Ni Lina, Sun Xiaoting, Li Xincheng, Zhang Jinquan
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Key Laboratory of the Ministry of Education for Embedded System and Service Computing, Tongji University, Shanghai 201804, China.
Comput Intell Neurosci. 2021 Jun 10;2021:5546758. doi: 10.1155/2021/5546758. eCollection 2021.
An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users' requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP-hard problem. In this paper, we propose a three-layer scheduling model based on whale-Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition-based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale-cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization.
云计算面临的一个重要挑战是如何正确有效地处理和服务数百万用户的请求。云计算中的高效任务调度直观上会影响整个系统的资源配置和运营成本。然而，云计算环境中的任务和资源调度是一个NP难问题。在本文中，我们提出了一种基于鲸鱼-高斯云的三层调度模型。在该模型的第二层，基于高斯云模型的鲸鱼优化策略（GCWOAS2）用于云计算中的多目标任务调度，即通过有效利用虚拟机资源来最小化任务完成时间，并保持每个虚拟机的负载平衡，降低系统的运营成本。在GCWOAS2策略中，首先使用基于反向学习的机制初始化调度策略以生成最优调度方案。然后，提出了一种自适应移动因子来动态扩展搜索范围。提出了基于高斯云模型的鲸鱼优化算法以增强搜索的随机性。最后，提出了一种基于高斯鲸鱼-云优化（GCWOA）的多目标任务调度算法，使得整个调度策略不仅可以扩展搜索范围，还能跳出局部最大值并获得全局最优调度策略。实验结果表明，与其他现有的元启发式算法相比，我们的策略不仅可以缩短任务完成时间，还能平衡虚拟机资源的负载，同时在资源利用率方面也具有更好的性能。
