In this book, we present novel methodologies for automatically
generating online scheduling strategies with the help of real life
workload data. The scheduling problem includes independent parallel
jobs, multiple identical machines, and a complex scheduling
objective. This objective is defined by the machine provider and
considers different priorities of user groups. In order to allow a
wide range of objective functions, we use a rule based scheduling
strategy. There, a rule system classifies all possible scheduling
states and assigns an appropriate scheduling strategy to the actual
state. The rule bases are developed in three different ways. We
evaluate our new scheduling strategies again on real workload data
and provide a comprehensive comparison of the different approaches
among each other. Further, we show the benefit of the developed
rule based scheduling systems by comparing them to the main
standard algorithms currently in use. To this end, we select
several exemplary objective functions that prioritize some user
groups over others.
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