5 papers
O^3-LSM: Maximizing Disaggregated LSM Write Performance via Three-Layer Offloading
Qi Lin, Gangqi Huang, Te Guo +5
Log-Structured Merge-tree-based Key-Value Stores (LSM-KVS) have been optimized and redesigned for disaggregated storage via techniques such as compaction offloading to reduce the n…
TD-Orch: Efficient Task-Data Orchestration for Distributed Systems with Application to Graph Processing
Yiwei Zhao, Qiushi Lin, Hongbo Kang +5
We introduce a task-data orchestration abstraction that supports a range of distributed applications. Given a batch of lambda tasks each requesting a data item, where both tasks an…
StorageXTuner: An LLM Agent-Driven Automatic Tuning Framework for Heterogeneous Storage Systems
Qi Lin, Zhenyu Zhang, Viraj Thakkar +3
Automatically configuring storage systems is hard: parameter spaces are large and conditions vary across workloads, deployments, and versions. Heuristic and ML tuners are often sys…
ELMo-Tune-V2: LLM-Assisted Full-Cycle Auto-Tuning to Optimize LSM-Based Key-Value Stores
Viraj Thakkar, Qi Lin, Kenanya Keandra Adriel Prasetyo +4
Log-Structured Merge-tree-based Key-Value Store (LSM-KVS) is a foundational storage engine serving diverse modern workloads, systems, and applications. To suit varying use cases, L…
Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions
Zuguang Li, Wen Wu, Shaohua Wu +3
Large models (LMs) have immense potential in Internet of Things (IoT) systems, enabling applications such as intelligent voice assistants, predictive maintenance, and healthcare mo…