DAG scheduling on heterogeneous distributed systems using learning automata

DAG scheduling is of great importance to optimal distribution of tasks in parallel and distributed systems. In this paper a novel approach to DAG scheduling, utilizing learning automata across distributed systems, is proposed. The learning process begins with an initial population of randomly generated learning automata. Each automaton by itself represents a stochastic scheduling. The scheduling is optimized within a learning process. Compared with current genetic approaches to DAG scheduling better results are achieved. The main reason underlying this achievement is that an evolutionary approach such as genetics looks for the best chromosomes within genetic populations whilst in the approach presented in this paper learning automata is applied to find the most suitable position for the genes in addition to looking for the best chromosomes. The scheduling resulted from applying our scheduling algorithm to some benchmark task graphs are compared with the existing ones.