روافد عبد الكاظم خيونأ. د. خلدون إبراهيم عارف2024-11-142024-11-142021-04-05https://dspace.utq.edu.iq/handle/123456789/281Cloud computing is an emerging computing technology that uses the internet and central remote services to maintain data and application which are provisioned on-demand and on pay-as-you-go basis. Wide adaptation of Cloud concepts has increased number of data centers worldwide resulting into significant amounts of power consumption by data centers which affects environment and economical aspects. Through virtualization, multiple virtual machines (VM) can be deployed onto one physical machine (PM). These VMs hold and execute the Cloud workload. An existing problem is how to allocate Virtual Machines (VMs) to Physical Machines (PMs) or hosts. This process is called VM placement. The decision on which physical machines (PM) to place each virtual machine (VM) is very important for the efficient cloud operation. On one hand, it is important that VMs obtain adequate resources (e.g., CPU, memory, and network) from the hosting PM so that its performance is not impaired. On the other hand, the cloud operator would like to consolidate VMs on as few PMs as possible to maximize resource utilization and lower energy consumption. Efficient VM placement is like the vector bin packing problem, which is known to be NP-hard. Effective physical machine allocation of VMs will lead to better use of resources and energy savings. In this study, the main goal is to provide an improved Policy on energy efficient VM placement to minimize energy consumption in the cloud environment. Placing the VMs in the addressed Bin-Packing mechanism and retaining the Service Level Agreement (SLA) between the provider and the cloud customer .Significant reduction in power consumption could even be made if reduction are implemented at software level. Energy-aware scheduling processes produces excellent performance by implementing Bin-packing energy efficiency mechanisms. The proposed algorithm has been enhanced for the two First Fit Decreasing (FFD( and Best Fit Decreasing) BFD( algorithms, which are considered the best among Bin packing algorithms. The enhanced algorithm adopts server power as the basis for arranging servers in the database, unlike the BFD, FFD algorithms that arrange servers according to their CPU. The purpose is to minimize the number of active servers without affecting the service level agreement The proposed algorithm has been practically tried using MATLAB 2020 programing language for samples of heterogeneous servers and virtual machines (each server and virtual machine has its own specifications for storage, processing unit, bandwidth, etc and different from the others ), whose specifications were chosen randomly. The proposed algorithm could decrease significantly energy consumption and resources utilization in comparison with the existing algorithms (FFD, BFD).entext::thesis::master thesis