公开(公告)号：US20200065157A1

公开(公告)日：2020-02-27

申请号：US16518763

申请日：2019-07-22

Applicant: VMware, Inc.

Inventor： Dev Nag ,  Yanislav Yankov ,  Dongni Wang ,  Gregory T. Burk ,  Nicholas Mark Grant Stephen

IPC: G06F9/50 ,  G06N20/00 ,  G06N3/02 ,  G06F17/16

Abstract: The current document is directed to automated reinforcement-learning-based application managers that learn and improve the reward function that steers reinforcement-learning-based systems towards optimal or near-optimal policies. Initially, when the automated reinforcement-learning-based application manager is first installed and launched, the automated reinforcement-learning-based application manager may rely on human-application-manager action inputs and resulting state/action trajectories to accumulate sufficient information to generate an initial reward function. During subsequent operation, when it is determined that the automated reinforcement-learning-based application manager is no longer following a policy consistent with the type of management desired by human application managers, the automated reinforcement-learning-based application manager may use accumulated trajectories to improve the reward function.

公开(公告)号：US20200065118A1

公开(公告)日：2020-02-27

申请号：US16518617

申请日：2019-07-22

Applicant: VMware, Inc.

Inventor： Dev Nag ,  Yanislav Yankov ,  Dongni Wang ,  Gregory T. Burk ,  Nicholas Mark Grant Stephen

IPC: G06F9/445 ,  G06N20/00 ,  G06F9/48 ,  G06F17/16

Abstract: The current document is directed to an administrator-monitored reinforcement-learning-based application manager that can be deployed in various different computational environments to manage the computational environments with respect to one or more reward-specified goals. Certain control actions undertaken by the administrator-monitored reinforcement-learning-based application manager are first proposed, to one or more administrators or other users, who can accept or reject the proposed control actions prior to their execution. The reinforcement-learning-based application manager can therefore continue to explore the state/action space, but the exploration can be parametrically constrained as well as by human-administrator oversight and intervention.

公开(公告)号：US20150370682A1

公开(公告)日：2015-12-24

申请号：US14312815

申请日：2014-06-24

Applicant: VMware, Inc.

Inventor： Naira Movses Grigoryan ,  Mazda A. Marvasti ,  Arnak Poghosyan ,  Ashot Nshan Harutyunyan ,  Yanislav Yankov

IPC: G06F11/32 ,  G06F11/34

CPC classification number: G06F11/3452 ,  G06F11/07 ,  G06F11/324 ,  G06F11/3433 ,  G06F11/3438 ,  G06F11/3495 ,  G06F2201/81 ,  G06F2201/875

Abstract: This disclosure is directed to data-agnostic computational methods and systems for adjusting hard thresholds based on user feedback. Hard thresholds are used to monitor time-series data generated by a data-generating entity. The time-series data may be metric data that represents usage of the data-generating entity over time. The data is compared with a hard threshold associated with usage of the resource or process and when the data violates the threshold, an alert is typically generated and presented to a user. Methods and systems collect user feedback after a number of alerts to determine the quality and significance of the alerts. Based on the user feedback, methods and systems automatically adjust the hard thresholds to better represent how the user perceives the alerts.

Abstract translation: 本公开涉及用于基于用户反馈来调整硬阈值的与数据无关的计算方法和系统。 硬阈值用于监视由数据生成实体生成的时间序列数据。 时间序列数据可以是表示数据生成实体随时间的使用的量度数据。 将数据与与资源或过程的使用相关联的硬阈值进行比较，并且当数据违反阈值时，通常生成警报并呈现给用户。 方法和系统通过多个警报收集用户反馈，以确定警报的质量和意义。 基于用户反馈，方法和系统自动调整硬阈值，以更好地表示用户如何感知警报。