This paper describes the hybrid approach to task allocation in distributed systems by using problem solving methods of the artificial intelligence. For static mapping the objective function is used to evaluate the optimality of the allocation of a task graph onto a processor graph. Together with our optimization method also augmented simulated annealing and heuristic move exchange methods in distributed form are implemented. For dynamic task allocation the semidistributed approach was designed based on the division of processor network topology into independent and symmetric spheres. Distributed static mapping (DSM) and dynamic load balancing (DLB) tools are controlled by user window interface. DSM and DLB tools are integrated together with software monitor (PG_PVM) in the graphical GRAPNEL environment.