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

    2 Citations (Scopus)

    Abstract

    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 multi-tenancy 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-tenancy-aware aggregation tree. Evaluation results demonstrate that MARINA can improve data aggregation performance by up to 81 percent compared with the conventional approach using statically partitioned resources and sequential aggregation.

    Original languageEnglish
    Pages (from-to)94-100
    Number of pages7
    JournalIEEE Network
    Volume37
    Issue number3
    DOIs
    Publication statusPublished - 2023 May 1

    Bibliographical note

    Publisher Copyright:
    © 1986-2012 IEEE.

    ASJC Scopus subject areas

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

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