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In the last decade we noticed a growth on studies regarding energy savings in data centers. The main reasons include political factors such as compliance with global protocols of conscious energy consumption, financial incentives such as tax reduction, and environmentally driven by concerns about sustainability issues such as emission of heat and gases harmful to the ozone layer. Most works aim to reduce the energy consumption of servers and cooling systems. However, network devices comprise also a significant slice of the total Data Center energy consumption, and most studies often neglect that. In this paper, we propose techniques to define flow paths in an SDN-based Data Center network respecting flow bandwidth requirements, while also enabling changing the operation state of network devices to a state of lower energy consumption in order to reduce the total consumption of the network layer. We evaluate the proposed techniques using different ratios of link demand oversubscription in a fat-tree topology with different POD sizes. Results show savings of up to 70% regarding energy consumption in the network layer.

Although IoT delivers several benefits, it also raises concerns regarding privacy and security, from revenue disruption in industrial facilities to life-threatening situations caused by smart houses hacking. As a consequence, anomaly detection algorithms stand out to improve data reliability. However, little has been said about the implications of running these computationally expensive programs in hardware-constrained edge devices. Therefore, in this paper, we present an evaluation of six anomaly detection algorithms running in an edge device regarding performance, accuracy, temperature, and power consumption. The results showed that time complexity, resources demand, and detection approach directly impact on the feasibility of running anomaly detection algorithms in edge devices. Based on these results, we present a recommendation on which algorithms would best satisfy the requirements of several IoT environments.

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