第二章论述了三维数据调度与场景管理的一般方法。
The Second chapter discusses some common way of data schedule and management of three dimensional scene.
而数据调度算法一直是P 2 P流媒体研究领域中的核心问题。
The data scheduling algorithm for P2P streaming media has been a research field in the core problem.
为改善以太网通信的实时性，系统引入了一种简单的数据调度模型。
To improve the real-time communication performance of Ethernet, a simple data schedule model is introduced.
本文提出一种基于改进粒子群算法的P 2 P流媒体数据调度方法。
This paper presents an improved particle swarm optimization algorithm based on P2P streaming media data scheduling method.
流媒体服务器集群系统中，如何分布视频数据，进行数据调度是影响系统性能的关键因素之一。
In stream media server cluster, how to distribute media data and scheduling between data is Paramount to the system's performance.
针对P2P流媒体中现有算法在数据调度时不够公平的问题，提出了一种新的具有激励作用的算法模型。
In order to solve the unfairness in the existing algorithm on data scheduling in P2P media streaming, a new algorithm model having incentive function was proposed.
另外，比较了多种内部负载调度和数据调度策略，提出了一种被动的第二级存储器命中优先法作为视频服务器的调度策略。
Furthermore, the load scheduling policy and cache disk scheduling policy are compared and the passive cache-hit priority approach is provided as the scheduling policy of the video server.
将这个全局变量保存到测试引擎的数据区域，这样允许不同的测试和不同调度的用户组进行访问。
Stores the global variable in the data area for the test engine, which allows access from different tests and different schedule user groups.
它提供强大且灵活的事务支持，可以处理安全、数据完整和资源调度。
It provides powerful and flexible transaction support, and handles security, data integrity, and resource scheduling.
生产监管者随后可以使用这些数据来支持决策制定，从而优化流程、库存控制和人员调度。
Manufacturing supervisors can then use this data for decision support to optimize the process, inventory control, and personnel scheduling.
为了捕获响应时间分解数据，您必须在一个测试或者调度中启动它，然后配置将要捕获的数据的信息。
To capture response time breakdown data, you must enable it in a test or schedule, and configure the amount of data to be captured.
HTTP/CLIAPI提供一个很便利的方法，从非 IBM环境启动或调度一个或多个数据规则执行。
The HTTP/CLI API provides a convenient way to start or schedule the execution of one or more data rules from a non-IBM environment.
要获取响应时间细分数据，您必须在一个测试或者调度中启用响应时间细分，并且设置数据的被俘获数量。
To capture response time breakdown data, you must enable response time breakdown in a test or schedule, and configure the amount of data to be captured.
这也使得收集有关调度器的数据变得简单，因为其可以在本地文件系统中轻松而又有效地收集数据并存储数据。
It also makes it simple to collect data about the scheduler, because it can easily and efficiently gather data and store it within the local file system.
如果这个任务被调度到CPU - 2上执行，那么数据就需要先在CPU - 1使其无效，并将其放到CPU - 2的缓存中。
If the task got rescheduled to CPU-2, its data would need to be invalidated in CPU-1 and brought into CPU-2.
跳过调度触发的结果通常由简单地将数据库中的时间和调度程序的当前时间相比较而产生。
This skipping over schedule triggers usually results from simply comparing the time in the database with the current time in the schedule program.
例如，一些系统以常规调度加载数据仓库执行数据解压。
For example, some systems perform data extracts for data warehouse loading on a regular schedule.
调度聚合数据报告的生成。
然而，数据网格通过提供调度、资源管理、存储保留、服务质量保证、监视和其他功能，从而优于Web服务。
However, a data grid goes beyond the Web service model by providing scheduling, resource management, storage reservations, quality-of-service assurance, monitoring, and other capabilities.
图表数据更新器的启动、调度和停止是通过调用CommonJTimer的TimerManager实现的(清单14)。
The start, schedule, and stop of the chart data updater are done by calling TimerManager of CommonJ Timer (Listing 14).
本文的下一个也是最后一个任务是，调度离线数据存储服务器上的 shell脚本 backup_remote_servers.sh 的执行。
Our next and final task involves scheduling the execution of the backup_remote_servers.sh shell script on the offsite data storage server.
第一个具有云计算能力的移动应用，有可能是商业上集中关注的移动生产力应用（business-focusedmobile productivityapplications），因为合作、数据共享、多任务以及调度都是其中的关键因素。
The first mobile apps powered by the cloud will likely be business-focused mobile productivity applications where collaboration, data sharing, multitasking, and scheduling are key factors.
通过使用PerformanceWarehouse，可以使用Process功能在长期数据上创建调度的报告，还可以使用查询或经验规则分析数据。
Using the Performance Warehouse, you can create scheduled reports on the long-term data using the Process function, or you can use queries or rules of thumb to analyze the data.
对于新数据库，要使用DB 2所提供的工具来调度备份和恢复命令。
For the new database, schedule backup and restore commands using the tools provided with DB2.
让我们了解一下实现这种新调度程序的一些关键数据结构。
Let's look at some of the key data structures implementing the new scheduler.
这比按一定周期调度数据库备份要更胜一筹。
This is beyond scheduling a database backup at some interval.
为从深层网络提取有意义的数据，搜索引擎必须分析用户的搜索词，找出如何最佳负责安排这些查询调度给特定的数据库。
To extract meaningful data from the Deep Web, search engines have to analyze users' search terms and figure out how to broker those queries to particular databases.
虚拟数据集不包含缓存刷新调度信息。
The virtual cubes do not have cache refresh scheduling information.
较小的银行将请求提交给承载于数据中心中的作业调度器。
Smaller Banks submit requests to the Job Scheduler hosted in the data center.
如果数据源数据很少变化或需要清理自动调度的缓存清理之间的缓存，那么手动清理缓存可能就是必须的。
It may be necessary to clear the cache manually if the data source data changes infrequently or if it is required to clear the cache in between automatically scheduled cache clearing.
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