DEX: Scalable Range Indexing on Disaggregated Memory [Extended Version]
Baotong Lu, Kaisong Huang, Chieh-Jan Mike Liang, Tianzheng Wang, Eric, Lo

TL;DR
This paper introduces DEX, a scalable B+-tree designed for disaggregated memory systems, employing techniques like logical partitioning and caching to reduce remote accesses and improve performance.
Contribution
DEX presents a novel scalable B+-tree architecture with techniques tailored for disaggregated memory, outperforming existing solutions significantly.
Findings
DEX outperforms state-of-the-art by 1.7--56.3X
Performance gains are consistent across different cache sizes
DEX maintains efficiency under various data skewness conditions
Abstract
Memory disaggregation can potentially allow memory-optimized range indexes such as B+-trees to scale beyond one machine while attaining high hardware utilization and low cost. Designing scalable indexes on disaggregated memory, however, is challenging due to rudimentary caching, unprincipled offloading and excessive inconsistency among servers. This paper proposes DEX, a new scalable B+-tree for memory disaggregation. DEX includes a set of techniques to reduce remote accesses, including logical partitioning, lightweight caching and cost-aware offloading. Our evaluation shows that DEX can outperform the state-of-the-art by 1.7--56.3X, and the advantage remains under various setups, such as cache size and skewness.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAlgorithms and Data Compression
