公开(公告)号：US10592169B2

公开(公告)日：2020-03-17

申请号：US15822612

申请日：2017-11-27

Applicant: VMware, Inc.

Inventor： Paul Pedersen ,  Darren Brown ,  Wei Li ,  Leah Nutman ,  Sergio Nakai

IPC: G06F3/06

Abstract: The current document is directed to methods and systems that collect metric data within computing facilities, including large data centers and cloud-computing facilities. In a described implementation, input metric data is compressed by replacing each metric data point with a one-bit, two-bit, four-bit, or eight-bit compressed data value. During a first time window following reception of a metric data point, the metric data point remains available in uncompressed form to facilitate data analysis and monitoring functionalities that use uncompressed metric data. During a second time window, the metric data point is compressed and stored in memory, where the compressed data point remains available for data analysis and monitoring functionalities that use compressed metric data for detection of peaks, periodic patterns, and other characteristics. Finally, the compressed data point is archived in mass storage, where it remains available to data-analysis and management functionalities for a lengthy time period.

公开(公告)号：US10212023B2

公开(公告)日：2019-02-19

申请号：US15286337

申请日：2016-10-05

Applicant: VMware, Inc.

Inventor： Darren Brown

IPC: G06F15/173 ,  H04L12/24 ,  H04L29/08

Abstract: Methods and systems to identify and respond to low-priority event messages are described. Methods identify types of event messages recorded in event-log files as low-priority event messages. Methods enable an information technology (“IT”) administrator, or other user, to determine which low-priority event messages may be deleted, how the low-priority event messages may be sampled for storage, or how long the low-priority event messages may be stored in a data-storage device.

公开(公告)号：US20160277268A1

公开(公告)日：2016-09-22

申请号：US14660461

申请日：2015-03-17

Applicant: VMware, Inc.

Inventor： Darren Brown ,  Nicholas Kushmerick ,  Junyuan Lin ,  Matt Roy McLaughlin ,  Jon Herlocker

IPC: H04L12/26 ,  H04L29/08

CPC classification number: H04L67/10 ,  G06F9/50 ,  G06F11/0778 ,  G06F11/3006 ,  G06F11/3452 ,  G06F11/3476 ,  G06F2201/86 ,  H04L41/142 ,  H04L43/04

Abstract: The current document is directed to systems, and methods incorporated within the systems, that carry out probability-distribution-based analysis of log-file entries. A monitoring subsystem within a distributed computer system uses probability-distribution-based analysis of log-file entries to detect changes in the state of the distributed computer system. A log-file-analysis subsystem within a distributed computer system uses probability-distribution-based analysis of log-file entries to identify subsets of log-file entries that predict anomalies and impending problems in the distributed computer system. In many implementations, a numerical comparison of probability distributions of log-file-entry types is used to detect state changes in the distributed computer system.

Abstract translation: 当前的文档针对系统和整合在系统中的方法，其对日志文件条目进行基于概率分布的分析。 分布式计算机系统中的监控子系统使用基于概率分布的日志文件条目分析来检测分布式计算机系统状态的变化。 分布式计算机系统中的日志文件分析子系统使用基于概率分布的日志文件条目分析来识别分布式计算机系统中预测异常和即将发生的问题的日志文件条目的子集。 在许多实现中，使用日志文件入口类型的概率分布的数值比较来检测分布式计算机系统中的状态变化。