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    • 2. 发明授权
    • Method and apparatus for efficiently determining rank in an LRU list
    • 用于有效地确定LRU列表中的等级的方法和装置
    • US07360043B1
    • 2008-04-15
    • US11205930
    • 2005-08-17
    • Jan L. Bonebakker
    • Jan L. Bonebakker
    • G06F12/00
    • G06F12/123G06F11/3457G06F2201/88
    • One embodiment of the present invention provides a system that manages an LRU list such that the rank, or position, of data records in the sequence can be determined efficiently. The system initializes an index field in each record to the record's initial rank. When a record is accessed, the system moves it to the beginning of the LRU list and appends the value of the record's index field to a “change list.” The system then sets the record's index field to zero. The change list effectively tracks the records accessed since initialization, and combined with the records' index fields can be used to efficiently compute the rank of any record in the list. This ability to efficiently compute the rank of the data record in the LRU list reduces the frequency with which the computationally-expensive initialization operation must be executed on the LRU list.
    • 本发明的一个实施例提供了一种管理LRU列表的系统,使得能够有效地确定序列中的数据记录的等级或位置。 系统将每个记录中的索引字段初始化为记录的初始等级。 当访问记录时,系统将其移动到LRU列表的开头,并将记录的索引字段的值追加到“更改列表”。 然后,系统将记录的索引字段设置为零。 更改列表有效地跟踪从初始化后访问的记录,并结合记录的索引字段可以有效地计算列表中任何记录的排名。 这种有效计算LRU列表中的数据记录的等级的能力降低了在LRU列表上必须执行计算昂贵的初始化操作的频率。
    • 3. 发明授权
    • System and method for automated workload characterization of an application server
    • 用于应用服务器自动化工作负载表征的系统和方法
    • US07716335B2
    • 2010-05-11
    • US11346900
    • 2006-02-03
    • Darpan DinkerHerbert D. SchwetmanJan L. Bonebakker
    • Darpan DinkerHerbert D. SchwetmanJan L. Bonebakker
    • G06F15/173
    • G06F11/3409G06F11/3461G06F11/3466G06F11/3476G06F2201/865G06F2201/87G06F2201/88G06F2201/885
    • An application server may be instrumented to provide a resource measurement framework to collect resource usage data regarding request processing by the application server and applications executing on the application server. The resource measurement framework of an application server may collect hardware and software resource usage data regarding request processing at interception points located at interfaces between application components and services or other components of the application server by instrumenting those interfaces. The resource measurement framework may collect resource usage by instrumenting standard interfaces and/or methods of various specifications, such as implemented by containers or other components of the application server. Thus, the resource measurement framework may collect resource usage for applications or application components that do not include any resource measuring capabilities. The collected resource usage data may be parsed and combined to create an overall characterization of resource usage corresponding to the application server's request processing.
    • 可以对应用服务器进行测试,以提供资源测量框架来收集关于应用服务器和在应用服务器上执行的应用的请求处理的资源使用数据。 应用服务器的资源测量框架可以通过测试这些接口来收集关于位于应用组件和应用服务或应用服务器的其他组件之间的接口处的拦截点处的请求处理的硬件和软件资源使用数据。 资源测量框架可以通过测试标准接口和/或各种规范的方法(例如由应用服务器的容器或其他组件实现)来收集资源使用。 因此,资源测量框架可以收集不包括任何资源测量能力的应用或应用组件的资源使用。 收集的资源使用数据可以被解析和组合,以创建与应用服务器的请求处理相对应的资源使用的总体特性。
    • 5. 发明授权
    • Method and apparatus for computing a distance metric between computer system workloads
    • 用于计算计算机系统工作负载之间的距离度量的方法和装置
    • US07398191B1
    • 2008-07-08
    • US11111151
    • 2005-04-20
    • Ilya GluhovskyJan L. Bonebakker
    • Ilya GluhovskyJan L. Bonebakker
    • G06F17/50G06F9/45G06F19/00G06F15/00
    • G06F11/3428G06F11/3447
    • One embodiment of the present invention provides a system that computes a distance metric between computer system workloads. During operation, the system receives a dataset containing metrics that have been collected for a number of workloads of interest. Next, the system uses splines to define bases for a regression model which uses a performance indicator y as a response and uses the metrics (represented by a vector x) as predictors. The system then fits the regression model to the dataset using a penalized least squares (PLS) criterion to obtain functions f1, . . . , fP, which are smooth univariate functions of individual metrics that add up to the regression function f, such that y=f(x)+ε= ∑ i = 1 P ⁢ f i ⁡ ( x i ) + ɛ , wherein ε represents noise. Finally, the system uses the fitted regression function to define the distance metric.
    • 本发明的一个实施例提供了一种计算计算机系统工作负载之间的距离度量的系统。 在操作期间,系统接收包含已针对感兴趣的多个工作负载收集的指标的数据集。 接下来,系统使用样条来定义回归模型的基数,该回归模型使用绩效指标y作为响应并使用度量(由向量x表示)作为预测变量。 然后,系统使用惩罚最小二乘法(PLS)准则将回归模型拟合到数据集,以获得函数f 1。 。 。 ,其是加在回归函数f上的各个度量的平滑单变量函数,使得y = f(x)+ε=&Sigma; maths id =“MATH-US-00001” num =“00001”> Σ i = < / MO> 1 P MI> i / MSUB> + ɛ > 其中epsilon表示噪声。 最后,系统使用拟合回归函数来定义距离度量。