Direct-FUSE: Removing the Middleman for High-Performance FUSE File System Support Yue Zhu *, Teng Wang*, Kathryn Mohror + , Adam Moody + , Kento Sato + , Muhib Khan*, Weikuan Yu* Florida State University* Lawrence Livermore National Laboratory +
Outline • Background & Motivation · Design · Performance Evaluation · Conclusion ROSS’18 S-2
Introduction n High-performance computing (HPC) systems needs efficient file system for supporting large-scale scientific applications Ø Different file systems are used for different kinds of data in a single job Ø Both kernel- and user-level file systems can be used in the applications Ø Due to kernel-level file systems’ development complexity, reliability and portability issues, user-level file systems are more leveraged for particular I/O workloads with special purpose n Filesystem in UserSpacE (FUSE) Ø A software interface for Unix-like computer operating systems Ø It allows non-privileged users to create their own file systems without modifying kernel code Ø User defined file system is run as a separate process in user-space Ø Example: SSHFS, GlusterFS client, FusionFS(BigData’14) ROSS’18 S-3
How does FUSE Work? n Execution path of a function call Ø Send the request to the user-level file system process o App program → VFS → FUSE kernel module → User-level file system process Ø Return the data back to the application program o User-level file system process → FUSE kernel module → VFS → App program Application Program User Level File System User Space Kernel Space Virtual File System (VFS) FUSE Ext4 Page Cache Storage Device ROSS’18 S-4
FUSE File System vs. Native File System FUSE File System Native File System # User-kernel 4 2 Mode Switch # Context 2 0 Switch # Memory 2 1 Copies Application Program User Level File System User Space Kernel Space Virtual File System (VFS) FUSE Ext4 Page Cache Storage Device ROSS’18 S-5
Number of Context Switches & I/O Bandwidth n The complexity added in FUSE file system execution path causes performance degradation in I/O bandwidth tmpfs : a file system that stores data in volatile memory Ø FUSE-tmpfs : a FUSE file system deployed on top of tmpfs Ø dd micro-benchmark and perf system profiling tool are used to gather the I/O Ø bandwidth and the number of context switches Experiment method: continually issue 1000 writes Ø Write Bandwidth # Context Switches Block FUSE-tmpfs tmpfs FUSE- tmpfs Size (KB) (MB/s) (GB/s) tmpfs 4 163 1.3 1012 7 16 372 1.6 1012 7 64 519 1.7 1012 7 128 549 2.0 1012 7 256 569 2.4 2012 7 ROSS’18 S-6
Breakdown of Metadata & Data Latency n The actual file system operations (i.e. metadata or data operations) only occupy a small amount of total execution time Ø Tests are on tmpfs and FUSE-tmpfs Ø Real Operation in metadata operation: the time of conducting operation Ø Data Movement : the actual time of write in a complete write function call Ø Overhead : the cost besides the above two, e.g. the time of context switches 250 600 11.18% Real Operation Data Movement 38.12% 500 200 Overhead Overhead Latency (ns) 400 Latency (ns) 150 300 37.86% 100 200 33.7% 2.17% 50 100 34.8% 15.82% 10.08% 0 0 tmpfs FUSE-tmpfs tmpfs FUSE-tmpfs Create Close 1 4 16 64 128 256 Metadata Operations Transfer Sizes (KB) Fig. 1. Time Expense in Metadata Operations Fig. 2. Time Expense in Data Operations ROSS’18 S-7
Existing Solution and Our Approach n How to reduce the overheads from FUSE? Ø Build an independent user-space library to avoid going through kernel (e.g., IndexFS (SC’14), FusionFS) Ø However, this approach cannot support multiple FUSE libraries with distinct file paths and file descriptors n We propose Direct-FUSE to support multiple backend I/O services to an application Ø We adapted libsysio to our purpose in Direct-FUSE o libsysio i s developed by Scalability team of Sandia National Lab): « a POSIX-like file I/O, and name space support for remote file systems from an application’s user-level address space. ROSS’18 S-8
Outline · Background & Motivation • Design · Performance Evaluation · Conclusion ROSS’18 S-9
The Overview of Direct-FUSE n Direct-FUSE mainly consists of three components Adapted-libsysio 1. o Intercept file path and file descriptor for backend services identification o Simplify metadata and data execution path in original libsysio lightweight-libfuse (not real libfuse) 2. o Abstract file system operations from backend services to unified APIs Backend services 3. o Provide defined file system operations (e.g., FusionFS) Application Program Direct-FUSE Adapted-libsysio lightweight-libfuse Backend FUSE-Ext4 FusionFS Client …. Services Ext4 FusionFS Server … ROSS’18 S-10
Path and File Descriptor Operations n To facilitate the interception of file system operations for multiple backends, the operations are categorized into two: File path operations 1. Intercept prefix and path (e.g., sshfs:/sshfs/test.txt) and return mount i. information Look up corresponding inode based on the mount information, and ii. redirect to defined operations File descriptor operations 2. Find open-file record based on given file descriptor i. « Open-file record contains pointers to inode, current stream position, etc Redirect to defined operations based inode info in open-file record ii. ROSS’18 S-11
Requirements for New Backends n Interact with FUSE high-level APIs n Separated as an independent user-space library Ø The library contains the fuse file system operations, initialization function, and also the unmount function Ø If a backend passes some specialized data to the fuse module via fuse_mount(), then the data has to be globalized for later file system operations n Implemented in C/C++ or has to be binary compatible with C/C++ ROSS’18 S-12
Outline · Background and Challenges · Design • Performance Evaluation · Conclusion ROSS’18 S-13
Experimental Methodology n We compare the bandwidth of Direct-FUSE with local FUSE file system and native file system on disk and memory by Iozone Ø Disk o Ext4-fuse: FUSE file system overlying Ext4 o Ext4-direct: Ext4-fuse bypasses the FUSE kernel o Ext4-native: original Ext4 on disk Ø Memory o tmpfs-fuse, tmpfs-direct, and tmpfs-native are similar to the three tests on disk n We also compare the I/O bandwidth of distributed FUSE file system with Direct-FUSE Ø FusionFS: a distributed file system that supports metadata- and write-intensive operations ROSS’18 S-14
Sequential Write Bandwidth n Direct-FUSE achieves comparable bandwidth performance to the native file system Ø Ext4-direct outperforms Ext4-fuse by 16.5% on average Ø tmpfs-direct outperforms tmpfs-fuse at least 2.15x Ext4-fuse Ext4-direct Ext4-native tmpfs-fuse tmpfs-direct tmpfs-native 10000 Bandwidth (MB/s) 1000 100 10 1 4 16 64 256 1024 Write Transfer Sizes (KB) ROSS’18 S-15
Sequential Read Bandwidth n Similar to the sequential write bandwidth, the read bandwidth of Direct-FUSE is comparable to the native file system Ø Ext4-direct outperforms Ext4-fuse by 2.5% on average Ø tmpfs-direct outperforms tmpfs-fuse at least 2.26x Ext4-fuse Ext4-direct Ext4-native tmpfs-fuse tmpfs-direct tmpfs-native 10000 Bandwidth (MB/s) 1000 100 10 1 4 16 64 256 1024 Read Transfer Sizes (KB) ROSS’18 S-16
Distributed I/O Bandwidth n Direct-FUSE outperforms FusionFS in write bandwidth and shows comparable read bandwidth Ø Writes benefit more from the FUSE kernel bypassing n Direct-FUSE delivers similar scalability results as the original FusionFS 10000 10000 fusionfs direct-fusionfs fusionfs direct-fusionfs Bandwidth (MB/s) Bandwidth (MB/s) 1000 1000 100 100 10 10 1 1 1 2 4 8 16 1 2 4 8 16 Number of Nodes Number of Nodes Write Read ROSS’18 S-17
Overhead Analysis n The dummy read/write occupies less than 3% of the complete I/O function time in Direct-FUSE, even when the I/O size is very small Ø Dummy write/read: no actual data movement, directly return once reach the backend service Ø Real write/read: the actual Direct-FUSE read and write I/O calls 10000 10000 dummy write real write dummy read real read 1000 1000 Latency (ns) Latency (ns) 100 100 10 10 1 1 1B 4B 16B 64B 256B 1KB 1B 4B 16B 64B 256B 1KB Transfer Sizes Transfer Sizes ROSS’18 S-18
Conclusions Ø We have revealed and analyzed the context switches count and time overheads in FUSE metadata and data operations Ø We have designed and implemented Direct-FUSE, which can avoid crossing kernel boundary and support multiple FUSE backends simultaneously Ø Our experimental results indicate that Direct-FUSE achieves significant performance improvement compared to original FUSE file systems ROSS’18 S-19
Sponsors of This Research ROSS’18 S-20
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