Leveraging Glocality for Fast Failure Recovery in Distributed RAM Storage

Yiming Zhang, Dongsheng Li, Ling Liu
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引用次数: 9

Abstract

Distributed RAM storage aggregates the RAM of servers in data center networks (DCN) to provide extremely high I/O performance for large-scale cloud systems. For quick recovery of storage server failures, MemCube [53] exploits the proximity of the BCube network to limit the recovery traffic to the recovery servers’ 1-hop neighborhood. However, the previous design is applicable only to the symmetric BCube(n,k) network with nk+1 nodes and has suboptimal recovery performance due to congestion and contention. To address these problems, in this article, we propose CubeX, which (i) generalizes the “1-hop” principle of MemCube for arbitrary cube-based networks and (ii) improves the throughput and recovery performance of RAM-based key-value (KV) store via cross-layer optimizations. At the core of CubeX is to leverage the glocality (= globality + locality) of cube-based networks: It scatters backup data across a large number of disks globally distributed throughout the cube and restricts all recovery traffic within the small local range of each server node. Our evaluation shows that CubeX not only efficiently supports RAM-based KV store for cube-based networks but also significantly outperforms MemCube and RAMCloud in both throughput and recovery time.
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利用局部性实现分布式RAM存储中的快速故障恢复
分布式RAM存储将数据中心网络(DCN)中服务器的RAM聚合在一起,为大规模云系统提供极高的I/O性能。为了快速恢复存储服务器故障,MemCube[53]利用BCube网络的邻近性,将恢复流量限制在恢复服务器的1跳邻居中。但是,上述设计仅适用于具有nk+1个节点的对称BCube(n,k)网络,并且由于拥塞和争用,恢复性能不是最优。为了解决这些问题,在本文中,我们提出了CubeX,它(i)将MemCube的“1-hop”原则推广到任意基于立方体的网络中,(ii)通过跨层优化提高基于ram的键值(KV)存储的吞吐量和恢复性能。CubeX的核心是利用基于多维数据集的网络的全局局部性(=全局性+局部性):它将备份数据分散到全局分布在整个多维数据集的大量磁盘上,并将所有恢复流量限制在每个服务器节点的小本地范围内。我们的评估表明,CubeX不仅有效地支持基于ram的基于立方体网络的KV存储,而且在吞吐量和恢复时间方面都明显优于MemCube和RAMCloud。
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