Patents by Inventor Madhan Kumar SRINIVASAN

Madhan Kumar SRINIVASAN has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20210342197
    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.
    Type: Application
    Filed: June 15, 2020
    Publication date: November 4, 2021
    Applicant: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Kishore Kumar Gajula
  • Publication number: 20210334191
    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.
    Type: Application
    Filed: June 10, 2020
    Publication date: October 28, 2021
    Applicant: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Guruprasad PV
  • Publication number: 20210334282
    Abstract: A system includes a multi-layer throughput optimization (TPO) stack to generate a token containing prescriptions for rightsizing database service throughput.
    Type: Application
    Filed: June 9, 2020
    Publication date: October 28, 2021
    Applicant: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Guruprasad PV, Samba Sivachari Rage
  • Publication number: 20210288880
    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.
    Type: Application
    Filed: March 13, 2020
    Publication date: September 16, 2021
    Applicant: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Kishore Kumar Gajula, Samba Sivachari Rage
  • Publication number: 20210280195
    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.
    Type: Application
    Filed: March 4, 2020
    Publication date: September 9, 2021
    Inventors: Madhan Kumar SRINIVASAN, Sumanta KAYAL, Moushom BORAH, Abhijit GHOSH
  • Patent number: 11108632
    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.
    Type: Grant
    Filed: March 13, 2020
    Date of Patent: August 31, 2021
    Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITED
    Inventors: Madhan Kumar Srinivasan, Kishore Kumar Gajula, Samba Sivachari Rage
  • Publication number: 20210256066
    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.
    Type: Application
    Filed: February 19, 2020
    Publication date: August 19, 2021
    Applicant: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Guruprasad PV, Manish Sharma Kolachalam
  • Patent number: 10999212
    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.
    Type: Grant
    Filed: May 28, 2019
    Date of Patent: May 4, 2021
    Assignee: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Arun Purushothaman, Manish Sharma Kolachalam
  • Patent number: 10922141
    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.
    Type: Grant
    Filed: March 15, 2018
    Date of Patent: February 16, 2021
    Assignee: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Arun Purushothaman, Manish Sharma Kolachalam, Michael S. Eisenstein
  • Patent number: 10871917
    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.
    Type: Grant
    Filed: February 26, 2019
    Date of Patent: December 22, 2020
    Assignee: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv, Kishore Kumar Gajula
  • Publication number: 20200382437
    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.
    Type: Application
    Filed: May 28, 2019
    Publication date: December 3, 2020
    Applicant: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Arun Purushothaman, Manish Sharma Kolachalam
  • Publication number: 20200348961
    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.
    Type: Application
    Filed: July 20, 2020
    Publication date: November 5, 2020
    Applicant: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad PV, Michael S. Eisenstein, Vijay Desai
  • Patent number: 10764370
    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.
    Type: Grant
    Filed: April 30, 2018
    Date of Patent: September 1, 2020
    Assignee: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv
  • Publication number: 20200272355
    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.
    Type: Application
    Filed: February 26, 2019
    Publication date: August 27, 2020
    Applicant: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad PV, Kishore Kumar Gajula
  • Patent number: 10742567
    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.
    Type: Grant
    Filed: December 13, 2018
    Date of Patent: August 11, 2020
    Assignee: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv, Manish Sharma Kolachalam
  • Patent number: 10719360
    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.
    Type: Grant
    Filed: October 12, 2018
    Date of Patent: July 21, 2020
    Assignee: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Arun Purushothaman, Vijaya Tapaswi Achanta
  • Patent number: 10719344
    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.
    Type: Grant
    Filed: March 15, 2018
    Date of Patent: July 21, 2020
    Assignee: Acceture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv, Michael S. Eisenstein, Vijay Desai
  • Patent number: 10712958
    Abstract: A system for elastic volume type selection and optimization is provided. The system may detect that a block storage volume was provisioned by a public cloud computing platform based on a first volume type identifier of a first volume type. The system may determine, based on a normalization model, a baseline operation rate and a baseline throughput rate for the provisioned block storage volume. The system may determine, based on a selected transition mode and historical performance measurements, a simulated operation rate and a simulated throughput rate. The system may communicate, in response to the simulated throughput being greater than the baseline throughput rate or the simulated operation rate being greater than the baseline operation rate, a provisioning instruction to re-provision the provisioned block storage volume on the cloud computing platform.
    Type: Grant
    Filed: October 8, 2018
    Date of Patent: July 14, 2020
    Assignee: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv
  • Publication number: 20200210443
    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.
    Type: Application
    Filed: December 28, 2018
    Publication date: July 2, 2020
    Applicant: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Arun Purushothaman, Vijaya Tapaswi Achanta
  • Publication number: 20200195571
    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.
    Type: Application
    Filed: December 13, 2018
    Publication date: June 18, 2020
    Applicant: Accenture Global Solutions Limited
    Inventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv, Manish Sharma Kolachalam