Abstract: A system includes a multi-layer block storage volume optimization (BSO) stack to generate a BSO token containing prescriptions to optimize block storage volume. The system may receive account information of storage accounts associated with block storage volumes; obtain respective storage regions and respective data redundancy types of the first storage account and the second storage account from the first account information; and generate the BSO token to include instructions to merge the storage accounts according to the respective storage regions and the respective data redundancy types.

Abstract: A system includes a multi-layer block storage volume optimization (BSO) stack to generate a BSO token containing prescriptions to optimize block storage volume. The system may receive account information of storage accounts associated with block storage volumes; obtain respective storage regions and respective data redundancy types of the first storage account and the second storage account from the first account information; and generate the BSO token to include instructions to merge the storage accounts according to the respective storage regions and the respective data redundancy types.

Abstract: A multi-layer tier requisition stack may generate prescriptive tier requisition tokens for controlling requisition of database-compute resources at database-compute tiers. The input layer of the tier requisition stack may obtain historical data and database-compute tolerance data. The coefficient layer may be used to determine activity coefficients for each data type within the historical data. The activity coefficients may then be combined to determine an overall activity factor. The tolerance layer may be used to select an initial database-compute tier based on the activity factor. The tolerance layer may then increase from the initial database compute tier to an adjusted database-compute tier while accommodating tolerances within the database-compute tolerance data. The requisition layer may generate a tier requisition token based on the adjusted database-compute tier and/or finalization directives obtained at the requisition layer.

Abstract: A system includes a multi-layer capacity configuration optimization (CCO) stack to generate a token containing prescriptions for optimize capacity configuration of a database container in a NoSQL database cloud service. The system may aggregate the capacity utilization data; predict, based on the aggregated capacity utilization data, respective prediction-based processing capacity utilizations for the database container; determine a target processing capacity utilization value from the prediction-based processing capacity utilizations; calculate respective provisioned processing capacity utilizations based on the target processing capacity utilization value; evaluate a consumption metric based on the prediction-based processing capacity utilizations and the provisioned processing capacity utilizations; select one of the predetermined capacity modes as a recommended capacity mode for the database container based on the consumption metric.

Abstract: A system includes a multi-layer throughput optimization (TPO) stack to generate a token containing prescriptions for rightsizing database service throughput.

Abstract: A multi-layer tier requisition stack may generate prescriptive tier requisition tokens for controlling requisition of database-compute resources at database-compute tiers. The input layer of the tier requisition stack may obtain historical data and database-compute tolerance data. The coefficient layer may be used to determine activity coefficients for each data type within the historical data. The activity coefficients may then be combined to determine an overall activity factor. The tolerance layer may be used to select an initial database-compute tier based on the activity factor. The tolerance layer may then increase from the initial database compute tier to an adjusted database-compute tier while accommodating tolerances within the database-compute tolerance data. The requisition layer may generate a tier requisition token based on the adjusted database-compute tier and/or finalization directives obtained at the requisition layer.

Abstract: A multi-layer cluster node optimization (CNO) stack may generate a token containing cluster node optimization prescriptions for detaching nodes from a storage cluster. A prescriptive engine layer of the CNO stack may select target computing resource nodes from a selected cluster based on the utilization tracking data, the optimization metric thresholds, and the CNO interval; utilize a prediction engine to predict respective storage utilizations over a next operation cycle for the nodes of the selected cluster; generate an aggregated storage utilization prediction for the selected cluster based on the predicted storage utilizations; determine a network traffic coefficient for the selected cluster based on the network traffic data; perform a cluster determination whether to execute a cluster node optimization for the selected cluster based on the aggregated storage utilization prediction and the network traffic coefficient; and generate a CNO token based on the cluster determination.

Abstract: A device may receive user personalized data and user activity data identifying tasks and actions performed by a user, and may perform natural language processing on the user personalized data and the user activity data to generate processed textual data. The device may train machine learning models based on the processed textual data to generate trained machine learning models, and may receive, from a client device, a command identifying a particular task to be performed. The device may process the command and the user activity data, with the trained machine learning models, to determine whether a particular action in the user activity data correlates with the particular task. The device may perform actions when the particular action correlates with the particular task.

Abstract: A mufti-layer duster node optimization (CNO) stack may generate a token containing cluster node optimization prescriptions for detaching nodes from a storage cluster. A prescriptive engine layer of the ONO stack may select target computing resource nodes from a selected cluster based on the utilization tracking data, the optimization metric thresholds, and the ONO interval; utilize a prediction engine to predict respective storage utilizations over a next operation cycle for the nodes of the selected cluster; generate an aggregated storage utilization prediction for the selected cluster based on the predicted storage utilizations; determine a network traffic coefficient for the selected cluster based on the network traffic data; perform a cluster determination whether to execute a cluster node optimization for the selected cluster based on the aggregated storage utilization prediction and the network traffic coefficient; and generate a CNO token based on the cluster determination.

Abstract: A multi-layer consumption unit estimation (CUE) stack may generate a consumption preview for prescribing cloud computing resource utilization. An input layer of the CUE stack may obtain computing resource utilization tracking data, consumption metric data, application execution tracking data, and computing resource reservation data for a set of computing resources. A configuration layer of the CUE stack may determine a CUE interval and determine consumption metric modifiers for a selected identity associated with the set of computing resources. A CUE engine layer may generate a consumption preview by advancing a dynamic consumption credit input/output flow analysis and executing a direct utilization consumption determination.

Abstract: A multi-layer resource aggregation (RA) stack may generate prescriptive activation timetables for controlling activation states for computing resources. To facilitate operator control and adjustment, the RA stack may, at an aggregation engine layer, aggregate the computing resource into one or more resource aggregates. The computing resources within the resource aggregates may have similar individual activation prescription patterns. Machine learning techniques may be used by the RA stack to identify these related individual activation prescription patterns and aggregate the computing resources accordingly. Once aggregated, the RA stack may make a uniform activation determination for the aggregates as single units. Therefore, the computing resources within the aggregate may be controlled and/or adjust together. Thus, the RA stack increases the scalability of implementation of prescriptive computing resource activation state determinations.

Abstract: A multi-layer committed compute reservation stack may generate prescriptive reservation matrices for controlling static reservation for computing resources. A transformation layer of the committed compute reservation stack may generate a time-mapping based on historical utilization and tagging data. An iterative analysis layer may determine a consumption-constrained committed compute state of a distribution of static reservation and dynamic requisition that achieves one or more consumption efficiency goals. Once the consumption-constrained committed compute state is determined, the prescriptive engine layer may generate a reservation matrix that may be used to control computing resource static reservation prescriptively.

Abstract: A multi-layer rate sizing stack may generate prescriptive operation-rate tokens for controlling sizing adjustments for operation-rates. The input layer of the rate sizing stack may generate operation pattern data based on operation-rate data received via network connection. A prescriptive engine layer may determine a prescriptive allowed operation-rate based on the operation pattern data. Based on the prescriptive allowed operation-rate, the prescriptive engine layer may generate the operation-rate tokens that that may be used to control rate sizing adjustments prescriptively.

Abstract: A multi-layer resource aggregation (RA) stack may generate prescriptive activation timetables for controlling activation states for computing resources. To facilitate operator control and adjustment, the RA stack may, at an aggregation engine layer, aggregate the computing resource into one or more resource aggregates. The computing resources within the resource aggregates may have similar individual activation prescription patterns. Machine learning techniques may be used by the RA stack to identify these related individual activation prescription patterns and aggregate the computing resources accordingly. Once aggregated, the RA stack may make a uniform activation determination for the aggregates as single units. Therefore, the computing resources within the aggregate may be controlled and/or adjust together. Thus, the RA stack increases the scalability of implementation of prescriptive computing resource activation state determinations.

Abstract: A multi-layer compute sizing correction stack may generate prescriptive compute sizing correction tokens for controlling sizing adjustments for computing resources. The input layer of the compute sizing correction stack may generate cleansed utilization data based on historical utilization data received via network connection. A prescriptive engine layer may generate a compute sizing correction trajectory detailing adjustments to sizing for the computing resources. Based on the compute sizing correction trajectory, the prescriptive engine layer may generate the compute sizing correction tokens that that may be used to control compute sizing adjustments prescriptively.

Abstract: A cloud migration tool manages and monitors a cloud migration project that migrates data from a legacy environment to a target data center environment. The cloud migration tool includes an analytics engine that applies data regression models to generate a delay risk prediction for activities that are scheduled during the cloud migration project.

Abstract: A multi-layer rate sizing stack may generate prescriptive operation-rate tokens for controlling sizing adjustments for operation-rates. The input layer of the rate sizing stack may generate operation pattern data based on operation-rate data received via network connection. A prescriptive engine layer may determine a prescriptive allowed operation-rate based on the operation pattern data. Based on the prescriptive allowed operation-rate, the prescriptive engine layer may generate the operation-rate tokens that that may be used to control rate sizing adjustments prescriptively.

Abstract: A multi-layer storage class placement stack may generate a token containing storage class placement prescriptions for controlling the placement of stored items within a selection of classes for storage. An input layer of the storage class placement stack may generate time-collated activity data based on historical access data, volume metric data, and/or tagging data. The time-collated activity data may include data groupings using timestamps or other timing indicators. A transformation layer may further process the time-collated activity data to generate defined-period summation data that provides summary detail for defined durations across a period of analysis. The defined-period summation data may be used by a prescriptive engine layer to generate prescriptions for placement of individual stored items by associating the prescriptions with storage identifiers for the individual items.

Abstract: A system may support distributed multiple tier multi-node serverless analytics task execution. At a data ingestion tier, data ingestion serverless tasks may receive detail data for analytic processing. data integration serverless tasks, executing at a data integration and consolidation tier and initiated by the data ingestion serverless tasks, may sort the detail data and identify patterns within the detail data to generate grouped pre-processed data. The data integration serverless tasks may initiate partitioning serverless tasks which may divide the grouped pre-processed data into data chunks. Multi-node analytic serverless tasks at an analytic tier, at least some of which being initiated by the partitioning serverless tasks, may analyze the data chunks and generate prescriptive outputs.

Abstract: A multi-layer compute sizing correction stack may generate prescriptive compute sizing correction tokens for controlling sizing adjustments for computing resources. The input layer of the compute sizing correction stack may generate cleansed utilization data based on historical utilization data received via network connection. A prescriptive engine layer may generate a compute sizing correction trajectory detailing adjustments to sizing for the computing resources. Based on the compute sizing correction trajectory, the prescriptive engine layer may generate the compute sizing correction tokens that that may be used to control compute sizing adjustments prescriptively.