Improving Task Scheduling in Cloud Datacenters by Implementation of an Intelligent Scheduling Algorithm

被引：0
作者：
Jasim Mohammad O.K. [1 ]
Salih B.M. [1 ]
机构：
[1] University of Fallujah, Fallujah Aar
来源：
Informatica (Slovenia) | 2024年 / 48卷 / 10期
关键词：
cloud computing; cloud datacenter; cuckoo intelligent algorithm; intelligent scheduling algorithm; task scheduling;
D O I：
10.31449/inf.v48i10.5843
中图分类号：
学科分类号：
摘要：
The need for mobile and online applications and services has resulted in a significant expansion of cloud computing services. The exponential expansion emphasizes the significance of minimizing scheduling time and optimizing resource utilization in a dynamic environment. Therefore, several scheduling algorithms have been developed to tackle these issues by utilizing intelligent scheduling methods, such as Genetic Algorithms, greedy algorithm, Antlion Optimizer, Ant Colony optimization, and Cuckoo Intelligent Algorithm. This paper presents a comprehensive analysis of intelligent optimization methodologies, with a particular emphasis on the Cuckoo intelligent methodology. Furthermore, it introduces a suggested deployment of a Cuckoo-based cloud computing system as a highly effective algorithm that is expected to produce enhanced outcomes in work scheduling. © 2024 Slovene Society Informatika. All rights reserved.
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页码：77 / 88
页数：11
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