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Небесная энциклопедия

Космические корабли и станции, автоматические КА и методы их проектирования, бортовые комплексы управления, системы и средства жизнеобеспечения, особенности технологии производства ракетно-космических систем

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Мониторинг СМИ и социальных сетей. Сканирование интернета, новостных сайтов, специализированных контентных площадок на базе мессенджеров. Гибкие настройки фильтров и первоначальных источников.

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27-02-2014 дата публикации

SYSTEM AND METHOD FOR MEAN ESTIMATION FOR A TORSO-HEAVY TAIL DISTRIBUTION

Номер: US20140059095A1
Автор: Greg D. Adams, Timothy W. Amato, Kumar R. Dandapani, Yiping Dou, Gurudev Karanth, Anthony Douglas Thrall, Mithun Yarlagadda, ADAMS GREG D, AMATO TIMOTHY W, DANDAPANI KUMAR R, DOU YIPING, KARANTH GURUDEV, THRALL ANTHONY DOUGLAS, YARLAGADDA MITHUN, ADAMS GREG D., AMATO TIMOTHY W., DANDAPANI KUMAR R.
Принадлежит: eBay Inc.

In various example embodiments, systems and methods for estimating the mean of a dataset having a fat tail. Data sets may be partitioned into components, a “torso” component and a “tail” component. For the “tail” component of the data set a more efficient estimator can be obtained (versus the traditionally calculated mean) by using the tail data to estimate parameters for a specific distribution and then deriving the mean from the estimated parameters. The estimated mean from the torso and the estimated mean from the tail may then be combined to obtain the estimated mean for the full data. This can be applied to gross merchandise bought (GMB) by various samples of visitors and apply the experience that was provided to the sample with the highest GMB to all visitors to increase gross revenue. 1. A method of estimating the mean of a heavy-tailed probability distribution comprising:using at least one computer processor, partitioning the probability distribution into a torso subgroup and a tail subgroup;using data from the tail subgroup to estimate parameters for a specific distribution; andderiving the mean of the tail subgroup from the estimated parameters.2. The method of further including estimating the mean of the torso subgroup and assembling the estimated mean of the torso subgroup and the estimated mean of the tail subgroup into an estimated overall-mean of the heavy-tail probability distribution.3. A method of determining the population mean of heavy-tailed data comprising:using at least one computer processor, partitioning the data into non-tail and tail components;estimating the mean and standard error of the non-tail component; andestimating the mean and standard error of the tail component by fitting a parametrically defined distribution to the tail component, deriving the mean of the tail from the fitted parameter, and estimating the standard error of the mean for the tail.4. The method of further including assembling an overall estimated population mean ...

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Номер записи: 1
10-09-2020 дата публикации

CLOUD RESOURCE MANAGEMENT USING MACHINE LEARNING

Номер: US20200285503A1
Автор: YIPING DOU, TANMAYEE PRAKASH KAMATH, ARUN RAMANATHAN CHANDRASEKHAR, CLAUDE REMILLARD, MARK STEVEN SCHNITZER, BALAN SUBRAMANIAN, NEELAKANTAN SUNDARESAN, YIJIN WEI, DOU YIPING, KAMATH TANMAYEE PRAKASH, RAMANATHAN CHANDRASEKHAR ARUN, REMILLARD CLAUDE, SCHNITZER MARK STEVEN, SUBRAMANIAN BALAN, SUNDARESAN NEELAKANTAN, WEI YIJIN, DOU, YIPING, KAMATH, TANMAYEE PRAKASH, RAMANATHAN CHANDRASEKHAR, ARUN, REMILLARD, CLAUDE, SCHNITZER, MARK STEVEN, SUBRAMANIAN, BALAN, SUNDARESAN, NEELAKANTAN, WEI, YIJIN
Принадлежит:

A cloud resource management system trains, through ensemble learning, multiple time series forecasting models to forecast a future idle time of a virtual machine operating on a cloud computing service. The models are trained on historical usage and metric data of the virtual machine. The metric data includes CPU usage, disk usage and network usage. A select one of the models having the best accuracy for a target virtual machine is used in a production run to predict when the virtual machine will be idle. At this time, the virtual machine may be automatically shutdown in order to reduce the expense associated with the continued operation of the virtual machine. 1. A system , comprising:at least one processor and a memory; receive metric data of a virtual machine, the metric data including CPU usage of the virtual machine at equally-spaced time points over a first time period;', 'train at least one time series forecasting model on the metric data for the first time period;', 'apply the time series forecasting model to determine the CPU usage of the virtual machine at a time interval succeeding the first time period; and', 'when the forecasted CPU usage is below a threshold, initiate actions to reduce resource consumption of the virtual machine., 'wherein the at least one processor is configured to2. The system of claim 1 , wherein the metric data includes one or more of disk I/O usage and network I/O usage.3. The system of claim 1 , wherein the at least one processor is further configured to:apply ensemble learning to train a plurality of time series forecasting models on the metric data simultaneously.4. The system of claim 3 , wherein the plurality of time series forecasting models includes at least one of ARIMA claim 3 , ETS claim 3 , TBATS claim 3 , or a decomposable time series forecasting model.5. The system of claim 4 , wherein the at least one processor is further configured to:select one of the plurality of time series forecasting models to forecast an idle ...

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Номер записи: 2