Dalia Abdulkareem Shafiq, Noor Zaman Jhanjhi, A. Abdullah
{"title":"Proposing A Load Balancing Algorithm For The Optimization Of Cloud Computing Applications","authors":"Dalia Abdulkareem Shafiq, Noor Zaman Jhanjhi, A. Abdullah","doi":"10.1109/MACS48846.2019.9024785","DOIUrl":null,"url":null,"abstract":"Cloud Computing (CC) is a fast growing services that make use of pay per use model. The technology provides various services in terms of storage, deployment, web services etc. however the expand of these services and the tremendous increase of user demand has resulted in many challenges to keep up the performance in line with QoS measurement and SLA document provided by cloud providers to enterprises. This expand resulted in challenges such as load balancing. Besides that, user's requirements became hard to fulfil in terms of response time and deadline regarding task scheduling. To address these challenges, this research proposes an optimized algorithm with the use of Machine Learning Classification technique based on deadline constraints. The main objective of the proposed algorithm is to enhance the efficiency, optimize the server resources by considering the priority of different users' tasks and avoid server breakdown. Our proposed algorithm will address the mentioned issues and current research gap based on the recent literature.","PeriodicalId":434612,"journal":{"name":"2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MACS48846.2019.9024785","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17
Abstract
Cloud Computing (CC) is a fast growing services that make use of pay per use model. The technology provides various services in terms of storage, deployment, web services etc. however the expand of these services and the tremendous increase of user demand has resulted in many challenges to keep up the performance in line with QoS measurement and SLA document provided by cloud providers to enterprises. This expand resulted in challenges such as load balancing. Besides that, user's requirements became hard to fulfil in terms of response time and deadline regarding task scheduling. To address these challenges, this research proposes an optimized algorithm with the use of Machine Learning Classification technique based on deadline constraints. The main objective of the proposed algorithm is to enhance the efficiency, optimize the server resources by considering the priority of different users' tasks and avoid server breakdown. Our proposed algorithm will address the mentioned issues and current research gap based on the recent literature.