Intuit, Inc.

Navin Kumar Jammula

Sumit Nagal

Saravanan Balasubramanian

Jesse Raymond Suen

Phanindra Padala

Sen Lin

Vijay Thomas

Chun-Che Peng

Raj Vasikarla

Adil Malik

Shreyas Badiger Mahadev

Yuxuan Zhu

Zihan Jiang

Gunjan Vora

Estela Ramirez Ramirez

Hui Luo

Suresh Vittala Gupta

Xiaotang Shao

Certain aspects of the disclosure provide techniques for predictive autoscaling. A method includes determining resource utilization metrics for a plurality of instances of a service running in a container-based cluster for a plurality of timestamps over a period of time; applying a smoothing filter to the resource utilization metrics to obtain smoothed resource utilization metrics; adjusting each of the smoothed resource utilization metrics by a nominal value; calculating a plurality of ratio metrics for the smoothed resource utilization metrics; processing, with a machine learning (ML) model trained to perform resource utilization forecasting, the plurality of ratio metrics and to predict a future ratio metric for the service after a prediction time window; determining a future resource utilization for the service after the prediction time window based on the future ratio metric; and automatically adjusting configuration parameter(s) to modify a state of the container-based cluster based on the future resource utilization.

Resource utilization forecasting for predictive autoscaling

A testing system and method for testing application code against various failure scenarios. The testing system and method generate a test workflow including test source code implementing a series of actions that affect an application component and or an infrastructure component included in application code. The testing system and method execute the test workflow to determine the performance of the application code during one or more failure scenarios caused by the series of actions included in the test workflow. Performance data generated by the test code is analyzed by a performance analysis service or method to identify limitations of the application code and build resiliency patterns that address the limitations and improve the performance of the application code.

Systems and methods for workflow based application testing in cloud computing environments

Certain embodiments of the present disclosure provide techniques for performing performance tests against services in a computing environment. The method generally includes deploying application code to an application namespace hosted on a first set of resources in the computing environment. Testing code is deployed to an infrastructure namespace hosted on a second set of resources in the computing environment. A request to test the application code is received. The request generally includes information identifying a load to be generated in testing the application code. A plurality of container instances implementing the test code are instantiated based on the identified load to be generated to test the application code. A test is executed against the application code through the instantiated plurality of container instances.

Method and system for scalable performance testing in cloud computing environments

Certain aspects of the present disclosure provide techniques for managing the deployment of software applications based on application performance data. Embodiments include transmitting, to a performance testing system, information identifying a test build of a software application component in a first deployment environment in a software development pipeline to test. Performance information for the test build of the software application component is received from the performance testing system. The received performance information for the test build of the software application component is compared to one or more of predetermined performance parameters or historical performance data for the software application component. Based on the comparing, the system takes one or more actions to manage deployment of the test build of the software application component to a second deployment environment in the software development pipeline.

Method and system for managing deployment of software application components based on software performance data

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Dublin, CA, United States of America

Navin Kumar Jammula