Accenture Global Solutions Limited

Samba Sivachari Rage

Madhan Kumar Srinivasan

Guruprasad Pv

Kishore Kumar Gajula

Abdul Hammed Shaik

Arun Krishnamurthy

Ashish Pant

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 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 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 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.

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Bengaluru, India

Samba Sivachari Rage