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Accelerating the performance of data analytics using network-centric processing

Authors:

Bochra Boughzala,

Boris KoldehofeAuthors Info & Claims

DEBS '21: Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems

Pages 192 - 195

https://doi.org/10.1145/3465480.3468162

Published: 28 June 2021 Publication History

Abstract

Distributed execution of real-time data analytics such as event stream processing is the key to scalability, performance and reliable detection of situation changes. Although real-time analytics is highly I/O centric, existing methods supporting the efficient execution of data analytics functions mostly rely on traditional compute models that are available in data centers, e.g., CPU or GPU based processing models, but treat the network mainly as a blackbox. However, with recent advance in software-defined networking (SDN) and the standardization of packet processing pipeline, data analytics functions can be offloaded to programmable switches and benefit from hardware acceleration in an easier and more flexible way than a decade ago. In this paper we focus on the potential of in-network processing to enhance the performance of the overall real-time data analytics application. We aim to contribute to an (i) understanding on how in-network processing can accelerate real-time data analytics and (ii) assess what models of in-network computing can accelerate which event processing functions considering the limitations of network models compared to traditional compute models. We motivate the potential and illustrate the research problems in the context of load balancing which is an important concept in the data-parallel execution of event processing systems.

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Cited By

Boughzala BKoldehofe B(2024)In-Network Management of Parallel Data Streams over Programmable Data Planes2024 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking62109.2024.10619812(50-58)Online publication date: 3-Jun-2024
https://doi.org/10.23919/IFIPNetworking62109.2024.10619812
Shirsath VChandane MLal CConti M(2024)SPARQComputer Communications10.1016/j.comcom.2023.12.027216:C(283-294)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.comcom.2023.12.027

Index Terms

Accelerating the performance of data analytics using network-centric processing
1. Networks
  1. Network services
    1. In-network processing
    2. Programmable networks
2. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Software infrastructure
        Middleware
        Message oriented middleware
    2. Software system structures
      1. Software architectures
        Publish-subscribe / event-based architectures

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Information

Published In

cover image ACM Conferences

DEBS '21: Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems

June 2021

207 pages

ISBN:9781450385558

DOI:10.1145/3465480

General Chairs:
Alessandro Margara
Politecnico di Milano, Italy
,
Emanuele Della Valle
Politecnico di Milano, Italy

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 28 June 2021

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DEBS '21: The 15th ACM International Conference on Distributed and Event-based Systems

June 28 - July 2, 2021

Virtual Event, Italy

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DEBS '21 Paper Acceptance Rate 7 of 26 submissions, 27%;

Overall Acceptance Rate 145 of 583 submissions, 25%

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Cited By

Boughzala BKoldehofe B(2024)In-Network Management of Parallel Data Streams over Programmable Data Planes2024 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking62109.2024.10619812(50-58)Online publication date: 3-Jun-2024
https://doi.org/10.23919/IFIPNetworking62109.2024.10619812
Shirsath VChandane MLal CConti M(2024)SPARQComputer Communications10.1016/j.comcom.2023.12.027216:C(283-294)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.comcom.2023.12.027

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