Abstract: A file system manager implemented at a provider network identifies a storage device of a first group of storage devices of a provider network as an initial location of a file system object. Based on an access metric associated with the object, the file system manager initiates a transfer of contents of the object to a second storage device of a different storage device group, without receiving a client request specifying the transfer. In response to an access request received via a file system programmatic interface, contents of the object are provided from the second storage device. Based on a second access metric, the object is transferred back to the first group of storage devices.

Abstract: Various aspects of the disclosure relate to automated conversion of time and/or date information from a first format to a second format and initiating computing functionality based on a successful conversion event. Mainframe computer hardware and software are provided for automatically converting differing time formats used across an enterprise computing network, automatically presenting times in multiple formats and/or translations of times across time zones for different regional computing systems of the enterprise computing network, and translating time formats based on an input and automatically pulling data records based on time zones of one or more of the involved computing systems and/or a location associated with the input source.

Abstract: Systems, computer program products, and methods are described herein for application anomaly detection using advanced computational AI machine learning modeling. In this way, performance metric data is extracted and the variances are derived by comparing the performance metrics of the application workload for the current time period against the same for a previous period. The AI model is trained at regular intervals by using the derived performance metrics data to identify only the candidate workloads which are degrading or underperforming at an early state, while avoiding reporting workloads that are not causing impact to performance stability of applications across an entity database network. The system monitors application workload across a relational database of an entity for degraded application performance and identifies changes in application workload performance and applies the anomaly detection artificial intelligence machine learning model.

Abstract: A file system manager implemented at a provider network identifies a storage device of a first group of storage devices of a provider network as an initial location of a file system object. Based on an access metric associated with the object, the file system manager initiates a transfer of contents of the object to a second storage device of a different storage device group, without receiving a client request specifying the transfer. In response to an access request received via a file system programmatic interface, contents of the object are provided from the second storage device. Based on a second access metric, the object is transferred back to the first group of storage devices.

Abstract: Systems, computer program products, and methods are described herein for determining application degradation using advanced computational models for data analysis and automated decision-making. The present disclosure is configured to receive one or more applications, wherein the one or more applications comprise one or more access paths; update the one or more access paths, wherein updating the one or more access paths comprises reconfiguring details associated with the access paths; create, in response to updating the one or more access paths, updated access paths; determine that the one or more updated access paths experience one or more application degradations; and implement one or more degradation solutions for the one or more application degradations to an artificial intelligence model.

Abstract: A file system manager implemented at a provider network identifies a storage device of a first group of storage devices of a provider network as an initial location of a file system object. Based on an access metric associated with the object, the file system manager initiates a transfer of contents of the object to a second storage device of a different storage device group, without receiving a client request specifying the transfer. In response to an access request received via a file system programmatic interface, contents of the object are provided from the second storage device. Based on a second access metric, the object is transferred back to the first group of storage devices.

Abstract: A file system manager implemented at a provider network identifies a storage device of a first group of storage devices of a provider network as an initial location of a file system object. Based on an access metric associated with the object, the file system manager initiates a transfer of contents of the object to a second storage device of a different storage device group, without receiving a client request specifying the transfer. In response to an access request received via a file system programmatic interface, contents of the object are provided from the second storage device. Based on a second access metric, the object is transferred back to the first group of storage devices.

Abstract: A file system manager implemented at a provider network identifies a storage device of a first group of storage devices of a provider network as an initial location of a file system object. Based on an access metric associated with the object, the file system manager initiates a transfer of contents of the object to a second storage device of a different storage device group, without receiving a client request specifying the transfer. In response to an access request received via a file system programmatic interface, contents of the object are provided from the second storage device. Based on a second access metric, the object is transferred back to the first group of storage devices.

Abstract: A file system manager implemented at a provider network identifies a storage device of a first group of storage devices of a provider network as an initial location of a file system object. Based on an access metric associated with the object, the file system manager initiates a transfer of contents of the object to a second storage device of a different storage device group, without receiving a client request specifying the transfer. In response to an access request received via a file system programmatic interface, contents of the object are provided from the second storage device. Based on a second access metric, the object is transferred back to the first group of storage devices.

Abstract: A recommendations manager (RM) of a file system service identifies a file system object group (FSOG) for which proactive placement recommendations are to be generated using statistical analyses. Usage metrics collected from the FSOG are used to train a model. Predictions obtained from the model are used to generate a recommendation to transfer a file system object proactively from one storage device group of the service to another. The recommendations are provided to an object migrator of the service to initiate the transfer.

Abstract: A plurality of computing resources are provisioned for a plurality of computing system elements based at least in part on input specification information, whereby the input specification specifies the plurality of computing resources. The plurality of computing resources include one or more computing resources of a first computing system element of the plurality of computing system elements that are provided for a second computing system element of the plurality of computing system elements. The plurality of computing system elements are evaluated to identify whether the plurality of computing system elements are successfully executed and a report is generated based at least in part on the evaluation.

Abstract: A method of enhancing an incomplete cloud infrastructure template may include receiving a template defining a cloud infrastructure stack associated with a customer account, wherein the template comprises at least one resource definition specifying a first cloud infrastructure resource. The method may further include performing a dependency check of the template. The method may further include identifying, in the resource definition, an unresolved reference to a second cloud infrastructure resource. The method may further include resolving the reference by producing, based on a plurality of templates associated with the customer account, a definition of the second cloud infrastructure resource. The method may further include modifying the template to include the definition of the second cloud infrastructure resource and causing the cloud infrastructure stack to be provisioned based on the template.

Abstract: Technology for capturing a configuration of an infrastructure to enable replication of the infrastructure is provided. In one example, a layout agent may be provided for deployment to an infrastructure, including a server, together with instructions for the layout agent to capture server layout data for the server and to communicate the server layout data to a replication application. The server layout data for the server may be received at the replication application. A system model for the infrastructure may be generated based on the server layout data from the layout agent. The replication of the infrastructure as a replicated infrastructure may be initiated based on the system model.

Abstract: A recommendations manager (RM) of a file system service identifies a file system object group (FSOG) for which proactive placement recommendations are to be generated using statistical analyses. Usage metrics collected from the FSOG are used to train a model. Predictions obtained from the model are used to generate a recommendation to transfer a file system object proactively from one storage device group of the service to another. The recommendations are provided to an object migrator of the service to initiate the transfer.

Abstract: A file system manager implemented at a provider network identifies a storage device of a first group of storage devices of a provider network as an initial location of a file system object. Based on an access metric associated with the object, the file system manager initiates a transfer of contents of the object to a second storage device of a different storage device group, without receiving a client request specifying the transfer. In response to an access request received via a file system programmatic interface, contents of the object are provided from the second storage device. Based on a second access metric, the object is transferred back to the first group of storage devices.

Abstract: A recommendations manager (RM) of a file system service identifies a file system object group (FSOG) for which proactive placement recommendations are to be generated using statistical analyzes. Usage metrics collected from the FSOG are used to train a model. Predictions obtained from the model are used to generate a recommendation to transfer a file system object proactively from one storage device group of the service to another. The recommendations are provided to an object migrator of the service to initiate the transfer.

Abstract: A file system manager implemented at a provider network identifies a storage device of a first group of storage devices of a provider network as an initial location of a file system object. Based on an access metric associated with the object, the file system manager initiates a transfer of contents of the object to a second storage device of a different storage device group, without receiving a client request specifying the transfer. In response to an access request received via a file system programmatic interface, contents of the object are provided from the second storage device. Based on a second access metric, the object is transferred back to the first group of storage devices.

Abstract: Technology for detecting processor usage of a physical host hosting computing instances is provided. In one example, a method may include identifying a location of a hardware register of a physical host containing data in a model specific register. The physical host hosts a plurality of computing instances using a hypervisor. The method may include determining power consumption by a processor of the physical host for the computing instances from the data. An upper level of acceptable processor power consumption may be identified. Processor usage may be determined based on the power consumption determined and the upper level of acceptable processor power consumption. The processor usage may be provided as data through the hypervisor.

Abstract: A file system manager implemented at a provider network identifies a storage device of a first group of storage devices of a provider network as an initial location of a file system object. Based on an access metric associated with the object, the file system manager initiates a transfer of contents of the object to a second storage device of a different storage device group, without receiving a client request specifying the transfer. In response to an access request received via a file system programmatic interface, contents of the object are provided from the second storage device. Based on a second access metric, the object is transferred back to the first group of storage devices.

Abstract: A file system manager implemented at a provider network identifies a storage device of a first group of storage devices of a provider network as an initial location of a file system object. Based on an access metric associated with the object, the file system manager initiates a transfer of contents of the object to a second storage device of a different storage device group, without receiving a client request specifying the transfer. In response to an access request received via a file system programmatic interface, contents of the object are provided from the second storage device. Based on a second access metric, the object is transferred back to the first group of storage devices.