Chenhao Ye

dblp:304/2139 · DBLP profile ↗
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4ranked-venue papers in the field
2as first author
4since 2021 · last 2026
—ORCID · conflict

Domains — venue-derived; a paper can count in several

Big Data, Cloud & Distributed Data Systems · 3 (1 first)Database Systems & Data Management · 1 (1 first)
YearPublicationVenuePosition
2026 Cache-Centric Multi-Resource Allocation for Storage Services
Chenhao Ye, Shawn Zhong, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau
FAST1
2025 Cloudscape: A Study of Storage Services in Modern Cloud Architectures
Sambhav Satija, Chenhao Ye, Ranjitha Kosgi, Romit Kankaria, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau, Kiran Srinivasan
FAST2
2023 MadFS: Per-File Virtualization for Userspace Persistent Memory Filesystems
Shawn Zhong, Chenhao Ye, Guanzhou Hu, Suyan Qu, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau, Michael M. Swift
FAST2
2023 Polaris: Enabling Transaction Priority in Optimistic Concurrency Control
abstract
Transaction priority is a critical feature for real-world database systems. Under high contention, certain classes of transactions should be given a higher chance to commit than others. Such a prioritization mechanism is commonly implemented in locking-based concurrency control protocols as some lock scheduling mechanisms, but it is rarely supported in the world of optimistic concurrency control. We present Polaris, an optimistic concurrency control protocol that supports multiple priority levels. To enforce priority, Polaris introduces a minimal amount of pessimism through a lightweight reservation mechanism. The protocol is fully optimistic among transactions within the same priority level and preserves the high throughput advantage of optimistic protocols. Our evaluation with YCSB workload shows that Polaris can make the p999 tail latency of high-priority transactions 13x lower than that of low-priority ones. With an abort-aware priority assignment policy, Polaris can deliver 1.9x higher throughput and 17x lower tail latency compared to Silo for high-contention workloads.
Chenhao Ye, Wuh-Chwen Hwang, Xiangyao Yu
Proc. ACM Manag. Data1