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• W4313193082 abstract "Today, world is moving towards digital economy; each and every application demands samples of data from real world to analyse every piece of information for better productivity. With the rapid reduction in the cost of sensors, in next five years, sensors can be deployed and used in all sectors and there will be seamless communications among all the applications. IoT devices will directly share data using open flow standards and connect to World Wide Web through Internet. Today, most of the data generated from physical sensors is data of specific applications which cannot be used as input for other domain applications. Many physical sensors deployed in one domain will only be used by that specific application. With the convergence of IoT and cloud computing, the data generated from physical sensors of different applications deployed at different scenarios can be imported on cloud environment which in turn can be used by all the applications. It is high time to address high usage of energy manageable data centres. It is time to identify and eliminate inefficiencies in delivering electric services to IT resources. This can be achieved by enhancing the efficiency of physical infrastructure, adopting efficient resource allocation and management algorithms. This paper focuses on energy management of IT equipments in virtualized sensor cloud data centre. This work emphasizes on minimizing energy consumption in virtualized sensor cloud infrastructure. We have implemented S-learning algorithm on sensor cloud environment and proved that the solution is able to handle dynamic requests. For dynamic request generation rate, the response time, violation of the SLA and the energy utilisation are measured. The algorithm is quite effective on three parameters: quick response time, reduced number of SLA violations and reduced energy consumption. The amount of energy conserved is measured and compared with other approaches. The model has built on a test bed with dynamic data requests and measures the number of SLA violations in the system wherein the proposed scheduling method is associated with other methods and found this solution is more efficient." @default.
• W4313193082 created "2023-01-06" @default.
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• W4313193082 date "2022-01-01" @default.
• W4313193082 modified "2023-09-28" @default.
• W4313193082 title "A Study and Analysis of Energy Efficient Machine Learning Algorithm for Virtualized Sensor Cloud Infrastructure" @default.
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• W4313193082 doi "https://doi.org/10.1007/978-981-19-2350-0_65" @default.
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