Lanfranco Muzi PSU – May 26 th , 2005

1. M. Rosenblum and J.K. OusterhoutThe design and implementation of a log-structured file systemProceedings of the 13th ACM Symposium onOperating Systems Principles, December 1991 Lanfranco Muzi PSU – May 26th, 2005

2. Presentation outline • Motivation • Basic functioning of Sprite LFS • Design issues and choices • Performance of Sprite LFS • Conclusions

3. Motivation“New” facts… • CPU speeds were dramatically increasing (1991 - and continued to do so…) • Memories become cheaper and larger • Disk have larger capacities, but performance does not keep up with other components: access time dominated by seek and rotational latency (mechanical issues Consequences…

4. Motivation…consequences • Applications become more disk-bound • Size of cache increases • Most read requests hit in cache • Allwrites must eventually go to disk (safety) • Higher write traffic fraction • But a file system optimized for reads pays a high cost during writes (to achieve logical locality)

5. Problems with conventional file systems - 1 Information is “spread around” on disk E.g. create new file in FFS requires 5 disk I/Os: • 2 for file i-node • 1 for file data • 2 for directory i-node and data Seek takes much longer than data writing in the case of small files, which is the target in this study

6. Problems with conventional file systems - 2 Tendency to write synchronously E.g. Unix FFS: • Data blocks written asynchronously… • …But metadata (inodes, directories) written synchronously • Synchronous writes slave application performance (and CPU usage) to the disk • Again, seeking for metadata update dominates write performance for small files

7. The Sprite LFS • Write asynchronously: buffer a series of writes in memory • Periodically copy buffer to disk • In a single write • On a single contiguous segment (data blocks, attributes, directories…) • Rewrite instead of updating in place • All info on disk is in a single sequential structure:the log

8. Sprite LFS – main issues ? How to retrieve information from the log? How to make large extents of free space always available for writing contiguous segments?

9. File location and reading Basic data structures analogous to Unix FFS: • One inode per file: attributes, address of first 10 blocks or indirect blocks …But inodes are in the log, i.e. NOT at fixed locations on disk… New data structure: inode map • Located in the log (no free list) • Fixed checkpoint region on disk holds addresses of all map blocks • Indexed by file identifying number, gives location of relative inode

10. Checkpoint regions • Contain addresses of all blocks in inode map and segment usage table, current time and pointer to last segment written • Two (for safety) • Located at fixed positions on disk • Used for crash recovery

11. Free space management - I GOAL: keep large free extents to write new data • Disk divided into fixed-length segments (512kB or 1MB) • A segment is always written sequentially from beginning to end • Segments written in succession until end of disk space (older segments get fragmented meanwhile) …and then?

12. Free space management - II Segment cleaning – copying live data out of a segment • Read a number of segments into memory • Identify live data • Write live data only back to smaller number of clean segments

13. Free space management - example Read these segments Old log end Free segment Writing memory buffer Cleaner thread: copy segments to memory buffer

14. Free space management - example Old log end New log end Writing memory buffer Cleaner thread: identify live blocks

15. Free space management - example Old log end New log end Writing memory buffer Cleaner thread: queue compacted data for writing

16. Free space management - example Old log end New log end Writing memory buffer Writer thread: write compacted and new data to segments, then mark old segments as free

17. Free space management - implementation Segment summary block – identify each piece of information in segment • E.g.: for a file, each data block identified by version number+inode number(=unique identifier, UID) and block number • Version number incremented in inode map when file deleted • If UID of block not equal to that in inode map when scanned, block is discarded

18. Free space management – cleaning policies ? Which segments to clean? How should live blocks be grouped when they are written out?

19. Free space management – cleaning policies Cleaning policies can be compared in terms of theWrite cost: N=number of segments read U=fraction of live data in read segments (0 u <1) • Average amount of time disk is busy per byte if new data written (seek and rot. Latency negligible in LFS) • Note: includes cleaning overhead • Note dependence on u

20. Free space mgmt – cleaning policies Low u = low write cost • Note: underutilized disk gives low write cost, but high storage cost! • …But u defined only for read segment (not overall) • Achievebimodal distribution: keep most segments nearly full, but a few nearly empty (have cleaner work on these)

21. How to achieve bimodal distribution? • First attempt: cleaner always chooses lowest u segments and sorts by age before writing – FAILURE! • Free space in “cold” (i.e. more stable) segments is more “valuable” (will last longer) • Assumption: stability of segment proportional to age of youngest block (i.e. older = colder) • Replace greedy policy with Cost-benefit criterion • Clean segments with higher ratio • Still group by age before rewriting

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

23. Support for Cost-benefit • Segment usage table:record number of live bytes and most recent modification time (used by cleaner to choose segments: u and age) • Segment summary:record age of youngest block (used by writer to sort live blocks)

24. Small file performance Performance – micro-benchmarks I • SunOS based on Unix FFS • NB: best case for SpriteLFS: no cleaning overhead • Sprite keeps disk 17% busy (85% for SunOS) and CPU saturated: will improve with CPU speed (right)

25. Large file performance Performance – micro-benchmarks II Single 100MB file • Traditional FS: logical locality – pay additional cost for organizing disk layout, assuming read patterns • LFS: temporal locality – group information created at the same time – not optimal for reading randomly written files

26. Performance – cleaning overhead • This experiment: statistics over several months of real usage • Previous results did not include cleaning • Write cost ranges 1.2-1.6 - more than half of cleaned segments empty • Cleaning overhead limits write performance: about 70% of bandwidth for writing • Improvement: cleaning could be performed at night or during idle periods

27. Conclusions • Prototype log-structured file system implemented and tested • Due to cleaning overhead, segment cleaning policies are crucial - tested in simulations before implementation • Results in tests (without cleaning overhead) • Higher performance than FFS in writes for both small and large files • Comparable read performance (except one case) • Results in real usage (with cleaning) • Simulation results confirmed • 70% of bandwidth can be used for writing

28. References • M. Rosenblum and J.Ousterhout, “The design and implementation of a log-structured file system”, Proceedings of the 13th ACM Symposium on Operating Systems Principles, December 1991 • Marshall K. McKusick, William N. Joy, Samuel J. Leffler, and Robert S. Fabry, “A Fast File System for Unix”, ACM Transactions on Computer Systems, 2(3), August 1984, pp. 181-197 • A. Tanenbaum “Modern operating systems” 2nd ed. (Chpt.4 “File systems”), Prentice Hall