Ge Weiqing* and Cui Yanru Pages 1 - 7 ( 7 )
Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm.
Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed.
Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm.
Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.
Cloud computing, genetic algorithm, task scheduling, min - min algorithm, max - min algorithm, NP problem.
City College of Dongguan University of Technologe, Dongguan City Guangdong Province, City College of Dongguan University of Technologe, Dongguan City Guangdong Province