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Gyuyeong Kim

Publications and source records attributed to Gyuyeong Kim.

4 recordsLinked to original sources

GateANN: I/O-Efficient Filtered Vector Search on SSDs

We present GateANN, an I/O-efficient SSD-based graph ANNS system that supports filtered vector search on an unmodified graph index. Existing SSD-based systems either waste I/O by post-filtering, or require expensive filter-aware index rebuilds. GateANN avoids both by decoupling graph traversal from vector retrieval. Our key insight is that traversing a node requires only its neighbor list and an approximate distance, neither of which needs the full-precision vector on SSD. Based on this, GateANN introduces graph tunneling. It checks each node's filter predicate in memory before issuing I/O and routes through non-matching nodes entirely in memory, preserving graph connectivity without any SSD read for non-matching nodes. Our experimental results show that it reduces SSD reads by up to 10x and improves throughput by up to 7.6x.

cs.OS

Database Normalization via Dual-LLM Self-Refinement

Database normalization is crucial to preserving data integrity. However, it is time-consuming and error-prone, as it is typically performed manually by data engineers. To this end, we present Miffie, a database normalization framework that leverages the capability of large language models. Miffie enables automated data normalization without human effort while preserving high accuracy. The core of Miffie is a dual-model self-refinement architecture that combines the best-performing models for normalized schema generation and verification, respectively. The generation module eliminates anomalies based on the feedback of the verification module until the output schema satisfies the requirement for normalization. We also carefully design task-specific zero-shot prompts to guide the models for achieving both high accuracy and cost efficiency. Experimental results show that Miffie can normalize complex database schemas while maintaining high accuracy.

cs.DB

Pushing the Limits of In-Network Caching for Key-Value Stores

We present OrbitCache, a new in-network caching architecture that can cache variable-length items to balance a wide range of key-value workloads. Unlike existing works, OrbitCache does not cache hot items in the switch memory. Instead, we make hot items revisit the switch data plane continuously by exploiting packet recirculation. Our approach keeps cached key-value pairs in the switch data plane while freeing them from item size limitations caused by hardware constraints. We implement an OrbitCache prototype on an Intel Tofino switch. Our experimental results show that OrbitCache can balance highly skewed workloads and is robust to various system conditions.

cs.NI

NetClone: Fast, Scalable, and Dynamic Request Cloning for Microsecond-Scale RPCs

Spawning duplicate requests, called cloning, is a powerful technique to reduce tail latency by masking service-time variability. However, traditional client-based cloning is static and harmful to performance under high load, while a recent coordinator-based approach is slow and not scalable. Both approaches are insufficient to serve modern microsecond-scale Remote Procedure Calls (RPCs). To this end, we present NetClone, a request cloning system that performs cloning decisions dynamically within nanoseconds at scale. Rather than the client or the coordinator, NetClone performs request cloning in the network switch by leveraging the capability of programmable switch ASICs. Specifically, NetClone replicates requests based on server states and blocks redundant responses using request fingerprints in the switch data plane. To realize the idea while satisfying the strict hardware constraints, we address several technical challenges when designing a custom switch data plane. NetClone can be integrated with emerging in-network request schedulers like RackSched. We implement a NetClone prototype with an Intel Tofino switch and a cluster of commodity servers. Our experimental results show that NetClone can improve the tail latency of microsecond-scale RPCs for synthetic and real-world application workloads and is robust to various system conditions.

cs.NI