Fundamentals of Quantitative Design and Analysis - Computer Architecture: A Quantitative Approach - page 59

One problem is that this number far exceeds the lifetime of a disk, which is commonly as-
sumed to be 5 years or 43,800 hours. For this large MTTF to make some sense, disk manufac-
turers argue that the model corresponds to a user who buys a disk and then keeps replacing
the disk every 5 years—the planned lifetime of the disk. The claim is that if many customers
(and their great-grandchildren) did this for the next century, on average they would replace a
disk 27 times before a failure, or about 140 years.
A more useful measure would be percentage of disks that fail. Assume 1000 disks with a
1,000,000-hour MTTF and that the disks are used 24 hours a day. If you replaced failed disks
with a new one having the same reliability characteristics, the number that would fail in a year
(8760 hours) is
Stated alternatively, 0.9% would fail per year, or 4.4% over a 5-year lifetime.
Moreover, those high numbers are quoted assuming limited ranges of temperature and vi-
bration; if they are exceeded, then all bets are of. A survey of disk drives in real environments
[ Gray and van Ingen 2005 ] found that 3% to 7% of drives failed per year, for an MTTF of about
125,000 to 300,000 hours. An even larger study found annual disk failure rates of 2% to 10%
[ Pinheiro, Weber, and Barroso 2007 ] . Hence, the real-world MTTF is about 2 to 10 times worse
than the manufacturer's MTTF.
Fallacy Peak Performance Tracks Observed Performance
The only universally true definition of peak performance is “the performance level a computer
is guaranteed not to exceed.” Figure 1.20 shows the percentage of peak performance for four
programs on four multiprocessors. It varies from 5% to 58%. Since the gap is so large and can
vary significantly by benchmark, peak performance is not generally useful in predicting ob-
served performance.