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    • 6. 发明授权
    • Response time benchmarking
    • 响应时间基准
    • US08849981B2
    • 2014-09-30
    • US11566684
    • 2006-12-04
    • Brian ZuzgaMark Jacob AddlemanRamesh Mani
    • Brian ZuzgaMark Jacob AddlemanRamesh Mani
    • G06F15/173H04L29/08G06F11/34H04L29/06H04L12/26
    • G06F11/3419G06F11/3428G06F11/3495G06F2201/87G06F2201/875H04L43/0852H04L43/12H04L67/02H04L69/28
    • A benchmark response time is determined for a browser application request sent to a network server over a network. The response time is determined by performance monitoring code that is loaded into and monitors the browser application from the client. The performance monitoring code automatically sends a request to a network server; the request is not sent in response to user input. The network server receives the request, generates a response and provides the response to the browser application. The response includes a fixed amount of randomly generated data. The browser application receives and processes the response, but does not display the bytes or change the content displayed in the browser application as a result of the response. The browser application sends the times at which the browser application sends the request and the browser application completes processing the response data to the network server for further processing.
    • 确定通过网络发送到网络服务器的浏览器应用程序请求的基准响应时间。 响应时间由从客户端加载到监视浏览器应用程序的性能监视代码来确定。 性能监控代码自动向网络服务器发送请求; 该请求不响应用户输入发送。 网络服务器接收请求,生成响应并向浏览器应用程序提供响应。 响应包括固定量的随机生成的数据。 浏览器应用程序接收并处理响应,但不会显示字节或更改作为响应的结果在浏览器应用程序中显示的内容。 浏览器应用程序发送浏览器应用程序发送请求的时间,浏览器应用程序完成处理响应数据到网络服务器进行进一步处理。
    • 7. 发明授权
    • Capacity planning based on resource utilization as a function of workload
    • 基于资源利用的容量规划作为工作负载的函数
    • US08402468B2
    • 2013-03-19
    • US12049840
    • 2008-03-17
    • David Isaiah SeidmanMark Jacob Addleman
    • David Isaiah SeidmanMark Jacob Addleman
    • G06F9/46G06F15/173
    • G06F11/3452G06F11/3442G06F11/3466G06F11/3476G06F2201/865
    • Capacity planning based on resource utilization as a function of workload is disclosed. The workload may include different types of requests such as login requests, requests to visit web pages, requests to purchase an item on an online shopping site, etc. In one embodiment, data is determined for each of a plurality of workloads. The data includes characteristics of a workload and resource utilization due at least in part processing that workload. Based on the data, utilization of each of the resources as a function of workload characteristics is estimated. Further, based on the estimated resource utilization, workload characteristics that are expected to cause each respective resource to reach a certain level are predicted. That level could be 100 percent utilization, but could be another level. Capacity planning is performed based on the workload characteristics that are expected to cause each respective resource to reach a certain level.
    • 披露了基于资源利用作为工作负载的功能的容量规划。 工作负载可以包括不同类型的请求,例如登录请求,访问网页的请求,在网络购物站点上购买项目的请求等。在一个实施例中,为多个工作负载中的每一个确定数据。 数据包括工作负载和资源利用率的特征,至少部分地处理该工作负载。 根据数据,估计作为工作负载特性的函数的每个资源的利用。 此外,基于估计的资源利用率,预测预期使各个资源达到一定水平的工作量特性。 该水平可能是100%的利用率,但可能是另一个水平。 基于预期导致每个相应资源达到一定水平的工作负载特征来执行容量规划。
    • 9. 发明授权
    • Two pass automated application instrumentation
    • 双程自动化应用仪器
    • US08938729B2
    • 2015-01-20
    • US12903102
    • 2010-10-12
    • David Brooke MartinMarco GagliardiMark Jacob Addleman
    • David Brooke MartinMarco GagliardiMark Jacob Addleman
    • G06F11/36G06F11/34
    • G06F11/3612G06F11/3466G06F11/3644
    • A two-pass technique for instrumenting an application is disclosed. One pass may be performed statically by analyzing the application and inserting probes while the application is not running. Another pass may be performed dynamically by analyzing data collected by the probes while the application runs to derive metrics for the probes. One or more metrics for each probe may be analyzed to determine whether to dynamically modify the probe. By dynamically modifying the probe, the application does not need to be shut down. Dynamically modifying the probe could include removing the probe from the application or moving the probe to another component (e.g., method) in the application, as examples. For example, the probe might be moved to a component that is either up or down the call graph from the component that the probe is presently in.
    • 公开了一种用于仪器应用的双程技术。 可以通过在应用程序未运行时分析应用程序和插入探针来静态执行一遍。 可以通过在应用程序运行时分析由探针收集的数据来导出探针的度量来动态执行另一遍。 可以分析每个探针的一个或多个度量以确定是否动态修改探针。 通过动态修改探测器,应用程序不需要关闭。 作为示例,动态修改探针可以包括从应用中去除探针或将探针移动到应用中的另一个组件(例如,方法)。 例如,探针可能被移动到从探头当前所在组件的调用图中向上或向下的组件。
    • 10. 发明授权
    • Capacity planning by transaction type
    • 按交易类型进行容量规划
    • US08631401B2
    • 2014-01-14
    • US11782346
    • 2007-07-24
    • Jyoti Kumar BansalDavid Isaiah SeidmanMark Jacob Addleman
    • Jyoti Kumar BansalDavid Isaiah SeidmanMark Jacob Addleman
    • G06F9/455
    • G06F11/3476G06F9/5011G06F9/5027G06F9/5077G06F9/5083G06F11/3409G06F11/3419G06F2201/87G06F2209/5019G06F2209/508
    • Capacity planning is performed based on expected transaction load and the resource utilization for each expected transaction. Resource usage is determined for one or more transactions or URLs based on transaction specific and non-transaction specific resource usage. Once the resource usage for each transaction is known, the expected resource usage may be determined for an expected quantity of each transaction. The actual resources needed to meet the expected resource usage are then determined. Resources may include hardware or software, such as a central processing unit, memory, hard disk bandwidth, network bandwidth, and other computing system components. The expected resource usage for a transaction may based on the usage directly related to the transaction and usage not directly related to the transaction but part of a process associated with the performed transactions.
    • 基于预期的事务负载和每个预期事务的资源利用率进行容量规划。 基于特定交易和非交易资源使用情况,为一个或多个交易或URL确定资源使用情况。 一旦知道每个事务的资源使用情况,就可以针对每个事务的预期数量确定预期的资源使用情况。 然后确定满足预期资源使用所需的实际资源。 资源可以包括诸如中央处理单元,存储器,硬盘带宽,网络带宽和其他计算系统组件的硬件或软件。 事务的预期资源使用可以基于与事务直接相关的使用和与事务直接相关的使用,而是与执行的事务相关联的过程的一部分。