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Cloud computing as an emerging technology, has revolutionized the information technology industry by elastic on-demand provisioning and De-provisioning of computing resources. Due to the huge amount of electrical energy consumption by large-scale Datacenters, it is essential to investigate various approaches in order to decrease simultaneously energy and its impacts on global economic crisis and ecological concerns. This study through virtualization technique applied a hybrid technique for resource management. This technique used k-means clustering for mapping task and dynamic consolidation method, which improved by micro-genetic algorithm. Experimental evaluation performed on CloudSim 3.0.3 and the results were analyzed with Expert-Choice software tools. We found that the proposed KMGA technique could provide a good trade-off between effectively reduce energy consumption of Datacenters and sustained quality of service. In addition, it minimized the number of virtual machine migrations and make-span, in comparison with particle swarm optimization and genetic algorithms in similar hybrid techniques. [ABSTRACT FROM AUTHOR]

Given the importance of using the railway networks for the reliable and safe transportation of goods and passengers, which plays an important role in the economy of rail transport of the country, it is necessary to use wireless sensor network techniques to raise the quality and quantity and also maintain the application of these facilities for better control and optimal use of relevant technologies in this regard. Regarding the development of wireless sensor networks for collecting and processing environmental information, we intended to employ them in order to maintain railways, and also prevent defects that might occur in mobile machines and parts, as well as to avoid probable events and reduce human errors. Considering the limited capacity of batteries for sensor nodes in a wireless sensor network, energy efficiency is an important issue; thus mobile sinks are presented as a possible solution. In this study, the proposed routing uses Ring Routing and an optimization gravitational search algorithm for the primary selection of the ring causes an increase in a sensor network's lifespan. In this study, by applying the proposed method in the railway transportation system, it is improved to reduce the existing errors by creating a system with a higher safety factor and high quality and efficiency, together with maximum efficiency and good speed. [ABSTRACT FROM AUTHOR]