Cloud computing is a dynamic technology with various application spheres because of its scalability, cost-effectiveness, and reliability. However, since the energy demand for information and Communication Technologies (ICT) is on the rise, cloud computing is facing new challenges related to environmental protection, power consumption, energy efficiency, and carbon dioxide emissions. The latest technologies that strive for sustainable energy efficiency and a reduced e-waste and carbon footprint are constantly being researched and deployed. These technologies have the potential to transform cloud computing into green cloud computing. In this survey, the authors investigated recent research methodologies such as algorithm-based, architecture-based, framework-based, model-based, methods-based, and general issue-based approaches. Many of these research projects are still in their infancy and are yet to be commercially implemented. The last thing that was talked about was some future research trends and some of the open challenges in green cloud computing.
Increased energy consumption in Cloud Data Centres (CDCs) increases the carbon footprint. Efficiency of the data centres thus needs to be improved through server consolidation using effective virtual machine (VM) placement and migration techniques and minimizing the number of active physical machines (PMs). One of the problems is how to operationally allocate the VMs to PMs. These allocations have both operational costs and energy consumption issues. To achieve the aim of ‘Green Computing’ a number of state-of-the-art machine learning algorithms have been proposed for the VM placement. The authors of this paper have provided a detailed discussion and comparison of some of the current research works on energy efficiency. and cons of each of these techniques have been discussed. Some future research prospects in this field have also been mentioned at the end.
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