Load Balancing in Multiprocessor Systems Using Modified Real-Coded Genetic Algorithm

Panwar, Poonam; Kaushal, Chetna; Singla, Ashish; Rattan, Vikas

doi:10.1007/978-981-19-0475-2_18

Cited by 1 publication

(1 citation statement)

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“…An algorithm is designed known as a 'Genetic' algorithm metaheuristic to sort out the problem of choosing the most favorable sorting for jobs. So, that is the method to balance the workload on a network [18]. A new technique for load balancing in a cloud environment: priority queue on the different stages using the binary heap is described.…”

Section: Literature Reviewmentioning

confidence: 99%

Load Balancing of Cloud Computing Service Model Empowered with Fuzzy Logic

Shirazi

Khan

Rasool³

et al. 2023

SSURJET

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Nowadays, cloud computing is a growing field in research and the marketplace, including virtualization, internet service, computer software, and web services. A cloud comprises multiple elements like a consumer's data center and servers. It provides high availability, efficiency, scalability, mobility, fault tolerance, decreased overhead of users, reduced expenses of ownership, on-demand services, etc. A Variety Of users need various Quality of Service (QoS) demands. Therefore, the cloud supplier needs to arrange the tasks to get maximum advantages regarding their services, and the consumer's quality of service demands are satisfied. Currently, the need for cloud is increasing day by day, people moved towards cloud simultaneously, so its scale is up that's why the service providers are required to deal with enormous requests. The biggest challenge is the availability of services and maintaining the performance equivalent or more effective whenever workload occurs. Multiple requests are processed simultaneously; that's why the workload increased. The load balancer uses to resolve that issue. Our research shows how to balance the load in a cloud environment using fuzzy logic. The processor speed, storage capacity, and assigned load of Virtual Machine (VM) utilize to balance the load in cloud computing through fuzzy logic to achieve better processing time and storage utilization.

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Section: Literature Reviewmentioning

confidence: 99%