蚁群优化算法在解决云计算主要问题中的作用

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-08-30 DOI:10.1080/0952813X.2021.1966841
Saied Asghari, N. J. Navimipour
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引用次数: 4

摘要

云计算中存在许多问题,研究者试图通过使用不同的技术来解决这些问题。大多数云挑战都是np难题;因此,许多元启发式技术被用于解决这些挑战。蚁群优化算法(Ant Colony optimization, ACO)作为一种著名而强大的元启发式算法,近年来被用于解决云计算中的许多挑战。然而,尽管蚁群算法在解决优化问题方面具有潜力,但它在以综述文章的形式解决云问题方面的应用迄今尚未得到研究。因此,本文对不同类型的蚁群算法进行了完整而详细的研究,以解决云计算中的重要问题和问题。此外,还显示了不同出版商和不同年份发表的论文数量。在本文中,可用的挑战被分为不同的组,包括调度、资源分配、负载平衡、整合、虚拟机放置、服务组合、能源消耗和复制。然后,通过应用选择过程,介绍了从每个类别中选择的一些重要技术。此外,本研究还对所研究的方法进行了比较,并突出了它们的主要要素。最后,强调了相关的开放问题和解释困难的一些线索。结果表明,在云环境中仍然存在一些未应用蚁群算法来解决的挑战。
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The role of an ant colony optimisation algorithm in solving the major issues of the cloud computing
ABSTRACT There are many issues and problems in cloud computing that researchers try to solve by using different techniques. Most of the cloud challenges are NP-hard problems; therefore, many meta-heuristic techniques have been used for solving these challenges. As a famous and powerful meta-heuristic algorithm, the Ant Colony Optimisation (ACO) algorithm has been recently used for solving many challenges in the cloud. However, in spite of the ACO potency for solving optimisation problems, its application in solving cloud issues in the form of a review article has not been studied so far. Therefore, this paper provides a complete and detailed study of the different types of ACO algorithms for solving the important problems and issues in cloud computing. Also, the number of published papers for various publishers and different years is shown. In this paper, available challenges are classified into different groups, including scheduling, resource allocation, load balancing, consolidation, virtual machine placement, service composition, energy consumption, and replication. Then, some of the selected important techniques from each category by applying the selection process are presented. Besides, this study shows the comparison of the reviewed approaches and also it highlights their principal elements. Finally, it highlights the relevant open issues and some clues to explain the difficulties. The results revealed that there are still some challenges in the cloud environments that the ACO is not applied to solve.
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来源期刊
CiteScore
6.10
自引率
4.50%
发文量
89
审稿时长
>12 weeks
期刊介绍: Journal of Experimental & Theoretical Artificial Intelligence (JETAI) is a world leading journal dedicated to publishing high quality, rigorously reviewed, original papers in artificial intelligence (AI) research. The journal features work in all subfields of AI research and accepts both theoretical and applied research. Topics covered include, but are not limited to, the following: • cognitive science • games • learning • knowledge representation • memory and neural system modelling • perception • problem-solving
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