In cloud computing, more often times cloud assets are underutilized because of poor allocation of task in virtual machine (VM). There exist inconsistent factors affecting the scheduling tasks to VMs. In this paper, an effective scheduling with multi-objective VM selection in cloud data centers is proposed. The proposed multi-objective VM selection and optimized scheduling is described as follows. Initially the input tasks are gathered in a task queue and tasks computational time and trust parameters are measured in the task manager. Then the tasks are prioritized based on the computed measures. Finally, the tasks are scheduled to the VMs in host manager. Here, multi-objectives are considered for VM selection. The objectives such as power usage, load volume, and resource wastage are evaluated for the VMs and the entropy is calculated for the measured objectives and based on the entropy value krill herd optimization algorithm prioritized tasks are scheduled to the VMs. The experimental results prove that the proposed entropy based krill herd optimization scheduling outperforms the existing general krill herd optimization, cuckoo search optimization, cloud list scheduling, minimum completion cloud, cloud task partitioning scheduling and round robin techniques.

在云计算中，由于虚拟机（VM）中的任务分配不佳，云资产的利用率经常不足。存在不一致的因素影响对VM的调度任务。本文提出了一种在云数据中心具有多目标虚拟机选择的有效调度方法。提出的多目标虚拟机选择和优化调度描述如下。最初，将输入任务收集在任务队列中，并在任务管理器中测量任务计算时间和信任参数。然后根据计算出的度量确定任务的优先级。最后，将任务调度到主机管理器中的VM。这里，多目标被考虑用于VM选择。目标，例如电力使用量，负载量，为VM评估资源浪费和资源浪费，并为测量的目标计算熵，并基于熵值Krill牛群优化算法为VM安排优先任务。实验结果证明，所提出的基于熵的磷虾群优化调度优于现有的一般磷虾群优化，布谷鸟搜索优化，云列表调度，最小完成云，云任务划分调度和轮询技术。