基于任务分类思维的云计算海量资源改进调度

对云计算海量数据下的资源调度的研究过程中,进行资源调度时资源分配无法到达合理化调度,存在资源调度效率低的问题。提出分类思维的云计算海量数据资源优化调度方法。该方法引入膜计算概念,将云计算下的海量资源调度的总任务划分为多个子任务,并详细计算每个子任务的资源调度任务量。将优化调度系统内部分解为主膜和辅助膜,利用蝙蝠算法在辅助膜内进行资源分配个体寻优,并将优化后的资源分配最优个体传送到主膜间进行云计算海量数据下的资源分配优化。实验仿真证明,基于改进膜计算蝙蝠算法的云计算海量数据下的资源优化调度方法调度效率高,分配较为均衡。

Based on task classification thinking of cloud computing resources to improve the scheduling

In cloud computing resources optimization scheduling research under the huge amounts of data process, using the current methods for resource optimization scheduling and scheduling, resource allocation is unable to be rationalization exist the problem of low efficiency of resource scheduling. A bat algorithm based on improved membrane computing cloud mass data resource optimization scheduling method. The method introduced membrane computing concept the massive resource scheduling under cloud computing by the addition of multiple sub divided into tasks, and details of the computing resource of each subtask scheduling tasks. Optimization scheduling system internal decomposition is given priority to film and auxiliary film, using the bat algorithm in the allocation of resources within the secondary membrane individual optimization, and the optimized allocation of resources between the optimal individual transmitted to the main membrane for cloud computing resource allocation optimization under the huge amounts of data, the experimental simulation proves that the calculation of the bat algorithm based on improved membrane cloud computing resources optimization scheduling method under the huge amounts of data scheduling efficiency is high, the distribution is more balanced.