Workload modeling for parallel computers
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Date
2006-03-01T09:42:13Z
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Abstract
The availability of good workload models is essential for the design
and analysis of parallel computer systems. A workload model can be
applied directly in an experimental or simulation environment to
verify new scheduling policies or strategies. Moreover, it can be
used for extrapolating and predicting future workload conditions. In
this work, we focus on the workload modeling for parallel computers.
To this end, we start with an examination of the overall features of
the available workloads. Here, we find a strong sequential
dependency in the submission series of computational jobs. Next, a
new approach using Markov chains is proposed that is capable of
describing the temporal dependency. Second, we analyze the missing
attributes in some workloads. Our results show that the missing
information can be still recovered when the relevant model is
trained from other complete data set. Based on the results of
overall workload analysis, we begin to inspect the workload
characteristics based on particular user-level features. That is, we
analyze in detail how the individual users use parallel computers.
In particular, we cluster the users into several manageable groups,
while each of these groups has distinct features. These different
groups provide a clear explanation for the global characteristics of
workloads. Afterwards, we examine the user feedbacks and present a
novel method to identify them. These evidences indicate that some
users have an adaptive tendency and a complete workload model should
not ignore the users' feedbacks. The work ends with a brief
conclusion on the discussed modeling aspects and gives an outlook on
future work.
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Keywords
Workload modeling, Parallel computer, Scheduling system, Simulation