ContainerStress：面向大数据ML用例的自主云节点范围界定框架
ContainerStress: Autonomous Cloud-Node Scoping Framework for Big-Data ML Use Cases
摘要：
在云环境中部署大数据机器学习（ML）服务对云供应商提出了针对任何给定客户用例的云容器配置大小调整方面的挑战。OracleLabs开发了一个自动化框架，该框架使用嵌套循环Monte Carlo模拟来自主扩展云CPU-GPU“配置”（云容器中可供最终客户使用的CPU和/或GPU配置）范围内的任何规模的客户机器学习用例。此外，OracleLabs和NVIDIA作者还合作进行了一项ML基准研究，该研究分析了任何ML预测算法的计算成本和GPU加速，并评估了由传统CPU和NVIDIA GPU组成的云容器中计算成本的降低。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
