Why disk arrays? CPUs improving faster than disks



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Why disk arrays? CPUs improving faster than disks - disks will increasingly be bottleneck New applications (audio/video) require big files (motivation for XFS) Disk arrays - make one logical disk out of many physical disks More disks results in two benefits - Higher data transfer rates on large data accesses - Higher I/O rates on small data accesses

Reliability implications of arrays JBOD array with 100 disks is 100 times more likely to fail! - Each disk 200,000 hours MTBF array 2,000 hours (3 months) [approximately double counts two disks failing] Use redundancy to improve reliability - But makes writes slower, since redundant info must be updated - Raises issues of consistency in the face of power failures

Disk array basics Data striping - balances load across disks Redundancy - improve reliability Many different schemes, depending on - granularity of data interleaving - fine grained high transfer rates for all requests - course grained allow parallel small requests to different disks - method in which redundant information computed & distributed

RAID 0 Nonredundant storage (JBOD - just a bunch of disks) - E.g., Stripe sequential 128K logical regions to different disk Offers best possible write performance (only one write per write) Offers best possible storage efficiency (no redundancy) Offers good read performance Use if speed more important that reliability (scientific computing)

RAID 1 Mirrored storage Each block stored on two disks Writing slower (twice as many operations) Storage efficiency 1/2 optimal Small reads better than other scheme - can read on disk with shortest seek

RAID 2 Use Hamming codes, like ECC memory - Multiply data by generator matrix G = ( I A ) G = 1 0 0 0 1 1 1 0 1 0 0 0 1 1 0 0 1 0 1 0 1 0 0 0 1 1 1 0 D = ( d 1 d 2 d 3 d 4 ) E = G D = d 1 d 2 d 3 d 4 d 1 + d 3 + d 4 d 1 + d 2 + d 4 d 1 + d 2 + d 3

hamming codes (continued) Decode by multiplying by H = ( A T I ) 1 0 1 1 1 0 0 H = 1 1 0 1 0 1 0 1 1 1 0 0 0 1 H E = ( d 1 d 2 d 3 d 4 0 0 0 ) Can recover any two missing bits Can even recover from one incorrect bit! - If one extra bit is 1, it is wrong - If two extra bits are 1, d 2, d 3, or d 4 is wrong - If all 3 extra bits are 1, d 1 is wrong

Properties of RAID 2 Small reads about like RAID 0 - Though more contention for data disks Writes must update multiple parity disks Storage more efficient than RAID 1 (uses more than half of disks) Recovers from errors (RAID 1 assumes fail-stop) - Is this overkill? - Most disks are fail-stop

RAID 3 Bit interleaved parity Assume fail stop disks, add one parity disk D = (d 1 d 2 d 3 d 4 ) E = (d 1 d 2 d 3 d 4 d 1 + d 2 + d 3 + d 4 ) Any read touches all data disks Any write touches all data disks plus parity disk

RAID 4 Block-interleaved parity Interleave data in blocks instead of bits Reads smaller than striping unit can access only one disk Writes must update data and compute and update parity block - Small writes require two reads plus two writes - Heavy contention for parity disk (all writes touch it)

RAID 5 Block-interleaved distributed parity - Distribute parity uniformly over all the disks - Want to access disks sequentially when sequentially accessing logical blocks: 0 1 2 3 p 0 5 6 7 p 1 4 10 11 p 2 8 9 15 p 3 12 13 14 p 4 16 17 18 19 Better load balancing than RAID 4 Small writes still require read-modify-write

RAID 6 P+Q Redundancy (rarely implemented) - Have two parity blocks instead of one - With Reed-Solomon codes, can lose any two blocks Now must read-modify-write two parity blocks plus data

RAID Summary RAID level SmRd SmWr BigRd BigWr Space 0 1 1 1 1 1 1 1 1/2 1 1/2 1/2 3 1/G 1/G (G-1)/G (G-1)/G (G-1)/G 5 1 max(1/g, 1/4) 1 (G-1)/G (G-1)/G 6 1 max(1/g, 1/6) 1 (G-2)/G (G-2)/G RAID 4 is strictly inferior to RAID 5 RAID 2 inferior to RAID 5 for fail-stop disks RAID 1 is just RAID 5 with parity group size G=2 RAID 3 is just like RAID 5 with a very small stripe unit

When/how do disks fail? Disks can fail very early in their lifetimes (manufacturing errors) Also tend to fail late in their lifetimes (when disk wears out) Systematic manufacturing defect can make entire batch of disks fail early - Beware disks with consecutive serial numbers! Environmental factors can kill a bunch of disks (air conditioning failure) Disks can fail when a bad block is read Bad block may exist for a while before being detected

Basic idea: - Add new disk Dealing with failures - Reconstruct failed disk s state on new disk Must store metadata information during recovery - Which disks are failed? - How much of failed disk has been reconstructed? System crashes become very serious in conjunction with disk failure - Parity may be inconsistent (particularly bad for P+Q) - You could lose a block other than the one you were writing - MUST log in NVRAM enough info to recover parity - Makes software-only implementation of RAID risky

Maximizing availability Want to keep operating after failure Demand reconstruction - Assumes spare disk immediately (or already) installed - Reconstruct blocks as accessed - Background thread reconstructs all blocks Parity sparing - Replaces parity block with reconstructed data block - Need extra metadata to keep track of this

Unrecoverable RAID failures Double disk failures (or tripple, if P+Q redundancy) System crash followed by disk failure Disk failure, then read and discover bad block during reconstruction

Parity logging RAID improvements - Log difference of old and new parity blocks - Delay updating actual parity - Further writes may save you from a read Declustered parity - Many parity groups, spread over many disks Parity sparing - Use spare disks to improve performance by spreading load

Tuning RAID What is optimal size of data stripe in RAID 0 disk array? (P X(L 1)Z)/N - P - average positioning time - X - disk transfer rate - L - concurrency of workload - Z - request size - N - size of array in disks What about in RAID 5? - Reads - similar to RAID 0 - Writes - optimal is a factor of 4 smaller than for reads (for 16 disks) - Seems to vary WITH #disks, while reads vary inversely! Conclusion: Very workload dependent!