publications

2023

1. 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.

2021

1. SIGCOMM

   Cost-Effective Data Analytics across Multiple Cloud Regions

   Junyi Shu, Xin Jin, Yun Ma, Xuanzhe Liu, and Gang Huang

   In Proceedings of the SIGCOMM ’21 Poster and Demo Sessions 2021

   Abs PDF Poster

   We propose a cloud-native data analytics engine for processing data stored among geographically distributed cloud regions with reduced cost. A job is split into subtasks and placed across regions based on factors including prices of compute resources and data transmission. We present its architecture which leverages existing cloud infrastructures and discuss major challenges of its system design. Preliminary experiments show that the cost is reduced by 15.1% for a decision support query on a four-region public cloud setup.