Crow–penguin optimizer for multiobjective task scheduling strategy in cloud computing,International Journal of Communication Systems

当前位置： X-MOL 学术 › Int. J. Commun. Syst. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Crow–penguin optimizer for multiobjective task scheduling strategy in cloud computing
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2020-06-29 , DOI: 10.1002/dac.4467
Harvinder Singh ₁ , Sanjay Tyagi ₂ , Pardeep Kumar ₂

Affiliation

Task scheduling in the cloud is the multiobjective optimization problem, and most of the task scheduling problems fail to offer an effective trade‐off between the load, resource utilization, makespan, and Quality of Service (QoS). To bring a balance in the trade‐off, this paper proposes a method, termed as crow–penguin optimizer for multiobjective task scheduling strategy in cloud computing (CPO‐MTS). The proposed algorithm decides the optimal execution of the available tasks in the available cloud resources in minimal time. The proposed algorithm is the fusion of the Crow Search optimization Algorithm (CSA) and the Penguin Search Optimization Algorithm (PeSOA), and the optimal allocation of the tasks depends on the newly designed optimization algorithm. The proposed algorithm exhibits a better convergence rate and converges to the global optimal solution rather than the local optima. The formulation of the multiobjectives aims at a maximum value through attaining the maximum QoS and resource utilization and minimum load and makespan, respectively. The experimentation is performed using three setups, and the analysis proves that the method attained a better QoS, makespan, Resource Utilization Cost (RUC), and load at a rate of 0.4729, 0.0432, 0.0394, and 0.0298, respectively.

中文翻译：

乌鸦企鹅优化器，用于云计算中的多目标任务调度策略

云中的任务调度是多目标优化问题，大多数任务调度问题未能在负载，资源利用率，制造期限和服务质量（QoS）之间提供有效的折衷。为了在折衷之间取得平衡，本文提出了一种称为“乌鸦企鹅优化器”的方法，用于云计算中的多目标任务调度策略（CPO-MTS）。所提出的算法决定了在最短时间内在可用云资源中最优任务的最佳执行。所提出的算法是乌鸦搜索优化算法（CSA）和企鹅搜索优化算法（PeSOA）的融合，任务的最佳分配取决于新设计的优化算法。所提出的算法具有更好的收敛速度，并且收敛于全局最优解而不是局部最优解。多目标的制定旨在通过分别获得最大的QoS和资源利用率以及最小的负载和制造期来实现最大值。使用三种设置进行了实验，分析证明该方法分别以0.4729、0.0432、0.0394和0.0298的速率获得了更好的QoS，有效期，资源利用成本（RUC）和负载。

更新日期：2020-06-29

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11