RACE:片面rdma感知的可扩展哈希

Pengfei Zuo, Qihui Zhou, Jiazhao Sun, Liu Yang, Shuangwu Zhang, Yu Hua, James Cheng, Rongfeng He, Huabing Yan
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引用次数: 4

摘要

内存分解在数据中心是一种很有前途的技术,它具有提高资源利用率、故障隔离和弹性的优点。散列索引已被广泛用于在分布式内存系统中提供快速查找服务。但是,传统的散列索引对于分解的内存来说效率很低,因为内存池中的计算能力太弱,无法执行复杂的索引请求。为了在分解内存场景中提供高效的索引服务,本文提出了RACE散列,这是一种单向rdma感知的可扩展散列索引,具有无锁远程并发控制和高效远程调整大小的功能。RACE散列允许通过仅使用单侧RDMA谓词有效地执行所有索引操作,而不涉及内存池中的任何计算资源。为了支持高性能的远程并发访问,RACE散列利用无锁远程并发控制方案,使不同的客户端能够以无锁的方式并发地操作内存池中相同的散列索引。为了以较低的开销调整哈希表的大小,RACE哈希利用一个可扩展的远程调整大小方案来减少由于可扩展调整大小引起的额外RDMA访问,并允许在调整大小期间并发执行请求。大量的实验结果表明,在YCSB混合工作负载中,RACE散列比最先进的分布式内存散列索引的性能高出1.4 - 13.7倍。
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RACE: One-sided RDMA-conscious Extendible Hashing
Memory disaggregation is a promising technique in datacenters with the benefit of improving resource utilization, failure isolation, and elasticity. Hashing indexes have been widely used to provide fast lookup services in distributed memory systems. However, traditional hashing indexes become inefficient for disaggregated memory, since the computing power in the memory pool is too weak to execute complex index requests. To provide efficient indexing services in disaggregated memory scenarios, this article proposes RACE hashing, a one-sided RDMA-Conscious Extendible hashing index with lock-free remote concurrency control and efficient remote resizing. RACE hashing enables all index operations to be efficiently executed by using only one-sided RDMA verbs without involving any compute resource in the memory pool. To support remote concurrent access with high performance, RACE hashing leverages a lock-free remote concurrency control scheme to enable different clients to concurrently operate the same hashing index in the memory pool in a lock-free manner. To resize the hash table with low overheads, RACE hashing leverages an extendible remote resizing scheme to reduce extra RDMA accesses caused by extendible resizing and allow concurrent request execution during resizing. Extensive experimental results demonstrate that RACE hashing outperforms state-of-the-art distributed in-memory hashing indexes by 1.4–13.7× in YCSB hybrid workloads.
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