In the era of data-intensive computing, large-scale applications, in both scientific and the BigData communities, demonstrate unique I/O requirements leading to a proliferation of different storage devices and software stacks, many of which have conflicting requirements. Further, new hardware technologies and system designs create a hierarchical composition that may be ideal for computational storage operations. In this article, we investigate how to support a wide variety of conflicting I/O workloads under a single storage system. We introduce the idea of a Label , a new data representation, and, we present LABIOS: a new, distributed, Label- based I/O system. LABIOS boosts I/O performance by up to 17× via asynchronous I/O, supports heterogeneous storage resources, offers storage elasticity, and promotes in situ analytics and software defined storage support via data provisioning. LABIOS demonstrates the effectiveness of storage bridging to support the convergence of HPC and BigData workloads on a single platform.

中文翻译：

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使用数据标签和异步 I/O 桥接存储语义

在数据密集型计算时代，科学和大数据社区中的大规模应用程序展示了独特的 I/O 要求，导致不同存储设备和软件堆栈的激增，其中许多具有相互冲突的要求。此外，新的硬件技术和系统设计创建了一个分层组合，可能是计算存储操作的理想选择。在本文中，我们将研究如何在单个存储系统下支持各种冲突的 I/O 工作负载。我们介绍一个概念标签，一种新的数据表示，并且，我们介绍了 LABIOS：一种新的、分布式的、基于标签的 I/O 系统。LABIOS 通过异步 I/O 将 I/O 性能提升高达 17 倍，支持异构存储资源，提供存储弹性，并促进原位通过数据配置提供分析和软件定义存储支持。LABIOS 展示了存储桥接支持在单个平台上融合 HPC 和 BigData 工作负载的有效性。