The goal of workload modeling is to predict a computer's 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 performance gain and 35% stability gain on the production
workloads. The second part of this book presents a user based
workload model. It identifies four stable user types and five
stable session types. It then deduces model parameters, the
distributions of the arrival and activity patterns for both users
and sessions, their dependencies and temporal structure. The
methodology and statistical toolset is explained to make it easier
to reuse in other domains. The insights and practical advice
provided will be of use to anyone building or operating a
large-scale parallel computer.
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