Erasure coding provides efficient fault tolerance for large-scale distributed storage systems. However, its data repair process is well-known to be resource-intensive. We find that conventional host-centric, TCP-based repair architectures suffer from severe resource contention. Even in high-bandwidth networks, such interference slows repair operations and degrades service quality. To address this limitation, we propose DPUEC, a novel architecture that fully offloads the entire repair process to a Data Processing Unit (DPU). DPUEC leverages Remote Direct Memory Access (RDMA) to enable kernel-bypass data transfers and utilizes the onboard multi-core processors of DPU for highly parallel decoding. This design establishes a physically isolated data plane for repair traffic, completely bypassing host resources. Our prototype evaluation shows that DPUEC improves repair throughput by 133% - 181% over the state-of-the-art approach across different workloads. More importantly, DPUEC delivers true performance isolation. It eliminates any interference with foreground applications and reduces host CPU utilization on the recovery node to nearly zero.
Integrating Knowledge Graphs (KGs) into Retrieval-Augmented Generation (RAG) can substantially improve LLM performance on complex question answering (QA) by reducing hallucinations and supplying structured context. However, building high-quality KGs over large corpora for edge scenarios is challenging: cloud-based processing introduces latency and dependency on remote services, while exhaustive on-device construction with LLMs is often computationally infeasible under limited hardware budgets. We observe that traditional non-LLM methods can efficiently capture explicit knowledge, and that real-world queries typically touch only a small, highly concentrated portion of the graph. As a result, static and exhaustive KG construction is redundant and inefficient. We propose Edge-AdaptiveKG, a resource-aware framework that combines an offline Seed KG (S-KG) with an online Query-driven KG (Q-KG). Lightweight non-LLM methods build the S-KG, while the LLM is invoked on demand during question answering to incrementally expand the Q-KG only when complex relations are needed. Experiments show that Edge-AdaptiveKG reduces computational overhead and inference latency, enabling KG-enhanced RAG on resource-constrained devices while maintaining competitive QA accuracy.
Yuyu Du, Juxin Niu, Chun Jason Xue et al.· IEEE International Conferenc...· 0 citations