Nowadays, Cloud computing is a new computing model in the field of information technology and research. Generally, the cloud environment aims in providing the resource that depends upon the user’s necessity. The major problem caused by cloud computing is task scheduling. Nevertheless, the previous scheduling methods concentrate only on the resource needs, memory, implementation time and cost. In this paper, we introduced an optimal task-scheduling algorithm of the local pollination-based moth search algorithm (LPMSA), which is the hybridization of moth search algorithm (MSA) and flower pollination algorithm (FPA). The proposed LPMSA chooses an optimal solution for proper task scheduling in the cloud. Moreover, the exploitation capacity of MSA is improved by using the local search of the FPA algorithm. In this work, we use 2-fold simulation processes that are implemented under the platform of JAVA. The proposed LPMSA for task-scheduling performance is evaluated using low and high heterogeneous machines with uniform and non-uniform parameters. The experimental analysis demonstrates that the proposed LPMSA approach is well suitable for cloud task scheduling thereby reducing the makespan and energy consumption during proper task scheduling.

中文翻译：

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基于局部授粉的飞蛾搜索算法用于任务调度异构云环境

如今，云计算是信息技术和研究领域中的一种新的计算模型。通常，云环境旨在提供取决于用户需求的资源。由云计算引起的主要问题是任务调度。尽管如此，以前的调度方法仅集中在资源需求，内存，实现时间和成本上。本文介绍了一种基于局部授粉的蛾类搜索算法（LPMSA）的最优任务调度算法，该方法是将蛾类搜索算法（MSA）和花朵授粉算法（FPA）混合在一起。提出的LPMSA为云中的正确任务调度选择了最佳解决方案。此外，通过使用FPA算法的本地搜索提高了MSA的开发能力。在这项工作中 我们使用在JAVA平台下实现的2倍仿真过程。使用具有统一和非统一参数的低异构机和高异构机，评估了用于任务调度性能的LPMSA。实验分析表明，所提出的LPMSA方法非常适合云任务调度，从而在适当的任务调度过程中减少了制造时间和能耗。