Bridging Storage Semantics Using Data Labels and Asynchronous I/O
Bridging Storage Semantics Using Data Labels and Asynchronous I/O
复制标题
DOI：
10.1145/3415579
复制
发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
Anthony Kougkas;H. Devarajan;Xian-He Sun
中科院分区：
文献类型：
--
作者：
Anthony Kougkas;H. Devarajan;Xian-He Sun
在数据密集型计算时代，科学和大数据社区的大规模应用程序都表现出独特的I/O需求，导致不同存储设备和软件堆栈的激增，其中许多都有相互冲突的需求。此外，新的硬件技术和系统设计创建了对于计算存储操作可能是理想的分层组成。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。我们介绍了标签的概念，一种新的数据表示，并提出了LABIOS：一个新的，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，并通过数据配置促进原位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。
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.