Multi-Tenancy- and Redundancy-Aware In-Network Aggregation using Programmable Switches

Sol Han, Hochan Lee, Subin Han, Heewon Kim, Sangheon Pack

Research output: Contribution to journalArticlepeer-review


Recent advances in programmable switches make it possible to aggregate data in partition-aggregation applications (e.g., MapReduce applications) at programmable switches. However, a well-designed aggregation scheme is indispensable for aggregating as much data as possible under the limited resources of programmable switches in an environment where multiple applications are running concurrently. In this article, we propose a multitenancy- and redundancy-aware in-network aggregation (MARINA) scheme that preferentially aggregates highly redundant data at a programmable switch and improves aggregation performance by constructing a multi-tenancyaware aggregation tree. Evaluation results demonstrate that MARINA can improve data aggregation performance by up to 81% compared with the conventional approach using statically partitioned resources and sequential aggregation.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalIEEE Network
Publication statusAccepted/In press - 2022


  • Aggregates
  • Data aggregation
  • Memory management
  • Random access memory
  • Servers
  • Steiner trees
  • Switches

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications


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