Publications, preprints, talks, and ongoing work.
My research addresses reliability and efficiency in large-scale distributed systems, with a secondary focus on applying learned models to classic systems problems. Below is a selected list of publications and preprints.
We present AdaptRaft, a variant of the Raft consensus algorithm that dynamically adjusts its timing parameters using a lightweight online learning model, reducing tail latency by up to 34% in geo-distributed deployments.
A structured survey of Byzantine fault-tolerant consensus protocols deployed since 2015, identifying the practical trade-offs between safety, liveness, and throughput that are often obscured in theoretical treatments.
We explore the applicability of learned index structures to distributed key-value stores, demonstrating that model-based indices can reduce lookup latency by 22% with a modest increase in memory footprint on skewed workloads.