Observations show that some HPC applications periodically alternate between (i) operations (computations, local data accesses) executed on the compute nodes, and (ii) I/O transfers of data and this behavior can be predicted before-hand. While the compute nodes are allocated separately to each application, the storage is shared, and thus, I/O access can be a bottleneck leading to contention. To tackle this issue, we design new static I/O scheduling algorithms that prescribe when each application can access the storage. To design a static algorithm, we emphasize on the periodic behavior of most applications. Scheduling the I/O volume of the different applications is repeated over time. This is critical since often the number of application runs is very high. In the following article, we develop a formal background for I/O scheduling. First, we define a model, bi-colored chain scheduling, and then, we go through related results existing in the literature and explore the complexity of this problem variants. Finally, to match the HPC context, we perform experiments based on use cases matching highly parallel applications or distributed learning framework

观察表明，一些 HPC 应用程序会定期在 (i) 在计算节点上执行的操作（计算、本地数据访问）和 (ii) 数据的 I/O 传输之间交替，并且可以预先预测这种行为。虽然计算节点单独分配给每个应用程序，但存储是共享的，因此 I/O 访问可能是导致争用的瓶颈。为了解决此问题，我们设计了新的静态I / O调度算法，该算法规定了每个应用程序何时可以访问存储。为了设计静态算法，我们强调大多数应用程序的周期性行为。随着时间的推移重复调度不同应用程序的 I/O 量。这很关键，因为应用程序运行的次数通常非常多。在下面的文章中，我们为 I/O 调度开发了正式的背景。第一的，我们定义了一个模型，双色链调度，然后，我们通过文献中存在的相关结果并探索这个问题变体的复杂性。最后，为了匹配 HPC 上下文，我们根据匹配高度并行应用程序或分布式学习框架的用例进行实验