MLLess: Achieving Cost Efficiency in Serverless Machine Learning Training

TL;DR

MLLess is a novel serverless machine learning training system that significantly reduces costs and improves speed for sparse models by employing innovative optimizations tailored to FaaS characteristics.

Contribution

The paper introduces MLLess, a FaaS-based ML training prototype with two novel optimizations: a significance filter and a scale-in auto-tuner, enhancing cost-efficiency and scalability.

Findings

MLLess is 15X faster than serverful systems for certain models.

MLLess achieves lower costs for sparse, fast-converging ML models.

The system scales effectively to larger serverless worker fleets.

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 100ms). 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.