An optimizing algorithm of static task scheduling problem based on hybrid genetic algorithm

To reduce resources consumption of parallel computation system,a static task scheduling optimization method based on hybrid genetic algorithm is proposed and validated,which can shorten the scheduling length of parallel tasks with precedence constraints.Firstly,the global optimal model and constraints are created to demonstrate the static task scheduling problem in heterogeneous distributed computing systems(HeDCSs).Secondly,the genetic population is coded with matrix and used to search the total available time span of the processors,and then the simulated annealing algorithm is introduced to improve the convergence speed and overcome the problem of easily falling into local minimum point,which exists in the traditional genetic algorithm.Finally,compared to other existed scheduling algorithms such as dynamic level scheduling(DLS),heterogeneous earliest finish time(HEFT),and longest dynamic critical path(LDCP),the proposed approach does not merely decrease tasks schedule length,but also achieves the maximal resource utilization of parallel computation system by extensive experiments.