The Design and Implementation of Log-Structure File System M. Rosenblum and J. Ousterhout

Introduction  CPU Speed increases dramatically  Memory Size increased Most “Read” hits in cache  Disk improves only on the size but access is still very slow due to seek and rotational latency “Write” must go to disk eventually  As a result “Write” dominate the traffic Application has disk-bound problem

Overview of LFS  Unix FFS Random write Scan entire disk Very slow restore consistency after crash  LFS Write new data to disk in sequence Eliminate “seek” Faster crash recovery The most recent log always at the end

Traditional Unix FFS  Spread information around the disk Layout file sequentially but physically separates different files Inode separate from file contents  Takes at least 5 I/O for each seek to create new file  Causes too many small access  Only use 5% disk bandwidth Most of the time spent on seeking

Sprite LFS  Inode not at fixed position It written to the log  Use inode map to maintain the current location of the inode It divided into blocks and store in the log Most of the time in cache for fast access (rarely need disk access) A fixed checkpoint on each disk store all the inode map location  Only one single write of all information to disk required + inode map update  All information in a single contiguous segment

Compare FFS/LFS TaskFFSLFS Allocate disk address Block creation Segment Write Allocate i-nodeFixed location Appended to log Map anode numbers into disk addresses Static address Lookup in i-node map Maintain free space BitmapCleaner Segment usage table

Space Management  Goal: keep large free extents to write new data  Disk divided into segments (512kB/1MB)  Sprite Segment Cleaner Threading between segments Copying within segment

Threading  Leave the live data in place  Thread the log through the free extents  Cons Free space become many fragmented Large contiguous write won’t be possible LFS can’t access faster

Copying and Compacted  Copy live data out of the log  Compact the data when it written back  Cons: Costly

Segment Cleaning Mechanism  Read a number of Segments into memory  Check if it is live data  If true, write it back to a smaller number of clean segments  Mark segment as clean

Segment summary block  Identify each piece of information in segment  Version number + inode = UID  Version number incremented in inode map when file deleted  If UID of block mismatch to that in inode map when scanned, discard the block

Cleaning Policies  Sprite starts cleaning segment when the number of clean segment drops below a threshold  It uses the write cost to compare the cleaning policies "write cost" is the average amount of time the disk is busy per byte of new data written

Disk space underutilized via performance  u < 0.8 will give better performance compare to current Unix FFS  u < 0.5 will give better performance compare to the improved Unix FFS

Simulate more real situation  Data random access pattern Uniform Hot and cold  10% is hot and select 90% of the time  90% is cold and select 10% of the time  Cleaner use “Greedy Policy” Choose the least-utilized segment to clean  Conclude hot and cold data should treat differently

Cost Benefit Policy  “Cold” data is more stable and will likely last longer  Assume “Cold” data = older (age)  Clean segment with higher ratio  Group by age before rewrite

Left: bimodal distribution achieved Cold cleaned at u=75%, hot at u=15% Right: cost-benefit better, especially at utilization>60% Cost Benefit Result

Crash Recovery  Traditional Unix FFS: Scan all metadata Very costly especially for large storage  Sprite LFS Last operations locate at the end of the log Fast access, recovery quicker Checkpoint & roll-forward Roll-forward hasn’t integrated to Sprite while the paper was written Not focus here

Micro-benchmarks (small files)  Fig (a) Shows performance of large number of files create, read and delete LFS 10 times faster than Sun OS in create and delete LFS kept the disk 17% busy while SunOS kept the disk busy 85%  Fig (b) Predicts LFS will improve by another factor of 4-6 as CPUs get faster No improvement can be expected in SunOS

Micro-benchmarks (large files)  100Mbyte file (with sequential, random) write, then read back sequentially  LFS gets higher write bandwidth  Same read bandwidth in both FS  In the case of reads require seek (reread) in LFS, the performance is lower than SunOS - SunOS: pay additional cost for organizing disk Layout - LFS: group information created at the same time, not optimal for reading randomly written files

Real Usage Statistics  Previous result doesn’t include cleaning overhead  The table shows better prediction  This real 4 months usage includes cleaning overhead  Write cost range is  More than half of cleaned segments empty  Cleaning overhead limits write performance about 70% of the bandwidth for sequential writing  In practice, possible to perform the cleaning at night or idle period

Thank You =) ~The end~