HYBRID APPROACH TO TASK ALLOCATION IN DISTRIBUTED SYSTEMS

This paper describes the hybrid approach to task allocation in distrib uted systems by using problem-solving methods of the artificial intell igence. For static mapping the objective function is used to evaluate the optimality of the allocation of a task graph onto a processor grap h. Together with our optimization method also augmented simulated anne aling and heuristic move exchange methods in distributed form are impl emented. For dynamic task allocation the semidistributed approach was designed based on the division of processor network topology into inde pendent and symmetric spheres. Distributed static mapping (DSM) and dy namic load balancing (DLB) tools are controlled by user window interfa ce. DSM and DLB tools are integrated together with software monitor (P GPVM) in the graphical GRAPNEL environment.