ata centers (DCs) are currently the largest closedloop systems in the information technology (IT) and networking worlds, continuously growing toward multi-million-node clouds [1]. DC operators manage and control converged IT and network infrastructures in order to offer a broad range of services and applications to their customers. Typical services and applications provided by current DCs range from traditional IT resource outsourcing (storage, remote desktop, disaster recovery, etc.) to a plethora of web applications (e.g., browsers, social networks, online gaming). Innovative applications and services are also gaining momentum to the point that they will become main representatives of future DC workloads. Among them, we can find high-performance computing (HPC) and big data applications [2]. HPC encompasses a broad set of computationally intensive scientific applications, aiming to solve highly complex problems in the areas of quantum mechanics, molecular modeling, oil and gas exploration, and so on. Big data applications target the analysis of massive amounts of data collected from people on the Internet to analyze and predict their behavior.All these applications and services require huge data exchanges between servers inside the DC, supported over the DC network (DCN): the intra-DC communication network. The DCN must provide ultra-large capacity to ensure high throughput between servers. Moreover, very low latencies are mandatory, particularly in HPC where parallel computing tasks running concurrently on multiple servers are tightly interrelated. Unfortunately, current multi-tier hierarchical tree-based DCN architectures relying on Ethernet or Infiniband electronic switches suffer from bandwidth bottlenecks, high latencies, manual operation, and poor scalability to meet the expected DC growth forecasts [3].These limitations have mandated a renewed investigation D Abstract Applications running inside data centers are enabled through the cooperation of thousands of servers arranged in racks and interconnected together through the data center network. Current DCN architectures based on electronic devices are neither scalable to face the massive growth of DCs, nor flexible enough to efficiently and cost-effectively support highly dynamic application traffic profiles. The FP7 European Project LIGHTNESS foresees extending the capabilities of today's electrical DCNs through the introduction of optical packet switching and optical circuit switching paradigms, realizing together an advanced and highly scalable DCN architecture for ultra-high-bandwidth and low-latency server-to-server interconnection. This article reviews the current DC and high-performance computing (HPC) outlooks, followed by an analysis of the main requirements for future DCs and HPC platforms. As the key contribution of the article, the LIGHTNESS DCN solution is presented, deeply elaborating on the envisioned DCN data plane technologies, as well as on the unified SDN-enabled control plane architectural solution that will empower OPS and OCS transm...

The incremented popularity of Internet of Things (IoT), thanks to improvements both in hardware and software of sensors over the last years, enables the possibility to monitor and gather any kind of data. Additionally, the arrangement of heterogeneous sensors, capable of perceiving information about their surroundings, into a rich Wireless Sensor Network (WSN), allows the appearance of complex systems in which resources are managed more efficiently. Smart cities, buildings, parkings, emergency services are appearing, where control over energy consumption and better sustainability are coupled with an improvement of the comfort of occupants. In this paper, we address the problem of energy optimization in smart buildings, considering both the planning and operational aspects. Specifically, the first aim is to propose an optimal deployment of the WSN inside a building. For this, we present a model able to identify the optimal locations for different types of sensors and gateways, by optimizing energy consumption while fulfilling connectivity, resource, protection, and clustering coverage constraints. Once the IoT system is deployed, we address the problem of how the building actually functions, according to the behaviour of the occupants. In particular, we propose a Building Management System (BMS) capable of efficiently and automatically manage the building elements using human behavioural models, thus lowering the overall building energy consumption whilst maintaining acceptable levels of comfort.

Abstract. Power management strategies that allow network infrastructures to achieve advanced functionalities with limited energy budget are expected to induce significant cost savings and positive effects on the environment, reducing Green House Gases (GHG) emissions. Power consumption can be drastically reduced on individual network elements by temporarily switching off or downclocking unloaded interfaces and line cards. At the state-of-the-art, Adaptive Link Rate (ALR) and Low Power Idle (LPI) are the most effective local-level techniques for lowering power demands during low utilization periods. In this paper, by modeling and analyzing in detail the aforementioned local strategies, we point out that the energy consumption does not depend on the data being transmitted but only depends on the interface link rate, and hence is throughput-independent. In particular, faster interfaces require lower energy per bit than slower interfaces, although, with ALR, slower interfaces require less energy per throughput than faster interfaces. We also note that for current technologies the energy/bit is the same both at 1 Gbps and 10 Gbps, meaning that the increase in the link rate has not been compensated at the same pace by a decrease in the energy consumption.

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This work analyzes a new and very subtle kind of security threat that can affect large scale cloudbased IT service infrastructures, by exploiting the computational resources of their component data center in order to waste as much energy as possible. The consequence of these threats range from increased costs in the energy bill, to penalization for exceeding the agreed quantity of green house gases (GHG) emissions, up to complete denial of service caused by electrical outages due to power budget exhaustion.We analyzed the different types of such attacks with their potential impacts on the energy consumption, modeled their behavior and quantified how current energyproportional technologies may provide attackers with great opportunities for raising the target facility emissions and costs. These efforts resulted in a simple model with some parametric reference values that can be used to estimate the impact of such attacks also in presence of very large infrastructures containing thousands or