The goal of workload modeling is to predict a
computers workload well enough to design it
correctly. A poor model will lead to degraded
performance and user satisfaction. Analyzing logs
from multiple real parallel computers uncovers
several statistical features - locality of sampling,
daily cycles, weekly cycles, self similarity and
flurries - that are missing from current workload
models. Their practical importance is demonstrated
by two new kinds of scheduling algorithms - adaptive
scheduling and shortest job backfill first
scheduling - which achieve an average 10% bottom-
line...