公开(公告)号：US20240046069A1

公开(公告)日：2024-02-08

申请号：US17970830

申请日：2022-10-21

Applicant: VMWARE, INC.

Inventor： MARIUS VILCU ,  Peter Rudy ,  Asmitha Rathis ,  Aiswaryaa Venugopalan

IPC: G06N3/04

CPC classification number: G06N3/0454

Abstract: The current document is directed to reinforcement-learning-based management-system agents that control distributed applications and the infrastructure environments in which they run. Management-system agents are initially trained in simulated environments and specialized training environments before being deployed to live, target distributed computer systems where they operate in a controller mode in which they do not explore the control-state space or attempt to learn better policies and value functions, but instead produce traces that are collected and stored for subsequent use. Each deployed management-system agent is associated with a twin training agent that uses the collected traces produced by the deployed management-system agent for optimizing its policy and value functions. To further ensure safe operational control of the environment, the management-system agents employ lookahead planning, action budgets, and action constraints to forestall issuance, by management-system controllers, of potentially deleterious actions.

公开(公告)号：US20240419457A1

公开(公告)日：2024-12-19

申请号：US18210032

申请日：2023-06-14

Applicant: VMware, Inc.

Inventor： Dongni Wang ,  Aiswaryaa Venugopalan ,  Arnav Chakravarthy ,  Marius Vilcu ,  Asmitha Rathis ,  Greg Burk

IPC: G06F9/445 ,  G06F9/50

Abstract: The disclosure provides a method for determining a target configuration for a container-based cluster. The method generally includes determining, by a virtualization management platform configured to manage components of the cluster, a current state of the cluster, determining, by the virtualization management platform, at least one of performance metrics or resource utilization metrics for the cluster based on the current state of the cluster, processing, with a model configured to generate candidate configurations recommended for the cluster, the current state and at least one of the performance metrics or the resource utilization metrics and thereby generate the candidate configurations, calculating a reward score for each of the candidate configurations, selecting the target configuration as a candidate configuration from the candidate configurations based on the reward score of the target configuration, and adjusting configuration settings for the cluster based on the target configuration to alter the current state of the cluster.