Abstract: A multi-layer database sizing stack may generate prescriptive tier requisition tokens for controlling requisition of database-compute resources at database-compute tiers. The input layer of the database sizing stack may obtain historical data. The threading layer may be used to flag occurrences of single threading application execution. The change layer may be used to determine potential for a step based on compute utilization type data and assert flags indicating the potential. The step layer may determine if potential steps may be taken based on operation-rate type data and flush type data. The requisition layer may generate a tier requisition token based on the provisional requisition tokens generated at other layers and/or finalization directives obtained at the requisition layer.

Abstract: A saltwater corrosion resistant hybrid composite is provided. The saltwater corrosion resistant hybrid composite coating includes at least one conductive polymer, crumb rubber, and a cured epoxy. The conductive polymer is dispersed in particles of the crumb rubber to form a network. The network is dispersed in the cured epoxy to form the saltwater corrosion resistant hybrid composite coating. A method of making of the saltwater corrosion resistant hybrid composite is also provided. A metal when coated with the resistant hybrid composite of the present disclosure is resistant to salt-water corrosion.

Abstract: Embodiments of this disclosure include a method and system for machine learning based evaluation of user experience on information technology (IT) support service. The method may include obtaining a field data of an IT support service ticket and obtaining a multi-score prediction engine. The method may further include predicting metric scores of a plurality of IT support service metrics for the support service ticket based on the field data by executing the multi-score prediction engine. The method may further include obtaining system-defined weights and user-defined weights for the plurality of service metrics and calculating a support service score for the support service ticket based on the metric scores, the system-defined weights, and the user-defined weights. The method may further include evaluating user experience based on the support service score.

Abstract: A biocompatible polymer hybrid nanocomposite coating on a surface of a substrate, such as titanium and its alloys. The coating can be achieved by an electrostatic spray coating, preferably using ultra-high molecular weight polyethylene (UHMWPE) as a matrix for the coating. For example, up to 2.95 wt. % carbon nanotubes can be used as reinforcement, as can up to 4.95 wt. % hydroxyapatite. A dispersion of CNTs and HA in the coating is substantially uniform. The tribological performance of such coatings include high hardness, improved scratch resistance, excellent wear resistance, and corrosion resistance compared to pure UHMWPE coatings.

Abstract: The present disclosure relates to systems and methods for carrying out predictive analysis where a plurality of data sets may be ingested from a data lake. A data analyzer may tag the ingested data sets, detect redundant occurrence of multiple attributes such as, a row, a column, and a list in the tagged data set. The data analyzer may eliminate the detected redundant multiple attributes. Further, a model selector and evaluator may execute a machine learning (ML) model to conduct predictive analysis on the data set. The execution may be done based on a predefined set of instructions stored in a database. The executed ML model may be validated upon determining that the predictive analysis yields a positive response for the transformed data set.

Abstract: A system may support multiple tier serverless data foundation creation to support large data set processing. At a data ingestion tier, data ingestion serverless tasks may receive source data for processing. The data integration serverless tasks may filter and group the source data into file-object stored items. Further, data integration serverless tasks may capture metadata that, when paired with the file-object stored items, establishes the data foundation. The data foundation facilitates database-like performance in data operations in a database-less system. At the processing tier, the processing serverless tasks access the data foundation by iterating across the file-object stored items to generate output-object stored items. At the directed storage tier, directed storage serverless tasks capture metadata for the output-object stored items to establish an output data foundation or prepare the output data for storage in a data warehouse.

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 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 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 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.