Disaggregated memory architecture decouples computing and memory resources into separate pools connected via high-speed interconnect technologies, offering substantial advantages in scalability and resource utilization. However, this architecture also poses unique challenges in designing effective index structures and concurrency protocols due to increased remote memory access overhead and its shared-everything nature. In this paper, we present DART, a lock-free two-layer hashed Adaptive Radix Tree (ART) designed to minimize remote memory access while ensuring high concurrency and crash consistency in the disaggregated memory architecture. DART incorporates a hash-based Express Skip Table at its upper layer, which reduces the round trips of remote memory access during index traversal. In the base layer, DART employs an Adaptive Hashed Layout within ART nodes, confining remote memory accesses during in-node searches to small hash buckets. By further leveraging Decoupled Metadata Organization, DART achieves lock-free atomic updates, enabling high scalability and ensuring crash consistency. Our evaluation demonstrates that DART outperforms state-of-the-art counterparts by up to 5.8× in YCSB workloads.
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Bin Zhang
Kunming University of Science and Technology
Shengan Zheng
Shanghai Jiao Tong University
Shi Shu
Jiangnan University
Proceedings of the ACM on Management of Data
Purdue University West Lafayette
Peking University
Shanghai Jiao Tong University
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Zhang et al. (Thu,) studied this question.
synapsesocial.com/papers/69d894326c1944d70ce051e0 — DOI: https://doi.org/10.1145/3786636