Patents by Inventor Sridhar Balachandriah
Sridhar Balachandriah 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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Publication number: 20240354218Abstract: Systems and methods are provided for utilization of optimal data access interface usage in machine learning pipelines. Examples of the systems and methods disclosed herein include identifying data access interfaces comprising at least a first data access interface for a persistent storage distributed across a plurality of storage nodes and at least a second data access interface for an in-memory object store, and receiving, from a compute node, a data operation request as part of a machine learning pipeline. Additionally, performance metrics are obtained for the plurality of access interfaces, and based on a type of data operation request, the data operation is executed using a data access interface selected from the plurality of data access interface based on the performance metrics and providing an object handle to the compute node.Type: ApplicationFiled: April 18, 2023Publication date: October 24, 2024Inventors: SRIDHAR BALACHANDRIAH, SATISH KUMAR MOPUR, LANCE MACKIMME EVANS, SHERIN THYIL GEORGE, KEVAN REHM
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Patent number: 12088476Abstract: The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.Type: GrantFiled: September 21, 2022Date of Patent: September 10, 2024Assignee: Hewlett Packard Enterprise Development LPInventors: Satish Kumar Mopur, Saikat Mukherjee, Gunalan Perumal Vijayan, Sridhar Balachandriah, Ashutosh Agrawal, KrishnaPrasad Lingadahalli Shastry, Gregory S. Battas
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Publication number: 20240220514Abstract: The present invention relates to a system and a method for updating data models. Input data received from a data source and/or prediction data obtained from a data model is reduced based on baseline reference data to obtain a plurality of representative points. The plurality of representative points are clustered to generate a plurality of clusters. An outlier cluster is detected from the plurality of clusters based on a maximum distance of the plurality of clusters from a highest density cluster and/or comparison of quantity and values of the plurality of representative points with predefined rules. Data drift is identified based on changes in densities of the plurality of clusters. The data model is updated using information corresponding to the outlier cluster and the data drift.Type: ApplicationFiled: March 18, 2024Publication date: July 4, 2024Inventors: Satish Kumar Mopur, Sridhar Balachandriah, Gunalan Perumal Vijayan, Suresh Ladapuram Soundararajan, Krishna Prasad Lingadahalli Shastry
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Patent number: 11954129Abstract: The present invention relates to a system and a method for updating data models. Input data received from a data source and/or prediction data obtained from a data model is reduced based on baseline reference data to obtain a plurality of representative points. The plurality of representative points are clustered to generate a plurality of clusters. An outlier cluster is detected from the plurality of clusters based on a maximum distance of the plurality of clusters from a highest density cluster and/or comparison of quantity and values of the plurality of representative points with predefined rules. Data drift is identified based on changes in densities of the plurality of clusters. The data model is updated using information corresponding to the outlier cluster and the data drift.Type: GrantFiled: April 8, 2021Date of Patent: April 9, 2024Assignee: Hewlett Packard Enterprise Development LPInventors: Satish Kumar Mopur, Sridhar Balachandriah, Gunalan Perumal Vijayan, Suresh Ladapuram Soundarajan, Krishna Prasad Lingadahalli Shastry
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Publication number: 20230017701Abstract: The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.Type: ApplicationFiled: September 21, 2022Publication date: January 19, 2023Inventors: Satish Kumar Mopur, Saikat Mukherjee, Gunalan Perumal Vijayan, Sridhar Balachandriah, Ashutosh Agrawal, KrishnaPrasad Lingadahalli Shastry, Gregory S. Battas
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Patent number: 11481665Abstract: A system and method for accounting for the impact of concept drift in selecting machine learning training methods to address the identified impact. Pattern recognition is performed on performance metrics of a deployed production model in an Internet-of-Things (IoT) environment to determine the impact that concept drift (data drift) has had on prediction performance. This concurrent analysis is utilized to select one or more approaches for training machine learning models, thereby accounting for the temporal dynamics of concept drift (and its subsequent impact on prediction performance) in a faster and more efficient manner.Type: GrantFiled: November 9, 2018Date of Patent: October 25, 2022Assignee: Hewlett Packard Enterprise Development LPInventors: Satish Kumar Mopur, Gregory S. Battas, Gunalan Perumal Vijayan, Krishnaprasad Lingadahalli Shastry, Saikat Mukherjee, Ashutosh Agrawal, Sridhar Balachandriah
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Patent number: 11469969Abstract: The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.Type: GrantFiled: October 4, 2018Date of Patent: October 11, 2022Assignee: Hewlett Packard Enterprise Development LPInventors: Satish Kumar Mopur, Saikat Mukherjee, Gunalan Perumal Vijayan, Sridhar Balachandriah, Ashutosh Agrawal, Krishnaprasad Lingadahalli Shastry, Gregory S. Battas
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Publication number: 20220215289Abstract: A system and a method for updating a Machine Learning (ML) model are described The method involves capturing reconstruction errors associated with reconstruction of images by a pre-trained autoencoder. Data points representing the reconstruction errors are clustered using affinity propagation. A preference value used by the affinity propagation for determining similarity between the data points is dynamically set through linear regression. Outliers and data drifts are determined from clusters of the data points. Classification output of the ML model is associated with the outliers and the data drift, for refinement of the ML model over a device hosting a training environment.Type: ApplicationFiled: April 22, 2021Publication date: July 7, 2022Inventors: Satish Kumar MOPUR, Sridhar BALACHANDRIAH, Krishnaprasad Lingadahalli SHASTRY, Maximilian CLASSEN, Milind B. DESAI
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Patent number: 11361245Abstract: The disclosure relates to technology that implements flow control for machine learning on data such as Internet of Things (“IoT”) datasets. The system may route outputs of a data splitter function performed on the IoT datasets to a designated target model based on a user specification for routing the outputs. In this manner, the IoT datasets may be dynamically routed to target datasets without reprogramming machine-learning pipelines, which enable rapid training, testing and validation of ML models as well as an ability to concurrently train, validate, and execute ML models.Type: GrantFiled: August 9, 2018Date of Patent: June 14, 2022Assignee: Hewlett Packard Enterprise Development LPInventors: Satish Kumar Mopur, Saikat Mukherjee, Gunalan Perumal Vijayan, Sridhar Balachandriah, Ashutosh Agrawal, Krishnaprasad Lingadahalli Shastry, Gregory S. Battas
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Publication number: 20210365478Abstract: The present invention relates to a system and a method for updating data models. Input data received from a data source and/or prediction data obtained from a data model is reduced based on baseline reference data to obtain a plurality of representative points. The plurality of representative points are clustered to generate a plurality of clusters. An outlier cluster is detected from the plurality of clusters based on a maximum distance of the plurality of clusters from a highest density cluster and/or comparison of quantity and values of the plurality of representative points with predefined rules. Data drift is identified based on changes in densities of the plurality of clusters. The data model is updated using information corresponding to the outlier cluster and the data drift.Type: ApplicationFiled: April 8, 2021Publication date: November 25, 2021Inventors: Satish Kumar MOPUR, Sridhar BALACHANDRIAH, Gunalan PERUMAL VIJAYAN, Suresh LADAPURAM SOUNDARAJAN, Krishna Prasad Lingadahalli SHASTRY
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Patent number: 10855791Abstract: A request that indicates a component that can be quiesced is received at a first node. It is determined that quiescence of the component might impact an endpoint. A request for identification of at least one path between a second node and the endpoint is sent to the second node. It is determined, based on a response received from the second node, whether the endpoint will be accessible to the second node if the component is quiesced. In response to a determination that the endpoint will be accessible to the second node if the component is quiesced, a positive analysis outcome is indicated. In response to a determination that the endpoint will not be accessible to the second node if the component is quiesced, a negative analysis outcome is indicated.Type: GrantFiled: November 25, 2014Date of Patent: December 1, 2020Assignee: NetApp, Inc.Inventors: Gunalan Perumal Vijayan, William D. Dallas, Sridhar Balachandriah, Bhaskar Singhal
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Publication number: 20200151619Abstract: A system and method for accounting for the impact of concept drift in selecting machine learning training methods to address the identified impact. Pattern recognition is performed on performance metrics of a deployed production model in an Internet-of-Things (IoT) environment to determine the impact that concept drift (data drift) has had on prediction performance. This concurrent analysis is utilized to select one or more approaches for training machine learning models, thereby accounting for the temporal dynamics of concept drift (and its subsequent impact on prediction performance) in a faster and more efficient manner.Type: ApplicationFiled: November 9, 2018Publication date: May 14, 2020Inventors: Satish Kumar MOPUR, Gregory S. BATTAS, Gunalan Perumal VIJAYAN, Krishnaprasad Lingadahalli SHASTRY, Saikat MUKHERJEE, Ashutosh AGRAWAL, Sridhar BALACHANDRIAH
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Publication number: 20200112490Abstract: The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.Type: ApplicationFiled: October 4, 2018Publication date: April 9, 2020Inventors: SATISH KUMAR MOPUR, SAIKAT MUKHERJEE, GUNALAN PERUMAL VIJAYAN, SRIDHAR BALACHANDRIAH, ASHUTOSH AGRAWAL, KRISHNAPRASAD LINGADAHALLI SHASTRY, GREGORY S. BATTAS
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Publication number: 20200050578Abstract: The disclosure relates to technology that implements flow control for machine learning on data such as Internet of Things (“IoT”) datasets. The system may route outputs of a data splitter function performed on the IoT datasets to a designated target model based on a user specification for routing the outputs. In this manner, the IoT datasets may be dynamically routed to target datasets without reprogramming machine-learning pipelines, which enable rapid training, testing and validation of ML models as well as an ability to concurrently train, validate, and execute ML models.Type: ApplicationFiled: August 9, 2018Publication date: February 13, 2020Inventors: SATISH KUMAR MOPUR, SAIKAT MUKHERJEE, GUNALAN PERUMAL VIJAYAN, SRIDHAR BALACHANDRIAH, ASHUTOSH AGRAWAL, KRISHNAPRASAD LINGADAHALLI SHASTRY, GREGORY S. BATTAS
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Publication number: 20190028407Abstract: Example implementations relate to managing compliance of workloads to quality of service (QoS) parameters. An example includes collection of time-series network performance data from server systems and fabric interconnects related to traffic generated by workloads of the server systems. Rapid trends and long term trends for the workloads are calculated, using the collected network performance data as the input. Compliance of a high priority workload to an associated QoS parameter with the high priority workload is managed based on monitoring a rapid analytic trend for the high priority workload. Compliance of all of the workloads to respective QoS parameters is managed based on monitoring of long term analytic trends for the workloads.Type: ApplicationFiled: July 20, 2017Publication date: January 24, 2019Inventors: Gunalan Perumal Vijayan, Saikat Mukherjee, Satish Kumar Mopur, Sridhar Balachandriah
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Publication number: 20160149828Abstract: A request that indicates a component that can be quiesced is received at a first node. It is determined that quiescence of the component might impact an endpoint. A request for identification of at least one path between a second node and the endpoint is sent to the second node. It is determined, based on a response received from the second node, whether the endpoint will be accessible to the second node if the component is quiesced. In response to a determination that the endpoint will be accessible to the second node if the component is quiesced, a positive analysis outcome is indicated. In response to a determination that the endpoint will not be accessible to the second node if the component is quiesced, a negative analysis outcome is indicated.Type: ApplicationFiled: November 25, 2014Publication date: May 26, 2016Inventors: Gunalan Perumal Vijayan, William D. Dallas, Sridhar Balachandriah, Bhaskar Singhal
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Patent number: 8874806Abstract: An embodiment of a method of managing multipathing in a computer system including the steps of establishing a plurality of concurrent multipathing processes on the computer system; disassociating a plurality of operational data paths from a first of the multipathing processes; and associating the operational data paths with a second of the multipathing processes.Type: GrantFiled: July 26, 2006Date of Patent: October 28, 2014Assignee: Hewlett-Packard Development Company, L.P.Inventors: Satish Kumar Mopur, Pruthviraj Herur Puttaiah, Sridhar Balachandriah
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Patent number: 8468385Abstract: Method and system for managing error related events while a system is processing input/output (“I/O”) requests for accessing storage space is provided. Various components are involved in processing the I/O requests. Some of these components may also have sub-components. Events related to the various components are classified with respect to their severity levels. Threshold values for a frequency of these events is set and stored in a data structure at a memory location. When an event occurs, the severity level and the threshold value for the event are determined from the data structure. The actual frequency is then compared to the stored threshold value. If the threshold value is violated and there is an alternate path to route the I/O request, then the affected component is restricted and the alternate path is used to route the I/O request.Type: GrantFiled: October 27, 2010Date of Patent: June 18, 2013Assignee: Netapp, Inc.Inventors: Sridhar Balachandriah, Bhaskar Singhal
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Patent number: 8261018Abstract: A method, system and computer program product for managing data storage systems. The data storage system being coupled to a volume storage pool as data storage resource, the data storage system presenting at least one virtual volume as a storage resource to a host device, the method for managing the data storage system comprising collecting the volume storage pool occupancy and the virtual volume consumption; trending the volume storage pool and the virtual volumes consumption; forecasting the volume storage pool occupancy and virtual volume consumption; and recommending at least one action based on the forecasted values of storage pool occupancy data and virtual volume consumption data. The method may further comprise detecting a rapid increase or surge in the volume storage pool occupancy data.Type: GrantFiled: July 11, 2009Date of Patent: September 4, 2012Assignee: Hewlett-Packard Development Company, L.P.Inventors: Sridhar Balachandriah, Satish Kumar Mopur, Duvvuri Rama Kiron
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Patent number: 8054763Abstract: A method, system and computer program product for migrating at least one switch in a storage area network is disclosed. The migration is done by analysing the I/O traffic to identify patterns in the I/O traffic of the switch; forecasting future I/O workload of the switch based on one or more identified patterns in the I/O traffic, determining appropriate timing for migration based on the identified patterns and administrator inputs; processing the storage area network configuration data to identify the storage network physical and logical access paths to the or each selected switch to create a first connectivity map; generating a second connectivity map based on the first connectivity map and administrator inputs; and migrating the or each switch migration based on the second connectivity map and the appropriate timing. The migration may comprise routing the I/O traffic from the switch to be migrated to the alternate switches in the storage area network.Type: GrantFiled: May 15, 2009Date of Patent: November 8, 2011Assignee: Hewlett-Packard Development Company, L.P.Inventors: Vivek Mehrotra, Satish Kumar Mopur, Saikat Mukherjee, Satyaprakash Rao, Gunalan Perumal Vijayan, Sridhar Balachandriah