公开(公告)号：US20250061354A1

公开(公告)日：2025-02-20

申请号：US18450102

申请日：2023-08-15

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Eitan Frachtenberg ,  Pedro Henrique Rocha Bruel ,  Dejan S. Milojicic

IPC: G06N5/025 ,  G06F18/2321

Abstract: In certain embodiments, a method includes obtaining, by a processing device, a pre-determined number of data points from an online experiment; processing, by the processing device, the data points to determine whether the data points exhibit at least one characteristic; selecting, by the processing device and in response to the data points exhibiting the at least one characteristic, a first stopping rule of a plurality of stopping rules, wherein the first stopping rule corresponds to the at least one characteristic; applying, by the processing device, the first stopping rule to determine whether a first convergence criterion is met; and stopping, when the first convergence criterion is met, the online experiment.

公开(公告)号：US20250165813A1

公开(公告)日：2025-05-22

申请号：US18518276

申请日：2023-11-22

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Eitan Frachtenberg ,  Sai Rahul Chalamalasetti ,  Izzat El Hajj

IPC: G06N5/022

Abstract: An ML model is trained to learn relationships between empirical distributions of telltale indicators and empirical distributions of variability benchmarks associated with executing an application on a first computer system having a first configuration. At least one empirical distribution of a first variability benchmark associated with the application is specified to the ML model. Output information indicative of at least one telltale indicator that is associated with the first variability benchmark is received from the ML model.