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Optimization of Particle Genetic Algorithm based on Time Load Balancing for Cloud Task Scheduling in Cloud Task Planning

Volume 14, Number 6, June 2018, pp. 1161-1170
DOI: 10.23940/ijpe.18.06.p7.11611170

Yenzhen Zhang, Shouming Hou, and Li Chang

School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, China

(Submitted on March 7, 2018; Revised on April 29, 2018; Accepted on May 24, 2018)


To solve the problems of long time consumption, imbalanced time load and low resource utilization for cloud task scheduling in cloud task planning, we propose an optimized strategy of particle genetic algorithm based on time load balancing. This strategy was adopted to improve the quality of particles by optimizing particle initialization operation. To ensure that better particles capable of more balanced time load are selected, a model of fitness in time load balancing was established. To prevent the particles from jumping out of the specified area in iterations, the element values of their location and velocity were processed in a standardized way. Finally, genetic crossover and mutation operators were introduced to avoid leading the algorithm to local optimization. This strategy could effectively improve the convergence rate of the particle genetic algorithm and the quality of solutions. The experimental results showed that the algorithm had greater power to search for a better global optimal solution, consumed less time, and reached a more balanced time load. With this algorithm, we may achieve better and more logical task scheduling sequences. Simultaneously, the idea owns a certain degree of practicality and generalization in many fields.


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