{"title":"蚁群优化算法在解决云计算主要问题中的作用","authors":"Saied Asghari, N. J. Navimipour","doi":"10.1080/0952813X.2021.1966841","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"216 1","pages":"755 - 790"},"PeriodicalIF":1.7000,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"The role of an ant colony optimisation algorithm in solving the major issues of the cloud computing\",\"authors\":\"Saied Asghari, N. J. Navimipour\",\"doi\":\"10.1080/0952813X.2021.1966841\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":15677,\"journal\":{\"name\":\"Journal of Experimental & Theoretical Artificial Intelligence\",\"volume\":\"216 1\",\"pages\":\"755 - 790\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2021-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Experimental & Theoretical Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1080/0952813X.2021.1966841\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Experimental & Theoretical Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1080/0952813X.2021.1966841","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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