ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (9): 1953-1963.doi: 10.7544/issn1000-1239.2016.20150157

• 系统结构 • 上一篇    下一篇

一种适用于异构存储系统的缓存管理算法

李勇1,王冉1,冯丹2,施展2   

  1. 1(中国电子科技集团公司第二十八研究所 南京 210007); 2(武汉光电国家实验室(筹)(华中科技大学) 武汉 430074) (liyong01@hust.edu.cn)
  • 出版日期: 2016-09-01
  • 基金资助: 
    国家“九七三”重点基础研究发展计划基金项目(2011CB302301);国家“八六三”高技术研究发展计划基金项目(2013AA013203);国家自然科学基金项目(61025008,6123004,61472153)

A Cache Management Algorithm for the Heterogeneous Storage Systems

Li Yong1, Wang Ran1, Feng Dan2, Shi Zhan2   

  1. 1(The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210007);2(Wuhan National Laboratory for Optoelectronics (Huazhong University of Science and Technology), Wuhan 430074)
  • Online: 2016-09-01

摘要: 当前数据中心广泛采用虚拟化、混合存储等技术以满足不断增长的存储容量和性能需求,这使得存储系统异构性变得越来越普遍.异构存储系统的一个典型问题是由于设备负载和服务能力不匹配,使得存储系统中广泛使用的条带等并行访问技术难以充分发挥作用,导致性能降低.针对这一问题,提出了一种基于负载特征识别和访问性能预测的缓存分配算法(access-pattern aware and performance prediction-based cache allocation algorithm, Caper),通过缓存分配来调节不同存储设备之间的I/O负载分布,使得存储设备上的负载和其本身服务能力相匹配,从而减轻甚至消除异构存储系统中的性能瓶颈.实验结果表明,Caper算法能够有效提高异构存储系统的性能,在混合负载访问下,比Chakraborty算法平均提高了约26.1%,比Forney算法平均提高了约28.1%,比Clock算法平均提高了约30.3%,比添加预取功能的Chakraborty算法和Forney算法分别平均提高了约7.7%和17.4%.

关键词: 异构, 存储系统, 缓存, 预取, 固态盘, 性能预测

Abstract: The scale of storage system is becoming larger with the rapid increase of the amount of produced data. Along with the development of computer technologies, such as cloud computing, cloud storage and big data, higher requirements are put forward to storage systems: higher capacity, higher performance and higher reliability. In order to satisfy the increasing requirement of capacity and performance, modern data center widely adopts multi technologies to implement the dynamic increasing of storage and performance, such as virtualization, hybrid storage and so on, which makes the storage systems trend more and more heterogeneous. The heterogeneous storage system introduces multiple new problems, of which one key problem is the degradation of performance as load unbalance. Thats because the difference of capacity and performance between heterogeneous storage devices make the parallelism technologies hardly to obtain high performance, such as RAID, Erasure code. For this problem, we propose a caching algorithm based on performance prediction and identification of workload characteristic, named Caper (access-pattern aware and performance prediction-based cache allocation algorithm). The main idea of Caper algorithm is to allocate the load according to the capacity of the storage devices, which aims to alleviate the load unbalance or eliminate the performance bottleneck in the heterogeneous storage systems. The Caper algorithm is composed of three parts: prediction of performance for I/O request, analysis of caching requirement for storage device, and caching replacement policy. The algorithm also classifies the application workload into three types: random access, sequential access, and looping access. In order to ensure high caching utility, the algorithm adjusts the size of logic cache partition based on the analysis of the caching requirement. Besides, in order to adapt to the heterogeneous storage system, the Caper algorithm improves the Clock cache replacement algorithm. The experimental results also show that the Caper algorithm can significantly improve the performance about 26.1% compared with Charkraborty algorithm under mixed workloads, 28.1% compared with Forney algorithm, and 30.3% compared with Clock algorithm. Even adding prefetching operation, Caper algorithm also improves the performance about 7.7% and 17.4% compared with Charkraborty algorithm and Forney algorithm respectively.

Key words: heterogeneous, storage system, cache, prefetch, solid state drives (SSDs), performance prediction

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