arXiv subjects

Myungcheol Lee

Publications and source records attributed to Myungcheol Lee.

4 recordsLinked to original sources

arXiv2026

DiaLSM: Towards Write-Stall-Free Performance via Shard-based LSM-tree

Log-Structured Merge-tree (LSM) aims to achieve high write throughput, but is known to experience the write stall problems when subjected to sustained write pressure. We quantify the occurrence probability and average duration of write stalls in LSM using a queuing model in the write--flush--compaction pipeline, moving beyond existing empirical analysis. The proposed model demonstrates that a monolithic LSM with a single pipeline cannot eliminate write stalls, revealing that internal sharding within the LSM offers an opportunity for fundamental write stall mitigation. To break this structural bottleneck, we propose DiaLSM, an internally shard-based LSM architecture. Instead of forcing all writes through one pipeline, DiaLSM splits the write--flush--compaction path into multiple independent shards and employs dynamic fallback redirection, allowing writes to proceed even when some shards stall. Implemented on RocksDB, DiaLSM achieves up to 2.4x higher throughput, 94% lower stalls, and significantly lower latency than state-of-the-art methods ADOC and Sub-Compaction, as demonstrated by db_bench, YCSB, and Sysbench OLTP evaluations.

cs.DB↗

arXiv2026

POaaS: Minimal-Edit Prompt Optimization as a Service to Lift Accuracy and Cut Hallucinations on On-Device sLLMs

Small language models (sLLMs) are increasingly deployed on-device, where imperfect user prompts--typos, unclear intent, or missing context--can trigger factual errors and hallucinations. Existing automatic prompt optimization (APO) methods were designed for large cloud LLMs and rely on search that often produces long, structured instructions; when executed under an on-device constraint where the same small model must act as optimizer and solver, these pipelines can waste context and even hurt accuracy. We propose POaaS, a minimal-edit prompt optimization layer that routes each query to lightweight specialists (Cleaner, Paraphraser, Fact-Adder) and merges their outputs under strict drift and length constraints, with a conservative skip policy for well-formed prompts. Under a strict fixed-model setting with Llama-3.2-3B-Instruct and Llama-3.1-8B-Instruct, POaaS improves both task accuracy and factuality while representative APO baselines degrade them, and POaaS recovers up to +7.4% under token deletion and mixup. Overall, per-query conservative optimization is a practical alternative to search-heavy APO for on-device sLLMs.

cs.AI↗

arXiv2024

A Host-SSD Collaborative Write Accelerator for LSM-Tree-Based Key-Value Stores

Log-Structured Merge (LSM) tree-based Key-Value Stores (KVSs) are widely adopted for their high performance in write-intensive environments, but they often face performance degradation due to write stalls during compaction. Prior solutions, such as regulating I/O traffic or using multiple compaction threads, can cause unexpected drops in throughput or increase host CPU usage, while hardware-based approaches using FPGA, GPU, and DPU aimed at reducing compaction duration introduce additional hardware costs. In this study, we propose KVACCEL, a novel hardware-software co-design framework that eliminates write stalls by leveraging a dual-interface SSD. KVACCEL allocates logical NAND flash space to support both block and key-value interfaces, using the key-value interface as a temporary write buffer during write stalls. This strategy significantly reduces write stalls, optimizes resource usage, and ensures consistency between the host and device by implementing an in-device LSM-based write buffer with an iterator-based range scan mechanism. Our extensive evaluation shows that for write-intensive workloads, KVACCEL outperforms ADOC by up to 1.17x in terms of throughput and performance-to-CPU-utilization efficiency. For mixed read-write workloads, both demonstrate comparable performance.

cs.AR↗

arXiv2023

An Analytical Model-based Capacity Planning Approach for Building CSD-based Storage Systems

The data movement in large-scale computing facilities (from compute nodes to data nodes) is categorized as one of the major contributors to high cost and energy utilization. To tackle it, in-storage processing (ISP) within storage devices, such as Solid-State Drives (SSDs), has been explored actively. The introduction of computational storage drives (CSDs) enabled ISP within the same form factor as regular SSDs and made it easy to replace SSDs within traditional compute nodes. With CSDs, host systems can offload various operations such as search, filter, and count. However, commercialized CSDs have different hardware resources and performance characteristics. Thus, it requires careful consideration of hardware, performance, and workload characteristics for building a CSD-based storage system within a compute node. Therefore, storage architects are hesitant to build a storage system based on CSDs as there are no tools to determine the benefits of CSD-based compute nodes to meet the performance requirements compared to traditional nodes based on SSDs. In this work, we proposed an analytical model-based storage capacity planner called CSDPlan for system architects to build performance-effective CSD-based compute nodes. Our model takes into account the performance characteristics of the host system, targeted workloads, and hardware and performance characteristics of CSDs to be deployed and provides optimal configuration based on the number of CSDs for a compute node. Furthermore, CSDPlan estimates and reduces the total cost of ownership (TCO) for building a CSD-based compute node. To evaluate the efficacy of CSDPlan, we selected two commercially available CSDs and 4 representative big data analysis workloads.

cs.DC↗