Accenture Global Solutions Limited

Sekhar A Badugu

Jagaran Das

Jayanti Vemulapati

Lisa Suzanne Wilson

Chethana Savalgi

Murtuza Chitalwala

Anuruddha S Khastgir

Prudhvi Laxmi Narayana Chelluri

Tri Phi Minh Nguyen

Prasun Chandra

This document describes a framework for measuring and improving the performance of applications, such as distributed applications and web applications. In one aspect, a method includes performing a test on an application. The test includes executing the application on one or more computers and, while executing the application, simulating a set of workload scenarios for which performance of the application is measured during the test. While performing the test, a set of performance metrics that indicate performance of individual components involved in executing the application during the test is obtained. A knowledge graph is queried using the set of performance metrics. The knowledge graph links the individual components to corresponding performance metrics and defines a set of hotspot conditions that are each based on one or more of the corresponding performance metrics for the individual components. A given hotspot condition is detected based on the set of performance metrics.

Enhanced application performance framework

A system access a session profile. The session profile may include log source identifiers and model identifiers. The system may deploy a log projection session based on the session profile. The system may receive, in response to deployment of the log projection session, a log stream from a log source corresponding to at least one of the log identifiers. The system may generate a log projection stream based on the log stream and an initial machine-learning model. The system may calibrate the session profile and select an alternative machine-learning model based on model performance metrics. The system may redeploy the log projection session based on the calibrated session profile. The system may automatically scale computer resources for improved job performance based on forecasted log information derived from the selected machine-learning model.

Calibratable log projection and error remediation system

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Bengaluru, India

Sekhar A Badugu