Opposition Learning-Based Grey Wolf Optimizer Algorithm for Parallel Machine Scheduling in Cloud Environment
Opposition Learning-Based Grey Wolf Optimizer Algorithm for Parallel Machine Scheduling in Cloud Environment
复制标题
云环境下基于对立学习的并行机调度灰狼优化算法
DOI：
10.22266/ijies2017.0228.20
复制
发表时间：
2017
影响因子：
--
通讯作者：
A. Chokkalingam
中科院分区：
文献类型：
--
作者：
Gobalakrishnan Natesan;A. Chokkalingam
云计算是一种新颖的开发计算范式，通过Internet提供实现，信息和IT服务。并行机器调度（任务资源）是云计算环境中的重要作用。但是，并行计算调度问题是与整个云计算设施的功效相关的首要任务。良好的安排算法必须减少实施时间和成本以及消费者的QoS必需品。为了克服平行计算计划中存在的问题，我们根据云计算环境的拟议成本和时间模型提出了基于对立的灰狼优化器（OGWO）。此外，基于反对的学习的概念与标准GWO一起使用，以增强其所提出方法的计算速度和收敛性。实验结果表明，所提出的方法在所有方法中都胜过所有方法，并提供了较少的内存利用和计算时间的质量时间表。
Cloud computing is a novel developing computing paradigm where implementations, information, and IT services are given over the internet. The parallel-machine scheduling (Task-Resource) is the important role in cloud computing environment. But parallel-machine scheduling issues are premier that associated with the efficacy of the whole cloud computing facilities. A good scheduling algorithm has to decrease the implementation time and cost along with QoS necessities of the consumers. To overcome the issues present in the parallel-machine scheduling, we have proposed an oppositional learning based grey wolf optimizer (OGWO) on the basis of the proposed cost and time model on cloud computing environment. Additionally, the concept of opposition based learning is used with the standard GWO to enhance its computational speed and convergence profile of the proposed method. The experimental results show that the proposed method outperforms among all methods and provides quality schedules with less memory utilization and computation time.
影响因子：
14.3
作者：
Noman, Nasimul;Iba, Hitoshi
通讯作者：
Iba, Hitoshi