GDL

Undergraduate Theses from gdlhub / 2021-08-24 11:55:00
Oleh : Bela Shrimali, Hiren Patel, STIKOM Dinamika Bangsa Jambi
Dibuat : 2021-08-06, dengan 0 file

Keyword : Power efficiency, Optimization, Resource allocation, Resource utilization, Cloud
Url : http://www.sciencedirect.com/science/article/pii/S1319157817302264
Sumber pengambilan dokumen : Web

Cloud computing is a hybrid paradigm which makes use of utility computing, high performance cluster computing and grid computing and it offers various benefits such as flexibility, expandability, little or almost no capital investment, disaster recovery, moveable work space and much more. However, due to constantly increasing number of data centers worldwide, the issue of energy consumption by these data centers has attracted attention of researchers. Resource allocation and resource utilization are the major criterion in which the problem of energy efficiency can be addressed. In this research, we aim to provide energy-efficient resource allocation using Multi-Objective Optimization (MOO) method. Further, We propose MOO-based virtual machine (VM) allocation policy and implement it in CloudSim environment. The results are compared with existing policies. The results depict that MOO-based policy leads to saving in energy due to efficient resource allocation, without compromising performance of data center operations.

Deskripsi Alternatif :

Cloud computing is a hybrid paradigm which makes use of utility computing, high performance cluster computing and grid computing and it offers various benefits such as flexibility, expandability, little or almost no capital investment, disaster recovery, moveable work space and much more. However, due to constantly increasing number of data centers worldwide, the issue of energy consumption by these data centers has attracted attention of researchers. Resource allocation and resource utilization are the major criterion in which the problem of energy efficiency can be addressed. In this research, we aim to provide energy-efficient resource allocation using Multi-Objective Optimization (MOO) method. Further, We propose MOO-based virtual machine (VM) allocation policy and implement it in CloudSim environment. The results are compared with existing policies. The results depict that MOO-based policy leads to saving in energy due to efficient resource allocation, without compromising performance of data center operations.

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