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Multiprocessor Task Graph Scheduling Using a Novel Graph-Like Learning Automata
2015
International Journal of Grid and Distributed Computing
Optimized task scheduling is one of the most important challenges in multiprocessor environments such as parallel and distributed systems. In such these systems, each parallel program is decomposed into the smaller segments so-called tasks. Task execution times, precedence constrains and communication costs are modeled by using a directed acyclic graph (DAG) named task graph. The goal is to minimize the program finish-time (makespan) by means of mapping the tasks to the processor elements in
doi:10.14257/ijgdc.2015.8.1.05
fatcat:njh677sgzber7oj5u2oc57v7eq