Abstract: There are provided systems and methods for a distributed autonomous patching system. During patching of a cloud computing architecture, an autonomous patching system may operator autonomously with no to minimal operator input to patch the host machines and corresponding computes of the cloud computing architecture's available applications. This may work by receiving a patch and determining corresponding patching factors for an availability zone of computes in the cloud. The system may then determine a patching topology map having an order of patching nodes for the selected computes of the application in the availability zone. Thereafter, while monitoring those computes, the system may select certain computes into an active processing funnel, where the patch is applied to those computes. Fingerprinting of before and after-patch states may be used to ensure proper patching.

Abstract: 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.

Abstract: There are provided systems and methods for a distributed autonomous patching system. During patching of a cloud computing architecture, an autonomous patching system may operator autonomously with no to minimal operator input to patch the host machines and corresponding computes of the cloud computing architecture's available applications. This may work by receiving a patch and determining corresponding patching factors for an availability zone of computes in the cloud. The system may then determine a patching topology map having an order of patching nodes for the selected computes of the application in the availability zone. Thereafter, while monitoring those computes, the system may select certain computes into an active processing funnel, where the patch is applied to those computes. Fingerprinting of before and after-patch states may be used to ensure proper patching.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: There are provided systems and methods for a distributed autonomous patching system. During patching of a cloud computing architecture, an autonomous patching system may operator autonomously with no to minimal operator input to patch the host machines and corresponding computes of the cloud computing architecture's available applications. This may work by receiving a patch and determining corresponding patching factors for an availability zone of computes in the cloud. The system may then determine a patching topology map having an order of patching nodes for the selected computes of the application in the availability zone. Thereafter, while monitoring those computes, the system may select certain computes into an active processing funnel, where the patch is applied to those computes. Fingerprinting of before and after-patch states may be used to ensure proper patching.

Abstract: 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.

Abstract: 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 executing on a distributed computing system; 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; performing a mitigative check to determine a performance degradation likelihood score and reducing allocation of the computing resource to a third level that is lower than the first level based on a determination that the performance degradation likelihood score does not meet a threshold score.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Aspects of the present disclosure involve a system and method for autoscaling application pools. The current disclosure presents a system that can be used to understand the throughput of one instance in an application. The throughput at the one instance in the application and/or traffic trend is used to right-size or auto-scale the application pool for the current traffic trend such that the application pool can be adjusted for varying traffic trends.

Abstract: 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.

Abstract: There are provided systems and methods for a distributed autonomous patching system. During patching of a cloud computing architecture, an autonomous patching system may operator autonomously with no to minimal operator input to patch the host machines and corresponding computes of the cloud computing architecture's available applications. This may work by receiving a patch and determining corresponding patching factors for an availability zone of computes in the cloud. The system may then determine a patching topology map having an order of patching nodes for the selected computes of the application in the availability zone. Thereafter, while monitoring those computes, the system may select certain computes into an active processing funnel, where the patch is applied to those computes. Fingerprinting of before and after-patch states may be used to ensure proper patching.

Abstract: There are provided systems and methods for a distributed autonomous patching system. During patching of a cloud computing architecture, an autonomous patching system may operator autonomously with no to minimal operator input to patch the host machines and corresponding computes of the cloud computing architecture's available applications. This may work by receiving a patch and determining corresponding patching factors for an availability zone of computes in the cloud. The system may then determine a patching topology map having an order of patching nodes for the selected computes of the application in the availability zone. Thereafter, while monitoring those computes, the system may select certain computes into an active processing funnel, where the patch is applied to those computes. Fingerprinting of before and after-patch states may be used to ensure proper patching.