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

• URL: http://arxiv.org/abs/2003.08011v1
• Date: Wed, 18 Mar 2020 01:51:42 GMT
• Title: ContainerStress: Autonomous Cloud-Node Scoping Framework for Big-Data ML Use Cases
• Authors: Guang Chao Wang, Kenny Gross, and Akshay Subramaniam
• Abstract summary: OracleLabs has developed an automated framework that uses nested-loop Monte Carlo simulation to autonomously scale any size customer ML use cases. OracleLabs and NVIDIA authors have collaborated on a ML benchmark study which analyzes the compute cost and GPU acceleration of any ML prognostic algorithm.
• Score: 0.2752817022620644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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