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    Dynamic resource allocation using known future benefits
    1.
    发明授权
    Dynamic resource allocation using known future benefits 失效
    动态资源分配使用已知的未来收益

    公开(公告)号：US07765301B1

    公开(公告)日：2010-07-27

    申请号：US11352328

    申请日：2006-02-13

    申请人： Tracy J. Kimbrel ,  Robert Krauthgamer ,  Maria Minkoff ,  Baruch M. Schieber ,  Maxim I. Sviridenko ,  Jayram S. Thathachar

    发明人： Tracy J. Kimbrel ,  Robert Krauthgamer ,  Maria Minkoff ,  Baruch M. Schieber ,  Maxim I. Sviridenko ,  Jayram S. Thathachar

    IPC分类号： G06F15/16 ,  G06F17/30

    CPC分类号： G06F9/50 ,  G06Q10/063 ,  G06Q10/06315 ,  H04L29/06 ,  H04L67/1002 ,  H04L67/1008 ,  H04L67/1023 ,  H04L2029/06054

    摘要： A benefit task system implements a policy for allocating resources to yield some benefit. The method implemented may be applied to a variety of problems, and the benefit may be either tangible (e.g., profit) or intangible (e.g., customer satisfaction). In one example, the method is applied to server allocation in a Web site server “farm” given full information regarding future loads to maximize profits for the Web hosting service provider. In another example, the method is applied to the allocation of telephone help in a way to improve customer satisfaction. In yet another example, the method is applied to distributed computing problem where the resources to be allocated are general purpose computers connected in a network and used to solve computationally intensive problems. Solution of the Web server “farm” problem is based on information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach which reduces the Web server farm problem to a minimum-cost network flow problem, which can be solved in polynomial time. Similar solutions are applicable to other resource allocation problems.

    摘要翻译： 利益任务系统实施分配资源以产生一些益处的政策。 实施的方法可以应用于各种问题，并且益处可以是有形的（例如，利润）或无形的（例如，客户满意度）。 在一个示例中，该方法应用于Web站点服务器“farm”中的服务器分配，给出了有关未来负载的完整信息以最大化Web托管服务提供商的利润。 在另一个例子中，该方法应用于电话帮助的分配，以提高客户满意度。 在另一个例子中，该方法应用于分布式计算问题，其中待分配的资源是连接在网络中的通用计算机，并用于解决计算密集型问题。 Web服务器“农场”问题的解决方案是基于有关未来负载的信息，以实现接近最大收入的假设，即假设收入与服务器的利用率成正比，并根据客户类别进行区分。 服务器分配的方法使用一种将Web服务器场问题降低到最小成本网络流问题的方法，可以在多项式时间内解决。 类似的解决方案适用于其他资源分配问题。

    Dynamic resource allocation using projected future benefits
    2.
    发明授权
    Dynamic resource allocation using projected future benefits 失效
    动态资源分配使用预计的未来收益

    公开(公告)号：US07546247B2

    公开(公告)日：2009-06-09

    申请号：US11861663

    申请日：2007-09-26

    申请人： Tracy J. Kimbrel ,  Robert Krauthgarner ,  Baruch M. Schieber ,  Maxim I. Sviridenko ,  Jayram S. Thathachar

    发明人： Tracy J. Kimbrel ,  Robert Krauthgarner ,  Baruch M. Schieber ,  Maxim I. Sviridenko ,  Jayram S. Thathachar

    IPC分类号： G06F17/30

    CPC分类号： G06F9/50 ,  G06Q10/0631 ,  G06Q10/06312 ,  G06Q10/06315 ,  H04L67/1002 ,  H04L67/1008 ,  H04L67/1023

    摘要： A method for server allocation in a Web server “farm” is based on limited information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach of “discounting the future”. Specifically, when the policy faces the choice between a guaranteed benefit immediately and a potential benefit in the future, the decision is made by comparing the guaranteed benefit value with a discounted value of the potential future benefit. This discount factor is exponential in the number of time units that it would take a potential benefit to be materialized. The future benefits are discounted because by the time a benefit will be materialized, things might change and the algorithm might decide to make another choice for a potential (even greater) benefit.

    摘要翻译： Web服务器“farm”中的服务器分配方法基于有限的关于未来负载的信息，以实现接近最大可能收入的假设，即假设收入与服务器的利用率成正比，并根据客户类别区分。 服务器分配的方法采用“贴现未来”的方法。 具体来说，当政策面临保证收益立即与潜在利益之间的选择时，通过将担保收益值与潜在未来收益的折扣价值进行比较来做出决策。 这个折扣因子是要实现潜在收益的时间单位数量的指数。 未来的利益是折扣的，因为在实现利益的时候，事情可能会改变，算法可能决定为潜力（甚至更大）的利益作出另一个选择。

    Method and system for recognizing end-user transactions
    5.
    发明授权
    Method and system for recognizing end-user transactions 失效
    用于识别最终用户交易的方法和系统

    公开(公告)号：US06925452B1

    公开(公告)日：2005-08-02

    申请号：US09575553

    申请日：2000-05-22

    申请人： Joseph L. Hellerstein ,  Irina Rish ,  Jayram S. Thathachar

    发明人： Joseph L. Hellerstein ,  Irina Rish ,  Jayram S. Thathachar

    IPC分类号： G06F15/00 ,  G05B13/00 ,  G06F9/46 ,  G06F15/18

    CPC分类号： G06N99/005

    摘要： A method and system are described for end-user transaction recognition based on server data such as sequences of remote procedure calls (RPCs). The method may comprise machine-learning techniques for pattern recognition such as Bayesian classification, feature extraction mechanisms, and a dynamic-programming approach to segmentation of RPC sequences. The method preferably combines information-theoretic and machine-learning approaches. The system preferably includes a learning engine and an operation engine. A learning engine may comprise a data preparation subsystem (feature extraction) and a Bayes Net learning subsystem (model construction). The operation engine may comprise transaction segmentation and transaction classification subsystems.

    摘要翻译： 基于诸如远程过程调用序列（RPC）的服务器数据描述用于最终用户事务识别的方法和系统。 该方法可以包括用于模式识别的机器学习技术，例如贝叶斯分类，特征提取机制和用于RPC序列分割的动态规划方法。 该方法优选地结合了信息理论和机器学习方法。 该系统优选地包括学习引擎和操作引擎。 学习引擎可以包括数据准备子系统（特征提取）和贝叶斯网络学习子系统（模型构造）。 操作引擎可以包括事务分段和事务分类子系统。

    Method of obtaining data samples from a data stream and of estimating the sortedness of the data stream based on the samples
    6.
    发明授权
    Method of obtaining data samples from a data stream and of estimating the sortedness of the data stream based on the samples 有权
    从数据流获取数据样本并基于样本估计数据流的排序的方法

    公开(公告)号：US07797326B2

    公开(公告)日：2010-09-14

    申请号：US11405994

    申请日：2006-04-18

    申请人： Parikshit Gopalan ,  Robert Krauthgamer ,  Jayram S. Thathachar

    发明人： Parikshit Gopalan ,  Robert Krauthgamer ,  Jayram S. Thathachar

    IPC分类号： G06F7/00 ,  G06F17/30

    CPC分类号： G06F7/22 ,  G06F17/30864

    摘要： Disclosed is a method of scanning a data stream in a single pass to obtain uniform data samples from selected intervals. The method comprises randomly selecting elements from the stream for storage in one or more data buckets and, then, randomly selecting multiple samples from the bucket(s). Each sample is associated with a specified interval immediately prior to a selected point in time. There is a balance of probabilities between the selection of elements stored in the bucket and the selection of elements included in the samples so that elements scanned during the specified interval are included in the sample with equal probability. Samples can then be used to estimate the degree of sortedness of the stream, based on counting how many elements in the sequence are the rightmost point of an interval such that majority of the interval's elements are inverted with respect to the interval's rightmost element.

    摘要翻译： 公开了一种在单次扫描中扫描数据流以从选定间隔获得均匀数据样本的方法。 该方法包括从流中随机选择元素以存储在一个或多个数据桶中，然后从桶随机选择多个样本。 每个样本在选定的时间点之前与指定的间隔相关联。 在存储在桶中的元素的选择和包含在样本中的元素的选择之间存在概率的平衡，使得在指定间隔期间扫描的元素以相等的概率被包含在样本中。 然后可以使用样本来估计流的排序程度，这是基于计数序列中的多少个元素是间隔的最右点，使得大部分间隔的元素相对于间隔的最右边的元素被反转。

    Dynamic resource allocation using projected future benefits
    7.
    发明授权
    Dynamic resource allocation using projected future benefits 失效
    动态资源分配使用预计的未来收益

    公开(公告)号：US07308415B2

    公开(公告)日：2007-12-11

    申请号：US10000149

    申请日：2001-12-04

    申请人： Tracy J. Kimbrel ,  Robert Krauthgamer ,  Maria Minkoff ,  Baruch M. Schieber ,  Maxim I. Sviridenko ,  Jayram S. Thathachar

    发明人： Tracy J. Kimbrel ,  Robert Krauthgamer ,  Maria Minkoff ,  Baruch M. Schieber ,  Maxim I. Sviridenko ,  Jayram S. Thathachar

    IPC分类号： G06F9/46

    CPC分类号： G06F9/50 ,  G06Q10/0631 ,  G06Q10/06312 ,  G06Q10/06315 ,  H04L67/1002 ,  H04L67/1008 ,  H04L67/1023

    摘要： A method for server allocation in a Web server “farm” is based on limited information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach of “discounting the future”. Specifically, when the policy faces the choice between a guaranteed benefit immediately and a potential benefit in the future, the decision is made by comparing the guaranteed benefit value with a discounted value of the potential future benefit. This discount factor is exponential in the number of time units that it would take a potential benefit to be materialized. The future benefits are discounted because by the time a benefit will be materialized, things might change and the algorithm might decide to make another choice for a potential (even greater) benefit.

    摘要翻译： Web服务器“farm”中的服务器分配方法基于有限的关于未来负载的信息，以实现接近最大可能收入的假设，即假设收入与服务器的利用率成正比，并根据客户类别区分。 服务器分配的方法采用“贴现未来”的方法。 具体来说，当政策面临保证收益立即与潜在利益之间的选择时，通过将担保收益值与潜在未来收益的折扣价值进行比较来做出决策。 这个折扣因子是要实现潜在收益的时间单位数量的指数。 未来的利益是折扣的，因为在实现利益的时候，事情可能会改变，算法可能决定为潜力（甚至更大）的利益作出另一个选择。

    System and method for generic automated tuning for performance management
    8.
    发明授权
    System and method for generic automated tuning for performance management 有权
    用于性能管理的通用自动调整的系统和方法

    公开(公告)号：US06718358B1

    公开(公告)日：2004-04-06

    申请号：US09541368

    申请日：2000-03-31

    申请人： Joseph Phillip Bigus ,  Joseph L. Hellerstein ,  Mark S. Squillante ,  Jayram S. Thathachar

    发明人： Joseph Phillip Bigus ,  Joseph L. Hellerstein ,  Mark S. Squillante ,  Jayram S. Thathachar

    IPC分类号： G06F900

    CPC分类号： G06F9/5061

    摘要： A system and method is described for generic automated tuning for performance management. The system comprises a target to be controlled and a generic automated tuning agent (GATA) that performs this control. The controlled target provides interfaces to metrics relating to workload, service levels, and configuration information, as well as a means to adjust tuning controls that determine resource allocations within the target. The GATA inputs the metrics, estimates new tuning control settings based on service objectives specified by administrators, and outputs the tuning control settings.

    摘要翻译： 描述了用于性能管理的通用自动调整的系统和方法。 该系统包括要控制的目标和执行该控制的通用自动调整代理（GATA）。 受控目标提供与工作负载，服务级别和配置信息相关的指标的接口，以及调整确定目标内资源分配的调整控制的方法。 GATA输入指标，根据管理员指定的服务目标估计新的调整控制设置，并输出调谐控制设置。

    COMPUTING CASCADED AGGREGATES IN A DATA STREAM
    10.
    发明申请
    COMPUTING CASCADED AGGREGATES IN A DATA STREAM 审中-公开
    在数据流中计算集合

    公开(公告)号：US20110251976A1

    公开(公告)日：2011-10-13

    申请号：US12759070

    申请日：2010-04-13

    申请人： Jayram S. Thathachar ,  David P. Woodruff

    发明人： Jayram S. Thathachar ,  David P. Woodruff

    IPC分类号： G06Q40/00 ,  G06F17/18

    CPC分类号： G06F17/16 ,  G06Q10/04 ,  G06Q40/025 ,  G06Q40/06

    摘要： A method for efficiently approximating cascaded aggregates in a data stream in a single pass over a dataset, with entries presented to the methodology in an arbitrary order includes receiving out-of-order data entries in the data stream, aggregating particular data entries into aggregated data sets from the data stream based on a first characteristic of the data entries, computing a normalized Euclidean norm around mean values of each of the aggregated data sets, calculating an average of all of the normalized Euclidean norms of each of the aggregated data sets, and calculating a value based on the first characteristic as a result of calculating the average of all of the normalized Euclidean norms.

    摘要翻译： 一种用于在数据集中的单次传递中有效地逼近数据流中的级联聚合的方法，其中以任意顺序呈现给方法的条目包括接收数据流中的无序数据条目，将特定数据条目聚合成聚合数据 基于数据条目的第一特征从数据流中设置数据流，计算关于每个聚合数据集的平均值的归一化欧几里德范数，计算每个聚合数据集的所有归一化欧几里得范数的平均值;以及 作为计算所有归一化欧几里德规范的平均值的结果，基于第一特征计算值。