公开(公告)号：US20150310139A1

公开(公告)日：2015-10-29

申请号：US14304760

申请日：2014-06-13

Applicant: Appnomic Systems Private Limited

Inventor： Padmanabhan Desikachari ,  Sumanth Narasappa

IPC: G06F17/50 ,  G06N99/00 ,  G06F17/10

CPC classification number: G06N20/00 ,  G06Q10/04 ,  G06Q10/06 ,  G06Q10/0631 ,  G06Q10/06315

Abstract: Various techniques employed by an application performance management service to generate an application behavior learning based capacity forecast model are disclosed. In some embodiments, such a capacity forecast model is at least in part generated by clustering collected transaction data into one or more usage patterns, analyzing collected usage pattern data, and solving a mathematical model generated from the usage pattern data to determine a sensitivity of a resource to each type of transaction associated with an application.

Abstract translation: 公开了应用性能管理服务采用的用于生成基于应用行为学习的容量预测模型的各种技术。 在一些实施例中，这样的容量预测模型至少部分地通过将收集的交易数据聚类成一个或多个使用模式，分析收集的使用模式数据以及求解从使用模式数据生成的数学模型来确定一个 资源到与应用程序相关联的每种类型的事务。

公开(公告)号：US11748227B2

公开(公告)日：2023-09-05

申请号：US16539969

申请日：2019-08-13

Applicant: Appnomic Systems Private Limited

Inventor： Padmanabhan Desikachari

IPC: G06F11/34 ,  G06F17/00 ,  H04L41/0896 ,  H04L41/142 ,  H04L41/147 ,  H04L41/16

CPC classification number: G06F11/3433 ,  G06F11/3447 ,  G06F17/00 ,  H04L41/0896 ,  H04L41/142 ,  H04L41/147 ,  G06F11/3409 ,  G06F2201/80 ,  G06F2201/87 ,  H04L41/16

Abstract: Disclosed herein is a computer implemented method and system for analyzing load responsive behavior of infrastructure components in an electronic environment for proactive management of the infrastructure components. Transaction data on multiple application transactions is collected. Load patterns are identified from the collected transaction data for generating load profiles. Data on infrastructure behavior in response to the application transactions is collected. Infrastructure behavior patterns are identified from the infrastructure behavior data for generating behavior profiles. The generated load profiles and the generated behavior profiles are correlated to create a load responsive behavior model. The created load responsive behavior model predicts behavior of the infrastructure components for different load patterns. A live data stream from current application transactions is analyzed using the load responsive behavior model to determine current load responsive behavior. Deviations of the current load responsive behavior from the predicted behavior are detected using the load responsive behavior model.

公开(公告)号：US11275667B2

公开(公告)日：2022-03-15

申请号：US16792914

申请日：2020-02-18

Applicant: APPNOMIC SYSTEMS PRIVATE LIMITED

Inventor： Padmanabhan Desikachari ,  Pranav Kumar Jha

IPC: G06F11/34 ,  G06F17/18 ,  G06N20/00 ,  G06F9/46 ,  G06F11/30

Abstract: According to an aspect of the present disclosure, a correlation data correlating resource usage with workload signatures is maintained, each workload signature representing a cluster of block signatures, each block signature characterizing the transaction instances initiated in a corresponding block duration. For the transactions received in a current block duration, if a current block signature is not contained in the correlation data and if the current transaction arrival rate (TAR) is greater than an expected TAR, a resource requirement for the current block signature is computed. Actions to manage capacity to handle transaction instances are triggered if the resource requirement is greater than the resource allocation in the current block duration. As an unknown current block signature and a higher TAR may be indicative of a workload surge, triggering suitable actions for such block signatures facilitates such surges to be handled by the software application.

公开(公告)号：US10198340B2

公开(公告)日：2019-02-05

申请号：US14599351

申请日：2015-01-16

Applicant: Appnomic Systems Private Limited

Inventor： Nageswara Rao Vesepogu ,  Sumanth Narasappa ,  Padmanabhan Desikachari

IPC: G06F11/34 ,  G06F11/07

Abstract: Various techniques associated with application performance monitoring are disclosed. In some embodiments, a subset of application methods is configured to capture response time metrics, and response time metrics for a prescribed application transaction are computed by summing corresponding response time metrics of methods of the subset that are executed during each transaction invocation. Method and transaction response time metrics are collected for each of a plurality of observation intervals, and the collected response time metrics are analyzed to identify anomalous method and transaction states. Co-occurring anomalous transaction and method states are correlated to identify a set of hotspot methods for the transaction, wherein hotspot methods comprise expected root causes for anomalies of the transaction.

公开(公告)号：US20150142414A1

公开(公告)日：2015-05-21

申请号：US14517582

申请日：2014-10-17

Applicant: Appnomic Systems Private Limited

Inventor： Padmanabhan Desikachari

IPC: G06F11/34

CPC classification number: G06F11/3433 ,  G06F11/3409 ,  G06F11/3447 ,  G06F17/00 ,  G06F2201/80 ,  G06F2201/87 ,  H04L41/0896 ,  H04L41/142 ,  H04L41/147 ,  H04L41/16

Abstract: Disclosed herein is a computer implemented method and system for analyzing load responsive behavior of infrastructure components in an electronic environment for proactive management of the infrastructure components. Transaction data on multiple application transactions is collected. Load patterns are identified from the collected transaction data for generating load profiles. Data on infrastructure behavior in response to the application transactions is collected. Infrastructure behavior patterns are identified from the infrastructure behavior data for generating behavior profiles. The generated load profiles and the generated behavior profiles are correlated to create a load responsive behavior model. The created load responsive behavior model predicts behavior of the infrastructure components for different load patterns. A live data stream from current application transactions is analyzed using the load responsive behavior model to determine current load responsive behavior. Deviations of the current load responsive behavior from the predicted behavior are detected using the load responsive behavior model.

Abstract translation: 本文公开了一种用于分析电子环境中的基础设施组件的负载响应行为的用于主动管理基础设施组件的计算机实现的方法和系统。 收集有关多个应用程序事务的事务数据。 从收集的交易数据中识别负载模式，以生成负载曲线。 收集有关应用程序事务响应的基础架构行为的数据。 基础设施行为模式从用于生成行为配置文件的基础结构行为数据中识别。 产生的负载曲线和生成的行为轮廓相关联，以创建负载响应行为模型。 创建的负载响应行为模型预测基础架构组件对不同负载模式的行为。 使用负载响应行为模型分析来自当前应用事务的实时数据流，以确定当前负载响应行为。 使用负载响应行为模型检测当前负载响应行为与预测行为的偏差。