Task spreading and shrinking on a network of workstations with various edge classes

We describe how our computational model can be used for the problems of processor allocation and task mapping on a network of workstations. The intended applications for this model include the dynamic mapping problems of shrinking or spreading an existing mapping when the available pool of workstations changes during execution of the problem. The concept of problem edge class and other features of our model are developed to realistically and efficiently support task partitioning and merging for static and dynamic mapping. Algorithms for shrinking and spreading are presented and execution times on a network of workstations are used to illustrate the utility of our model