Junyi (Kyle) Shu

151 N. Zhongguancun Ave

Yanyuan Mansion, Rm 818

Beijing, China

I am a final-year Ph.D. candidate at Peking University. My research interests lie in building scalable and elastic cloud storage atop modern network/storage hardware (e.g., RDMA NIC, DPU & NVMe SSD) for evolving use cases (e.g., cloud computing, model training/inference). Before that, I graduated from University of California at Berkeley with a Bachelor’s degree in Applied Mathematics and Computer Science.

I am a foodie and enjoy making new dishes. Japanese, Italian, Vietnamese and Mediterranean are among my favorites. I am also a wine lover. I passed WSET Level 2 and may pursue Level 3 someday. I like Gewürztraminer, Sauvignon Blanc, Riesling, and Pinot Noir.

I am currently on the job market, looking for faculty and postdoctoral positions in academia.

news

Mar 22, 2024	Burstable Cloud Block Storage with Data Processing Units is accepted by OSDI ‘24.
Feb 6, 2023	Disaggegated RAID Storage in Modern Datacenters is accepted by ASPLOS ‘23.
Dec 6, 2022	Successfully passed the Ph.D. qualifying examination. Many thanks to Prof. Tao Xie, Prof. Minghui Zhou, Prof. Xuanzhe Liu, Prof. Xin Jin, and Prof. Xin Zhang.

selected publications

OSDI

Burstable Cloud Block Storage with Data Processing Units

Junyi Shu, Kun Qian, Ennan Zhai, Xuanzhe Liu, and Xin Jin

In 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24) 2024

Abs PDF Slides

Cloud block storage (CBS) is a key pillar of public clouds. Today’s CBS distinguishes itself from physical counterparts (e.g., SSDs) by offering unique burst capability as well as enhanced throughput, capacity, and availability. We conduct an initial characterization of our CBS product, a globally deployed cloud block storage service at public cloud provider Alibaba Cloud. A key observation is that the storage agent (SA) running on a data processing unit (DPU) which connects user VMs to the backend storage is the major source of performance fluctuation with burst capability provided. In this paper, we propose a hardware-software co-designed I/O scheduling system BurstCBS to address load imbalance and tenant interference at SA. BurstCBS exploits high-performance queue scaling to achieve near-perfect load balancing at line rate. To mitigate tenant interference, we design a novel burstable I/O scheduler that prioritizes resource allocation for base-level usage while supporting bursts. We employ a vectorized I/O cost estimator for comprehensive measurements of the consumed resources of different types of I/Os. Our evaluation shows that BurstCBS reduces average latency by up to 85% and provides up to 5× throughput for base-level tenants under congestion with minimal overhead. We verify the benefits brought by BurstCBS with a database service that internally relies on CBS, and show that up to 83% latency reduction is observed on customer workloads.
ASPLOS

Disaggregated RAID Storage in Modern Datacenters

Junyi Shu, Ruidong Zhu, Yun Ma, Gang Huang, Hong Mei, Xuanzhe Liu, and Xin Jin

In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3 2023

Abs PDF Code Poster Slides

RAID (Redundant Array of Independent Disks) has been widely adopted for decades, as it provides enhanced throughput and redundancy beyond what a single disk can offer. Today, enabled by fast datacenter networks, accessing remote block devices with acceptable overhead (i.e. disaggregated storage) becomes a reality (e.g., for serverless applications). Combining RAID with remote storage can provide the same benefits while creating better fault tolerance and flexibility than its monolithic counterparts. The key challenge of disaggregated RAID is to handle extra network traffic generated by RAID, which can consume a vast amount of NIC bandwidth. We present dRAID, a disaggregated RAID system that achieves near-optimal read and write throughput. dRAID exploits peer-to-peer disaggregated data access to reduce bandwidth consumption in both normal and degraded states. It employs non-blocking multi-stage writes to maximize inter-node parallelism, and applies pipelined I/O processing to maximize inter-device parallelism. We introduce bandwidth-aware reconstruction for better load balancing. We show that dRAID provides up to 3x bandwidth improvement. The results on a lightweight object store show that dRAID brings 1.5x-2.35x throughput improvement on various workloads.