Container-aware I/O Stack: Bridging the Gap between Container Storage Drivers and Solid State Devices
Solid State Devices (SSDs) have been widely adopted in containerized cloud platforms as they provide parallel and high speed data accesses for critical data-intensive applications. Unfortunately, the I/O stack of the physical host overlooks the layered and independent nature of containers, thus I/O operations require expensive file redirect (between the storage driver, Overlay2/EXT4, and the virtual file system, VFS) and are scheduled sequentially. Moreover, containers suffer from significant I/O contention as resources at the native file system are shared between them. This paper presents a Container-aware I/O stack (CAST). CAST is made up of Layer-aware VFS (LaVFS) and Container-aware Native File System (CaFS). LaVFS locates files based on layer information and enables simultaneous copy-on-write (cow) and thus avoids the overhead of searching and modifying files. CaFS, on the other hand, provides contention-free access by designing fine-grain resource allocation at the native file system. Experimental results using a NVMe SSD with micro-benchmarks and real-world applications show that CAST achieves 216%-219% (38%-98%, respectively) improvement over the original I/O stack.
Tue 1 MarDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
10:15 - 11:35 | Session-1: System VirtualizationResearch Papers at Online Chair(s): Antonio Barbalace The University of Edinburgh | ||
10:15 20mTalk | Portkey: Hypervisor-assisted container migration in nested cloud environments Research Papers Chandra Prakash Indian Institute of Technology Bombay, Debadatta Mishra , Purushottam Kulkarni Indian Institute of Technology, Bombay, Umesh Bellur IIT Bombay | ||
10:35 20mTalk | Container-aware I/O Stack: Bridging the Gap between Container Storage Drivers and Solid State Devices Research Papers Song Wu Huazhong University of Science and Technology, China, Zhuo Huang Huazhong University of Science and Technology, Pengfei Chen Huazhong University of Science and Technology, Hao Fan Huazhong University of Science and Technology, Shadi Ibrahim Inria, Hai Jin Huazhong University of Science and Technology | ||
10:55 20mTalk | ClusterRR: A Record and Replay Framework for Virtual Machine Cluster Research Papers | ||
11:15 20mTalk | EOP: Efficient Operator Partition for Deep Learning Inference Over Edge Servers Research Papers Yuanjia XU University of Chinese Academy of Sciences; Institute of Software, Chinese Academy of Sciences, Heng WU Institute of Software, Chinese Academy of Sciences, Wenbo ZHANG Institute of Software, Chinese Academy of Sciences; State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, Yi HU University of Chinese Academy of Sciences; Institute of Software, Chinese Academy of Sciences |
The Zoom room for Session 1 is at https://rochester.zoom.us/j/98375917164?pwd=ZHRvcy85elRVUWtDaGRZQkl6dENTQT09.