1,721,005 research outputs found
Energy Management in Large Data Center Networks
Full text linkIn the era of digitalization, one of the most challenging research topic regards the energy consumption reduction of ICT equipment to contrast the global climate change. The ICT world is very sensitive to the problem of Greenhouse Gas emissions (GHG) and for several years has begun to implement some countermeasures to reduce consumption waste and increase efficiency of infrastructure: the total embodied emissions of end-use devices have significantly decreased, networks have become more energy efficient, and trends such as virtualization and dematerialization will continue to make equipment more efficient.
One of the main contributor to GHG emissions is data centers industry, which provision end users with the necessary computing and communication resources to access the vast majority of services online and on a pay-as-you-go basis. Data centers require a tremendous amount of energy to operate, since the efficiency of cooling systems is increasing, more research efforts should be put in making green the IT system, which is becoming the major contributor to energy consumption.
Being the network one of the non-negligible contributors to energy consumption in data centers, several architectures have been designed with the goal of improving energy-efficient of data centers. These architectures are called Data Center Networks (DCNs) and provide interconnections among the computing servers and between the servers and the Internet, according to specific layouts.In my PhD I have extensively investigated on energy efficiency of data center, working on different projects which try to tackle the problems from different views. The research can be divided into two main parts with the Energy Proportionality as connection argument.
The main focus of the work is about the trade-off between size and energy efficiency of data centers, with the aim to find a relationship between scalability and energy proportionality of data centers. In this regard, the energy consumption of different data center architectures have been analyzed, varying the dimension in terms of number of server and switches. Extensive simulation experiments, performed in small and large scale scenarios, unveil the ability of network-aware allocation policies in loading the the data center in a energy-proportional manner and the robustness of classical two- and three-tier design under network-oblivious allocation strategies.
The concept of energy proportionality, applied to the whole DCN and used as efficiency metric, is one of the main contributions of the work. Energy proportionality is a property defining the degree of proportionality between load and the energy spent to support such load, thus devices are energy proportional when any increase of the load corresponds to a proportional increase of energy consumption. A peculiar feature of our analysis is in the consideration of the whole data center, i.e., both computing and communication devices are taken into account. Our methodology consists of an asymptotic analysis of data center consumption, whenever its size (in terms of servers) become very large.
In our analysis, we investigate the impact of three different allocation policies on the energy proportionality of computing and networking equipment for different DCNs, including 2-Tier, 3-Tier and Jupiter topologies. For evaluation, the size of the DCNs varies to accommodate up to several thousands of computing servers. Validation of the analysis is conducted through simulations. We propose new metrics with the objective to characterize in a holistic manner the energy proportionality in data centers. The experiments unveil that, when consolidation policies are in place and regardless of the type of architecture, the size of the DCN plays a key role, i.e., larger DCNs containing thousands of servers are more energy proportional than small DCNs
SISTEMA E METODO PER ESEGUIRE OPERAZIONI SU UN OGGETTO TRAMITE DRONI
Full text linkA system for executing an operation on an object (O) by means of a fleet of drones (20) equipped with tools for executing said operation. The system comprises: - processing means (10) adapted to receive as input information for calculating the trajectories required for bringing the fleet of drones (20) into the desired position for the execution of said operation, wherein said processing means (10) are adapted to transmit flight trajectories and instructions for executing the operation on the object (O) to said fleet of drones (20), - a local positioning system (L) comprising a plurality of anchor nodes in Ultra- Wide- Band technology (UWB-A1,UWB-A2,UWB-A3,UWB-Ax,UWB-AN) arranged around said object (O) along the sides of the surfaces of the ideal polyhedron that circumscribes the object (O). Each drone (20) of said fleet is equipped with: - a UWB transceiver (26) in communication with said anchor nodes (UWB-A1,UWB- A2,UWB-A3,UWB-Ax,UWB-AN), wherein each drone (20) is at all times in communication with at least four anchor nodes (UWB-A1,UWB-A2,UWB-A3,UWB- Ax,UWB-AN), and - a plurality of ultrasound sensors (24) for measuring the distance of each drone (20) from the other drones and from obstacles, wherein said fleet of drones (20), by following the way points (WP) of said trajectories sent by said processing means (10), executes said operation on said object (O) by means of said tools
Digital Identity in the EU: Promoting eIDAS Solutions Based on Biometrics
Full text linkToday, more than ever before, technological progress is evolving rapidly, and in the absence of adequate regulatory frameworks, the big players in the digital market (the so-called Big Techs) are exploiting personal data (name, address, telephone numbers) and private data (political opinions, religious beliefs, financial information, or health status) in an uncontrolled manner. A crucial role in this scenario is played by the weakness of international regulatory frameworks due to the slow response time of legislators who are incapable, from a regulatory point of view, of keeping pace with technological evolution and responding to the new requirements coming from the social context, which is increasingly characterized by the pervasive presence of new technologies, such as smartphones and wearable devices. At the European level, the General Data Protection Regulation (GDPR) and the Regulation on Electronic Identification, Authentication and Trust Services (eIDAS) have marked a significant turning point in the regulatory landscape. However, the mechanisms proposed present clear security issues, particularly in light of emerging concepts such as digital identity. Moreover, despite the centrality of biometric issues within the European regulatory framework and the practical introduction of biometric data within electronic national identity (eID) cards, there are still no efforts to use biometric features for the identification and authentication of a person in a digital context. This paper clarifies and precisely defines the potential impact of biometric-based digital identity and hypothesizes its practical use for accessing network-based services and applications commonly used in daily life. Using the Italian eID card as a model, an authentication scheme leveraging biometric data is proposed, ensuring full compliance with GDPR and eIDAS regulations. The findings suggest that such a scheme can significantly improve the security and reliability of electronic identification systems, promoting broader adoption of eIDAS solutions
Performance Analysis of WRF Simulations in a Public Cloud and HPC Environment
No full textThe Weather Research and Forecasting (WRF) Model is a numerical weather prediction system designed for both atmospheric research and operational forecasting needs. WRF requires a large amount of CPU power which increases drastically if WRF is used to model a big geographical area with a high resolution. To satisfy the computational demand WRF requires large number of computing resources through infrastructures such as clusters in grid or cloud. In this paper the performance analysis of different WRF simulations to the Amazon Web Services (AWS) cloud computing environment (single node and cluster) compared to that of a HCP cluster is presented
Enhancing Job Scheduling of an Atmospheric Intensive Data Application
Full text linkNowadays, e-Science applications involve great deal of data to have more accurate analysis. One of its application domains is the Radio Occultation which manages satellite data. Grid Processing Management is a physical infrastructure geographically distributed based on Grid Computing, that is implemented for the overall processing Radio Occultation analysis. After a brief description of algorithms adopted to characterize atmospheric profiles, the paper presents an improvement of job scheduling in order to decrease processing time and optimize resource utilization. Extension of grid computing capacity is implemented by virtual machines in existing physical Grid in order to satisfy temporary job requests. Also scheduling plays an important role in the infrastructure that is handled by a couple of schedulers which are developed to manage data automaticall
Automatic Dynamic Allocation of Cloud Storage for Scientific Applications
No full textParticularly in scientific community the size of digital data to be stored is ramping up. For those application characterized by very dynamic workloads is difficult to estimate the real size of storage to be allocated and avoid over-provisioning, also in extremely elastic environments as cloud computing. DAViS (Dynamic Allocation of Virtual Storage) is a prototype of a system for dynamically providing virtual block storage to Virtual Machines (VMs), optimizing the physical resources utilization, through dynamic and autonomous resizing of the storage
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