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Internet of Things (IoT) as one of the last technology in the network domain is an environment of associated physical objects that are reachable through the Internet. Virtualization as the primary technology of IoT has a significant role in its popularization. The visualization strategies have a great impact on the IoT, but, as far as we know, there is not a comprehensive and systematic study in this field. Also, there is no systematic and complete way to discuss and analyze the related strategies. Henceforth, a systematic survey of virtualization techniques is presented in this paper. Different parameters based on the examination of the prevailing methods are considered in three main categorizations. Furthermore, the benefits and weaknesses connected with selected techniques have been discussed, and the significant issues of these techniques are addressed to improve the more efficient virtualization technique for the future. K E Y W O R D SCloud, Fog, network, Internet of Things, systematic literature review, virtualization

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Summary Due to the growth of applications and the integration of new customers into the world of computing systems, computing needs to be changed and to become more powerful and flexible than before. Meanwhile, cloud computing is presented as a model beyond a system that is currently capable of answering most request needs. Flexible infrastructure for cloud computing and virtualization technology provide new features to support business activities. Clouds are a very important topic that used secure management tools for storage, security and securing data centers in a flexible manner. One of the important matters in cloud technologies is virtual machine migration (VMM). There are different ways to implement the VMM, but because of the limitation of resources' energy, energy management is also very important and challenging. Due to the NP‐hard nature of this problem, this paper presents an energy‐aware VMM engine for cloud computing using the discrete bacterial foraging algorithm as a new collective behavior‐based metaheuristics algorithm. The CloudSim simulator is employed to investigate the efficiency of this method. The obtained results have shown that the proposed method improves the energy consumption and the migration count.

Green finance can be referred to as financial investments made on sustainable projects and policies that focus on a sustainable economy. The procedures include promoting renewable energy sources, energy efficiency, water sanitation, industrial pollution control, transportation pollution control, reduction of deforestation, and carbon emissions, etc. Mainly, these green finance initiatives are carried out by private and public agents like business organizations, banks, international organizations, government organizations, etc. Green finance provides a financial solution to create a positive impact on society and leads to environmental development. In the age of artificial intelligence, all industries adopt AI technologies. In this research, we see the applications of the intelligent model to examine the green finance for ecological advancement with regard to artificial intelligence. Feasible transportation and energy proficiency and power transmission are two significant fields to be advanced and focused on minimizing the carbon impression in these industries. Renewable sources like solar energies for power generation and electric vehicles are to be researched and developed. This R&D requires a considerable fund supply, thus comes the green finance. Globally, green finance plays a vital role in creating a sustainable environment. In this research, for performing the green finance analysis, financial maximally filtered graph (FMFG) algorithm is implemented in different domains. The proposed algorithm is compared with the neural model and observed that the proposed model has obtained 98.85% of accuracy which is higher than the neural model.

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