Abstract
We investigate the problem arising in scheduling parallel applications that follow a master–worker paradigm in order to maximize both resource efficiency and application performance. Based on the results obtained in a previous simulation study, we have derived a self-adjusting strategy that can be used to dynamically adjust the number of processors allocated to the application. The effectiveness of the proposed strategy has been assessed in two different scenarios: first, we implemented and tested this strategy on a cluster of homogeneous workstations. Secondly, we extended the self-adjusting strategy to be applied on heterogeneous clusters. We assessed the effectiveness of our strategy using an image-thinning application as a practical example of master–worker application.
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