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Jasmine Kaur
Karmanbir Singh


Pay-as-you-go access to computer resources is a major selling point of the cloud computing model. Cloud tenants demand complete networking of their dedicated resources to simply implement network functions and services, in addition to the conventional computer resources. The flexibility and convenience of on-demand resource provisioning make cloud computing a compelling computing platform. The key to meeting fluctuating needs and maximizing return on investment from Cloud-supporting infrastructure is dynamic resource allocation and reallocation. For traditional IaaS, we offer an energy-efficient resource allocation strategy based on bin packing. In this paper, we present an accurate energy-conscious method for initial resource allocation by casting the issue of energy-efficient resource allocation as a bin-packing model. The available VMs (virtual machines) employ a modified version of the max-min scheduling technique, which saves money and resources. The results of this study give a framework for comparing and contrasting the many different resource distribution approaches that have been proposed by other researchers. The importance of efficient data centers for the cloud is growing. Power consumption has been a major problem due to its expanding size and widespread usage. The overarching purpose of this effort is to create models and algorithms for resource allocation that are both energy-efficient and take into account a variety of relevant factors.


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Author Biography

Jasmine Kaur

Ph.D. Scholar,Department of Computer Science,

Thapar Institute of Engineering & Technology,



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