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公开(公告)号：US10591383B2
公开(公告)日：2020-03-17
申请号：US15248526
申请日：2016-08-26
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Anton A. Bougaev , Aleksey M. Urmanov , Kalyanaraman Vaidyanathan , David K. McElfresh
Abstract: The disclosed embodiments relate to a system that characterizes I/O performance of a computing device in terms of energy consumption across a range of vibrational operating environments. During operation, the system executes a test script on a computing device that is affixed to a programmable vibration table, wherein the test script causes the computing device to perform a predetermined I/O workload. While the test script is executing, the system controls the programmable vibration table to subject the computing device to different vibrational operating environments. At the same time, the system obtains test results by monitoring a progress of the test script and an associated power consumption of the computing device. Finally, the system uses the obtained test results to characterize the I/O performance of the computing device in terms of energy consumption across the range of vibrational operating environments.
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42.发明申请公开(公告)号：US20200081817A1
公开(公告)日：2020-03-12
申请号：US16128071
申请日：2018-09-11
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Guang C. Wang
Abstract: During operation, the system obtains the time-series sensor signals, which were gathered from sensors in a monitored system. Next, the system classifies the time-series sensor signals into stair-stepped signals and un-stair-stepped signals. The system then replaces stair-stepped values in the stair-stepped signals with interpolated values determined from un-stair-stepped values in the stair-stepped signals. Next, the system divides the time-series sensor data into a training set and an estimation set. The system then trains an inferential model on the training set, and uses the trained inferential model to replace interpolated values in the estimation set with inferential estimates. Next, the system switches roles of the training and estimation sets to produce a new training set and a new estimation set. The system then trains the inferential model on the new training set, and uses the trained inferential model to replace interpolated values in the new estimation set with inferential estimates.
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43.发明授权公开(公告)号：US10540612B2
公开(公告)日：2020-01-21
申请号：US15248807
申请日：2016-08-26
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Kalyanaraman Vaidyanathan , Guang-Tong Zhou
Abstract: The disclosed embodiments relate to a system for validating a prognostic-surveillance mechanism, which detects anomalies that arise during operation of a computer system. During operation, the system obtains telemetry data comprising a set of raw signals gathered from sensors in the computer system during operation of the computer system, wherein the telemetry signals are gathered over a monitored time period. Next, for each raw signal in the set of raw signals, the system decomposes the raw signal into deterministic and stochastic components. The system then generates a corresponding set of synthesized signals based on the deterministic and stochastic components of the raw signals, wherein the synthesized signals are generated for a simulated time period, which is longer than the monitored time period. Finally, the system uses the set of synthesized signals to validate one or more performance metrics of the prognostic-surveillance mechanism.
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公开(公告)号：US20200003812A1
公开(公告)日：2020-01-02
申请号：US16022269
申请日：2018-06-28
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Sanjeev Sondur , Richard A. Kroes
IPC: G01R22/10
Abstract: The disclosed embodiments provide a system that estimates greenhouse gas (GHG) emissions for a server computer system. During operation, the system receives time-series telemetry signals that were gathered from sensors in the server during operation of the server. Next, the system estimates a power consumption for the server based on the received time-series telemetry signals. The system then multiplies the estimated power consumption by a time interval to estimate a power consumption for the server over the time interval. Finally, the system converts the estimated power consumption for the server over the time interval into an estimate for GHG emissions for the server over the time interval.
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公开(公告)号：US20190286725A1
公开(公告)日：2019-09-19
申请号：US15925427
申请日：2018-03-19
Applicant: Oracle International Corporation
Inventor： Dieter Gawlick , Kenny C. Gross , Zhen Hua Liu , Adel Ghoneimy
Abstract: The disclosed embodiments relate to a system that preprocesses sensor data to facilitate prognostic-surveillance operations. During operation, the system obtains training data from sensors in a monitored system during operation of the monitored system, wherein the training data comprises time-series data sampled from signals produced by the sensors. The system also obtains functional requirements for the prognostic-surveillance operations. Next, the system performs the prognostic-surveillance operations on the training data and determines whether the prognostic-surveillance operations meet the functional requirements when tested on non-training data. If the prognostic-surveillance operations do not meet the functional requirements, the system iteratively applies one or more preprocessing operations to the training data in order of increasing computational cost until the functional requirements are met.
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Patent Agency Ranking
46.发明申请公开(公告)号：US20190243799A1
公开(公告)日：2019-08-08
申请号：US15887234
申请日：2018-02-02
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Mengying Li , Alan Paul Wood , Steven T. Jeffreys , Avishkar Misra , Lawrence L. Fumagalli, JR.
CPC classification number: G06N20/00 , G05B19/048 , G06F16/2474 , G06F17/14 , G06F17/18 , G06K9/6256 , H04W4/38
Abstract: The disclosed embodiments relate to a system that facilitates development of machine-learning techniques to perform prognostic-surveillance operations on time-series data from a monitored system, such as a power plant and associated power-distribution system. During operation, the system receives original time-series signals comprising sequences of observations obtained from sensors in the monitored system. Next, the system decomposes the original time-series signals into deterministic and stochastic components. The system then uses the deterministic and stochastic components to produce synthetic time-series signals, which are statistically indistinguishable from the original time-series signals. Finally, the system enables a developer to use the synthetic time-series signals to develop machine-learning (ML) techniques to perform prognostic-surveillance operations on subsequently received time-series signals from the monitored system.
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47.发明申请公开(公告)号：US20190243407A1
公开(公告)日：2019-08-08
申请号：US16052638
申请日：2018-08-02
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Guang C. Wang , Steven T. Jeffreys , Alan Paul Wood , Coleen L. MacMillan
Abstract: The disclosed embodiments relate to a system that compactly stores time-series sensor signals. During operation, the system receives original time-series signals comprising sequences of observations obtained from sensors in a monitored system. Next, the system formulizes the original time-series sensor signals to produce a set of equations, which can be used to generate synthetic time-series signals having the same correlation structure and the same stochastic properties as the original time-series signals. Finally, the system stores the formulized time-series sensor signals in place of the original time-series sensor signals.
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48.发明申请公开(公告)号：US20190236162A1
公开(公告)日：2019-08-01
申请号：US15885600
申请日：2018-01-31
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Mengying Li , Dieter Gawlick , Zhen Hua Liu
IPC: G06F17/30
CPC classification number: G06F16/1744 , G06F16/2237 , G06F16/2453 , G06F16/24561
Abstract: The disclosed embodiments relate to a system that caches time-series data in a time-series database system. During operation, the system receives the time-series data, wherein the time-series data comprises a series of observations obtained from sensor readings for each signal in a set of signals. Next, the system performs a multivariate memory vectorization (MMV) operation on the time-series data, which selects a subset of observations in the time-series data that represents an underlying structure of the time-series data for individual and multivariate signals that comprise the time-series data. The system then performs a geometric compression aging (GAC) operation on the selected subset of time-series data. While subsequently processing a query involving the time-series data, the system: caches the selected subset of the time-series data in an in-memory database cache in the time-series database system; and accesses the selected subset of the time-series data from the in-memory database cache.
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49.发明申请公开(公告)号：US20180058976A1
公开(公告)日：2018-03-01
申请号：US15248526
申请日：2016-08-26
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Anton A. Bougaev , Aleksey M. Urmanov , Kalyanaraman Vaidyanathan , David K. McElfresh
IPC: G01M7/02 , G01R21/133
Abstract: The disclosed embodiments relate to a system that characterizes I/O performance of a computing device in terms of energy consumption across a range of vibrational operating environments. During operation, the system executes a test script on a computing device that is affixed to a programmable vibration table, wherein the test script causes the computing device to perform a predetermined I/O workload. While the test script is executing, the system controls the programmable vibration table to subject the computing device to different vibrational operating environments. At the same time, the system obtains test results by monitoring a progress of the test script and an associated power consumption of the computing device. Finally, the system uses the obtained test results to characterize the I/O performance of the computing device in terms of energy consumption across the range of vibrational operating environments.
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50.发明申请公开(公告)号：US20170351964A1
公开(公告)日：2017-12-07
申请号：US15173220
申请日：2016-06-03
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Kalyanaraman Vaidyanathan , Anton A. Bougaev , Aleksey M. Urmanov
CPC classification number: G06N20/00 , G06F13/37 , G06F13/4022 , G06F13/4282
Abstract: The disclosed embodiments relate to a system that reduces bandwidth requirements for transmitting telemetry data from sensors in a computer system. During operation, the system obtains a cross-imputability value for each sensor in a set of sensors that are monitoring the computer system, wherein a cross-imputability value for a sensor indicates how well a sensor value obtained from the sensor can be predicted based on sensor values obtained from other sensors in the set. Next, the system clusters sensors in the set of sensors into two or more groups based on the determined cross-imputability values. Then, while transmitting sensor values from the set of sensors, for a group of sensors having cross-imputability values exceeding a threshold, the system selectively transmits sensor values from some but not all of the sensors in the group to reduce a number of sensor values transmitted.
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