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公开(公告)号：US20140095693A1
公开(公告)日：2014-04-03
申请号：US13630852
申请日：2012-09-28
IPC分类号： G06F15/173 , G06F9/50
CPC分类号： G06F9/505 , G06F2209/508 , H04L67/1004 , H04L67/1008 , H04L67/101 , H04L67/1012 , H04L67/22
摘要： According to one general aspect, a method may include monitoring, via a communications network, an actual system resource usage of each of a plurality of target computing devices configured to execute one or more respective workload tasks. The method may also include receiving a request for a suggestion for an assigned target computing device to be assigned a new workload task. The method may further include providing the suggestion regarding the assigned target computing device to be assigned a new workload task, wherein the suggestion suggests one or more target computing device(s) that is included in the plurality of target computing devices. The method may also include adjusting a system resource usage profile of the assigned target computing device to include an estimated system resource usage for the new workload task and an actual system resource usage of the assigned target computing device that was previously monitored.
摘要翻译： 根据一个一般方面，一种方法可以包括经由通信网络监视被配置为执行一个或多个相应工作负载任务的多个目标计算设备中的每一个的实际系统资源使用。 该方法还可以包括接收对被分配的目标计算设备的建议的请求以被分配新的工作负载任务。 该方法还可以包括提供关于被分配的新的工作负载任务的所分配的目标计算设备的建议，其中该建议建议包括在多个目标计算设备中的一个或多个目标计算设备。 该方法还可以包括调整所分配的目标计算设备的系统资源使用简档以包括用于新工作负载任务的估计系统资源使用以及之前被监视的所分配的目标计算设备的实际系统资源使用。
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公开(公告)号：US09160634B2
公开(公告)日：2015-10-13
申请号：US13650827
申请日：2012-10-12
发明人： Paolo Cremonesi , Kanika Dhyani , Stefano Visconti
摘要： Embodiments provide a method for performing an automatic execution of a Box and Jenkins method for forecasting the behavior of said dataset. The method may include pre-processing the dataset including providing one or more missing values to the dataset, removing level discontinuities and outliers, and removing one or more last samples from the dataset, obtaining a trend of the pre-processed dataset including identifying and filtering the trend out of the dataset based on a coefficient of determination methodology, detecting seasonality to obtain a resulting stationary series including computing an auto correlation function of the dataset, repeating the detecting step on an aggregate series of a previous dataset, and removing detected seasonality based on a seasonal differencing process, and modeling the resulting stationary series under an autoregressive-moving-average (ARMA) model.
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公开(公告)号：US09356846B2
公开(公告)日：2016-05-31
申请号：US13650827
申请日：2012-10-12
发明人： Paolo Cremonesi , Kanika Dhyani , Stefano Visconti
CPC分类号： H04L43/04 , G06F11/3452 , G06N5/022 , G06N5/047 , G06Q10/06 , H04L41/142 , H04L41/147
摘要： Embodiments provide a method for performing an automatic execution of a Box and Jenkins method for forecasting the behavior of said dataset. The method may include pre-processing the dataset including providing one or more missing values to the dataset, removing level discontinuities and outliers, and removing one or more last samples from the dataset, obtaining a trend of the pre-processed dataset including identifying and filtering the trend out of the dataset based on a coefficient of determination methodology, detecting seasonality to obtain a resulting stationary series including computing an auto correlation function of the dataset, repeating the detecting step on an aggregate series of a previous dataset, and removing detected seasonality based on a seasonal differencing process, and modeling the resulting stationary series under an autoregressive-moving-average (ARMA) model.
摘要翻译： 实施例提供了一种用于执行Box和Jenkins方法的自动执行以预测所述数据集的行为的方法。 该方法可以包括预处理数据集，包括向数据集提供一个或多个缺失值，去除级别不连续性和异常值，以及从数据集中移除一个或多个最后样本，获得预处理数据集的趋势，包括识别和过滤 基于确定方法系数的数据集的趋势，检测季节性以获得所得到的固定系列，包括计算数据集的自相关函数，重复前一数据集的聚集序列上的检测步骤，以及基于检测到的季节性 在季节性差异过程中，在自回归移动平均（ARMA）模型下对所得到的固定系列进行建模。
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公开(公告)号：US20160105327A9
公开(公告)日：2016-04-14
申请号：US13650827
申请日：2012-10-12
发明人： Paolo Cremonesi , Kanika Dhyani , Stefano Visconti
CPC分类号： H04L43/04 , G06F11/3452 , G06N5/022 , G06N5/047 , G06Q10/06 , H04L41/142 , H04L41/147
摘要： Embodiments provide a method for performing an automatic execution of a Box and Jenkins method for forecasting the behavior of said dataset. The method may include pre-processing the dataset including providing one or more missing values to the dataset, removing level discontinuities and outliers, and removing one or more last samples from the dataset, obtaining a trend of the pre-processed dataset including identifying and filtering the trend out of the dataset based on a coefficient of determination methodology, detecting seasonality to obtain a resulting stationary series including computing an auto correlation function of the dataset, repeating the detecting step on an aggregate series of a previous dataset, and removing detected seasonality based on a seasonal differencing process, and modeling the resulting stationary series under an autoregressive-moving-average (ARMA) model.
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公开(公告)号：US09350627B2
公开(公告)日：2016-05-24
申请号：US13650767
申请日：2012-10-12
发明人： Paolo Cremonesi , Kanika Dhyani , Stefano Visconti
IPC分类号： G06F15/173 , H04L12/26 , G06Q10/06 , G06F11/34
CPC分类号： H04L43/04 , G06F11/3452 , G06N5/022 , G06N5/047 , G06N99/005 , G06Q10/06 , H04L41/0896 , H04L41/142 , H04L41/147 , H04L43/0876
摘要： Embodiments provide a method for upgrading resources in a system including normalizing a collected dataset, scattering data from the normalized dataset, obtaining a plurality of clusters based on the scattered data, discarding one or more clusters from the plurality of clusters with less than a percentage of a total number of observations, in each cluster, performing clusterwise regression and obtaining linear sub-clusters in a defined number, reducing one or more sub-clusters including applying a refinement procedure, removing one or more sub-clusters that fit to outliers and merging pairs of clusters that fit an equivalent model, updating one or more clusters with the reduced sub-clusters, removing one or more globular clusters, reducing a number of clusters with the refinement procedure, and de-normalizing one or more results.
摘要翻译： 实施例提供了一种用于升级系统中的资源的方法，包括归一化收集的数据集，从归一化数据集散射数据，基于分散数据获得多个聚类，从多个聚类中丢弃一个或多个聚类， 在每个集群中，总共观察数量执行聚类回归并且以定义的数量获得线性子集群，减少一个或多个子集群，包括应用细化过程，去除适合异常值并合并的一个或多个子集群 适合等效模型的群集对，用减少的子群集更新一个或多个群集，去除一个或多个球状群集，通过细化过程减少群集数量，以及对一个或多个结果进行归一化。
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公开(公告)号：US09135076B2
公开(公告)日：2015-09-15
申请号：US13630852
申请日：2012-09-28
IPC分类号： G06F15/173 , G06F9/50 , H04L29/08
CPC分类号： G06F9/505 , G06F2209/508 , H04L67/1004 , H04L67/1008 , H04L67/101 , H04L67/1012 , H04L67/22
摘要： According to one general aspect, a method may include monitoring, via a communications network, an actual system resource usage of each of a plurality of target computing devices configured to execute one or more respective workload tasks. The method may also include receiving a request for a suggestion for an assigned target computing device to be assigned a new workload task. The method may further include providing the suggestion regarding the assigned target computing device to be assigned a new workload task, wherein the suggestion suggests one or more target computing device(s) that is included in the plurality of target computing devices. The method may also include adjusting a system resource usage profile of the assigned target computing device to include an estimated system resource usage for the new workload task and an actual system resource usage of the assigned target computing device that was previously monitored.
摘要翻译： 根据一个一般方面，一种方法可以包括经由通信网络监视被配置为执行一个或多个相应工作负载任务的多个目标计算设备中的每一个的实际系统资源使用。 该方法还可以包括接收对被分配的目标计算设备的建议的请求以被分配新的工作负载任务。 该方法还可以包括提供关于被分配的新的工作负载任务的所分配的目标计算设备的建议，其中该建议建议包括在多个目标计算设备中的一个或多个目标计算设备。 该方法还可以包括调整所分配的目标计算设备的系统资源使用简档以包括用于新工作负载任务的估计系统资源使用以及之前被监视的所分配的目标计算设备的实际系统资源使用。
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公开(公告)号：US20130041644A1
公开(公告)日：2013-02-14
申请号：US13650827
申请日：2012-10-12
发明人： Paolo Cremonesi , Kanika Dhyani , Stefano Visconti
IPC分类号： G06G7/62
CPC分类号： H04L43/04 , G06F11/3452 , G06N5/022 , G06N5/047 , G06Q10/06 , H04L41/142 , H04L41/147
摘要： Embodiments provide a method for performing an automatic execution of a Box and Jenkins method for forecasting the behavior of said dataset. The method may include pre-processing the dataset including providing one or more missing values to the dataset, removing level discontinuities and outliers, and removing one or more last samples from the dataset, obtaining a trend of the pre-processed dataset including identifying and filtering the trend out of the dataset based on a coefficient of determination methodology, detecting seasonality to obtain a resulting stationary series including computing an auto correlation function of the dataset, repeating the detecting step on an aggregate series of a previous dataset, and removing detected seasonality based on a seasonal differencing process, and modeling the resulting stationary series under an autoregressive-moving-average (ARMA) model.
摘要翻译： 实施例提供了一种用于执行Box和Jenkins方法的自动执行以预测所述数据集的行为的方法。 该方法可以包括预处理数据集，包括向数据集提供一个或多个缺失值，去除级别不连续性和异常值，以及从数据集中移除一个或多个最后样本，获得预处理数据集的趋势，包括识别和过滤 基于确定方法系数的数据集的趋势，检测季节性以获得所得到的固定系列，包括计算数据集的自相关函数，重复前一数据集的聚集序列上的检测步骤，以及基于检测到的季节性 在季节性差异过程中，在自回归移动平均（ARMA）模型下对所得到的固定系列进行建模。
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