StreamCSD: SSD-Autonomous Stream Management via In-Storage Content Learning

Write amplification (WA) from migrating valid pages during garbage collection (GC) degrades SSD performance and lifespan. Although stream management based on high-level software semantics reduces WA, existing solutions require host modifications, hindering their adoption. We introduce StreamCSD, an SSD-autonomous stream management approach using in-storage content learning, eliminating host-side changes. Leveraging compression ratios from embedded compressors in computational storage drives (CSDs), StreamCSD employs a streaming Kmeans algorithm to cost-efficiently cluster data into streams. Evaluations show that StreamCSD reduces WA from 1.7 to 1.06 under multimodal generative AI workloads, matching state-of-the-art methods with minimal impact on bandwidth. StreamCSD operates without host modifications, promoting broader adoption of multi-stream SSDs.