公开(公告)号：US20230177345A1

公开(公告)日：2023-06-08

申请号：US17545403

申请日：2021-12-08

Applicant: VMware, Inc.

Inventor： Marius Vilcu ,  Dongni Wang ,  Asmitha Rathis ,  Greg Burk

IPC: G06N3/08 ,  G06N3/04 ,  G06F9/50

CPC classification number: G06N3/088 ,  G06N3/04 ,  G06F9/505

Abstract: The current document is directed to methods and systems that determine workload characteristics of computational entities from stored data and that evaluate deployment/configuration policies in order to facilitate deploying, launching, and controlling distributed applications, distributed-application components, and other computational entities within distributed computer systems. Deployment/configuration policies are powerful tools for assisting managers and administrators of distributed applications and distributed computer systems, but constructing deployment/configuration policies and, in particular, evaluating the relative effectiveness of deployment/configuration policies in increasingly complex distributed-computer-system environments may be difficult or practically infeasible for many administrators and managers and may be associated with undesirable or intolerable levels of risk. The currently disclosed machine-learning-based deployment/configuration-policy evaluation methods and systems represent a significant improvement to policy-based management and control that address both of these problems.

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.