公开(公告)号：US11010694B2

公开(公告)日：2021-05-18

申请号：US15938988

申请日：2018-03-28

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Andrew I. Vakhutinsky ,  DeJun Li ,  Bradley R. Williams ,  Sungpack Hong

IPC: G06Q10/04 ,  G06Q50/06 ,  G06Q10/00 ,  H02J3/00

Abstract: The disclosed embodiments relate to a system that facilitates deployment of utility repair crews to nodes in a utility network. During operation, the system determines a node criticality for each node in the utility network based on a network-reliability analysis, which considers interconnections among the nodes in the utility network. The system also determines a node failure probability for each node in the utility network based on historical weather data, historical node failure data and weather forecast information for the upcoming weather event. The system uses the determined node criticalities and the determined node failure probabilities to determine a deployment plan for deploying repair crews to nodes in the utility network in preparation for the upcoming weather event. The system then presents the deployment plan to a person who uses the deployment plan to deploy repair crews to be available to service nodes in the utility network.

公开(公告)号：US10984106B2

公开(公告)日：2021-04-20

申请号：US16419861

申请日：2019-05-22

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Andrew J. Lewis ,  Guang C. Wang ,  Michael H. S. Dayringer

IPC: G06F21/56 ,  G06F11/30 ,  G01R29/08

Abstract: The disclosed embodiments provide a system that detects execution of malicious cryptomining software in a target computing system. During operation, the system monitors target electromagnetic interference (EMI) signals generated during operation of the target computing system. Next, the system generates a target EMI fingerprint from the target EMI signals. The system then compares the target EMI fingerprint against a set of malicious EMI fingerprints for different pieces of malicious cryptomining software to determine whether the target computing system is executing malicious cryptomining software.

公开(公告)号：US10796242B2

公开(公告)日：2020-10-06

申请号：US15247251

申请日：2016-08-25

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Kalyanaraman Vaidyanathan ,  Craig R. Schelp ,  Andrew E. Brownsword

IPC: G06N20/00 ,  G06F11/07 ,  G06F11/34 ,  G06F11/00 ,  G06F11/30

Abstract: The disclosed embodiments relate to a technique for training a prognostic pattern-recognition system to detect incipient anomalies that arise during execution of a computer system. During operation, the system gathers and stores telemetry data obtained from n sensors in the computer system during operation of the computer system. Next, the system uses the telemetry data gathered from the n sensors to train a baseline model for the prognostic pattern-recognition system. The prognostic pattern-recognition system then uses the baseline model in a surveillance mode to detect incipient anomalies that arise during execution of the computer system. The system also uses the stored telemetry data to train a set of additional models, wherein each additional model is trained to operate with one or more missing sensors. Finally, the system stores the additional models to be used in place of the baseline model when one or more sensors fail in the computer system.

公开(公告)号：US10705580B2

公开(公告)日：2020-07-07

申请号：US15247264

申请日：2016-08-25

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Kalyanaraman Vaidyanathan ,  Sanjeev Sondur

IPC: G06F1/20 ,  H05K7/20

Abstract: The disclosed embodiments relate to a system that controls cooling in a computer system. During operation, this system monitors a temperature of one or more components in the computer system. Next, the system determines a thermal-headroom margin for each of the one or more components in the computer system by subtracting the temperature of the component from a pre-specified maximum operating temperature of the component. Then, the system controls a cooling system that regulates an ambient air temperature for the computer system based on the determined thermal-headroom margins for the one or more components. In some embodiments, controlling the cooling system additionally involves minimizing a collective energy consumption of the computer system and the cooling system.

公开(公告)号：US10699007B2

公开(公告)日：2020-06-30

申请号：US15457523

申请日：2017-03-13

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Eric S. Chan ,  Dieter Gawlick

IPC: H04L29/06 ,  G06F21/55 ,  G06F11/30 ,  G06F11/34 ,  G06N20/00 ,  G06F16/2458 ,  G06F16/2455

Abstract: The disclosed embodiments relate to a system for analyzing telemetry data. During operation, the system obtains telemetry data gathered from sensors during operation of a monitored system. Next, the system applies a univariate model to the telemetry data to identify an operational phase for the monitored system, wherein the univariate model analyzes an individual signal in the telemetry data without reference to other signals in the telemetry data. The system then selects a phase-specific multivariate model based on the identified operational phase, wherein the phase-specific multivariate model was previously trained based on telemetry data gathered while the system was operating in the identified operational phase. Finally, the system uses the phase-specific multivariate model to monitor the telemetry data to detect incipient anomalies associated with the operation of the monitored system.

公开(公告)号：US10664324B2

公开(公告)日：2020-05-26

申请号：US15993446

申请日：2018-05-30

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Sanjeev Sondur

IPC: G06F9/50 ,  H04L12/803 ,  G06F1/3296 ,  G06F9/48

Abstract: The disclosed embodiments provide a system that intelligently migrates workload between servers in a data center to improve efficiency in associated power supplies. During operation, the system receives time-series signals associated with the servers during operation of the data center, wherein the servers include low-priority servers and high-priority servers. Next, the system analyzes the time-series signals to predict a load utilization for the servers. The system then migrates workload between the servers in the data center based on the predicted load utilization so that: the high-priority servers have sufficient workload to ensure that associated power supplies for the high-priority servers operate in a peak-efficiency range; and the low-priority servers operate with less workload or no workload.

公开(公告)号：US10565185B2

公开(公告)日：2020-02-18

申请号：US15850027

申请日：2017-12-21

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Dieter Gawlick ,  Zhen Hua Liu ,  Mengying Li

IPC: G06F17/00 ,  G06F16/23 ,  G06F16/2458

Abstract: The disclosed embodiments relate to a system that certifies provenance of time-series data in a time-series database. During operation, the system retrieves time-series data from the time-series database, wherein the time-series data comprises a sequence of observations comprising sensor readings for each signal in a set of signals. The system also retrieves multivariate state estimation technique (MSET) estimates, which were computed for the time-series data, from the time-series database. Next, the system performs a reverse MSET computation to produce reconstituted time-series data from the MSET estimates. The system then compares the reconstituted time-series data with the time-series data. If the reconstituted time-series data matches the original time-series data, the system certifies provenance for the time-series data.

公开(公告)号：US10452510B2

公开(公告)日：2019-10-22

申请号：US15793742

申请日：2017-10-25

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Mengying Li ,  Alan Paul Wood

IPC: G06F11/30 ,  G06F11/00 ,  G06F21/55 ,  G06N7/00 ,  G06F17/18 ,  G06N3/08 ,  G06N20/00

Abstract: The disclosed embodiments relate to a system for performing prognostic surveillance operations on sensor data. During operation, the system obtains a group of signals from sensors in a monitored system during operation of the monitored system. Next, if possible, the system performs a clustering operation, which divides the group of signals into groups of correlated signals. Then, for one or more groups of signals that exceed a specified size, the system randomly partitions the groups of signals into smaller groups of signals. Next, for each group of signals, the system trains an inferential model for a prognostic pattern-recognition system based on signals in the group of signals. Then, for each group of signals, the system uses a prognostic pattern-recognition system in a surveillance mode and the inferential model to detect incipient anomalies that arise during execution of the monitored system.

公开(公告)号：US20190310781A1

公开(公告)日：2019-10-10

申请号：US15947446

申请日：2018-04-06

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Dieter Gawlick

IPC: G06F3/06

Abstract: The disclosed embodiments provide a system that proactively resilvers a disk array when a disk drive in the array is determined to have an elevated risk of failure. The system receives time-series signals associated with the disk array during operation of the disk array. Next, the system analyzes the time-series signals to identify at-risk disk drives that have an elevated risk of failure. If one or more disk drives are identified as being at-risk, the system performs a proactive resilvering operation on the disk array using a background process while the disk array continues to operate using the at-risk disk drives.

公开(公告)号：US20190310617A1

公开(公告)日：2019-10-10

申请号：US15947548

申请日：2018-04-06

Applicant: Oracle International Corporation

Inventor： Mengying Li ,  Kenny C. Gross

IPC: G05B23/02 ,  G06F11/30 ,  G06F11/00 ,  G06F17/14

Abstract: The disclosed embodiments relate to a system that removes quantization effects from a set of time-series signals to produce highly accurate approximations of a set of original unquantized signals. During operation, for each time-series signal in the set of time-series signals, the system determines a number of quantization levels (NQL) in the time-series signal. Next, the system performs a fast Fourier transform (FFT) on the time-series signal to produce a set of Fourier modes for the time-series signal. The system then determines an optimal number of Fourier modes (Nmode) to reconstruct the time-series signal based on the determined NQL for the time-series signal. Next, the system selects Nmode largest-amplitude Fourier modes from the set of Fourier modes for the time-series signal. The system then performs an inverse FFT operation using the Nmode largest-amplitude Fourier modes to produce a dequantized time-series signal to be used in place of the time-series signal.