MLLess: Achieving cost efficiency in serverless machine learning training

Gimeno Sarroca, Pablo; Sánchez-Artigas, Marc

doi:10.1016/j.jpdc.2023.104764

MLLess: Achieving cost efficiency in serverless machine learning training

Pablo Gimeno Sarroca, Marc Sánchez-Artigas

Journal of Parallel and Distributed Computing, 2024

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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 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.

Cite this paper

@article{gimeno2024mlless,
  title     = {{MLLess}: Achieving cost efficiency in serverless machine learning training},
  author    = {Gimeno Sarroca, Pablo and S{\'a}nchez-Artigas, Marc},
  journal   = {Journal of Parallel and Distributed Computing},
  volume    = {183},
  pages     = {104764},
  publisher = {Elsevier},
  year      = {2024},
  doi       = {10.1016/j.jpdc.2023.104764}
}