Out of band server utilization estimation and server workload characterization for datacenter resource optimization and forecasting
US11423327B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 10, 2018 |
| Grant date | Aug 23, 2022 |
| Priority date | — |
| Expiry date | Nov 20, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/088
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.