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@bnyeggen
Created July 11, 2011 22:54
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A RAID MTBF calculator
#redundancy is the max number of survivable failures, so eg 1 for RAID5
#mtbf_array is an array of either actual mean-time-between-failures, or a nested RAID array
# RAID([100]*7,2) #7 disk RAID 6
# RAID([RAID([100]*3,1),RAID([1000]*3,1)],0) # RAID 50, 2 arrays of 3
# RAID([100,100,50,50],1) #RAID 5 with varying reliabilities
from random import random
class RAID(object):
def __init__(self,mtbf_ary,redundancy):
self.mtbf_ary=mtbf_ary
self.redundancy=redundancy
self.mtbf_cache=False
return
def mtbf(self,sims=10000,recalc=False):
if self.mtbf_cache and not recalc: return self.mtbf_cache
failtimes=[None]*sims
failrates=[None]*len(self.mtbf_ary)
for idx,item in enumerate(self.mtbf_ary):
if type(item) is float or type(item) is int:
failrates[idx]=1.0 / float(item)
if isinstance(item,RAID):
failrates[idx]=1.0 / item.mtbf(sims)
for i in range(sims):
trials=0
failcount=0
alive=[True]*len(self.mtbf_ary)
while failcount<=self.redundancy:
trials+=1
for idx,item in enumerate(self.mtbf_ary):
if random()<failrates[idx] and alive[idx]:
failcount+=1
alive[idx]=False
failtimes[i]=trials
self.mtbf_cache=float(sum(failtimes)) / len(failtimes)
return self.mtbf_cache #mean time to array failure
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