公开(公告)号：US20210124739A1

公开(公告)日：2021-04-29

申请号：US16990506

申请日：2020-08-11

Applicant: Microsoft Technology Licensing, LLC

Inventor： Konstantinos KARANASOS ,  Matteo INTERLANDI ,  Fotios PSALLIDAS ,  Rathijit SEN ,  Kwanghyun PARK ,  Ivan POPIVANOV ,  Subramaniam VENKATRAMAN KRISHNAN ,  Markus WEIMER ,  Yuan YU ,  Raghunath RAMAKRISHNAN ,  Carlo Aldo CURINO ,  Doris Suiyi XIN ,  Karla Jean SAUR

IPC: G06F16/2458 ,  G06N5/04 ,  G06N20/00 ,  G06F16/28

Abstract: The description relates to executing an inference query relative to a database management system, such as a relational database management system. In one example a trained machine learning model can be stored within the database management system. An inference query can be received that applies the trained machine learning model on data local to the database management system. Analysis can be performed on the inference query and the trained machine learning model to generate a unified intermediate representation of the inference query and the trained model. Cross optimization can be performed on the unified intermediate representation. Based upon the cross-optimization, a first portion of the unified intermediate representation to be executed by a database engine of the database management system can be determined, and, a second portion of the unified intermediate representation to be executed by a machine learning runtime can be determined.

公开(公告)号：US20240289818A1

公开(公告)日：2024-08-29

申请号：US18332118

申请日：2023-06-09

Applicant: Microsoft Technology Licensing, LLC

Inventor： Jesus CAMACHO RODRIGUEZ ,  Kwanghyun PARK ,  Fotios PSALLIDAS ,  Xiaoyong ZHU ,  Jinghui MO ,  Rathijit SEN ,  Matteo INTERLANDI ,  Yuanyuan TIAN ,  Rui LIU ,  Konstantinos KARANASOS

IPC: G06Q30/0201 ,  G06N20/00

CPC classification number: G06Q30/0201 ,  G06N20/00

Abstract: The described technology provides a method including receiving a new feature definition; the new feature definition specifying parameters of the feature, comparing the new feature definition with a plurality of computed feature definitions stored in a feature store, and in response to determining that the new feature definition is at least partially contained in a matched feature definition of the plurality of computed feature definitions, generating an alternative feature definition based on the new feature definition and the matched feature definitions, and selecting an execution alternative from an execution of a PIT join using the alternative feature definition and an execution of a PIT join using the new feature definition.

公开(公告)号：US20240111739A1

公开(公告)日：2024-04-04

申请号：US18534559

申请日：2023-12-08

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yiwen ZHU ,  Subramaniam Venkatraman KRISHNAN ,  Konstantinos KARANASOS ,  Carlo CURINO ,  Isha TARTE ,  Sudhir DARBHA

IPC: G06F16/21 ,  G06F11/30 ,  G06F11/34 ,  G06F16/17 ,  G06F16/182 ,  G06F16/188 ,  G06N20/00

CPC classification number: G06F16/217 ,  G06F11/3006 ,  G06F11/3433 ,  G06F16/1727 ,  G06F16/1734 ,  G06F16/182 ,  G06F16/1834 ,  G06F16/188 ,  G06N20/00

Abstract: An automated tuning service is used to automatically tune, or modify, the operational parameters of a large-scale cloud infrastructure. The tuning service performs automated and fully data/model-driven configuration based from learning various real-time performance of the cloud infrastructure. Such performance is identified through monitoring various telemetric data of the cloud infrastructure. The tuning service leverages a mix of domain knowledge and principled data-science to capture the essence of our cluster dynamic behavior in a collection of descriptive machine learning (ML) models. The ML models power automated optimization procedures for parameter tuning, and inform administrators in most tactical and strategical engineering/capacity decisions (such as hardware and datacenter design, software investments, etc.). Rich “observational” models (models collected without modifying the system) are combined with judicious use of “fighting” (testing in production), allowing the tuning service to automatically configure operational parameters of a large cloud infrastructure for a broad range of applications.

公开(公告)号：US20250156430A1

公开(公告)日：2025-05-15

申请号：US19022565

申请日：2025-01-15

Applicant: Microsoft Technology Licensing, LLC

Inventor： Konstantinos KARANASOS ,  Matteo INTERLANDI ,  Fotios PSALLIDAS ,  Rathijit SEN ,  Kwanghyun PARK ,  Ivan POPIVANOV ,  Subramaniam VENKATRAMAN KRISHNAN ,  Markus WEIMER ,  Yuan YU ,  Raghunath RAMAKRISHNAN ,  Carlo Aldo CURINO ,  Doris Suiyi XIN ,  Karla Jean SAUR

IPC: G06F16/2458 ,  G06F16/28 ,  G06N5/04 ,  G06N20/00

Abstract: The description relates to executing an inference query relative to a database management system, such as a relational database management system. In one example a trained machine learning model can be stored within the database management system. An inference query can be received that applies the trained machine learning model on data local to the database management system. Analysis can be performed on the inference query and the trained machine learning model to generate a unified intermediate representation of the inference query and the trained model. Cross optimization can be performed on the unified intermediate representation. Based upon the cross-optimization, a first portion of the unified intermediate representation to be executed by a database engine of the database management system can be determined, and, a second portion of the unified intermediate representation to be executed by a machine learning runtime can be determined.

公开(公告)号：US20230244662A1

公开(公告)日：2023-08-03

申请号：US17587952

申请日：2022-01-28

Applicant: Microsoft Technology Licensing, LLC

Inventor： Matteo INTERLANDI ,  Konstantinos KARANASOS ,  Dong HE ,  Dalitso Hansini BANDA ,  Jesus CAMACHO RODRIGUEZ ,  Rathijit SEN ,  Supun Chathurang NAKANDALA

IPC: G06F16/2453 ,  G06F16/2458 ,  G06N3/04

CPC classification number: G06F16/24542 ,  G06F16/2458 ,  G06N3/04

Abstract: Example aspects include techniques for query processing over deep neural network runtimes. These techniques may include receiving a query including one or more query operators and determining a query representation based on the one or more query operators. In addition, the techniques may include determining a neural network program based on the query representation, the neural network program including one or more neural network operators for performing the query in a neural network runtime, generating a neural network data structure based on a dataset associated with the query, and executing the neural network program in the neural network runtime over the neural network data structure to generate a query result.

公开(公告)号：US20220164327A1

公开(公告)日：2022-05-26

申请号：US17221755

申请日：2021-04-02

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yiwen ZHU ,  Subramaniam Venkatraman KRISHNAN ,  Konstantinos KARANASOS ,  Carlo CURINO ,  Isha TARTE ,  Sudhir Darbha

IPC: G06F16/21 ,  G06F11/34 ,  G06F11/30 ,  G06N20/00

Abstract: An automated tuning service is used to automatically tune, or modify, the operational parameters of a large-scale cloud infrastructure. The tuning service performs automated and fully data/model-driven configuration based from learning various real-time performance of the cloud infrastructure. Such performance is identified through monitoring various telemetric data of the cloud infrastructure. The tuning service leverages a mix of domain knowledge and principled data-science to capture the essence of our cluster dynamic behavior in a collection of descriptive machine learning (ML) models. The ML models power automated optimization procedures for parameter tuning, and inform administrators in most tactical and strategical engineering/capacity decisions (such as hardware and datacenter design, software investments, etc.). Rich “observational” models (models collected without modifying the system) are combined with judicious use of “fighting” (testing in production), allowing the tuning service to automatically configure operational parameters of a large cloud infrastructure for a broad range of applications.

公开(公告)号：US20190236189A1

公开(公告)日：2019-08-01

申请号：US15884282

申请日：2018-01-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Alekh JINDAL ,  Konstantinos KARANASOS ,  Hiren Shantilal PATEL ,  Sriram S RAO

IPC: G06F17/30

Abstract: 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.

公开(公告)号：US20180300174A1

公开(公告)日：2018-10-18

申请号：US15954311

申请日：2018-04-16

Applicant: Microsoft Technology Licensing, LLC

Inventor： Konstantinos KARANASOS ,  Sriram RAO ,  Srikanth KANDULA ,  Milan VOJNOVIC ,  Jeffrey Thomas RASLEY ,  Rodrigo Lopes Cancado FONSECA

IPC: G06F9/48 ,  G06F9/50

Abstract: Embodiments for efficient queue management for cluster scheduling and managing task queues for tasks which are to be executed in a distributed computing environment. Both centralized and distributed scheduling is provided. Task queues may be bound by length-based bounding or delay-based bounding. Tasks may be prioritized and task queues may be dynamically reordered based on task priorities. Job completion times and cluster resource utilization may both be improved.