RACE: One-sided RDMA-conscious Extendible Hashing

Author:

Zuo Pengfei1ORCID,Zhou Qihui2ORCID,Sun Jiazhao1ORCID,Yang Liu1ORCID,Zhang Shuangwu1ORCID,Hua Yu3ORCID,Cheng James2ORCID,He Rongfeng4ORCID,Yan Huabing5ORCID

Affiliation:

1. Huawei Cloud, Shenzhen, Guangdong, China

2. The Chinese University of Hong Kong, Hong Kong, China

3. Huazhong University of Science and Technology, Wuhan, Hubei, China

4. Huawei, Shenzhen, Guangdong, China

5. Huawei, Chengdu, Sichuan, China

Abstract

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.

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture

Reference48 articles.

1. 2022. Gen-Z Technology. Retrieved from https://genzconsortium.org/.

2. 2022. Memcached—A Distributed Memory Object Caching System. Retrieved from https://memcached.org/.

3. 2022. Redis. Retrieved from https://redis.io/.

4. Marcos K. Aguilera, Nadav Amit, Irina Calciu, Xavier Deguillard, Jayneel Gandhi, Stanko Novakovic, Arun Ramanathan, Pratap Subrahmanyam, Lalith Suresh, Kiran Tati, Rajesh Venkatasubramanian, and Michael Wei. 2018. Remote regions: A simple abstraction for remote memory. In Proceedings of the USENIX Annual Technical Conference (USENIX ATC’18). 775–787.

5. Designing Far Memory Data Structures

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