Disaggregating memory from compute offers the opportunity to better utilize stranded memory in cloud data centers. It is important to cache data in the compute nodes and maintain cache coherence across multiple compute nodes. However, the limited computing power on disaggregated memory servers makes traditional cache coherence protocols suboptimal, particularly in the case of stranded memory. This paper introduces SELCC; a Shared-Exclusive Latch Cache Coherence protocol that maintains cache coherence without imposing any computational burden on the remote memory side. It aligns the state machine of the shared-exclusive latch protocol with the MSI protocol, thereby ensuring both atomicity of data access and cache coherence with sequential consistency. SELCC embeds cache-ownership metadata directly into the RDMA latch word, enabling efficient cache ownership management via RDMA atomic operations. SELCC can serve as an abstraction layer over disaggregated memory with APIs that resemble main-memory accesses. A concurrent B-tree and three transaction concurrency control algorithms are realized using SELCC's abstraction layer. Experimental results show that SELCC significantly outperforms RPC-based protocols for cache coherence under limited remote computing power. Applications on SELCC achieve comparable or superior performance over disaggregated memory compared to competitors.

The emerging trend of memory disaggregation where CPU and memory are physically separated from each other and are connected via ultra-fast networking, e.g., over RDMA, allows elastic and independent scaling of compute (CPU) and main memory. This paper investigates how indexing can be efficiently designed in the memory disaggregated architecture. Although existing research has optimized the B-tree for this new architecture, its performance is moderate. This paper focuses on LSM-based indexing and proposes dLSM, the first highly optimized LSM-tree for disaggregated memory. dLSM introduces a suite of optimizations including reducing software overhead, leveraging near-data computing, tuning for byte-addressability, and an instantiation over RDMA as a case study with RDMA-specific customizations to improve system performance. Experiments illustrate that dLSM achieves 1.6× to 11.7× higher write throughput than running the optimized B-tree and four adaptations of existing LSM-tree indexes over disaggregated memory. dLSM is written in C++ (with approximately 41,000 LOC), and is open-sourced.

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.