skip to main content
10.1145/3409964.3461824acmconferencesArticle/Chapter ViewAbstractPublication PagesspaaConference Proceedingsconference-collections
extended-abstract

Toward Self-Adjusting Networks for the Matching Model

Published: 06 July 2021 Publication History

Abstract

Self-adjusting networks (SANs) utilize novel optical switching technologies to support dynamic physical network topology reconfiguration. SANs rely on online algorithms to exploit this topological flexibility to reduce the cost of serving network traffic, leveraging locality in the demand. Models in prior work assign uniform cost for traversing and adjusting a single link (e.g. both cost 1). In this paper, we initiate the study of online algorithms for SANs in a more realistic cost model, the Matching Model (MM), in which the network topology is given by the union of a constant number of bipartite matchings (realized by optical switches), and in which changing an entire matching incurs a fixed cost a. The cost of routing is given by the number of hops packets need to traverse. We present online SAN algorithms in the MM with cost O(√α) times the cost of reference algorithms in the uniform cost model.

References

[1]
Chen Avin, Manya Ghobadi, Chen Griner, and Stefan Schmid. 2020a. On the complexity of traffic traces and implications. Proceedings of the ACM on Measurement and Analysis of Computing Systems, Vol. 4, 1 (2020), 1--29.
[2]
Chen Avin, Chen Griner, Iosif Salem, and Stefan Schmid. 2020b. An Online Matching Model for Self-Adjusting ToR-to-ToR Networks. CoRR, Vol. abs/2006.11148 (2020). arxiv: 2006.11148 https://arxiv.org/abs/2006.11148
[3]
Chen Avin and Stefan Schmid. 2019. Toward demand-aware networking: A theory for self-adjusting networks. ACM SIGCOMM Computer Communication Review, Vol. 48, 5 (2019), 31--40.
[4]
Chen Avin and Stefan Schmid. 2021. ReNets: Statically-Optimal Demand-Aware Networks. In 2nd Symposium on Algorithmic Principles of Computer Systems, APOCS 2020, Virtual Conference, January 13, 2021, Michael Schapira (Ed.). SIAM, 25--39. https://doi.org/10.1137/1.9781611976489.3
[5]
Monia Ghobadi, Ratul Mahajan, Amar Phanishayee, Nikhil Devanur, Janardhan Kulkarni, Gireeja Ranade, Pierre-Alexandre Blanche, Houman Rastegarfar, Madeleine Glick, and Daniel Kilper. 2016. Projector: Agile reconfigurable data center interconnect. In Proceedings of the 2016 ACM SIGCOMM Conference. 216--229.
[6]
William M. Mellette, Rob McGuinness, Arjun Roy, Alex Forencich, George Papen, Alex C. Snoeren, and George Porter. 2017. RotorNet: A Scalable, Low-complexity, Optical Datacenter Network. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication, SIGCOMM 2017, Los Angeles, CA, USA, August 21--25, 2017 . ACM, 267--280. https://doi.org/10.1145/3098822.3098838
[7]
Nick Reingold, Jeffery Westbrook, and Daniel D Sleator. 1994. Randomized competitive algorithms for the list update problem. Algorithmica, Vol. 11, 1 (1994), 15--32.
[8]
Stefan Schmid, Chen Avin, Christian Scheideler, Michael Borokhovich, Bernhard Haeupler, and Zvi Lotker. 2015. Splaynet: Towards locally self-adjusting networks. IEEE/ACM Transactions on Networking, Vol. 24, 3 (2015), 1421--1433.
[9]
Daniel D Sleator and Robert E Tarjan. 1985a. Amortized efficiency of list update and paging rules. Commun. ACM, Vol. 28, 2 (1985), 202--208.
[10]
Daniel Dominic Sleator and Robert Endre Tarjan. 1985b. Self-adjusting binary search trees. Journal of the ACM (JACM), Vol. 32, 3 (1985), 652--686.

Cited By

View all
  • (2023)OpticNet: Self-Adjusting Networks for ToR-Matching-ToR Optical Switching ArchitecturesIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10228883(1-10)Online publication date: 17-May-2023

Index Terms

  1. Toward Self-Adjusting Networks for the Matching Model

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      SPAA '21: Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures
      July 2021
      463 pages
      ISBN:9781450380706
      DOI:10.1145/3409964
      • General Chair:
      • Kunal Agrawal,
      • Program Chair:
      • Yossi Azar
      Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 06 July 2021

      Check for updates

      Author Tags

      1. matching model
      2. online algorithms
      3. self-adjusting networks

      Qualifiers

      • Extended-abstract

      Funding Sources

      Conference

      SPAA '21
      Sponsor:

      Acceptance Rates

      Overall Acceptance Rate 447 of 1,461 submissions, 31%

      Upcoming Conference

      SPAA '25
      37th ACM Symposium on Parallelism in Algorithms and Architectures
      July 28 - August 1, 2025
      Portland , OR , USA

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)4
      • Downloads (Last 6 weeks)1
      Reflects downloads up to 05 Mar 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2023)OpticNet: Self-Adjusting Networks for ToR-Matching-ToR Optical Switching ArchitecturesIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10228883(1-10)Online publication date: 17-May-2023

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media