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Analysis of virtual Machine placement and optimization using Swarm intelligence
Abstract
Internet of Things devices are highly distributed over a large geographical area, and these devices have limited resources in terms of computing, connectivity, energy, and memory. If the virtual machine is placed nearer to the Internet of Things nodes, it increases their efficiency by manifold. Virtual machine placement optimization is a trial and error method. Many new algorithms will be proposed and their results are tested against the desired metrics, and the successful ones are continuously modified to get better results. Placement of the virtual machines is the main goal. Resources should be available based on the need and cannot be allocated statistically based on the peak workload elasticity of cloud traffic engineering. In this area, nature-inspired algorithms are preferred as they are capable of finding a better candidate solution in a vast problem search space. A few notable nature-inspired algorithms are flower pollination algorithm, particle swarm optimization algorithm, ant colony algorithm, ant bee colony algorithm, and firefly algorithm. Out of all these algorithms, particle swarm optimization and ant colony algorithms are the ones that attracted many researchers. In this chapter we discuss about how efficiently we managed the placement of the virtual machines, using these two nature-inspired algorithms, so that efficiency of the Internet of Things network is increased.
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Today, in the digital age, the problem of pattern recognition is very relevant. In particular, the task of text recognition is important in banking, for the automatic reading of documents and their control; in video control systems, for example, to identify the license plate of a car that violated traffic rules; in security systems, for example, to check banknotes at an ATM and in many other areas. A large number of methods are known for solving the problem of pattern recognition, but the main advantage of neural networks over other methods is their learning ability. It is this feature that makes neural networks attractive to study. The article proposes a basic neural network model. The main algorithms are considered and a programming model is implemented in the Python programming language. In the course of research, the following shortcomings of the basic model were revealed: low learning rate (the number of correctly recognized digits in the first epochs of learning); retraining - the network has not learned to generalize the knowledge gained; low probability of recognition - 95.13%.To solve the above disadvantages, various techniques were used that increase the accuracy and speed of work, as well as reduce the effect of network retraining.
One of the crucial elements in decision-making is the calculation of criteria weights. In this paper, a new Modified Integrated Weighting (MIW) method was proposed to combine the weights obtained using different weight calculation methods into a single set of weights. The weights express the relative significance of the criteria and play an essential role in making correct decisions. The proposed method considered both subjective knowledge of the experts and the objectivity of the problem by combining the subjective and objective weight assignment methods. The proposed weight calculation method was applied to the agriculture dataset for the evaluation of groundnut crop sites. A decision-making model was developed via the proposed MIW method and Complex Proportional Assessment (COPRAS) method to rank the given groundnut crop site dataset. The ranking results of the developed decision model were compared with the ranking results of average yield data and other methods for validation purposes. The developed model exhibited better results for the given dataset and could be used to solve various other decision-making problems, thereby realizing sustainable development.
AI is providing a significant basis for future technological innovation. The financial sector will be transformed by AI, offering the opportunity for better and more tailor made services, cost reduction, and the development of new business models. The Federal and the Hessen government recently published roadmaps for the further development of AI in Germany and Hessen, respectively. The federal government will invest 3 Bill. Euro over the next 5 years in a variety of research and business sectors whereas the State of Hessen will set up a new AI oriented institute of applied research and business development and spend one billion Euro over the next 5 years on digitalization development. The public strategies for building AI Hubs are still extremely diverse. However, the focus is on stronger application of research results in business activities, on increasing networks and ecosystems and predominantly on building on existing centers of excellence. Especially the Frankfurt Rhein Main Region, already a strong hub for fintech, cyber security, and AI will benefit from these programs. The financial center Frankfurt offers a vivid and fast growing tech and start up community a well as an academic and data infrastructure unprecedented in Europe: the largest data and cloud service hub in continental Europe, the worlds largest internet knot, universities, research institutes with global quality research in AI, companies and consultancies specialized in AI and neighboring areas such as fintech and cyber security.
Renewable energy reliability has been the main agenda nowadays, where the internet of things (IoT) is a crucial research direction with a lot of opportunities for 5 improvement and challenging work. Data obtained from IoT is converted into actionable information to improve wind turbine performance, driving wind energy cost down and reducing risk. However, the implementation in IoT is a challenging task because the wind turbine system level 10 and component level need real-time control. So, this paper is dedicated to investigating wind resource assessment and lifetime estimation of wind power modules using IoT. To illustrate this issue, a model is built with sub-models of an aerodynamic rotor connected directly to a multi-pole 15 variable speed permanent magnet synchronous generator (PMSG) with variable speed control, pitch angle control and full-scale converter connected to the grid. Besides, a large number of various sensors for measurement of wind parameters are integrated with IoT. Simulations are con-20 structed with Matlab/Simulink and IoT 'Thingspeak' Math-works web service. IoT has proved to increase the reliability of measurement strategies, monitoring accuracy, and quality assurance.
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Cloud computing is made possible through virtualization technology which makes virtual machines (VMs) to be placed in physical servers. The process of assigning VMs to servers is called virtual machine placement. VM placement in cloud is done by focusing on many objectives like VM allocation time, energy consumption, SLA violation, utilization of resources, etc. In this paper, a multi-objective hybrid ACO-PSO optimization algorithm is proposed for minimizing resource wastage, minimizing power consumption and for load balancing in physical servers. Simulation results show that the proposed algorithm reduces resource wastage and power consumption and also provides load balancing in servers when compared to the existing multi-objective ant colony system algorithm.
The changes in population demography and technology have made social connections more and more difficult, although fundamental to human nature, health, and well-being. A review of the theory and measurement evolution of social relations and their early empirical evidence is analyzed in this chapter. We consider how social relationships have changed over time and how the fundamental characteristics of social relations have shifted through analysis of different techniques for the digital ranking. The emerging impact of technology on contacts, particularly on the evolving ways in which technology can be used to strengthen, decrease, maintain, or prevent social relations is also discussed. The role and influence of the smart Internet in the negative as well as in the positive aspects of these new technologies on the well-being of smart people are elaborated. Successful navigation of our complex social environment calls for the ability to identify and judge others’ relativity. Social comparison processes are therefore very important and contribute to intelligent individuals and urban development for efficient interpersonal decision-making.