Many problems in science and engineering are usually emulated as a set of mutually interacting models, resulting in a coupled or multiphysics application. These component models show challenges originating from their interdisciplinary nature and from their computational and algorithmic complexities. In general, these models are independently developed and maintained, so that they commonly employ the global file system for exchanging their data in the coupled application. To effectively use the local file cache on the compute node for exchanging the data among the processes of such applications, and consequently boosting I/O performance, this article presents a novel mechanism to migrate a process from one compute node to another node on the basis of block I/O dependency. In this newly proposed mechanism, the block I/O dependency between two involved processes running on the different nodes is profiled as block access similarity by taking advantage of the Cohen’s kappa statistic . Then, the process is supposed to be dynamically migrated from its source node to the destination node, on which there is another process having heavy block I/O dependency. As a result, both processes can exchange their data by utilizing the local file cache instead of the global file system to reduce I/O time. The experimental results demonstrate that the I/O performance can be significantly improved, and the time required for executing the application can be resultantly decreased, as expected.

中文翻译：

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用于在本地文件缓存中交换数据的耦合应用程序中的自适应进程迁移

科学和工程中的许多问题通常被模拟为一组相互交互的模型，从而产生耦合或多物理场应用。这些组件模型显示了源自其跨学科性质以及计算和算法复杂性的挑战。通常，这些模型是独立开发和维护的，因此它们通常使用全局文件系统在耦合应用程序中交换它们的数据。为了有效地使用计算节点上的本地文件缓存在此类应用程序的进程之间交换数据，从而提高 I/O 性能，本文提出了一种新的机制，将进程从一个计算节点迁移到另一个节点。块 I/O 依赖性。在这个新提出的机制中，Cohen 的 kappa 统计量. 然后，该进程应该从其源节点动态迁移到目标节点，在该目标节点上存在另一个具有重块 I/O 依赖性的进程。因此，两个进程可以通过使用本地文件缓存而不是全局文件系统来交换它们的数据，以减少 I/O 时间。实验结果表明，可以显着提高 I/O 性能，并且可以减少执行应用程序所需的时间，正如预期的那样。