| 1. | To perform a calculation , the data and computation components need to be aligned on the same grid node
要执行计算，需要将数据组件和计算组件调节到同一网格节点上。 |
| 2. | What we really need , then , is an extended metascheduler - one that can take care of both data and compute aspects of the problem
我们真正需要的是一个扩展的元调度器它可以控制这个问题中的数据和计算组件。 |
| 3. | The test of safety supervision software system shows a satisfactory performance on a pc as well as on internet
在单机环境和internet上分别对安全监察管理软件和通风计算组件主页进行测试，注册过的组件运行正常。 |
| 4. | We must take the relative localities of executables and data into account to ensure that both data and computation are co - located on the same physical system
我们必须利用可执行程序与数据之间的相对本地性，从而确保数据和计算组件位于相同的物理系统上。 |
| 5. | In an environment where different grid middleware solutions are aligning data and computation components onto the same grid node , we need an entity that will make high - level decisions
在不同的网格中间件解决方案会将数据和计算组件调节到同一网格节点上的环境中，我们需要一个制定高层决策的实体。 |
| 6. | This understanding gives us a framework within which we can investigate the challenges we face when trying to align the data and computation components of a job onto the same grid node
这些知识为我们提供了一个框架，在这个框架中，我们可以调研在试图将某个任务的数据和计算组件调节到同一网格节点上时所面临的挑战。 |
| 7. | An understanding of this gives us a framework within which we can investigate the challenges we face when trying to align the data and computation components of a job onto the same grid node
这些知识为我们提供了一个框架，在这个框架中，我们可以调研在试图将某个任务的数据和计算组件调节到同一网格节点上时所面临的挑战。 |
| 8. | In part 3 , we ? ll discuss ways to overcome the decisions made by various grid middleware solutions when they try to align the data and computation components of jobs onto the same grid node
在下一篇文章中，我们将讨论如何解决在试图将某个任务的数据和计算组件调节到同一网格节点上时由各种网格中间件解决方案制定的决策之间的冲突。 |
| 9. | The real challenge here is that when we create some form of workflow that incorporates web services the calculation component from our earlier discussion and data services the data component , we need to have a workflow view of how the data will be used
此处真正的挑战是，当我们创建一些合并web服务（前面讨论的计算组件）和数据服务（数据组件）的任务流形式时，我们需要有一个任务流视图来了解数据是如何使用的。 |
| 10. | When the decision is obvious , the decisions made by the job scheduler the entity moving the compute component to the node and the data mover the entity moving the data component align , and both computation and data align on the same node
当决策非常明显时，由任务调度程序（将计算组件移动到节点上的实体）和数据移动程序（移动数据组件的实体）所制定的决策就可以进行调整，计算和数据组件也可以在同一节点上进行调整。 |
