Abstract: Implementations described herein relate to methods, systems, and computer-readable media to synchronize configuration settings. In some implementations, a method may include performing an optimization of one or more computing resources in a distributed computing system, determining, based on the optimization, that a setting of at least one computing resource is to be adjusted, determining that performing an adjustment to the at least one computing resource would cause a mismatch between a setting for the at least one computing resource in the distributed computing system and a corresponding setting stored in a version control repository for the at least one computing resource, generating an updated configuration file, wherein the update configuration file is indicative of an adjusted setting for the at least one computing resource, and transmitting a request to a version control system to add the updated configuration file to the version control repository.

Abstract: A vehicle including: a first row of one or more first-type storage devices, each first-type storage device including a first carrier configured to be moved outwards from a first stowed position to a first deployed position substantially outside the footprint of the vehicle; a second row of one or more second-type storage devices located vertically above the first row of first-type storage devices, wherein each of the one or more second-type storage devices comprises a second carrier configured to be moved outwards from a second stowed position to a second deployed position substantially outside the footprint of the vehicle; wherein the spatial volume occupied by one of the second carriers when in the second deployed position occupies at least a portion of the spatial volume occupied by one of the first carriers when in the first deployed position.

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: This disclosure relates to a system having a first transport device including one of a guide and/or drive connected to a surface of a second transport device, having another guide and/or drive. The drive is configured to interact with the guide to effect movement of one of the first or second transport device relative to the other of the first or second transport device. The guide is configured to change between an active state, in which it is securely engaged with the drive means to effect movement of one of the first or second transport device, and a passive state in which the drive is not engaged, allowing the first and second transport devices to be separated.

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 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: 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 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 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 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 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: The present invention relates to a modular energy absorption device (9) comprising: a support (91) having fastening portions (81) forming single fastening points for fastening said device to a steering column, a deforming member (94) securely connected to the support, an absorber (93) coiled around the deforming member and fitted securely at each of its ends to the support, a coupling member (96) formed of a part separate from the support and having a first side securely connected to the absorber and a second side having at least a first engaging shape (96a), said device being designed such that the exertion of forces in opposite directions on the support and on the tooth, respectively, brings about stress on the coiled portion of the absorber, tending to deform the latter.