Flash-Level Asynchronous Execution for In-Storage LSM-Tree Operations

Computational storage devices (CSDs) promise to reduce host--storage data movement by executing computation inside the storage device. This is especially appealing for LSM-tree-based key--value stores, whose reads and background maintenance move large amounts of data through the host. In practice, however, in-storage LSM-tree operations often fail to deliver their full benefit. Existing CSDs largely follow a host-managed, copy-then-execute model that drives in-storage operators through synchronous or weakly overlapped I/O paths. This serializes compute-flash interactions, underutilizes device-side compute resources, and forces host storage software to handle complex coordination logic. As a result, the expected benefits of in-storage processing are often limited or even negated on realistic workloads.

We argue that the missing abstraction is asynchronous execution with device-resident dependency management. We present FLAX, a computational storage architecture that makes asynchronous, overlapped in-device I/O execution a first-class design goal for LSM-tree operations. FLAX tracks fine-grained dependencies inside the device, allowing the host to issue large batches of work while the CSD schedules and executes commands asynchronously. To further improve utilization, FLAX combines adaptive data loading with dynamic on-device buffer management, enabling higher concurrency under limited device resources. We integrate FLAX into RocksDB and offload both compaction and reads. FLAX improves throughput by up to 3.1× over host-only execution and 1.8× over a host-managed CSD baseline.