分类存储系统网络拥塞控制中的I/O吞吐量降低

Danlin Jia, Yiming Xie, Li Wang, Xiaoqian Zhang, Allen Yang, Xuebin Yao, Mahsa Bayati, Pradeep Subedi, B. Sheng, N. Mi
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引用次数: 0

摘要

业界普遍采用分散式存储系统,为超大规模架构提供高质量的服务。这种基础设施使组织能够访问可以独立管理、配置和扩展的存储资源。它得到了全闪存阵列和NVMe-over- fabric协议的最新进展的支持,可以通过不同的网络结构远程访问NVMe设备。针对传统远程直接存储器访问协议(RDMA)中的网络拥塞问题,人们提出了大量的研究。然而,NVMe-oF给分布式存储系统的拥塞控制带来了新的挑战。在这项工作中,我们研究了传统网络拥塞控制机制导致的存储节点读吞吐量的性能下降。我们设计了一个存储端速率控制(SRC)来缓解网络拥塞,同时避免存储节点的性能下降。首先,在NVMe驱动层设计I/O吞吐量控制机制,实现对存储节点的吞吐量控制。其次,我们构建了吞吐量预测模型来学习工作负载特征与I/O吞吐量之间的映射函数。第三,我们在存储节点上部署SRC,以配合NVMe-over-RDMA架构下的传统网络拥塞控制。最后,我们用不同的工作负载、SSD配置和网络拓扑来评估SRC。实验结果表明,该算法取得了显著的性能提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SRC: Mitigate I/O Throughput Degradation in Network Congestion Control of Disaggregated Storage Systems
The industry has adopted disaggregated storage systems to provide high-quality services for hyper-scale architectures. This infrastructure enables organizations to access storage resources that can be independently managed, configured, and scaled. It is supported by the recent advances of all-flash arrays and NVMe-over-Fabric protocol, enabling remote access to NVMe devices over different network fabrics. A surge of research has been proposed to mitigate network congestion in traditional remote direct memory access protocol (RDMA). However, NVMe-oF raises new challenges in congestion control for disaggregated storage systems.In this work, we investigate the performance degradation of the read throughput on storage nodes caused by traditional network congestion control mechanisms. We design a storage-side rate control (SRC) to relieve network congestion while avoiding performance degradation on storage nodes. First, we design an I/O throughput control mechanism in the NVMe driver layer to enable throughput control on storage nodes. Second, we construct a throughput prediction model to learn a mapping function between workload characteristics and I/O throughput. Third, we deploy SRC on storage nodes to cooperate with traditional network congestion control on an NVMe-over-RDMA architecture. Finally, we evaluate SRC with varying workloads, SSD configurations, and network topologies. The experimental results show that SRC achieves significant performance improvement.
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