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The adoption of virtualization technologies in networking is promoting a radical innovation in the way network services are managed and delivered. Indeed, some network services may be provisioned to cope with complex and unpredictable traffic demands by dynamically creating a sequence of Virtual Network Functions (VNFs) and steering traffic flows through them. In this context, the optimized deployment of network services, composed of VNFs that may be instantiated in multiple Data Centers (DCs), is one of the most challenging orchestration target. VNF placement is the problem of choosing the set of optimal locations for a chain of VNFs according to the service request and the current characteristics of available computing resources and network links. With respect to the state of the art, our original contribution reflects a multi-stakeholder perspective (subscriber, service providers, infrastructure providers) in a multi-DC environment. We thus consider the problem of placing VNFs to maximize primarily the number of accepted requests from a set of incoming requests and secondarily the satisfaction of subscribers' preferences. Our model also allows to differentiate service requests in priority levels and guarantees that Quality of Service objectives for accepted service requests are fulfilled, including also a requirement on network service instantiation time. We provide an integer linear programming formulation of this problem that leverages a layered auxiliary graph built for each request in a set. Experimental evaluation is described in detail and

Abstract:In this paper, we address the problem of energy conservation and optimization in residential environments by providing users with useful information to solicit a change in consumption behavior. Taking care to highly limit the costs of installation and management, our work proposes a Non-Intrusive Load Monitoring (NILM) approach, which consists of disaggregating the whole-house power consumption into the individual portions associated to each device. State of the art NILM algorithms need monitoring data sampled at high frequency, thus requiring high costs for data collection and management. In this paper, we propose an NILM approach that relaxes the requirements on monitoring data since it uses total active power measurements gathered at low frequency (about 1 Hz). The proposed approach is based on the use of Factorial Hidden Markov Models (FHMM) in conjunction with context information related to the user presence in the house and the hourly utilization of appliances. Through a set of tests, we investigated how the use of these additional context-awareness features could improve disaggregation results with respect to the basic FHMM algorithm. The tests have been performed by using Tracebase, an open dataset made of data gathered from real home environments.

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The rational use and management of energy is considered a key societal and technological challenge. Home energy management systems (HEMS) have been introduced especially in private home domains to support users in managing and controlling energy consuming devices. Recent studies have shown that informing users about their habits with appliances as well as their usage pattern can help to achieve energy reduction in private households. This requires instruments able to monitor energy consumption at fine grain level and provide this information to consumers. While the most existing approaches for load disaggregation and classification require high-frequency monitoring data, in this paper we propose an approach that exploits low-frequency monitoring data gathered by meters (i.e., Smart Plugs) displaced in the home. Moreover, while the most existing works dealing with appliance classification delegate the classification task to a remote central server, we propose a distributed approach where data processing