基于改进遗传算法的异构环境混合调度策略研究

Reseach of Hybrid Scheduling Strategy in Heterogeneous Environment Based on Improved Genetic Algorithm

异构环境下任务调度是NP问题,它关注大规模的资源和任务调度,要求采用的调度算法能够具有高效性.随着任务数和资源数的增加,遗传算法表现出慢速收敛的缺点.为了克服其缺点,在改进的遗传算法的基础上,增加了分组和负载平衡处理策略,提出了一种混合遗传调度策略.仿真实验表明,基于改进遗传算法的混合调度策略比传统的调度策略性能更优,其算法更符合复杂的异构环境,能更好满足系统的时间特性和最小化资源开销的问题.

Task scheduling in heterogeneous environment is a NP problem, it is concerned about the large-scale resource and task scheduling, requires the scheduling algorithm is highly efficient. As the number of tasks and resources to increase the number of genetic algorithm to show the shortcomings of slow convergence. In order to overcome its shortcomings, this paper improved genetic algorithm based on the increased packet processing and load balancing strategy, a hybrid genetic scheduling policy, simulation experiments show that the hybrid genetic algorithm-based scheduling policy than the traditional scheduling strategy better performance, the algorithm more complex, heterogeneous environment, the system can better meet the characteristics of time and resources to minimize the overhead issue.