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The Case for Distributed Shared-Memory Databases with RDMA-Enabled Memory Disaggregation

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arxiv 2207.03027 v1 pith:FNGNLZHS submitted 2022-07-07 cs.DB

classification cs.DB
keywords memorycasecomputedatabasedatabasesdesigndisaggregationdistributed
verification ladder T0 review T1 audit T2 compute T3 formal

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Memory disaggregation (MD) allows for scalable and elastic data center design by separating compute (CPU) from memory. With MD, compute and memory are no longer coupled into the same server box. Instead, they are connected to each other via ultra-fast networking such as RDMA. MD can bring many advantages, e.g., higher memory utilization, better independent scaling (of compute and memory), and lower cost of ownership. This paper makes the case that MD can fuel the next wave of innovation on database systems. We observe that MD revives the great debate of "shared what" in the database community. We envision that distributed shared-memory databases (DSM-DB, for short) - that have not received much attention before - can be promising in the future with MD. We present a list of challenges and opportunities that can inspire next steps in system design making the case for DSM-DB.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Outback: Fast and Communication-efficient Index for Key-Value Store on Disaggregated Memory

    cs.DB 2025-02 conditional novelty 6.0 of 10

    Outback's decoupled dynamic minimal perfect hashing index serves disaggregated-memory key-value lookups in one RDMA round trip with minimal memory-node CPU work.

  2. Efficient Vector Search on Disaggregated Memory with d-HNSW

    cs.DB 2025-05 reject novelty 5.0 of 10

    A disaggregated RDMA-based HNSW design using a small cached routing index and batched cluster fetches, with evaluation only against self-defined baselines.

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