Constrained In-network Computing with Low Congestion in Datacenter Networks

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


Distributed computing has become a common practice nowadays, where recent focus has been given to the usage of smart networking devices with in-network computing capabilities. State-of-the-art switches with near-line rate computing and aggregation capabilities enable acceleration and improved performance for various modern applications like big data analytics and large-scale distributed and federated machine learning.In this work, we formulate and study the theoretical algorithmic foundations of such approaches, and focus on how to deploy and use constrained in-network computing capabilities within the data center. We focus our attention on reducing the network congestion, i.e., the most congested link in the network, while supporting the given workload(s). We present an efficient optimal algorithm for tree-like network topologies and show that our solution provides as much as an x13 improvement over common alternative approaches. In particular, our results show that having merely a small fraction of network devices that support in-network aggregation can significantly reduce the network congestion, both for single and multiple workloads.

Original languageEnglish
Title of host publicationINFOCOM 2022 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781665458221
StatePublished - 1 Jan 2022
Event41st IEEE Conference on Computer Communications, INFOCOM 2022 - Virtual, Online, United Kingdom
Duration: 2 May 20225 May 2022

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Conference41st IEEE Conference on Computer Communications, INFOCOM 2022
Country/TerritoryUnited Kingdom
CityVirtual, Online

ASJC Scopus subject areas

  • Computer Science (all)
  • Electrical and Electronic Engineering


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