S. Kalarani, V. Sharmila, Suma. S, Jayakumari Ag, K. Sudha
{"title":"Scalable Computing in Resource Allocation","authors":"S. Kalarani, V. Sharmila, Suma. S, Jayakumari Ag, K. Sudha","doi":"10.1109/ICECAA58104.2023.10212395","DOIUrl":null,"url":null,"abstract":"Computational humanity is enormously voluminous and complex. One of the computing industry's fastest-growing approaches is cloud computing. It is a cutting-edge method for providing IT service over the World Wide Web. Through the Internet, this concept offers computing resources to users in a pool. Resource scheduling and allocation for various aggregate web services is a crucial and challenging problem in cloud computing. This research looks at resource allocation using scalable computing. Infrastructure as a Service (IaaS), or the service of renting out computer resources through the Internet, is offered to users by cloud computing. The client can select from a variety of computing resources depending on their needs. This approach uses the IaaS model to allocate resources for real-time tasks. Real-Time jobs must be finished ahead of schedules. Elasticity or scalable computing refers to the ability to scale up the resource in this situation in accordance with the demands. The resources are scalable and open to a vast user base. In order to finish real-time work ahead of schedules, the user can choose any number of Virtual Machines (VMs) based on speed and rate. The client leases the virtual machines. Hence the fee is set just for the duration of the rental. Additionally, a method is devised to assign VMs to programs with real-time tasks. The allocation is presented as a problem of restricted optimization.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECAA58104.2023.10212395","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Computational humanity is enormously voluminous and complex. One of the computing industry's fastest-growing approaches is cloud computing. It is a cutting-edge method for providing IT service over the World Wide Web. Through the Internet, this concept offers computing resources to users in a pool. Resource scheduling and allocation for various aggregate web services is a crucial and challenging problem in cloud computing. This research looks at resource allocation using scalable computing. Infrastructure as a Service (IaaS), or the service of renting out computer resources through the Internet, is offered to users by cloud computing. The client can select from a variety of computing resources depending on their needs. This approach uses the IaaS model to allocate resources for real-time tasks. Real-Time jobs must be finished ahead of schedules. Elasticity or scalable computing refers to the ability to scale up the resource in this situation in accordance with the demands. The resources are scalable and open to a vast user base. In order to finish real-time work ahead of schedules, the user can choose any number of Virtual Machines (VMs) based on speed and rate. The client leases the virtual machines. Hence the fee is set just for the duration of the rental. Additionally, a method is devised to assign VMs to programs with real-time tasks. The allocation is presented as a problem of restricted optimization.