公开(公告)号：US20250021558A1

公开(公告)日：2025-01-16

申请号：US18902195

申请日：2024-09-30

Applicant: Snowflake Inc.

Inventor： Qiming Jiang ,  Orestis Kostakis ,  John Reumann

IPC: G06F16/2453 ,  G06F16/27

Abstract: A method for improving query scheduling on a computing cluster using reinforcement learning is provided. A series of queries to be executed using resources of the computing cluster is received. For each query, a query execution plan is generated and a resource profile for executing the query is predicted. Current state data of the cluster resources is received and assignment data to execute the query on the cluster resources is generated by applying the reinforcement learning technique. The query is executed on the computing cluster based on the generated assignment data, and query results are stored.

公开(公告)号：US12026221B2

公开(公告)日：2024-07-02

申请号：US18112944

申请日：2023-02-22

Applicant: Snowflake Inc.

Inventor： Michel Adar ,  Boxin Jiang ,  Qiming Jiang ,  John Reumann ,  Boyu Wang ,  Jiaxun Wu

IPC: G06F17/18

CPC classification number: G06F17/18

Abstract: Using an attributes model of a time series forecasting model, determine a set of features based on time series data, the set of features including periodic components. The time series data may be divided into a set of segments. For each segment of the set of segments, a weight may be assigned using an age of the segment, resulting in a set of weighted segments of time series data. Using a trend detection model of the time series forecasting model, trend data from the set of weighted segments of time series data may be determined. A time series forecast may be generated by combining the set of features and the trend data.

公开(公告)号：US12130811B2

公开(公告)日：2024-10-29

申请号：US18362869

申请日：2023-07-31

Applicant: Snowflake Inc.

Inventor： Qiming Jiang ,  Orestis Kostakis ,  John Reumann

IPC: G06F16/00 ,  G06F16/2453 ,  G06F16/27

CPC classification number: G06F16/24542 ,  G06F16/27

Abstract: A system for improving task scheduling on a cloud data platform is provided. A task to be executed using resources of a computing cluster is received. A task execution plan is generated and information about data to be used for the ask is accessed. Resource requirements for executing the task are predicted by applying machine learning to the task execution plan and the information about the data. Assignment data is generated to execute the task on the resources by applying machine learning information about a current state of the resources and predicted resource requirements.

公开(公告)号：US20240078235A1

公开(公告)日：2024-03-07

申请号：US18362869

申请日：2023-07-31

Applicant: Snowflake Inc.

Inventor： Qiming Jiang ,  Orestis Kostakis ,  John Reumann

IPC: G06F16/2453 ,  G06F16/27

CPC classification number: G06F16/24542 ,  G06F16/27

Abstract: A system for improving task scheduling on a cloud data platform is provided. A task to be executed using resources of a computing cluster is received. A task execution plan is generated and information about data to be used for the ask is accessed. Resource requirements for executing the task are predicted by applying machine learning to the task execution plan and the information about the data. Assignment data is generated to execute the task on the resources by applying machine learning information about a current state of the resources and predicted resource requirements.

公开(公告)号：US11755576B1

公开(公告)日：2023-09-12

申请号：US18104256

申请日：2023-01-31

Applicant: Snowflake Inc.

Inventor： Qiming Jiang ,  Orestis Kostakis ,  John Reumann

IPC: G06F16/00 ,  G06F16/2453 ,  G06F16/27

CPC classification number: G06F16/24542 ,  G06F16/27

Abstract: A system for improving task scheduling on a cloud data platform is provided. A task is received, from a user of a cloud data platform, for execution on a dataset of a cloud data platform using a plurality of resources. A task graph is generated, and metadata related to the dataset is accessed for use in execution of the task. A predicted resource profile is generated by applying a first machine learning scheme to the task graph and the metadata of the dataset. Assignment data is generated to execute processes of the task on the plurality of resources. The assignment data generated by applying a second machine learning scheme to current state data of a current computational state of the plurality of resources and the predicted resource profile generated by the first machine learning scheme.

公开(公告)号：US11609970B1

公开(公告)日：2023-03-21

申请号：US17877588

申请日：2022-07-29

Applicant: Snowflake Inc.

Inventor： Michel Adar ,  Boxin Jiang ,  Qiming Jiang ,  John Reumann ,  Boyu Wang ,  Jiaxun Wu

IPC: G06F17/18

Abstract: A processing device may analyze a set of time series data using a time series forecasting model comprising an attributes model and a trend detection model. The attributes model may comprise a modified gradient boosting decision tree (GBDT) based algorithm. Analyzing the set of time series data comprises determining a set of features of the set of time series data, the set of features including periodic components as well as arbitrary components. A trend of the set of time series data may be determined using the trend detection model and the set of features and the trend may be combined to generate a time series forecast.

公开(公告)号：US20230401283A1

公开(公告)日：2023-12-14

申请号：US18112944

申请日：2023-02-22

Applicant: Snowflake Inc.

Inventor： Michel Adar ,  Boxin Jiang ,  Qiming Jiang ,  John Reumann ,  Boyu Wang ,  Jiaxun Wu

IPC: G06F17/18

CPC classification number: G06F17/18

Abstract: Using an attributes model of a time series forecasting model, determine a set of features based on time series data, the set of features including periodic components. The time series data may be divided into a set of segments. For each segment of the set of segments, a weight may be assigned using an age of the segment, resulting in a set of weighted segments of time series data. Using a trend detection model of the time series forecasting model, trend data from the set of weighted segments of time series data may be determined. A time series forecast may be generated by combining the set of features and the trend data.

公开(公告)号：US11620289B1

公开(公告)日：2023-04-04

申请号：US17930277

申请日：2022-09-07

Applicant: Snowflake Inc.

Inventor： Qiming Jiang ,  Orestis Kostakis ,  John Reumann

IPC: G06F16/00 ,  G06F16/2453 ,  G06F16/27

Abstract: Embodiments of the present disclosure may provide a database optimization system that can generate computational values through a database compiler and assignment data for execution of a query by a plurality of nodes of a database system. The computational values and assignment data can be generated by one or more machine learning schemes. The machine learning schemes can be trained on previous computational values and previous assignment data.