公开(公告)号：US20220351023A1

公开(公告)日：2022-11-03

申请号：US17867552

申请日：2022-07-18

Applicant: Oracle International Corporation

Inventor： Pravin Shinde ,  Felix Schmidt ,  Onur Kocberber

IPC: G06N3/04 ,  G06N3/08 ,  G06F11/07

Abstract: Embodiments use a hierarchy of machine learning models to predict datacenter behavior at multiple hardware levels of a datacenter without accessing operating system generated hardware utilization information. The accuracy of higher-level models in the hierarchy of models is increased by including, as input to the higher-level models, hardware utilization predictions from lower-level models. The hierarchy of models includes: server utilization models and workload/OS prediction models that produce predictions at a server device-level of a datacenter; and also top-of-rack switch models and backbone switch models that produce predictions at higher levels of the datacenter. These models receive, as input, hardware utilization information from non-OS sources. Based on datacenter-level network utilization predictions from the hierarchy of models, the datacenter automatically configures its hardware to avoid any predicted over-utilization of hardware in the datacenter. Also, the predictions from the hierarchy of models can be used to detect anomalies of datacenter hardware behavior.

公开(公告)号：US11443166B2

公开(公告)日：2022-09-13

申请号：US16173655

申请日：2018-10-29

Applicant: Oracle International Corporation

Inventor： Pravin Shinde ,  Felix Schmidt ,  Onur Kocberber

IPC: G06F9/44 ,  G06N3/04 ,  G06N3/08 ,  G06F11/07 ,  G06F21/57

Abstract: Embodiments use a hierarchy of machine learning models to predict datacenter behavior at multiple hardware levels of a datacenter without accessing operating system generated hardware utilization information. The accuracy of higher-level models in the hierarchy of models is increased by including, as input to the higher-level models, hardware utilization predictions from lower-level models. The hierarchy of models includes: server utilization models and workload/OS prediction models that produce predictions at a server device-level of a datacenter; and also top-of-rack switch models and backbone switch models that produce predictions at higher levels of the datacenter. These models receive, as input, hardware utilization information from non-OS sources. Based on datacenter-level network utilization predictions from the hierarchy of models, the datacenter automatically configures its hardware to avoid any predicted over-utilization of hardware in the datacenter. Also, the predictions from the hierarchy of models can be used to detect anomalies of datacenter hardware behavior.

公开(公告)号：US11423327B2

公开(公告)日：2022-08-23

申请号：US16156925

申请日：2018-10-10

Applicant: Oracle International Corporation

Inventor： Onur Kocberber ,  Felix Schmidt ,  Craig Schelp ,  Andrew Brownsword ,  Nipun Agarwal

IPC: H04L29/08 ,  G06N20/00 ,  H04L67/01 ,  G06F1/26

Abstract: Techniques are described herein for estimating CPU, memory, and I/O utilization for a workload via out-of-band sensor readings using a machine learning model. The framework involves receiving sensor data associated with executing benchmark applications, obtaining ground truth utilization values for the benchmarks, preprocessing the training data to select a set of enhanced sequences, and using the enhanced sequences to train a random forest model to estimate CPU, memory, and I/O utilization given sensor monitoring data. Prior to the training phase, a machine learning model is trained using a set of predefined hyper-parameters. The trained models are used to generate estimations for CPU, memory, and I/O utilizations values. The utilization values are used with workload context information to assess the deployment and generate one or more recommendations for machine types that will best serve the workload in terms of system utilization.