Deploying big-data Machine Learning (ML) services in a cloud environment presents a challenge to the cloud vendor with respect to the cloud container configuration sizing for any given customer use case. OracleLabs has developed an automated framework that uses nested-loop Monte Carlo simulation to autonomously scale any size customer ML use cases across the range of cloud CPU-GPU "Shapes" (configurations of CPUs and/or GPUs in Cloud containers available to end customers). Moreover, the OracleLabs and NVIDIA authors have collaborated on a ML benchmark study which analyzes the compute cost and GPU acceleration of any ML prognostic algorithm and assesses the reduction of compute cost in a cloud container comprising conventional CPUs and NVIDIA GPUs.
@article{arxiv.2003.08011,
title = {ContainerStress: Autonomous Cloud-Node Scoping Framework for Big-Data ML Use Cases},
author = {Guang Chao Wang and Kenny Gross and Akshay Subramaniam},
journal= {arXiv preprint arXiv:2003.08011},
year = {2020}
}
Comments
To be published in 6th Annual Conf. on Computational Science & Computational Intelligence (CSCI'19)