco. 2018. Can the Network Be the AI Accelerator?. In Proceedings of the 2018 Morning Workshop on In-Network Computing (Budapest, Hungary) (NetCompute '18). Association for Computing Machinery, New York, NY, USA, 20--25. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Amedeo Sapio, Ibrahim Abdelaziz, Abdulla Aldilaijan, Marco Canini, and Panos Kalnis. 2017. In-Network Computation is a Dumb Idea Whose Time Has Come. In Proceedings of the 16th ACM Workshop on Hot Topics in Networks (Palo Alto, CA, USA) (HotNets-XVI). Association for Computing Machinery, New York, NY, USA, 150--156. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Muhammad Shahbaz, Sean Choi, Ben Pfaff, Changhoon Kim, Nick Feamster, Nick McKeown, and Jennifer Rexford. 2016. Pisces: A programmable, protocol-independent software switch. In Proceedings of the 2016 ACM SIGCOMM Conference. 525--538.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Apoorv Shukla, Kevin Nico Hudemann, Artur Hecker, and Stefan Schmid. 2019. Runtime Verification of P4 Switches with Reinforcement Learning. In Proceedings of the 2019 Workshop on Network Meets AI & ML (Beijing, China) (NetAI'19). Association for Computing Machinery, New York, NY, USA, 1--7. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Manmeet Singh, Maninder Singh, and Sanmeet Kaur. 2019. 10 Days DNS Network Traffic from April-May, 2016. Google ScholarGoogle ScholarCross RefCross Ref
  32. P. Talasila and D. E. Lucani. 2019. Generalized Deduplication: Lossless Compression by Clustering Similar Data. In IEEE 8th International Conference on Cloud Networking (CloudNet). 1--4. Google ScholarGoogle ScholarCross RefCross Ref
  33. Tom Tofigh and Nic Viljoen. 2016. Dynamic Analytics for Programmable NICs Utilizing P4. In P4 Workshop.Google ScholarGoogle Scholar
  34. Yuta Tokusashi, Huynh Tu Dang, Fernando Pedone, Robert Soulé, and Noa Zilberman. 2019. The Case For In-Network Computing On Demand. In Proceedings of the Fourteenth EuroSys Conference 2019 (Dresden, Germany) (EuroSys '19). Association for Computing Machinery, New York, NY, USA, Article 21, 16 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. R. Vestergaard, D. E. Lucani, and Q. Zhang. 2019. Generalized Deduplication: Lossless Compression for Large Amounts of Small IoT Data. In European Wireless 2019; 25th European Wireless Conference. 1--5.Google ScholarGoogle Scholar
  36. R. Vestergaard, Q. Zhang, and D. E. Lucani. 2019. Generalized Deduplication: Bounds, Convergence, and Asymptotic Properties. In IEEE Global Communications Conference (GLOBECOM). 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. R. Vestergaard, Q. Zhang, and D. E. Lucani. 2019. Lossless Compression of Time Series Data with Generalized Deduplication. In IEEE Global Communications Conference (GLOBECOM). 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Daryle Walker. 2020. Boost.CRC. https://www.boost.org/doc/libs/1_73_0/doc/html/crc.htmlGoogle ScholarGoogle Scholar
  39. Shie-Yuan Wang, Jun-Yi Li, and Yi-Bing Lin. 2020. Aggregating and disaggregating packets with various sizes of payload in P4 switches at 100 Gbps line rate. Journal of Network and Computer Applications 165 (2020), 102676. Google ScholarGoogle ScholarCross RefCross Ref
  40. Shie-Yuan Wang, Chia-Ming Wu, Yi-Bing Lin, and Ching-Chun Huang. 2019. High-speed data-plane packet aggregation and disaggregation by P4 switches. Journal of Network and Computer Applications 142 (2019), 98--110.Google ScholarGoogle ScholarCross RefCross Ref
  41. Niloofar Yazdani and Daniel E Lucani. 2019. Protocols to Reduce CPS Sensor Traffic using Smart Indexing and Edge Computing Support. In 2019 IEEE Globecom Workshops (GC Wkshps). 1--6. Google ScholarGoogle ScholarCross RefCross Ref
  42. Niloofar Yazdani and Daniel E Lucani. 2020. Age of Information Analysis for Instantly Decompressible IoT Protocols. In ICC 2020 - 2020 IEEE International Conference on Communications (ICC). 1--6. Google ScholarGoogle ScholarCross RefCross Ref
  43. Niloofar Yazdani, Lars Nielsen, and Daniel E Lucani. 2020. Memory-aware Online Compression of CAN Bus Data for Future Vehicular Systems. 2020 IEEE Global Communications Conference (GLOBECOM) (2020).Google ScholarGoogle Scholar
  44. Yu Zhou, Jun Bi, Yunsenxiao Lin, Yangyang Wang, Dai Zhang, Zhaowei Xi, Jiamin Cao, and Chen Sun. 2019. P4Tester: Efficient Runtime Rule Fault Detection for Programmable Data Planes. In Proceedings of the International Symposium on Quality of Service (Phoenix, Arizona) (IWQoS '19). Association for Computing Machinery, New York, NY, USA, Article 5, 10 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. ZipLine: in-network compression at line speed

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            CoNEXT '20: Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies
            November 2020
            585 pages
            ISBN:9781450379489
            DOI:10.1145/3386367

            Copyright © 2020 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 24 November 2020

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • short-paper

            Acceptance Rates

            Overall Acceptance Rate198of789submissions,25%

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader