公开(公告)号：US20200257968A1

公开(公告)日：2020-08-13

申请号：US16271642

申请日：2019-02-08

Applicant: Adobe Inc.

Inventor： Subrata Mitra ,  Nikhil Sheoran ,  Ramanuja Narasimha Simha ,  Shanka Subhra Mondal ,  Neeraj Jagdish Dhake ,  Ravinder Nehra

IPC: G06N3/08 ,  H04L29/08 ,  G06F9/48

Abstract: The technology described herein is directed to a self-learning application scheduler for improved scheduling distribution of resource requests, e.g., job and service scheduling requests or tasks derived therefrom, initiated by applications on a shared compute infrastructure. More specifically, the self-learning application scheduler includes a reinforcement learning agent that iteratively learns a scheduling policy to improve scheduling distribution of the resource requests on the shared compute infrastructure. In some implementations, the reinforcement learning agent learns inherent characteristics and patterns of the resource requests initiated by the applications and orchestrates placement or scheduling of the resource requests on the shared compute infrastructure to minimize resource contention and thereby improve application performance for better overall user-experience.

公开(公告)号：US11989647B2

公开(公告)日：2024-05-21

申请号：US16271642

申请日：2019-02-08

Applicant: Adobe Inc.

Inventor： Subrata Mitra ,  Nikhil Sheoran ,  Ramanuja Narasimha Simha ,  Shanka Subhra Mondal ,  Neeraj Jagdish Dhake ,  Ravinder Nehra

IPC: G06N3/08 ,  G06F9/48 ,  H04L67/51 ,  H04L67/60

CPC classification number: G06N3/08 ,  G06F9/4881 ,  H04L67/51 ,  H04L67/60

Abstract: The technology described herein is directed to a self-learning application scheduler for improved scheduling distribution of resource requests, e.g., job and service scheduling requests or tasks derived therefrom, initiated by applications on a shared compute infrastructure. More specifically, the self-learning application scheduler includes a reinforcement learning agent that iteratively learns a scheduling policy to improve scheduling distribution of the resource requests on the shared compute infrastructure. In some implementations, the reinforcement learning agent learns inherent characteristics and patterns of the resource requests initiated by the applications and orchestrates placement or scheduling of the resource requests on the shared compute infrastructure to minimize resource contention and thereby improve application performance for better overall user-experience.