云存储多卷负载均衡的LSM键值存储系统  

TANGO:a Balancer for LSM Stores with Multiple Cloud Volumes

作　　者：徐鹏[1] 周元辉 陈书宁 刘玮 李大平 万继光[1] XU Peng;ZHOU Yuan-hui;CHEN Shu-ning;LIU Wei;LI Da-ping;WAN Ji-guang(Wuhan National Laboratory for Optoelectronics,School of Computer Science&Technology in Huazhong University of Science and Technology,Key Laboratory of Information Storage System Ministry of Education of China,Wuhan 430074,China;PingCAP,Beijing 100192,China)

机构地区：[1]华中科技大学计算机科学与技术学院武汉光电国家研究中心,武汉430074 [2]北京平凯星辰科技发展有限公司,北京100192

出　　处：《小型微型计算机系统》2023年第10期2157-2165,共9页Journal of Chinese Computer Systems

基　　金：国家自然科学基金面上项目(62072196)资助;国家自然科学基金创新研究群体项目(61821003)资助.

摘　　要：单个云存储卷的IOPS和带宽性能受到限制,通过组合使用多个云存储卷的方式能以较低的费用获得更高的性能.但是,现有工作缺乏针对多云存储卷优化的LSM键值存储系统的探究.首先将现有多路径或哈希负载均衡的方案应用于使用多云存储卷的LSM键值存储系统,相对单个大容量卷的性能有显著提升;但是,现有多卷负载均衡方案的写数据策略,无法感知LSM键值存储系统的数据布局特点,导致各成员卷之间仍然存在负载不均衡的问题,不能充分发挥出多卷的最大性能.为此,提出一种云存储多卷负载均衡的LSM键值存储系统TANGO.在LSM键值存储系统由compaction新生成的sstable落盘之前,先根据统计的各个成员卷的关键信息,判断sstable与各成员卷的键范围重叠情况,然后选择键范围重叠最小的成员卷进行写入;针对读为主的负载,无法通过compaction达到负载均衡,TANGO采用后台数据迁移方式进一步达到负载均衡.在亚马逊云存储卷上的评估表明,相比相同存储容量的单卷,采用了TANGO方案的同等容量的多卷可提高7倍左右的性能;相比其它多卷方案,TANGO能提升20%以上的性能,且各成员卷间负载更加均衡.Cloud service providers(e.g.,AWS)limit the IOPS and bandwidth provisioned for a cloud storage volume,so users can join multiple volumes together to get higher IOPS and bandwidth at almost no extra cost.However,few existing works focus on LSM stores with multiple cloud volumes.Even when naively marrying existing multi-path based or hash-based workload balancing approaches to build an LSM store using multiple small cloud volumes,we observe a significant improvement when compared to using a single large cloud volume.Existing literature,on the other hand,is unaware of key-value distributions of LSM stores among volumes,resulting in I/O pressure imbalance.As a result,it is inefficient to fully unleash aggregated performance of multiple volumes and yields suboptimal performance.In this paper,we propose TANGO,a multiple-cloud-volume-based LSM store.It effectively uses LSM compaction operations to prevent potential I/O jams to a single volume by sensing LSM data layouts and collected data distributions among volumes.TANGO also actively offloads I/O pressures from bottleneck volumes through selective duplications.Evaluation results on AWS EBS volumes demonstrate that,TANGO improves performance by up to 7×when compared to a single cloud volume with the same cost.TANGO also achieves a more balanced I/O among volumes when joining multiple volumes for LSM stores,outperforming existing approaches by 20%.

关 键 词：云存储 多卷 键值存储 日志结构归并树 负载均衡 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]