基于Intel缓存加速软件的Ceph存储性能研究:在Ceph存储上解耦Hadoop MapReduce和HDFS

V. Shankar, Roscoe Lin
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引用次数: 6

摘要

如今,对于大多数互联网和云服务提供商来说,数据中心的存储需求正在急剧增长。由于交货时间、费用和灵活性的原因,越来越多的服务提供商正在采用软件定义存储(SDS),而不是传统的基于光纤通道的存储设备。然而,由于存储I/O无法满足不断增长的需求,数据中心受到阻碍,导致系统无法充分发挥其性能潜力。英特尔缓存加速软件(英特尔CAS)与高性能固态硬盘(ssd)相结合,通过智能缓存提高数据中心的性能,而不是极端的支出。这个案例研究展示了Apache Hadoop集群中计算和存储的解耦,这样计算和存储就可以独立扩展。当将Hadoop HDFS存储从本地硬盘驱动器解耦到外部Ceph存储时,该研究通过几个基准测试任务演示了英特尔缓存加速软件如何帮助在解耦架构下提高性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Study of Ceph Storage with Intel Cache Acceleration Software: Decoupling Hadoop MapReduce and HDFS over Ceph Storage
Storage demands in the data centers are growing dramatically for most internet and cloud service providers today. More and more service providers are adopting Software-Defined Storage (SDS) instead of traditional fiber channel based storage appliances due to the lead time, expense, and flexibility. However, data centers are held back by storage I/O that cannot keep up with ever-increasing demand, preventing systems from reaching their full performance potential. Intel Cache Acceleration Software (Intel CAS), combined with highperformance Solid State Drives (SSDs), increases data center performance via intelligent caching rather than extreme spending. This case study shows the decoupling of compute and storage in the Apache Hadoop cluster so the compute and storage can be expanded independently. While decoupling Hadoop HDFS storage from local hard drives to external Ceph storage, the study demonstrates how the Intel Cache Acceleration Software helps the increase of the performance under the decoupled architecture by several benchmarking tasks.
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