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As classic controller, pole placement approach is suggested and results reveal weaknesses in modeling the uncertainties.
Carrera et al. [12] presented a utility-based web application placement approach to maximize application performance on clusters of PMs.
In this paper, we give a hybrid approach which combined genetic encoding and evolution scheme with the proposed placement approach.
The idea of the placement approach is to consider the given container to be divided into X and Y coordinates.
However, during the workload placement approach, this work did not consider the effect of cache sharing on power efficiency.
Four performance metrics are used for evaluating the effectiveness of the proposed VM placement approach and comparing it with the baseline heuristic method.
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The chapter concludes with a discussion of other placement approaches and useful resources to placement research.
We then adapt and develop several degree-based server placement approaches and greedy client assignment algorithms.
Focused placement approaches allow placement of nanoscale building blocks with precision much higher than the precision with which guiding templates are defined.
Also, we have presented an exploration of various thermal-aware placement approaches for both the 2D and 3D stacked systems.
Two controllers based on feedback linearization (FBL) and pole placement approaches are designed to preserve the dynamic system around the desired tracking paths.
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