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Analysis of institutional authors

Sánchez-Artigas MAuthor

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October 6, 2023
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MLLESS: Achieving cost efficiency in serverless machine learning training

Publicated to: Journal Of Parallel And Distributed Computing. 183 104764- - 2024-01-01 183(), DOI: 10.1016/j.jpdc.2023.104764

Authors:

Sarroca, PG; Sánchez-Artigas, M
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Affiliations

Univ Rovira i Virgili, Comp Sci & Maths, Tarragona, Spain - Author
Universitat Rovira i Virgili - Author

Abstract

Function-as-a-Service (FaaS) has raised a growing interest in how to “tame” serverless computing to enable domain-specific use cases such as data-intensive applications and machine learning (ML), to name a few. Recently, several systems have been implemented for training ML models. Certainly, these research articles are significant steps in the correct direction. However, they do not completely answer the nagging question of when serverless ML training can be more cost-effective compared to traditional “serverful” computing. To help in this endeavor, we propose MLLESS, a FaaS-based ML training prototype built atop IBM Cloud Functions. To boost cost-efficiency, MLLESS implements two innovative optimizations tailored to the traits of serverless computing: on one hand, a significance filter, to make indirect communication more effective, and on the other hand, a scale-in auto-tuner, to reduce cost by benefiting from the FaaS sub-second billing model (often per 100 ms). Our results certify that MLLESS can be 15X faster than serverful ML systems [27] at a lower cost for sparse ML models that exhibit fast convergence such as sparse logistic regression and matrix factorization. Furthermore, our results show that MLLESS can easily scale out to increasingly large fleets of serverless workers.
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Keywords

Function-as-a-serviceMachine learningServerless computing

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 33/147, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Theory & Methods.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-05:

  • WoS: 10
  • Scopus: 15
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-05:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 31 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: http://hdl.handle.net/20.500.11797/imarina9330487
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Gimeno Sarroca P) and Last Author (Sanchez Artigas, Marc).

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