Patents by Inventor Arun Purushothaman
Arun Purushothaman 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).
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Patent number: 11334590Abstract: 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: GrantFiled: December 28, 2018Date of Patent: May 17, 2022Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Vijaya Tapaswi Achanta
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Prescriptive analytics based compute sizing correction stack for cloud computing resource scheduling
Patent number: 11314542Abstract: 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: GrantFiled: July 20, 2020Date of Patent: April 26, 2022Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad PV, Michael S. Eisenstein, Vijay Desai -
Publication number: 20220027744Abstract: A resource data modeling, forecasting, and simulation system analyzes data pertaining to the data processing tasks and the resources assigned to the data processing tasks to generate short-term forecasts and long-term forecasts of task volumes. The forecasted task volumes are further optimized based on different factors to determine the resources required to handle the forecasted task volume. Various simulations of hypothetical what-if scenarios are also generated based on the forecasts and the resource requirements. The resource data modeling, forecasting and simulation system is based on multi-algorithmic ensemble models for forecasting, automated model selection and the unique simulation methodology based on multiple parameters.Type: ApplicationFiled: July 22, 2020Publication date: January 27, 2022Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Mythili KRISHNAN, Dnyaneshwar AMBHORE, Kunal BHOWMICK, Lalitha SUNDAR, Vinita NAIR, Arun PURUSHOTHAMAN, Anand JANARDHANAN, Bhavana RAO
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Patent number: 10999212Abstract: 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: GrantFiled: May 28, 2019Date of Patent: May 4, 2021Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Manish Sharma Kolachalam
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Patent number: 10922141Abstract: 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: GrantFiled: March 15, 2018Date of Patent: February 16, 2021Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Manish Sharma Kolachalam, Michael S. Eisenstein
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Patent number: 10871917Abstract: 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: GrantFiled: February 26, 2019Date of Patent: December 22, 2020Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv, Kishore Kumar Gajula
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Publication number: 20200382437Abstract: 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: ApplicationFiled: May 28, 2019Publication date: December 3, 2020Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Manish Sharma Kolachalam
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PRESCRIPTIVE ANALYTICS BASED COMPUTE SIZING CORRECTION STACK FOR CLOUD COMPUTING RESOURCE SCHEDULING
Publication number: 20200348961Abstract: 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: ApplicationFiled: July 20, 2020Publication date: November 5, 2020Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad PV, Michael S. Eisenstein, Vijay Desai -
Patent number: 10764370Abstract: 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: GrantFiled: April 30, 2018Date of Patent: September 1, 2020Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv
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Publication number: 20200272355Abstract: 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: ApplicationFiled: February 26, 2019Publication date: August 27, 2020Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad PV, Kishore Kumar Gajula
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Patent number: 10742567Abstract: 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: GrantFiled: December 13, 2018Date of Patent: August 11, 2020Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv, Manish Sharma Kolachalam
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Prescriptive analytics based compute sizing correction stack for cloud computing resource scheduling
Patent number: 10719344Abstract: 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: GrantFiled: March 15, 2018Date of Patent: July 21, 2020Assignee: Acceture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv, Michael S. Eisenstein, Vijay Desai -
Patent number: 10719360Abstract: 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: GrantFiled: October 12, 2018Date of Patent: July 21, 2020Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Vijaya Tapaswi Achanta
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Patent number: 10712958Abstract: 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: GrantFiled: October 8, 2018Date of Patent: July 14, 2020Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv
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Publication number: 20200210443Abstract: 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: ApplicationFiled: December 28, 2018Publication date: July 2, 2020Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Vijaya Tapaswi Achanta
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Publication number: 20200195571Abstract: 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: ApplicationFiled: December 13, 2018Publication date: June 18, 2020Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv, Manish Sharma Kolachalam
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Publication number: 20200034057Abstract: 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: ApplicationFiled: October 8, 2018Publication date: January 30, 2020Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Guruprasad Pv
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Patent number: 10459757Abstract: 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. The input layer may receive one or more resource configurations that may be applied to implement the sizing correction. A prescriptive engine layer may generate a compute sizing correction trajectory indicative of a sizing adjustment to a computing resource. The compute sizing correction trajectory may account of historic processor, network, and memory utilization. Based on the compute sizing correction trajectory and a selected resource configuration, the prescriptive engine layer may generate the compute sizing correction tokens that that may be used to control compute sizing adjustments prescriptively.Type: GrantFiled: May 13, 2019Date of Patent: October 29, 2019Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITEDInventors: Madhan Kumar Srinivasan, Guruprasad Pv, Arun Purushothaman
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Publication number: 20190317808Abstract: 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: ApplicationFiled: October 12, 2018Publication date: October 17, 2019Applicant: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Vijaya Tapaswi Achanta
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Patent number: 10445209Abstract: A multi-layer activation timetable stack may generate prescriptive activation timetables for controlling activation states for computing resources. An input layer of the activation timetable stack may generate time-scaled pattern data. A transformation layer may identify trends and variables at era timescales. A data treatment layer may flag activation states based on the trends identified at the era timescales. Once the activation states a flagged, the prescriptive engine layer may generate an activation timetable that may be used to control computing resource activation prescriptively.Type: GrantFiled: November 13, 2017Date of Patent: October 15, 2019Assignee: Accenture Global Solutions LimitedInventors: Madhan Kumar Srinivasan, Arun Purushothaman, Manish Sharma Kolachalam, Michael S. Eisenstein