PrismDB: Read-aware Log-structured Merge Trees for Heterogeneous Storage

In recent years, emerging hardware storage technologies have focused on divergent goals: better performance or lower cost-per-bit of storage. Correspondingly, data systems that employ these new technologies are optimized either to be fast (but expensive) or cheap (but slow). We take a different approach: by combining multiple tiers of fast and low-cost storage technologies within the same system, we can achieve a Pareto-efficient balance between performance and cost-per-bit. This paper presents the design and implementation of PrismDB, a novel log-structured merge tree based key-value store that exploits a full spectrum of heterogeneous storage technologies (from 3D XPoint to QLC NAND). We introduce the notion of "read-awareness" to log-structured merge trees, which allows hot objects to be pinned to faster storage, achieving better tiering and hot-cold separation of objects. Compared to the standard use of RocksDB on flash in datacenters today, PrismDB's average throughput on heterogeneous storage is 2.3$\times$ faster and its tail latency is more than an order of magnitude better, using hardware than is half the cost

No Provisioned Concurrency: Fast RDMA-codesigned Remote Fork for Serverless Computing

Serverless platforms essentially face a tradeoff between container startup time and provisioned concurrency (i.e., cached instances), which is further exaggerated by the frequent need for remote container initialization. This paper presents MITOSIS, an operating system primitive that provides fast remote fork, which exploits a deep codesign of the OS kernel with RDMA. By leveraging the fast remote read capability of RDMA and partial state transfer across serverless containers, MITOSIS bridges the performance gap between local and remote container initialization. MITOSIS is the first to fork over 10,000 new containers from one instance across multiple machines within a second, while allowing the new containers to efficiently transfer the pre-materialized states of the forked one. We have implemented MITOSIS on Linux and integrated it with FN, a popular serverless platform. Under load spikes in real-world serverless workloads, MITOSIS reduces the function tail latency by 89% with orders of magnitude lower memory usage. For serverless workflow that requires state transfer, MITOSIS improves its execution time by 86%.Comment: To appear in OSDI'2

Replicating Persistent Memory Key-Value Stores with Efficient RDMA Abstraction

Combining persistent memory (PM) with RDMA is a promising approach to performant replicated distributed key-value stores (KVSs). However, existing replication approaches do not work well when applied to PM KVSs: 1) Using RPC induces software queueing and execution at backups, increasing request latency; 2) Using one-sided RDMA WRITE causes many streams of small PM writes, leading to severe device-level write amplification (DLWA) on PM. In this paper, we propose Rowan, an efficient RDMA abstraction to handle replication writes in PM KVSs; it aggregates concurrent remote writes from different servers, and lands these writes to PM in a sequential (thus low DLWA) and one-sided (thus low latency) manner. We realize Rowan with off-the-shelf RDMA NICs. Further, we build Rowan-KV, a log-structured PM KVS using Rowan for replication. Evaluation shows that under write-intensive workloads, compared with PM KVSs using RPC and RDMA WRITE for replication, Rowan-KV boosts throughput by 1.22X and 1.39X as well as lowers median PUT latency by 1.77X and 2.11X, respectively, while largely eliminating DLWA.Comment: Accepted to OSDI 202