Accenture Global Solutions Limited

Madhan Kumar Srinivasan

Guruprasad Pv

Arun Purushothaman

Manish Sharma Kolachalam

Samba Sivachari Rage

Kishore Kumar Gajula

Michael S Eisenstein

Vijay Desai

Vijaya Tapaswi Achanta

Abhijit Ghosh

Sumanta Kayal

Moushom Borah

Ashish Pant

Abdul Hammed Shaik

Arun Krishnamurthy

Guruprasad P V

Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for allocating computation resources for a plurality of databases. For each database, the system identifies a respective initial computation capacity tier for the respective database based at least on the respective utilization of the respective database. For each of a set of optimization orders, the system determines a respective set of candidate resource pools for accommodating the plurality of databases. The system selects an optimization order and determines a final set of resource pools for the plurality of databases. The system outputs data specifying the final set of resource pools.

Computational resource allocation advisor for elastic cloud databases

Embodiments of this disclosure disclose a method and system for optimizing resources for cloud-based scalable distributed search data analytics service. The method may include generating a computing capacity rightsizing recommendation on the computing capacity based on the computing utilization metrics and generating a memory capacity rightsizing recommendation on the memory capacity based on the memory utilization metrics. The method may further include determining a recommended instance type for the service resource unit based on the computing capacity rightsizing recommendation and the memory capacity rightsizing recommendation. The method may further include performing a storage volume check on the service resource unit to obtain a storage volume check result. The method may further include determining whether to accept the recommended instance type as a final optimization recommendation based on the storage volume check result, the computing capacity rightsizing recommendation, and the memory capacity rightsizing recommendation.

System for optimizing resources for cloud-based scalable distributed search data analytics service

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.

Systems and methods for multi machine learning based predictive analysis

A method and system for cloud based service provider performance evaluation are disclosed. The method may include extracting service record data and ticket status change record data from the service ticket data and aggregating the service record data and the ticket status change record data. The method may include calculating ticket level performance metric data based on the aggregated record data and generating ticket level performance scores based on the ticket level performance metric data. The method may further include generating service level performance scores based on the service level performance metric data and generating service feedback performance scores based on the service feedback metric data. The method may further include merging the ticket level performance scores, the service level performance scores, and the service feedback performance scores to generate performance vectors and evaluating overall performances of the service providers based on the set of performance vectors.

Method and system for machine learning based service performance intelligence

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.

Performance-centric cloud database virtual core sizing stack

A system includes a multi-layer throughput optimization (TPO) stack to generate a token containing prescriptions for rightsizing database service throughput. A prescriptive engine layer of the TPO stack may obtain consumed throughputs from the utilization tracking data; predict, based on the consumed throughputs, a prediction-based throughput for the target NoSQL database service; select target consumed throughputs from the consumed throughputs based on a time granularity; determine a statistical throughput consumption value from the target consumed throughputs; calculate a statistics-based throughput over a TPO interval based on the statistical throughput consumption value; extract database service performance metric data from the utilization tracking data; select target performance metric data from the database service performance metric data based on the time granularity; determine a statistical performance metric value from the target performance metric data; select one of the prediction-based throughput and the statistics-based throughput as based on the statistical performance metric value.

Prescriptive analytics based NoSQL database service optimization system for cloud computing

A multi-layer elastic requisition stack may generate pool requisition tokens for controlling requisition of pooled database-compute resources. The elastic requisition stack may determine candidate databases for inclusion in elastic pools by analyzing historical utilization data and generating predicted utilization data. Based on the historical and predicted utilization data, the elastic requisition stack may determine multiplexing characteristics for the candidate databases and complement factors among the databases. The elastic requisition stack may compare unpooled database performance to pooled database performance to determine whether to pool the candidate databases.

Prescriptive analytics based multi-tier elastic-pool database requisition stack for cloud computing

A multi-layer serverless sizing stack may determine a compute sizing correction for a serverless function. The serverless sizing stack may analyze historical data to determine a base compute allocation and compute buffer range. The serverless sizing stack may traverse the compute buffer range in an iterative analysis to determine a compute size for the serverless function to support efficient computational-operation when the serverless function is instantiated.

Prescriptive analytics-based performance-centric dynamic serverless sizing

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.

Infrastructure automation platform to assist in performing actions in response to tasks

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.

Cloud-based database-less serverless framework using data foundation

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. The pre-prescriptive analysis layer may apply a filter metric to mark the preservable computing resource and the correctable computing resource for exclusion from prescriptive utilization processing and initiate the prescriptive utilization processing on the set of computing resources without computing resources marked for exclusion.

Consumption unit estimation analytics for prescribing cloud computing resources utilization

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. The system may determine a recommended capacity throughput based on the recommended capacity mode selected and the target processing capacity utilization value.

NOSQL database capacity configuration optimization system for cloud computing

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.

Prescriptive analytics based compute sizing correction stack for cloud computing resource scheduling

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. The system may further obtain historical resource utilization data for the block storage volume; generate historical resource utilization metrics from the historical resource utilization data; determine a resource configuration baseline based on the historical resource utilization metrics; and select a recommended resource configuration from a plurality of resource configurations based on the first resource configuration baseline.

System of selecting and optimizing resources for cloud block storage volume

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.

Growing community of inventors

Bengaluru, India

Madhan Kumar Srinivasan