Learning Based Genetic Algorithm for Task Graph Scheduling

Nowadays, parallel and distributed based environments are used extensively; hence, for using these environments effectively,scheduling techniques are employed. The scheduling algorithm aims to minimize the makespan (i.e., completion time) of a parallelprogram. Due to the NP-hardness of the scheduling problem, in the literature, several genetic algorithms have been proposed tosolve this problem, which are effective but are not efficient enough. An effective scheduling algorithm attempts to minimize themakespan and an efficient algorithm, in addition to that, tries to reduce the complexity of the optimization process. The majorityof the existing scheduling algorithms utilize the effective scheduling algorithm, to search the solution space without consideringhow to reduce the complexity of the optimization process. This paper presents a learner genetic algorithm (denoted by LAGA) toaddress static scheduling for processors in homogenous computing systems. For this purpose, we proposed two learning criterianamed Steepest AscentLearning Criterion and Next AscentLearning Criterion wherewe use the concepts of penalty and reward for learning. Hence, we can reach an efficient search method for solving scheduling problem, so that the speed of finding a schedulingimproves sensibly and is prevented from trapping in local optimal. It also takes into consideration the reuse idle time criterionduring the scheduling process to reduce the makespan. The results on some benchmarks demonstrate that the LAGA provides always better scheduling against existing well-known scheduling approaches