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CWOA: Hybrid Approach for Task Scheduling in Cloud Environment
The Computer Journal ( IF 1.077 ) Pub Date : 2021-05-03 , DOI: 10.1093/comjnl/bxab028
K Pradeep, L Javid Ali, N Gobalakrishnan, C J Raman, N Manikandan

A cloud computing system typically comprises of a huge number of interconnected servers that are organized in a datacentre. Such servers dynamically cater to the on-demand requests put forward by the clients seeking solutions to their applications through an interface. The scheduling activity concerned with scientific applications is designated under the NP hard problem category since they make use of heterogeneous resources of dynamic capabilities. Recently cloud computing researchers had developed numerous meta-heuristic approaches for providing solutions to the challenges arising in the task scheduling activities. Scheduling of tasks poses a major concern in cloud computing environment. This decreases the efficiency of the system considerably, if not handled properly. Hence, an improvised task scheduling algorithm that enhances the performance of the cloud is needed. There are two factors that affect the cloud environment: service quality and energy usage. To increase the performance in above suggested factors (memory, makespan and energy efficiency), an efficient hybridized algorithm, obtained by integrating the Cuckoo Search Algorithm (CSA) and Whale Optimization Algorithm (WOA), called the CWOA had been proposed in this work. The performance of our proposed CWOA algorithm had been compared with Ant Colony Optimization, CSA and WOA and it was found to produce an improvement of 5.62%, 4.36% and 2.27% with respect to makespan, 16.36%, 19.19% and 13.13% with respect to memory utilization and 19.08%, 19.34% and 16.75% with respect to energy consumption parameters, respectively. Comprehensive results have been tabulated in the result section of this article.

中文翻译:

CWOA:云环境中任务调度的混合方法

云计算系统通常包括在数据中心内组织的大量互连服务器。这样的服务器动态地满足由客户端通过接口为他们的应用程序寻求解决方案的客户端提出的按需请求。与科学应用有关的调度活动被指定为NP难题类别,因为它们利用了动态能力的异构资源。最近,云计算研究人员开发了许多元启发式方法来提供解决方案,以应对任务调度活动中出现的挑战。任务调度是云计算环境中的一个主要问题。如果处理不当,会大大降低系统的效率。因此,需要一种可增强云性能的简易任务调度算法。影响云环境的因素有两个:服务质量和能源使用。为了提高上述建议因素(内存,制造期限和能源效率)的性能,在这项工作中提出了一种通过将布谷鸟搜索算法(CSA)和鲸鱼优化算法(WOA)集成而获得的高效混合算法,称为CWOA。我们将我们提出的CWOA算法的性能与蚁群优化,CSA和WOA进行了比较,发现在制造跨度方面分别提高了5.62%,4.36%和2.27%,在改进方面分别提高了16.36%,19.19%和13.13%相对于能耗参数而言,它们分别占内存利用率和19.08%,19.34%和16.75%。
更新日期:2021-05-04
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