Patents by Inventor Hiren Shantilal PATEL
Hiren Shantilal PATEL 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: 11934874Abstract: A serverless query processing system receives a query and determines whether the query is a recurring query or a non-recurring query. The system may predict, in response to determining that the query is the recurring query, a peak resource requirement during an execution of the query. The system may compute, in response to determining that the query is the non-recurring query, a tight resource requirement corresponding to an amount of resources that satisfy a performance requirement over the execution of the query, where the tight resource requirement is less than the peak resource requirement. The system allocates resources to the query based on an applicable one of the peak resource requirement or the tight resource requirement. The system then starts the execution of the query using the resources.Type: GrantFiled: August 24, 2022Date of Patent: March 19, 2024Assignee: Microsoft Technology Licensing, LLCInventors: Hiren Shantilal Patel, Shi Qiao, Alekh Jindal, Malay Kumar Bag, Rathijit Sen, Carlo Aldo Curino
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Publication number: 20230342359Abstract: Methods of machine learning for system deployments without performance regressions are performed by systems and devices. A performance safeguard system is used to design pre-production experiments for determining the production readiness of learned models based on a pre-production budget by leveraging big data processing infrastructure and deploying a large set of learned or optimized models for its query optimizer. A pipeline for learning and training differentiates the impact of query plans with and without the learned or optimized models, selects plan differences that are likely to lead to most dramatic performance difference, runs a constrained set of pre-production experiments to empirically observe the runtime performance, and finally picks the models that are expected to lead to consistently improved performance for deployment. The performance safeguard system enables safe deployment not just for learned or optimized models but also for additional of other ML-for-Systems features.Type: ApplicationFiled: June 30, 2023Publication date: October 26, 2023Inventors: Irene Rogan SHAFFER, Remmelt Herbert Lieve AMMERLAAN, Gilbert ANTONIUS, Marc T. FRIEDMAN, Abhishek ROY, Lucas ROSENBLATT, Vijay Kumar RAMANI, Shi QIAO, Alekh JINDAL, Peter ORENBERG, H M Sajjad Hossain, Soundararajan Srinivasan, Hiren Shantilal PATEL, Markus WEIMER
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Patent number: 11748350Abstract: Methods of machine learning for system deployments without performance regressions are performed by systems and devices. A performance safeguard system is used to design pre-production experiments for determining the production readiness of learned models based on a pre-production budget by leveraging big data processing infrastructure and deploying a large set of learned or optimized models for its query optimizer. A pipeline for learning and training differentiates the impact of query plans with and without the learned or optimized models, selects plan differences that are likely to lead to most dramatic performance difference, runs a constrained set of pre-production experiments to empirically observe the runtime performance, and finally picks the models that are expected to lead to consistently improved performance for deployment. The performance safeguard system enables safe deployment not just for learned or optimized models but also for additional of other ML-for-Systems features.Type: GrantFiled: April 3, 2020Date of Patent: September 5, 2023Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Irene Rogan Shaffer, Remmelt Herbert Lieve Ammerlaan, Gilbert Antonius, Marc T. Friedman, Abhishek Roy, Lucas Rosenblatt, Vijay Kumar Ramani, Shi Qiao, Alekh Jindal, Peter Orenberg, H M Sajjad Hossain, Soundararajan Srinivasan, Hiren Shantilal Patel, Markus Weimer
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Publication number: 20220413914Abstract: A serverless query processing system receives a query and determines whether the query is a recurring query or a non-recurring query. The system may predict, in response to determining that the query is the recurring query, a peak resource requirement during an execution of the query. The system may compute, in response to determining that the query is the non-recurring query, a tight resource requirement corresponding to an amount of resources that satisfy a performance requirement over the execution of the query, where the tight resource requirement is less than the peak resource requirement. The system allocates resources to the query based on an applicable one of the peak resource requirement or the tight resource requirement. The system then starts the execution of the query using the resources.Type: ApplicationFiled: August 24, 2022Publication date: December 29, 2022Inventors: Hiren Shantilal PATEL, Shi QIAO, Alekh JINDAL, Malay Kumar BAG, Rathijit SEN, Carlo Aldo CURINO
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Patent number: 11455192Abstract: A serverless query processing system receives a query and determines whether the query is a recurring query or a non-recurring query. The system may predict, in response to determining that the query is the recurring query, a peak resource requirement during an execution of the query. The system may compute, in response to determining that the query is the non-recurring query, a tight resource requirement corresponding to an amount of resources that satisfy a performance requirement over the execution of the query, where the tight resource requirement is less than the peak resource requirement. The system allocates resources to the query based on an applicable one of the peak resource requirement or the tight resource requirement. The system then starts the execution of the query using the resources.Type: GrantFiled: November 27, 2019Date of Patent: September 27, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Hiren Shantilal Patel, Shi Qiao, Alekh Jindal, Malay Kumar Bag, Rathijit Sen, Carlo Aldo Curino
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Patent number: 11416487Abstract: Techniques are described herein that are capable of selecting checkpoints of a database job. For instance, at compile time, temporal indicators associated with the query plans of the database job are determined. Each temporal indicator indicates first and second subsets of stages of the respective query plan. Values of attributes of each stage in at least each first subset are predicted using a machine learning technique. At the compile time, candidate stage(s) for each query plan are identified based on the respective candidate stage being a child of stage(s) in the corresponding second subset or not being a child of another stage in the respective query plan. The candidate stage(s) for each query plan are selectively chosen as respective checkpoint(s) based on whether the values of the attributes of each stage in at least the first subset of the stages of the respective query plan satisfy one or more criteria.Type: GrantFiled: September 22, 2020Date of Patent: August 16, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Yiwen Zhu, Alekh Jindal, Malay Kumar Bag, Hiren Shantilal Patel
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Publication number: 20220100763Abstract: Solutions for optimizing job runtimes via prediction-based token allocation includes receiving training data comprising historical run data, the historical run data comprising job characteristics, runtime results, and a token count for each of a plurality of prior jobs, and the job characteristics comprising an intermediate representation and job graph data; based at least on the training data, training a token estimator, the token estimator comprising a machine learning (ML) model; receiving job characteristics for a user-submitted job; based at least on the received job characteristics, generating, with the token estimator, token prediction data for the user-submitted job; selecting a token count for the user-submitted job, based at least on the token prediction data; identifying the selected token count to an execution environment; and executing, with the execution environment, the user-submitted job in accordance with the selected token count.Type: ApplicationFiled: September 30, 2020Publication date: March 31, 2022Inventors: Rathijit SEN, Alekh JINDAL, Anish Yatin PIMPLEY, Shuo LI, Anubha SRIVASTAVA, Vishal Lalchand ROHRA, Yi ZHU, Hiren Shantilal PATEL, Shi QIAO, Marc Todd FRIEDMAN, Clemens Alden SZYPERSKI
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Publication number: 20220092067Abstract: Techniques are described herein that are capable of selecting checkpoints of a database job. For instance, at compile time, temporal indicators associated with the query plans of the database job are determined. Each temporal indicator indicates first and second subsets of stages of the respective query plan. Values of attributes of each stage in at least each first subset are predicted using a machine learning technique. At the compile time, candidate stage(s) for each query plan are identified based on the respective candidate stage being a child of stage(s) in the corresponding second subset or not being a child of another stage in the respective query plan. The candidate stage(s) for each query plan are selectively chosen as respective checkpoint(s) based on whether the values of the attributes of each stage in at least the first subset of the stages of the respective query plan satisfy one or more criteria.Type: ApplicationFiled: September 22, 2020Publication date: March 24, 2022Inventors: Yiwen Zhu, Alekh Jindal, Malay Kumar Bag, Hiren Shantilal Patel
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Publication number: 20210263932Abstract: Methods of machine learning for system deployments without performance regressions are performed by systems and devices. A performance safeguard system is used to design pre-production experiments for determining the production readiness of learned models based on a pre-production budget by leveraging big data processing infrastructure and deploying a large set of learned or optimized models for its query optimizer. A pipeline for learning and training differentiates the impact of query plans with and without the learned or optimized models, selects plan differences that are likely to lead to most dramatic performance difference, runs a constrained set of pre-production experiments to empirically observe the runtime performance, and finally picks the models that are expected to lead to consistently improved performance for deployment. The performance safeguard system enables safe deployment not just for learned or optimized models but also for additional of other ML-for-Systems features.Type: ApplicationFiled: April 3, 2020Publication date: August 26, 2021Inventors: Irene Rogan Shaffer, Remmelt Herbert Lieve Ammerlaan, Gilbert Antonius, Marc T. Friedman, Abhishek Roy, Lucas Rosenblatt, Vijay Kumar Ramani, Shi Qiao, Alekh Jindal, Peter Orenberg, H M Sajjad Hossain, Soundararajan Srinivasan, Hiren Shantilal Patel, Markus Weimer
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Publication number: 20210096915Abstract: A serverless query processing system receives a query and determines whether the query is a recurring query or a non-recurring query. The system may predict, in response to determining that the query is the recurring query, a peak resource requirement during an execution of the query. The system may compute, in response to determining that the query is the non-recurring query, a tight resource requirement corresponding to an amount of resources that satisfy a performance requirement over the execution of the query, where the tight resource requirement is less than the peak resource requirement. The system allocates resources to the query based on an applicable one of the peak resource requirement or the tight resource requirement. The system then starts the execution of the query using the resources.Type: ApplicationFiled: November 27, 2019Publication date: April 1, 2021Inventors: Hiren Shantilal PATEL, Shi QIAO, Alekh JINDAL, Malay Kumar BAG, Rathijit SEN, Carlo Aldo CURINO
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Patent number: 10726014Abstract: Described herein is a system and method for selecting subexpressions to be materialized. For a predefined storage budget, subexpressions of a set of candidate subexpressions to be materialized to minimize query evaluation cost are selected based upon a calculated utility of the set of candidate subexpressions, interactions of the candidate subexpressions, and, a cost of evaluating the candidate subexpressions. Based upon the subexpressions selected to be materialized, subexpression(s) of the set of candidate subexpressions to use when evaluating particular queries of the set of queries to minimize query evaluation cost are determined.Type: GrantFiled: January 30, 2018Date of Patent: July 28, 2020Assignee: Microsoft Technology Licensing, LLCInventors: Alekh Jindal, Konstantinos Karanasos, Hiren Shantilal Patel, Sriram S Rao
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Publication number: 20190236189Abstract: Described herein is a system and method for selecting subexpressions to be materialized. For a predefined storage budget, subexpressions of a set of candidate subexpressions to be materialized to minimize query evaluation cost are selected based upon a calculated utility of the set of candidate subexpressions, interactions of the candidate subexpressions, and, a cost of evaluating the candidate subexpressions. Based upon the subexpressions selected to be materialized, subexpression(s) of the set of candidate subexpressions to use when evaluating particular queries of the set of queries to minimize query evaluation cost are determined.Type: ApplicationFiled: January 30, 2018Publication date: August 1, 2019Applicant: Microsoft Technology Licensing, LLCInventors: Alekh JINDAL, Konstantinos KARANASOS, Hiren Shantilal PATEL, Sriram S RAO