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In this paper we propose a monitoring system of a data center that is able to infer when the data center is getting into an anomalous behavior by analyzing the power consumption at each server and the data center network traffic. The monitoring system is non-intrusive in the sense that there is no need to install software on the data center servers. The monitoring architecture embeds two Elman Recurrent Networks (RNNs) to predict power consumed by each data center component starting from data center network traffic and viceversa. Results obtained along six mounts of experiments, within a data center, show that the architecture is able to classify anomalous system behaviors and normal ones by analyzing the error between the actual values of power consumption and network traffic and the ones inferred by the two RNNs.

Abstract. Smart home applications are currently implemented by vendor-specific systems managing mainly a few number of homogeneous sensors and actuators. However, the sharp increase of the number of intelligent devices in a house and the foreseen explosion of the smart home application market will change completely this vendor centric scenario towards open, expandable systems made up of a large number of cheap heterogeneous devices. As a matter of fact, new smart home solutions have to be able to takle with scalability, dynamicity and heterogeneity requirements. In this paper we present the architecture of a basic building block, namely a distributed repository service, for smart home systems. The repository stores data from heterogeneous devices deployed in the house that can be then retrieved by context aware applications implementing some home automation functionalities. Our architecture, based on a DHT, offers a completely decentralized and reliable storage service able to offer complex query functionalities.

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