  The paper presents a solution to the dynamic DAG scheduling problem in Grid
environments. It presents a distributed, scalable, efficient and fault-tolerant
algorithm for optimizing tasks assignment. The scheduler algorithm for tasks
with dependencies uses a heuristic model to optimize the total cost of tasks
execution. Also, a method based on genetic algorithms is proposed to optimize
the procedure of resources assignment. The experiments used the MonALISA
monitoring environment and its extensions. The results demonstrate very good
behavior in comparison with other scheduling approaches for this kind of DAG
scheduling algorithms.

Distributed, Parallel, and Cluster Computing