DAIET: A system for data aggregation inside the network

Amedeo Sapio, Ibrahim Abdelaziz, Marco Canini, Panagiotis Kalnis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Many data center applications nowadays rely on distributed computation models like MapReduce and Bulk Synchronous Parallel (BSP) for data-intensive computation at scale [4]. These models scale by leveraging the partition/aggregate pattern where data and computations are distributed across many worker servers, each performing part of the computation. A communication phase is needed each time workers need to synchronize the computation and, at last, to produce the final output. In these applications, the network communication costs can be one of the dominant scalability bottlenecks especially in case of multi-stage or iterative computations [1].

Original languageEnglish (US)
Title of host publicationSoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing
PublisherAssociation for Computing Machinery, Inc
Number of pages1
ISBN (Electronic)9781450350280
DOIs
StatePublished - Sep 24 2017
Event2017 Symposium on Cloud Computing, SoCC 2017 - Santa Clara, United States
Duration: Sep 24 2017Sep 27 2017

Publication series

NameSoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing

Conference

Conference2017 Symposium on Cloud Computing, SoCC 2017
CountryUnited States
CitySanta Clara
Period09/24/1709/27/17

Keywords

  • In-network processing
  • On-path aggregation
  • P4

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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