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    • 1. 发明授权
    • Dynamic resource allocation using known future benefits
    • 动态资源分配使用已知的未来收益
    • US07765301B1
    • 2010-07-27
    • US11352328
    • 2006-02-13
    • Tracy J. KimbrelRobert KrauthgamerMaria MinkoffBaruch M. SchieberMaxim I. SviridenkoJayram S. Thathachar
    • Tracy J. KimbrelRobert KrauthgamerMaria MinkoffBaruch M. SchieberMaxim I. SviridenkoJayram S. Thathachar
    • G06F15/16G06F17/30
    • G06F9/50G06Q10/063G06Q10/06315H04L29/06H04L67/1002H04L67/1008H04L67/1023H04L2029/06054
    • 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.
    • 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。
    • 5. 发明授权
    • Method and system for recognizing end-user transactions
    • 用于识别最终用户交易的方法和系统
    • US06925452B1
    • 2005-08-02
    • US09575553
    • 2000-05-22
    • Joseph L. HellersteinIrina RishJayram S. Thathachar
    • Joseph L. HellersteinIrina RishJayram S. Thathachar
    • G06F15/00G05B13/00G06F9/46G06F15/18
    • G06N99/005
    • 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.
    • 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。
    • 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
    • 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
    • US07797326B2
    • 2010-09-14
    • US11405994
    • 2006-04-18
    • Parikshit GopalanRobert KrauthgamerJayram S. Thathachar
    • Parikshit GopalanRobert KrauthgamerJayram S. Thathachar
    • G06F7/00G06F17/30
    • G06F7/22G06F17/30864
    • 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.
    • 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。