34 citations · 43 across the 13 of their papers we have counts for
13 papers
InfraBench: Evaluating Infrastructure Agents Across Layers, Lifecycle, and Risk
Yuan Gao, Zeren Yang, Junnan Li +6
Managing modern computing infrastructure has become a steadily harder problem due to the ever-increasing complexity. Recent advances in AI agents create a timely opportunity to aut…
TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training
Chenhao Ye, Huaizheng Zhang, Mingcong Han +11
Modern LLM reinforcement learning (RL) workloads require a high-performance weight transfer system to scale training across heterogeneous compute resources. However, efficiently tr…
Don't Let AI Agents YOLO Your Files: Information and Control in Agent-Native Filesystems
Shawn Wanxiang Zhong, Junxuan Liao, Jing Liu +3
AI coding agents regularly misuse their filesystem access, causing data corruption, loss, and leakage. We conduct the first systematic study of this problem through an analysis of…
OBASE: Object-Based Address-Space Engineering to Improve Memory Tiering
Vinay Banakar, Suli Yang, Kan Wu +3
Hardware and OS mechanisms for memory tiering are widely deployed, yet datacenters still overprovision DRAM. The root cause is hotness fragmentation: allocators place objects by si…
Getting the MOST out of your Storage Hierarchy with Mirror-Optimized Storage Tiering
Kaiwei Tu, Kan Wu, Andrea C. Arpaci-Dusseau +1
We present Mirror-Optimized Storage Tiering (MOST), a novel tiering-based approach optimized for modern storage hierarchies. The key idea of MOST is to combine the load balancing a…
Tidying Up the Address Space
Vinay Banakar, Suli Yang, Kan Wu +3
Memory tiering in datacenters does not achieve its full potential due to hotness fragmentation -- the intermingling of hot and cold objects within memory pages. This fragmentation…
Bodega: Serving Linearizable Reads Locally from Anywhere at Anytime via Roster Leases
Guanzhou Hu, Andrea Arpaci-Dusseau, Remzi Arpaci-Dusseau
We present Bodega, the first consensus protocol that serves linearizable reads locally from any desired node, regardless of interfering writes. Bodega achieves this via a novel ros…
Crossword: Adaptive Consensus for Dynamic Data-Heavy Workloads
Guanzhou Hu, Yiwei Chen, Andrea Arpaci-Dusseau +1
We present Crossword, a flexible consensus protocol for dynamic data-heavy workloads, a rising challenge in the cloud where replication payload sizes span a wide spectrum and intro…
Zerrow: True Zero-Copy Arrow Pipelines in Bauplan
Yifan Dai, Jacopo Tagliabue, Andrea Arpaci-Dusseau +2
Bauplan is a FaaS-based lakehouse specifically built for data pipelines: its execution engine uses Apache Arrow for data passing between the nodes in the DAG. While Arrow is known…
Foreactor: Exploiting Storage I/O Parallelism with Explicit Speculation
Guanzhou Hu, Andrea Arpaci-Dusseau, Remzi Arpaci-Dusseau
We introduce explicit speculation, a variant of I/O speculation technique where I/O system calls can be parallelized under the guidance of explicit application code knowledge. We p…
A Unified, Practical, and Understandable Model of Non-transactional Consistency Levels in Distributed Replication
Guanzhou Hu, Andrea Arpaci-Dusseau, Remzi Arpaci-Dusseau
We present a practical model of non-transactional consistency levels in the context of distributed data replication. Unlike prior work, our simple Shared Object Pool (SOP) model de…
WiscSort: External Sorting For Byte-Addressable Storage
Vinay Banakar, Kan Wu, Yuvraj Patel +3
We present WiscSort, a new approach to high-performance concurrent sorting for existing and future byte-addressable storage (BAS) devices. WiscSort carefully reduces writes, exploi…
From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees
Yifan Dai, Yien Xu, Aishwarya Ganesan +4
We introduce BOURBON, a log-structured merge (LSM) tree that utilizes machine learning to provide fast lookups. We base the design and implementation of BOURBON on empirically-grou…