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Nowadays, service providers' (SPs) need for efficient resource utilization solutions is more demanding than ever. The optimal use of the physical and virtual infrastructures guarantees that the waste of resources due to overdesign is minimized while the provided services enjoy the required quality of service levels. However, the prediction of the exact amount of the required resources per service at any time of its lifecycle is not an easy process. For this purpose, we propose a solution that handles the infrastructure in a holistic manner introducing a novel architecture that exploits the monitoring data from three layers (hardware, virtualization, and application) and uses them to train machine learning models, which can accurately predict the exact amount of the required resources per service. Its implementation using open-source tools and its performance are also presented. © 1999-2012 IEEE.

Billions of embedded processors are being attached to everyday objects and houseware equipment to enhance daily activities and enable smart living. These embedded processors have enough processing capabilities to process sensor data to produce smart insights, and are designed to operate for months without the need of physical interventions. Despite the compelling features of Internet of Things (IoT), applied at several home-oriented use cases (e.g., lighting, security, heating, comfort), due to the lack of a physical flow of information (e.g., absence of switches and cable-based gateways), the security of such networks is impeding their rapid deployment. In this work we look into IPv6 based IoT deployments, since it is the leading standard for interconnecting the wireless devices with the Internet and we propose a data-driven anomaly detection system that operates at the transport-layer of 6LoWPAN deployments. We present a comprehensive experimental evaluation carried out using both simulated and real-world experimentation facilities that demonstrates the accuracy of our system against well-known network attacks against 6LoWPAN networks. © Springer Nature Switzerland AG 2019.