ContainerStress: Autonomous Cloud-Node Scoping Framework for Big-Data ML Use Cases

Autor:	Wang, Guang Chao, Gross, Kenny, Subramaniam, Akshay
Rok vydání:	2020
Předmět:	Computer Science - Distributed Parallel and Cluster Computing Computer Science - Machine Learning Computer Science - Performance Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: To be published in 6th Annual Conf. on Computational Science & Computational Intelligence (CSCI'19)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2003.08011 Zobrazit plný text záznamu View this record from Arxiv