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Power management is an expensive and important issue for large computational infrastructures such as datacenters, large clusters, and computational grids. However, measuring energy consumption of scalable systems may be impractical due to both cost and complexity for deploying power metering devices on a large number of machines. In this paper, we propose the use of information about resource utilization (e.g. processor, memory, disk operations, and network traffic) as proxies for estimating power consumption. We employ machine learning techniques to estimate power consumption using such information which are provided by common operating systems. Experiments with linear regression, regression tree, and multilayer perceptron on data from different hardware resulted into a model with 99.94% of accuracy and 6.32 watts of error in the best case.
The energy consumption of the HVAC system accounts for a significant portion of the energy consumption of the public building system, and using an efficient energy consumption prediction model can assist it in carrying out effective energy-saving tra
The vast majority of todays mobile malware targets Android devices. This has pushed the research effort in Android malware analysis in the last years. An important task of malware analysis is the classification of malware samples into known families.
The world is increasingly urbanizing and the building industry accounts for more than 40% of energy consumption in the United States. To improve urban sustainability, many cities adopt ambitious energy-saving strategies through retrofitting existing
The enormous power consumption of Bitcoin has led to undifferentiated discussions in science and practice about the sustainability of blockchain and distributed ledger technology in general. However, blockchain technology is far from homogeneous - no
In informal learning scenarios the popularity of multimedia content, such as video tutorials or lectures, has significantly increased. Yet, the users interactions, navigation behavior, and consequently learning outcome, have not been researched exten