As the capacity of Solid-State Drives (SSDs) is constantly being optimised and boosted with gradually reduced cost, the SSD cluster is now widely deployed as part of the hybrid storage system in various scenarios such as cloud computing and big data processing. However, despite its rapid developments, the performance of the SSD cluster remains largely under-investigated, leaving its sub-optimal applications in reality. To address this issue, in this paper we conduct extensive empirical studies for a comprehensive understanding of the SSD cluster in diverse settings. To this end, we configure a real SSD cluster and gather the generated trace data based on some often-used benchmarks, then adopt analytical methods to analyse the performance of the SSD cluster with different configurations. In particular, regression models are built to provide better performance predictability under broader configurations, and the correlations between influential factors and performance metrics with respect to different numbers of nodes are investigated, which reveal the high scalability of the SSD cluster. Additionally, the cluster's network bandwidth is inspected to explain the performance bottleneck. Finally, the knowledge gained is summarised to benefit the SSD cluster deployment in practice.

翻译：随着固态硬盘（SSD）容量持续优化提升且成本逐渐降低，SSD集群已广泛部署为混合存储系统的组成部分，应用于云计算和大数据处理等多种场景。然而，尽管SSD技术发展迅速，其集群性能尚未得到充分研究，导致实际应用中存在次优部署问题。为解决这一挑战，本文针对不同场景下的SSD集群开展了系统性实证研究。具体而言，我们部署了真实SSD集群环境，基于常用基准测试工具采集运行时迹数据，并采用分析方法评估不同配置下SSD集群的性能表现。研究重点包括：通过构建回归模型提升更广泛配置条件下的性能可预测性；探究不同节点规模下影响因素与性能指标间的关联性，揭示SSD集群的高扩展性特征；同时通过检查集群网络带宽揭示性能瓶颈成因。最终，我们归纳总结研究结果，为SSD集群的实际部署提供指导性建议。