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Local Filesystems (part 1)

Local Filesystems (part 1). CPS210 Spring 2006. Papers. The Design and Implementation of a Log-Structured File System Mendel Rosenblum File System Logging Versus Clustering: A Performance Comparison Margo Seltzer. Surface organized into tracks. Parallel tracks form cylinders.

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Local Filesystems (part 1)

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  1. Local Filesystems (part 1) CPS210 Spring 2006

  2. Papers • The Design and Implementation of a Log-Structured File System • Mendel Rosenblum • File System Logging Versus Clustering: A Performance Comparison • Margo Seltzer

  3. Surface organized into tracks

  4. Parallel tracks form cylinders

  5. Tracks broken up into sectors

  6. Disk head position

  7. Rotation is counter-clockwise

  8. About to read a sector

  9. After reading blue sector After BLUEread

  10. Red request scheduled next After BLUEread

  11. Seek to red’s track After BLUEread Seek for RED SEEK

  12. Wait for red sector to reach head After BLUEread Seek for RED Rotational latency SEEK ROTATE

  13. Read red sector After BLUEread Seek for RED Rotational latency After RED read SEEK ROTATE

  14. Unix index blocks • Intuition • Many files are small • Length = 0, length = 1, length < 80, ... • Some files are huge (3 gigabytes) • “Clever heuristic” in Unix FFS inode • 12 (direct) block pointers: 12 * 8 KB = 96 KB • Availability is “free” - you need inode to open() file anyway • 3 indirect block pointers • single, double, triple

  15. Unix index blocks

  16. Unix index blocks

  17. Unix index blocks

  18. Unix index blocks

  19. Unix index blocks

  20. Log-structured file system • What is the high level motivation? • Caches are getting bigger • Disk reads are less important • Disk traffic will be dominated by writes • Why a log? • Eliminate seeks (make all disk writes sequential) • Easy crash-recovery • Most writes are small meta-data updates • Consecutive small writes will trigger seeks • Some file systems perform these synchronously

  21. LFS challenges • Writes are easier, what about reads? • How do we ensure large open spaces? • Why does this matter?

  22. LFS on disk structures

  23. Segment cleaner • LFS requires large open spaces • Fragmentation will kill performance • Use notion of segments • Large contiguous areas of live/dead data • 512 kb or 1MB • Segment cleaner defragments disk • Separate the old from the young • Old data rarely changes • Clean two differently

  24. Threading vs copying • Thread between segments

  25. Segment cleaning • Three steps • Read segments into memory • Identify live blocks in those segments • Write live blocks to a small, clean segment • Must also update file inodes • Segment block summary for each segment

  26. Segment block summaries • Contains info about blocks in a segment • For each file data block • SBS has the file number and block number • Also used to identify live/dead blocks • Use file number from SBS with actual inode • If same, block is live • If different, block is dead • Optimization: just keep inode versions

  27. Cleaner policy questions • When should the cleaner run? • Continuously, at night, high utilization • How many segments per cleaning? • Tens, hundreds, … • Which segments should be cleaned? • How should live blocks be grouped?

  28. Write cost • Percent of bandwidth used for new data • 1.0 is perfect (we only write new data) • 10.0 isn’t great (1/10 written data is new) • Ideal bimodal distribution of segments • High utilization of old segments • Low utilization of young segments • Combines high utilization and low write cost

  29. Initial simulation results Surprise!

  30. Why the surprise? These segments decay slowly collecting dead blocks. In aggregate, they contain a lot of free blocks

  31. Instead of greatest yield • Use cost-benefit analysis • Benefit/cost = • (Yield * age)/cost = • (1-u)*age/(1+u) • Result • Clean cold segments at higher utilization

  32. Result

  33. What do LFS results really mean? • Workloads matter • When is LFS better than FFS? • When is FFS better than LFS?

  34. More terminology • Blocks • Partial blocks, aka fragments • Contiguous ranges of blocks, aka clusters • Want to allocate indoes + data • In same cylinder (no seeks) • Want data • In clusters on same track (also no seeks)

  35. Challenges for FFS • Reduce (eliminate?) synchronous writes • Avoid fragmentation • Why are meta-data writes synchronous?

  36. Sequential performance vs file size • Four phase benchmark: • Create • Read • Overwrite • Delete • Ideal conditions • Blank file system, no cleaner

  37. Create results

  38. Read results

  39. Overwrite results

  40. Delete results

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