1 / 21

The Design and Implementation of Log-Structure File System

The Design and Implementation of Log-Structure File System. M. Rosenblum and J. Ousterhout. presented by Candy Yiu. Introduction. CPU Speed increases dramatically Memory Size increased Most “Read” hits in cache

jason-patterson
Télécharger la présentation

The Design and Implementation of Log-Structure File System

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Design and Implementation of Log-Structure File System M. Rosenblum and J. Ousterhout presented by Candy Yiu

  2. 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

  3. 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

  4. 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

  5. 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

  6. Compare FFS/LFS

  7. 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

  8. 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

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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

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

  17. 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

  18. 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(small files)

  19. 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 Micro-benchmarks(large files) • - SunOS: pay additional cost for organizing disk • Layout • LFS: group information created at the same time, • not optimal for reading randomly written files

  20. 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 1.2-1.6 • 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

  21. Thank You =) ~The end~

More Related