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Analysis of job scheduling algorithms for heterogeneous multiprocessor computing systems

The problem of scheduling independent jobs on heterogeneous multiprocessor
models (i.e., those with non-identical or uniform processors) with independent
memories has been studied. Actually, a number of demand scheduling nonpreemptive
algorithms have been evaluated, with respect to their mean flow and completion time performance criterion. In particular, the deterministic
analysis has been used to predict the worst-case performance whereas simulation
techniques have been applied to estimate the expected performance of the
algorithms. As a result from the deterministic analysis, informative worstcase
bounds have been proven, from which the behaviour of the extreme
performance of the considered algorithms can be well predicted. However, relaxing some or a combination of the system parameters then, our model corresponds to versions which have already been studied. (i.e. the classical
homogeneous and heterogeneous models or the homogeneous one with independent
memories). For such cases, the proven bounds in this thesis either agree or
are better and more informative than the ones found for these simpler models..
Finally, the analysis of the worst-case and expected performance results
reveals that there is a high degree of correlation in the behaviour of the
algorithms as predicted or estimated by these two performance measurements,
respectively.