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      Towards transforming OpenFlow rulesets to fit fixed-function pipelines

      Sanger, Richard; Luckie, Matthew John; Nelson, Richard
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      Ruleset_fitting_SOSR2020_Camera_Ready.pdf
      Accepted version, 708.2Kb
      DOI
       10.1145/3373360.3380844
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      Sanger, R., Luckie, M. J., & Nelson, R. (2020). Towards transforming OpenFlow rulesets to fit fixed-function pipelines. In Proceedings of 2020 Symposium on SDN Research (SOSR 2020) (pp. 123–134). New York, NY, USA: ACM. https://doi.org/10.1145/3373360.3380844
      Permanent Research Commons link: https://hdl.handle.net/10289/13710
      Abstract
      OpenFlow feature support differs between devices due to devicespecific hardware constraints. OpenFlow places the burden of addressing these differences on the controller, which increases development cost and restricts device interoperability. This paper investigates reducing this burden by algorithmically transforming an existing ruleset to fit an incompatible fixed-function pipeline to improve device interoperability. Existing rule-fitting schemes in the literature require metadata to link rules between tables into a path through the pipeline, but not all pipelines support metadata. We developed a novel approach that does not rely on any particular pipeline features, like metadata, and considers the pipeline's constraints, including both the matches and actions available. This paper presents our implementation, including ruleset preprocessing techniques, methods of transforming rules, and how we use a partially constrained boolean satisfiability problem to select from these transformations and build the final ruleset. While future work remains towards real-world deployment, our approach demonstrates fitting rulesets to fixed-function pipelines without metadata is feasible, and our techniques to reduce the size of the problem are beneficial.
      Date
      2020
      Type
      Conference Contribution
      Publisher
      ACM
      Rights
      This is an author’s accepted version of an article published in the Proceedings of 2020 Symposium on SDN Research (SOSR 2020). © 2020 Association for Computing Machinery.
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      • Computing and Mathematical Sciences Papers [1452]
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