Sedai Inc.

Hari Chandrasekhar

Suresh Mathew

Nikhil Gopinath Kurup

Benjamin Thomas

Aby Jacob

S Meenakshi

Mathew Koshy Karunattu

Sayanth S

Rajat Usha Krishna

Akash Vijayan

Adharsh Sabukumar

Vaibhav Desai

Implementations described herein relate to methods, systems, and computer-readable media to provide an alert based on a release of a software application implemented in a distributed computing system. In some implementations, the method includes receiving, at a processor, an indication of the release of the software application, obtaining a first set of metric values for each metric of a list of metrics for a first time period preceding a time of release of the release, obtaining a second set of metric values for each metric of the list of metrics for a second time period following the time of release, comparing the first set of metric values to the second set of metric values to determine a deviation score, generating an alert based on the deviation score, and transmitting the alert via one of a user interface and a communication channel.

Autonomous release management in distributed computing systems

Implementations described herein relate to methods, systems, and computer-readable media to manage a computing resource allocation for a software application. In some implementations, a method may include executing a first test function using the distributed computing system at a first plurality of allocation setpoints for the computing resource, based on the execution, obtaining one or more performance metrics for the first test function for each setpoint of the first plurality of allocation setpoints, training a machine learning model based on the obtained one or more performance metrics; and utilizing the trained machine learning model to manage the computing resource for a second function.

Autonomous application management for distributed computing systems

Implementations described herein relate to methods, systems, and computer-readable media to monitor a distributed computing system. In some implementations, a method may include obtaining a first plurality of monitoring metrics of a respective application of one or more applications executing over the distributed computing system, obtaining time-series data of the first plurality of monitoring metrics, programmatically analyzing the time-series data of the first plurality of monitoring metrics to determine a second plurality of monitoring metrics, wherein the second plurality of monitoring metrics is determined to be a predictive set of the first plurality of monitoring metrics, and monitoring the distributed computing system by monitoring the second plurality of monitoring metrics.

Autonomous management of computing systems

Implementations described herein relate to run-time management of a serverless function in a serverless computing environment. In some implementations, a method includes receiving, at a processor, based on historical run-time invocation data for the serverless function in the serverless computing environment, a first number of expected invocations of the serverless function for a first time period, determining, by the processor, based on the first number of expected invocations of the serverless function for the first time period, a second number of warm-up invocation calls to be made for the first time period, and periodically invoking the second number of instances of an extended version of the serverless function during the first time period, wherein the extended version of the serverless function is configured to load and initialize the serverless function and terminate without executing the serverless function.

Autonomous concurrency for serverless functions

Implementations described herein relate to methods, systems, and computer-readable media to identify anomalous applications. In some implementations, the methods may include obtaining application metric data that includes application level metrics and instance level metrics for each instance of a plurality of instances associated with a respective application of a plurality of applications operating over a distributed computing system, generating a first anomaly detection score based on the instance level metrics; generating a second anomaly detection score based on one or more input metrics associated with the respective application, generating a third anomaly detection score based on seasonal metric data associated with the respective application and identifying at least one application of the plurality of applications as an anomalous application based on the first anomaly detection score, the second anomaly detection score, and the third anomaly detection score.

Cloud management system with autonomous aberrant behavior detection

Implementations described herein relate to methods, systems, and computer-readable media to manage a computing resource allocation for a software application. In some implementations, a method may include receiving first metric data associated with the software application, determining, based on the first metric data, that an allocation of a computing resource for the software application is to be reduced from a first level of allocation, increasing allocation of the computing resource to a second level of allocation greater than the first level of allocation; executing the software application on the distributed computing system at the second level of allocation, obtaining second metric data based on execution of the software application at the second level of allocation, and reducing allocation to a third level that is lower than the first level based on a determination that the second metric data does not indicate a performance improvement for the software application.

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Highlands Ranch, CO, United States of America

Hari Chandrasekhar