Myeongjae Jeon

ShoreKeeper: Accelerating Iterative Data Analytics in the Cloud via Distributed Recovery-Cost-Aware Caching

Large-scale data-intensive workloads powered by iterative graph or machine learning processing have an insatiable demand for memory due to the continuous generation of new intermediate data. Although existing caching strategies strive to keep performance-critical data in memory, they often fail in practice because they...

DIALER: A Case for Improving Rare-Class Accuracy in Retraining-Free Edge Video Analytics

DIALER is designed, a system that exploits the spare compute cycles freed by retraining-free VFM inference to mitigate rare-class misclassifications and improves rare-class accuracy by up to 14.0% without interfering with real-time VFM inference for multi-stream analytics.

Arachne: Learning to Plan Distributed Training on Dynamic Heterogeneous Clusters

FaaSHive: Addressing Limited Visibility in Function-as-a-Service via Worker-driven Scheduling

FaaSHive, a worker-driven FaaS scheduling framework that mitigates this visibility gap by partitioning workers into groups and shifting final request placement from the central manager to those groups, is presented.

Heterogeneous LLM Serving with General-Purpose Processing-Near-Memory for Retrieval-Based Sparse Attention

A heterogeneous decode-phase serving system that relocates the KV cache out of GPU memory, motivated by the retrieval-based sparse attention that recent frontier LLMs adopt to serve million-token contexts and proposes KARAT, a general-purpose PNM design that is the design point meeting all four requirements.