Aya A. Salah Farrag, Safia A. Mahmoud, El-Sayed M. El-Horbaty
{"title":"Intelligent cloud algorithms for load balancing problems: A survey","authors":"Aya A. Salah Farrag, Safia A. Mahmoud, El-Sayed M. El-Horbaty","doi":"10.1109/INTELCIS.2015.7397223","DOIUrl":null,"url":null,"abstract":"Cloud computing services are growing very fast especially with the high demand of mobile and online applications (Apps) and services. This exponential growth emphasis on the need of minimizing the makespan scheduling and utilizing the resources efficiently based on dynamic environment. Accordingly, many load balancing algorithms have been developed to overcome these issues using intelligent optimization methodologies, such as Genetic Algorithms (GA), Ant Colony optimization (ACO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). This paper surveys the above intelligent optimization techniques and focuses on the Ant Lion Optimizer (ALO) intelligent technique, also it proposes an implementation of ALO based cloud computing environment as efficient algorithm that expected to supplies better outcomes in load balancing.","PeriodicalId":6478,"journal":{"name":"2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"41","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INTELCIS.2015.7397223","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 41
Abstract
Cloud computing services are growing very fast especially with the high demand of mobile and online applications (Apps) and services. This exponential growth emphasis on the need of minimizing the makespan scheduling and utilizing the resources efficiently based on dynamic environment. Accordingly, many load balancing algorithms have been developed to overcome these issues using intelligent optimization methodologies, such as Genetic Algorithms (GA), Ant Colony optimization (ACO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). This paper surveys the above intelligent optimization techniques and focuses on the Ant Lion Optimizer (ALO) intelligent technique, also it proposes an implementation of ALO based cloud computing environment as efficient algorithm that expected to supplies better outcomes in load balancing.