1. Bandwidth and latency optimizations Jinyang Li w/ speculator slides from Ed Nightingale

2. What we’ve learnt so far Programming tools Consistency Fault tolerance Security Today: performance boosting techniques –Caching –Leases –Group commit –Compression –Speculative execution

3. Performance metrics Throughput –Measures the achievable rate (ops/sec) –Limited by the bottleneck resource 10Mbps link: max ~150 ops/sec for writing 8KB blocks –Reduce b/w by using less bottleneck resource Latency –Measures the latency of a single client response –Reduce latency by pipelining multiple operations

4. Caching (in NFS) NFS clients cache file content and directory name mappings Caching saves network bandwidth, improves latency GETATTR READ data

5. Leases (not in NFS) Leases eliminate latency in freshness check, at the cost of keeping extra state at the server READ READ fh1 LEASE fh1, data fh1: C1 INVAL fh1 WRITE fh1 OK

6. Group commit (in NFS) Group commit reduces the latency of a sequence of writes COMMIT WRITE

7. Two cool tricks Further optimization for b/w and latency is necessary for wide area –Wide area network challenges Low bandwidth (10~100Mbps) High latency (10~100ms) Promising solutions: –Compression (LBFS) –Speculative execution (Speculator)

8. Low Bandwidth File System Goal: avoid redundant data transfer between clients and the server Why isn’t caching enough? –A file with duplicate content  duplicate cache blocks –Two files that share content  duplicate cache blocks –A file that’s modified  previous cache is useless

9. LBFS insights: name by content hash Traditional cache naming: (fh#, offset) LBFS naming: SHA-1(cached block) Same contents have the same name –Two identical files share cached blocks Cached blocks keep the same names despite file changes

10. Naming granularity Name each file by its SHA-1 hash –It’s rare for two files to be exactly identical –No cache reuse across file modifications Cut a file into 8KB blocks, name each [ x*8K,(x+1)*8K ) range by hash –If block boundaries misalign, two almost identical files could share no common block –If block boundaries misalign, a new file could share no common block with its old version SHA-1(8K)

11. Align boundaries across different files Idea: determine boundary based on the actual content –If two boundaries have the same 48-byte content, they probably correspond to the same position in a contiguous region of identical content

12. Align boundaries across different files ab9f..0a 87e6b..f5

13. LBFS content-based chunking Examine every sliding window of 48-bytes Compute a 2-byte Rabin fingerprint f of 48- byte window If the lower 13-bit of f is equal to v, f corresponds to a breakpoint

14. LBFS chunking Two files with same content of length x have x identical fingerprints and x/8K aligned breakpoints f1 f2 f3 f4 f1 f2 f3 f4

15. Why Rabin fingerprints? Why not use the lower 13 bit of every 2- byte sliding window for breakpoints? –Data is not random, resulting in extreme variable chunk size Rabin fingerprints computes a random 2-byte value out of 48-bytes data

16. Rabin fingerprint is fast Treat 48-byte data D as a 48 digit radix-256 number f 47 = fingerprint of D[0…47] = ( D[47] + 256*D[46] + … + 256 46 *D[1]+ …+256 47 *D[0] ) % q f 48 = fingerprint of D[1..48] = ((f 47 - D[0]*256 47 )* 256 + D[48] ) % q A new fingerprint is computed from the old fingerprint and the new shifted-in byte

17. LBFS reads GETHASH File not in cache (h1, size1, h2, size2, h3, size3) READ(h1,size1) READ(h2,size2) Ask for missing Chunks: h1, h2 Reconstruct file as h1,h2,h3 Fetching missing chunks Only saves b/w by reusing common cached blocks across different files or different versions of the same file

18. LBFS writes MKTMPFILE(fd) CONDWRITE(fd, h1,size1, h2,size2, h3,size3) HASHNOTFOUND(h1,h2) TMPWRITE(fd, h1) TMPWRITE(fd, h2) COMMITTMP(fd, target_fhandle) Create tmp file fd Reply with missing chunks h1, h2 Construct tmp file from h1,h2,h3 copy tmp file content to target file Transferring missing chunks saves b/w if different files or different versions of the same file have pieces of identical content

19. LBFS evaluations In practice, there are lots of content overlap among different files and different version of the same file –Save a Word document –Recompile after a header change –Different versions of a software package LBFS results in ~1/10 b/w use

20. Speculative Execution in a Distributed File System Nightingale et al. SOSP’05

21. How to reduce latency in FS? What are potentially “wasteful” latencies? Freshness check –Client issues GETATTR before reading from cache –Incurs an extra RTT for read –Why wasteful? Most GETATTRs confirm freshness ok Commit ordering –Client waits for commit on modification X to finish before starting modification Y –No pipelining of modifications on X & Y –Why wasteful? Most commits succeed!

22. RPC Req Client RPC Resp Guarantees without blocking I/O! Server Block!2) Speculate! 1) Checkpoint Key Idea: Speculate on RPC responses 3) Correct? Yes: discard ckpt.No: restore process & re-execute RPC Req RPC Resp RPC Req RPC Resp

23. Conditions of useful speculation Operations are highly predictable Checkpoints are cheaper than network I/O –52 µs for small process Computers have resources to spare –Need memory and CPU cycles for speculation

24. Undo log Implementing Speculation Process Checkpoint Spec 1) System call2) Create speculation Time

25. Speculation Success Undo log Checkpoint 1) System call2) Create speculation Process 3) Commit speculation Time Spec

26. Speculation Failure Undo log Checkpoint 1) System call 2) Create speculation Process 3) Fail speculation Process Time Spec

27. Ensuring Correctness Speculative processes hit barriers when they need to affect external state –Cannot roll back an external output Three ways to ensure correct execution –Block –Buffer –Propagate speculations (dependencies) Need to examine syscall interface to decide how to handle each syscall

28. Handle systems calls Block calls that externalize state –Allow read-only calls (e.g. getpid) –Allow calls that modify only task state (e.g. dup2) File system calls -- need to dig deeper –Mark file systems that support Speculator getpid reboot mkdir Call sys_getpid() Block until specs resolved Allow only if fs supports Speculator

29. Output Commits “stat worked” “mkdir worked” Undo log Checkpoint Spec (stat) Spec (mkdir) 1) sys_stat2) sys_mkdir Process Time 3) Commit speculation

30. Multi-Process Speculation Processes often cooperate –Example: “make” forks children to compile, link, etc. –Would block if speculation limited to one task Allow kernel objects to have speculative state –Examples: inodes, signals, pipes, Unix sockets, etc. –Propagate dependencies among objects –Objects rolled back to prior states when specs fail

31. Spec 1 Multi-Process Speculation Spec 2 pid 8001 Checkpoint inode 3456 Chown -1 Write -1 pid 8000 Checkpoint Chown -1 Write -1

32. Multi-Process Speculation What’s handled: –DFS objects, RAMFS, Ext3, Pipes & FIFOs –Unix Sockets, Signals, Fork & Exit What’s not handled (i.e. block) –System V IPC –Multi-process write-shared memory

33. Example: NFSv3 Linux Client 1Client 2Server Open B Getattr Modify B Write Commit

34. Example: SpecNFS Modify B speculate Getattr Open B speculate Open B Getattr speculate Write+Commit Client 1Client 2Server

35. Problem: Mutating Operations bar depends on speculative execution of “cat foo” If bar’s state could be speculative, what does client 2 view in bar? Client 1 1. cat foo > bar Client 2 2. cat bar

36. Solution: Mutating Operations Server determines speculation success/failure –State at server is never speculative Clients send server hypothesis speculation based on –List of speculations an operation depends on Server reports failed speculations Server performs in-order processing of messages

37. Server checks speculation’s status Client 1 Server Cat foo>bar Write+ Commit Foo v=1 Check if foo indeed has version=1, if no fail

38. Group Commit Previously sequential ops now concurrent Sync ops usually committed to disk Speculator makes group commit possible write commit Client Server

39. Putting it Together: SpecNFS Apply Speculator to an existing file system Modified NFSv3 in Linux 2.4 kernel –Same RPCs issued (but many now asynchronous) –SpecNFS has same consistency, safety as NFS –Getattr, lookup, access speculate if data in cache –Create, mkdir, commit, etc. always speculate

40. Putting it Together: BlueFS Design a new file system for Speculator –Single copy semantics –Synchronous I/O Each file, directory, etc. has version number –Incremented on each mutating op (e.g. on write) –Checked prior to all operations. –Many ops speculate and check version async

41. Apache Benchmark SpecNFS up to 14 times faster

42. Rollback cost is small All files out of date SpecNFS up to 11x faster

43. What we’ve learnt today Traditional Performance boosting techniques –Caching –Group commit –Leases Two new techniques –Content-based hash and chunking –Speculative execution