Cloud Computing Transformed IT Organizations With Its On-Demand Model, Notably Infrastructure as a Service (IaaS). Cloud Providers Manage Extensive Physical Devices, Consuming Substantial Energy and Bandwidth for Data Traffic During Virtual Machine D

Abstract

Cloud computing transformed IT organizations
 with its on-demand model, notably Infrastructure
 as a Service (IaaS). Cloud providers manage
 extensive physical devices, consuming
 substantial energy and bandwidth for data traffic
 during virtual machine deployment. Balancing
 energy and bandwidth are critical. This thesis
 presents BOHGOA integrated with ACO to
 optimize virtual machine placement in clouds,
 simultaneously considering bandwidth and
 energy. It yields Pareto-optimal solutions for this
 trade-off. Using CloudSim, BOHGOA
 outperformed GA, ACO, and FFD algorithms,
 reducing bandwidth consumption by 54.14%,
 32.11%, 57.47% (240 VMs) and 38.12%,
 22.76%, 47.05% (500 VMs) respectively.
 Additionally, it decreased physical machine
 energy usage by 37.70%, 34.01%, 40.14% (240
 VMs) and 27.50%, 22.28%, 30.52% (500 VMs)
 respectively. These results underscore
 BOHGOA's effectiveness in optimizing cloud
 VM placement.