公开(公告)号：US20200065128A1

公开(公告)日：2020-02-27

申请号：US16261253

申请日：2019-01-29

Applicant: VMware, Inc.

Inventor： Dev Nag ,  Gregory T. Burk ,  Janislav Jankov ,  Nick Stephen ,  Dongni Wang

IPC: G06F9/455 ,  G06F9/50 ,  G06F9/48 ,  G06N20/00 ,  G06N3/08

Abstract: The current document is directed to a modular reinforcement-learning-based application manager that can be deployed in various different computational environments without extensive modification and interface development. The currently disclosed modular reinforcement-learning-based application manager interfaces to observation and action adapters and metadata that provide a uniform and, in certain implementations, self-describing external interface to the various different computational environments which the modular reinforcement-learning-based application manager may be operated to control. In addition, certain implementations of the currently disclosed modular reinforcement-learning-based application manager interface to a user-specifiable reward-generation interface to allow the rewards that provide feedback from the computational environment to the modular reinforcement-learning-based application manager to be tailored to meet a variety of different user expectations and desired control policies.

公开(公告)号：US20220391279A1

公开(公告)日：2022-12-08

申请号：US17342423

申请日：2021-06-08

Applicant: VMware, Inc

Inventor： Naira Movses Grigoryan ,  Ashot Nshan Harutyunyan ,  Amak Poghosyan ,  Nicholas Kushmerick ,  Janislav Jankov

IPC: G06F11/07 ,  G06N20/00 ,  G06N5/04 ,  G06F16/28

Abstract: Methods and systems are directed to discovering problem incidents in a distributed computing system. Events corresponding to historical problems incidents for the distributed computing system are retrieved from a data base. Sets of representative events of the various historical problem incidents for the distributed computing system are determined. A runtime problem incident in the distributed computing system is characterized by runtime events. The runtime problem incident is classified as corresponding to a historical problem incident of the historical problem incidents based on the runtime events and the sets of representative events. Remedial measures used to correct the historical problem incident may be used to correct the runtime problem.

公开(公告)号：US10802864B2

公开(公告)日：2020-10-13

申请号：US16261253

申请日：2019-01-29

Applicant: VMware, Inc.

Inventor： Dev Nag ,  Gregory T. Burk ,  Janislav Jankov ,  Nick Stephen ,  Dongni Wang

IPC: G06F9/455 ,  G06F9/50 ,  G06N20/00 ,  G06N3/08 ,  G06F9/48

Abstract: The current document is directed to a modular reinforcement-learning-based application manager that can be deployed in various different computational environments without extensive modification and interface development. The currently disclosed modular reinforcement-learning-based application manager interfaces to observation and action adapters and metadata that provide a uniform and, in certain implementations, self-describing external interface to the various different computational environments which the modular reinforcement-learning-based application manager may be operated to control. In addition, certain implementations of the currently disclosed modular reinforcement-learning-based application manager interface to a user-specifiable reward-generation interface to allow the rewards that provide feedback from the computational environment to the modular reinforcement-learning-based application manager to be tailored to meet a variety of different user expectations and desired control policies.

公开(公告)号：US20180165693A1

公开(公告)日：2018-06-14

申请号：US15377824

申请日：2016-12-13

Applicant: VMware, Inc.

Inventor： Lalit Jain ,  Janislav Jankov

IPC: G06Q30/02 ,  G06F9/455

CPC classification number: G06Q30/0201 ,  G06F9/45558 ,  G06F2009/4557 ,  G06F2009/45591

Abstract: Methods and systems that identify objects of a data center that exhibit correlated-extreme behavior are described. The objects may be, but are not limited to, virtual machines (“VMs”), containers, server computers, clusters of server computers, and the data center itself. Metric data is collected for the various objects and the methods identify the objects that exhibit correlated-extreme behavior. In particular, the methods and systems narrow a search for correlated-extreme behavior of consumers of computational resources of a data center when a provider of the computational resources exhibits unexpected or extreme behavior.