公开(公告)号：US11907250B2

公开(公告)日：2024-02-20

申请号：US17871092

申请日：2022-07-22

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Urvashi Oswal ,  Marc Jolles ,  Onur Kocberber ,  Seema Sundara ,  Nipun Agarwal

IPC: G06F16/24 ,  G06F16/25 ,  G06F16/21 ,  G06F11/34 ,  G06F16/2458

CPC classification number: G06F16/258 ,  G06F11/3409 ,  G06F16/21 ,  G06F16/2462

Abstract: Techniques are described for executing machine learning models trained for specific operators with feature values that are based on the actual execution of a workload set. The machine learning models generate an estimate of benefit gain/cost for executing operations on data portions in the alternative encoding format. Such data potions may be sorted based on the estimated benefit, in an embodiment. Using cost estimation machine learning models for memory space, the data portions with the most benefits that comply with the existing memory space constraints are recommended and/or are automatically encoded into the alternative encoding format.

公开(公告)号：US20230297573A1

公开(公告)日：2023-09-21

申请号：US17699607

申请日：2022-03-21

Applicant: Oracle International Corporation

Inventor： Urvashi Oswal ,  Jian Wen ,  Farhan Tauheed ,  Onur Kocberber ,  Seema Sundara ,  Nipun Agarwal

IPC: G06F16/2453 ,  G06F16/21 ,  G06F16/22 ,  G06F11/34 ,  G06F16/27

CPC classification number: G06F16/24544 ,  G06F16/211 ,  G06F16/2282 ,  G06F11/3409 ,  G06F16/278

Abstract: Embodiments implement a prediction-driven, rather than a trial-driven, approach to automatic data placement recommendations for partitioning data across multiple nodes in a database system. The system is configured to extract workload-specific features of a database workload running at a database system and dataset-specific features of a database running on the database system. The workload-specific features characterize utilization of the database workload. The dataset-specific features characterize how data is organized within the database. The system identifies a plurality of candidate keys for determining how to partition data stored in the database across nodes. Based at least in part on the workload-specific features, the dataset specific features, and the plurality of candidate keys, a set of candidate key combinations for partitioning data is generated. Using a machine learning model, determine a particular candidate key combination that optimizes query execution performance benefit based on the workload-specific features and the dataset specific features. Generate data placement commands to allocate the database tables across the nodes.

公开(公告)号：US11657256B2

公开(公告)日：2023-05-23

申请号：US17867552

申请日：2022-07-18

Applicant: Oracle International Corporation

Inventor： Pravin Shinde ,  Felix Schmidt ,  Onur Kocberber

IPC: G06F9/44 ,  G06N3/04 ,  G06N3/084 ,  G06N3/088 ,  G06F11/07 ,  G06F21/57

CPC classification number: G06N3/0454 ,  G06F11/0709 ,  G06F11/0751 ,  G06F11/0787 ,  G06N3/0445 ,  G06N3/084 ,  G06N3/088 ,  G06F21/57 ,  G06F2221/034

Abstract: Embodiments use a hierarchy of machine learning models to predict datacenter behavior at multiple hardware levels of a datacenter without accessing operating system generated hardware utilization information. The accuracy of higher-level models in the hierarchy of models is increased by including, as input to the higher-level models, hardware utilization predictions from lower-level models. The hierarchy of models includes: server utilization models and workload/OS prediction models that produce predictions at a server device-level of a datacenter; and also top-of-rack switch models and backbone switch models that produce predictions at higher levels of the datacenter. These models receive, as input, hardware utilization information from non-OS sources. Based on datacenter-level network utilization predictions from the hierarchy of models, the datacenter automatically configures its hardware to avoid any predicted over-utilization of hardware in the datacenter. Also, the predictions from the hierarchy of models can be used to detect anomalies of datacenter hardware behavior.

公开(公告)号：US11579951B2

公开(公告)日：2023-02-14

申请号：US16144912

申请日：2018-09-27

Applicant: Oracle International Corporation

Inventor： Onur Kocberber ,  Felix Schmidt ,  Arun Raghavan ,  Nipun Agarwal ,  Sam Idicula ,  Guang-Tong Zhou ,  Nitin Kunal

IPC: G06F11/00 ,  G06N20/00 ,  G06F11/30 ,  G06F17/18 ,  G06K9/62 ,  G06N3/08 ,  G06F11/22

Abstract: Techniques are described herein for predicting disk drive failure using a machine learning model. The framework involves receiving disk drive sensor attributes as training data, preprocessing the training data to select a set of enhanced feature sequences, and using the enhanced feature sequences to train a machine learning model to predict disk drive failures from disk drive sensor monitoring data. Prior to the training phase, the RNN LSTM model is tuned using a set of predefined hyper-parameters. The preprocessing, which is performed during the training and evaluation phase as well as later during the prediction phase, involves using predefined values for a set of parameters to generate the set of enhanced sequences from raw sensor reading. The enhanced feature sequences are generated to maintain a desired healthy/failed disk ratio, and only use samples leading up to a last-valid-time sample in order to honor a pre-specified heads-up-period alert requirement.

公开(公告)号：US11520834B1

公开(公告)日：2022-12-06

申请号：US17387841

申请日：2021-07-28

Applicant: Oracle International Corporation

Inventor： Tomas Karnagel ,  Suratna Budalakoti ,  Onur Kocberber ,  Nipun Agarwal ,  Alan Wood

IPC: G06F16/00 ,  G06F16/9035

Abstract: Techniques are described for generating an approximate frequency histogram using a series of Bloom filters (BF). For example, to estimate the f1 and f2 cardinalities in a dataset, an ordered chain of three BFs is established (“BF1”, “BF2”, and “BF3”). An insertion operation is performed for each datum in the dataset, whereby the BFs are tested in order (starting at BF1) for the datum. If the datum is represented in a currently-tested BF, the subsequent BF in the chain is tested for the datum. If the datum is not represented in the currently-tested BF, the datum is added to the BF, a counter for the BF is incremented, and the insertion operation for the current datum ends. To estimate the cardinality of f1-values in the dataset, the BF2-counter is subtracted from the BF1-counter. Similarly, to estimate the cardinality of f2-values in the dataset, the BF3-counter is subtracted from the BF2-counter.

公开(公告)号：US11379456B2

公开(公告)日：2022-07-05

申请号：US17060999

申请日：2020-10-01

Applicant: Oracle International Corporation

Inventor： Onur Kocberber ,  Mayur Bency ,  Marc Jolles ,  Seema Sundara ,  Nipun Agarwal

IPC: G06F16/23 ,  G06F16/245

Abstract: Systems and methods for adjusting parameters for a spin-lock implementation of concurrency control are described herein. In an embodiment, a system continuously retrieves, from a resource management system, one or more state values defining a state of the resource management system. Based on the one or more state values, the system determines that the resource management system has reached a steady state and, in response adjusts a plurality of parameters for spin-locking performed by said resource management system to identify optimal values for the plurality of parameters. After adjusting the plurality of parameters, the system detects, based on one or more current state values, a workload change in the resource management system and, in response, readjusts the plurality of parameters for spin-locking performed by said resource management system to identify new optimal values for the parameters.

公开(公告)号：US20210365805A1

公开(公告)日：2021-11-25

申请号：US16877882

申请日：2020-05-19

Applicant: Oracle International Corporation

Inventor： Tomas Karnagel ,  Onur Kocberber ,  Farhan Tauheed ,  Nipun Agarwal

IPC: G06N5/04 ,  G06N20/00

Abstract: Techniques for estimating the number of distinct values in a data set using machine learning are provided. In one technique, a sample of a data set is retrieved where the sample is a strict subset of the data set. The sample is analyzed to identify feature values of multiple features of the sample. The feature values are inserted into a machine-learned model that computes a prediction regarding a number of distinct values in the data set. An estimated number of distinct values that is based on the prediction is stored in association with the data set.

公开(公告)号：US20200259722A1

公开(公告)日：2020-08-13

申请号：US16271535

申请日：2019-02-08

Applicant: Oracle International Corporation

Inventor： Onur Kocberber ,  Felix Schmidt ,  Craig Schelp ,  Pravin Shinde

IPC: H04L12/24 ,  H04L12/26 ,  G06F9/455

Abstract: Herein are computerized techniques for autonomous and artificially intelligent administration of a computer cloud health monitoring system. In an embodiment, an orchestration computer automatically detects a current state of network elements of a computer network by processing: a) a network plan that defines a topology of the computer network, and b) performance statistics of the network elements. The network elements include computers that each hosts virtual execution environment(s). Each virtual execution environment hosts analysis logic that transforms raw performance data of a network element into a portion of the performance statistics. For each computer, a configuration specification for each virtual execution environment of the computer is automatically generated based on the network plan and the current state of the computer network. At least one virtual execution environment is automatically tuned and/or re-provisioned based on a generated configuration specification.

公开(公告)号：US12014286B2

公开(公告)日：2024-06-18

申请号：US16914816

申请日：2020-06-29

Applicant: Oracle International Corporation

Inventor： Farhan Tauheed ,  Onur Kocberber ,  Tomas Karnagel ,  Nipun Agarwal

IPC: G06N5/04 ,  G06F16/22 ,  G06N20/00

CPC classification number: G06N5/04 ,  G06F16/2282 ,  G06N20/00

Abstract: Herein are approaches for self-optimization of a database management system (DBMS) such as in real time. Adaptive just-in-time sampling techniques herein estimate database content statistics that a machine learning (ML) model may use to predict configuration settings that conserve computer resources such as execution time and storage space. In an embodiment, a computer repeatedly samples database content until a dynamic convergence criterion is satisfied. In each iteration of a series of sampling iterations, a subset of rows of a database table are sampled, and estimates of content statistics of the database table are adjusted based on the sampled subset of rows. Immediately or eventually after detecting dynamic convergence, a machine learning (ML) model predicts, based on the content statistic estimates, an optimal value for a configuration setting of the DBMS.

公开(公告)号：US11868261B2

公开(公告)日：2024-01-09

申请号：US17381072

申请日：2021-07-20

Applicant: Oracle International Corporation

Inventor： Peyman Faizian ,  Mayur Bency ,  Onur Kocberber ,  Seema Sundara ,  Nipun Agarwal

IPC: G06F16/2455 ,  G06F12/0842

CPC classification number: G06F12/0842 ,  G06F16/24552 ,  G06F2212/6022

Abstract: Techniques are described herein for prediction of an buffer pool size (BPS). Before performing BPS prediction, gathered data are used to determine whether a target workload is in a steady state. Historical utilization data gathered while the workload is in a steady state are used to predict object-specific BPS components for database objects, accessed by the target workload, that are identified for BPS analysis based on shares of the total disk I/O requests, for the workload, that are attributed to the respective objects. Preference of analysis is given to objects that are associated with larger shares of disk I/O activity. An object-specific BPS component is determined based on a coverage function that returns a percentage of the database object size (on disk) that should be available in the buffer pool for that database object. The percentage is determined using either a heuristic-based or a machine learning-based approach.