公开(公告)号：US20160337226A1

公开(公告)日：2016-11-17

申请号：US14711648

申请日：2015-05-13

Applicant: VMware, Inc.

Inventor： Pradeep Padala ,  Neelima Mukiri ,  Mazda A. Marvasti

IPC: H04L12/26 ,  G06N7/00 ,  G06F9/455 ,  H04L29/08

CPC classification number: H04L43/10 ,  G06F9/45558 ,  G06F2009/45562 ,  G06F2009/45595 ,  G06N7/005 ,  H04L43/0817 ,  H04L67/10 ,  H04L67/20 ,  H04L67/2804 ,  H04L69/40

Abstract: The current document is directed to an analysis subsystem within a large distributed computing system, such as a virtual data center or cloud-computing facility, that monitors the operational states associated with a multi-tiered application and provides useful information for determining one or more causes of various types of failures and undesirable operational states that may arise during operation of the multi-tiered application. In one implementation, the analysis subsystem collects metrics provided by various different types of metrics sources within the computational system and employs principal feature analysis to select a generally small subset of the collected metrics particularly relevant to monitoring a multi-tiered application and diagnosing underlying causes of operational states of the multi-tiered application. The analysis subsystem develops one or more conditional probability distributions with respect to the subset of metrics. These one or more conditional probability distributions, in turn, allow the analysis subsystem to provide useful information for analysis of the causes of failures and undesirable system states associated with the multi-tiered application.

Abstract translation: 当前文档针对大型分布式计算系统（如虚拟数据中心或云计算设施）中的分析子系统，该分析计算系统监视与多层应用程序相关联的操作状态，并提供用于确定一个或多个原因的有用信息 各种类型的故障和在多层应用的操作期间可能出现的不期望的操作状态。 在一个实现中，分析子系统收集由计算系统内的各种不同类型的度量源提供的度量，并且使用主要特征分析来选择与监测多层应用特别相关的所收集的度量的一般小的子集，并且诊断潜在的原因 多层应用程序的运行状态。 分析子系统针对度量子集开发一个或多个条件概率分布。 这些一个或多个条件概率分布反过来又允许分析子系统提供用于分析与多层应用相关联的故障原因和不期望的系统状态的有用信息。

公开(公告)号：US09882798B2

公开(公告)日：2018-01-30

申请号：US14711648

申请日：2015-05-13

Applicant: VMware, Inc.

Inventor： Pradeep Padala ,  Neelima Mukiri ,  Mazda A. Marvasti

IPC: G06F15/173 ,  H04L12/26 ,  H04L29/08 ,  G06N7/00 ,  G06F9/455 ,  H04L29/14

CPC classification number: H04L43/10 ,  G06F9/45558 ,  G06F2009/45562 ,  G06F2009/45595 ,  G06N7/005 ,  H04L43/0817 ,  H04L67/10 ,  H04L67/20 ,  H04L67/2804 ,  H04L69/40

Abstract: The current document is directed to an analysis subsystem within a large distributed computing system, such as a virtual data center or cloud-computing facility, that monitors the operational states associated with a multi-tiered application and provides useful information for determining one or more causes of various types of failures and undesirable operational states that may arise during operation of the multi-tiered application. In one implementation, the analysis subsystem collects metrics provided by various different types of metrics sources within the computational system and employs principal feature analysis to select a generally small subset of the collected metrics particularly relevant to monitoring a multi-tiered application and diagnosing underlying causes of operational states of the multi-tiered application. The analysis subsystem develops one or more conditional probability distributions with respect to the subset of metrics. These one or more conditional probability distributions, in turn, allow the analysis subsystem to provide useful information for analysis of the causes of failures and undesirable system states associated with the multi-tiered application.