Patent · US Active

Machine-learning based heap memory tuning

US11340924B2 · kind B2 · utility

0Cited by

5References

17Claims

0Family size

Assignee

International Business Machines Corporation · US

Inventors

Hong Bing Zhang · Beijing, CN
Huan Da Wang · Beijing, CN
Wei Huang · Austin, US
Yi Yao · Beijing, CN
Hong Chen · Shanghai, CN

Key dates

Filing date	Jun 27, 2019
Grant date	May 24, 2022
Priority date	—
Expiry date	Sep 28, 2040

Classification

Technology area (CPC G)Physics
CPC primaryG06N3/088
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

In an approach for JAVA Virtual Machine (JVM) heap memory tuning, one or more computer processors obtain a feature vector of an application running on the JVM. The one or more computer processors input the feature vector to a predictive model trained with historical application data collected in one or more production environments. The one or more computer processors receive an output of the predictive model based on the feature vector with at least one memory tuning recommendation for the JVM. The one or more computer processors tune the memory of the JVM based on the at least one memory tuning recommendation.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.