IndigoStore:延迟优化的云规模块存储分布式存储后端

Yichao Li, Li Wang, Fanfan Cheng, Ji Wu, Yiyun Zhang, Yiming Zhang
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引用次数: 0

摘要

分布式块存储与云计算平台集成的主要用途是为VM(虚拟机)实例提供存储。传统的桌面和服务器应用程序往往以小I/O为主编写,并且并行性有限。因此,为这些迁移到云的应用程序提供服务的块存储的性能在很大程度上取决于小I/O的延迟。本文介绍了IndigoStore,一个优化的Ceph后端,用于实现云规模的块存储,为云虚拟机提供虚拟磁盘。indiostore的设计旨在优化Ceph BlueStore后端,最先进的分布式存储后端,以减少小I/O的平均和尾部延迟,同时不浪费磁盘带宽为大I/O服务。我们使用微基准测试和我们的生产工作负载来证明,与BlueStore相比,IndigoStore的平均延迟降低了29% ~ 44%,尾延迟高达1.23×lower 99.99百分位数,对其他性能指标没有任何明显的负面影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IndigoStore: Latency Optimized Distributed Storage Backend for Cloud-Scale Block Storage
The major usage of a distributed block storage integrated with a cloud computing platform is to provide the storage for VM (virtual machine) instances. Traditional desktop and server applications tend to be written with small I/O being dominant, and in limited parallelism. Hence the performance of block storage serving these applications migrated to cloud is largely determined by latency of small I/O. This paper presents IndigoStore, an optimized Ceph backend to implement cloud-scale block storage that provides virtual disks for cloud VMs. The design of IndigoStore aims to optimize Ceph BlueStore backend, the state-of-the-art distributed storage backend, to reduce both average and tail latency of small I/O, meanwhile not waste disk bandwidth serving large I/O. We use both microbenchmarks and our production workloads to demonstrate that IndigoStore achieves 29%∼44 % lower average latency, and up to 1.23×lower 99.99th percentile tail latency than BlueStore, without any notable negative effects on other performance metrics.
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