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Persico V.,University of Naples Federico II | Montieri A.,NM2 S.r.l. | Pescape A.,University of Naples Federico II
Proceedings - 2016 5th IEEE International Conference on Cloud Networking, CloudNet 2016 | Year: 2016

The advances in networking technologies and the increase in the need for storage resources have prompted many companies to outsource their storage needs. Cloud-storage providers offer clean and simple file-system interfaces, abstracting away the complexities of direct hardware management. At the same time, however, such services eliminate the direct oversight of performance that final users with high service-level requirements traditionally expect. While several works in literature have addressed security-related issues (such as privacy, integrity, availability, etc.) few of them have targeted the network performance of this kind of services. In this work we propose the analysis of the performance of the network associated to the storage service offered by Amazon: S3. Thanks to a large-scale distributed campaign performed by leveraging the Bismark measurement platform, we have characterized how the performance of the network may impact the quality of service experienced by final users on the basis of their location and the configuration of services. We found how performance heavily changes (up to 1553 KiB/s) accordingto the location of the customers and the cloud region they rely on (up to 2117 KiB/s), also deriving a number of usageguidelines for the customers. In addition we characterize the impact of leveraging the Amazon CDN service to distributecontents, finding that while it guarantees up to a 275-percent performance improvement, cases exist for which additional costs may lead to worse performance. © 2016 IEEE.


Garofalo M.,University of Naples Federico II | Botta A.,University of Naples Federico II | Botta A.,NM2 Srl | Ventre G.,University of Naples Federico II
Proceedings of the International Astronomical Union | Year: 2016

Nowadays there is no field research which is not flooded with data. Among the sciences, astrophysics has always been driven by the analysis of massive amounts of data. The development of new and more sophisticated observation facilities, both ground-based and spaceborne, has led data more and more complex (Variety), an exponential growth of both data Volume (i.e., in the order of petabytes), and Velocity in terms of production and transmission. Therefore, new and advanced processing solutions will be needed to process this huge amount of data. We investigate some of these solutions, based on machine learning models as well as tools and architectures for Big Data analysis that can be exploited in the astrophysical context. © Copyright International Astronomical Union 2017.


Ciuonzo D.,NM2 S.r.l. | Aubry A.,University of Naples Federico II | Carotenuto V.,University of Naples Federico II
IEEE Transactions on Signal Processing | Year: 2017

In this paper, we study channel-aware decision fusion (DF) in a wireless sensor network (WSN), where the sensors transmit their decisions simultaneously for spectral efficiency purposes and the DF center (DFC) is equipped with multiple antennas. Also, each sensor-DFC channel is described via a Rician model. As opposed to the existing literature, in order to account for stringent energy constraints in the WSN, only statistical channel information is assumed for the non-line-of-sight (scattered) fading terms. For such a scenario, suboptimal fusion rules are developed in order to deal with the exponential complexity of the likelihood ratio test (LRT) and impractical (complete) system knowledge. Furthermore, the considered model is extended to the case of (partially unknown) jamming-originated interference. Then, the obtained fusion rules are modified with the use of composite hypothesis testing framework and generalized LRT. Coincidence and statistical equivalence among them are also investigated under some relevant simplified scenarios. Numerical results compare the proposed rules and highlight their jamming-suppression capability. © 1991-2012 IEEE.


Aceto G.,University of Naples Federico II | Montieri A.,NM2 Srl | Pescape A.,University of Naples Federico II
IEEE International Conference on Communications | Year: 2017

Users trying to access censored content may experience different results, depending on the technique adopted to enforce Internet Censorship, that in turn depends on different factors. Administrative control of the network (i.e. the entity managing network devices) is one of such factors. To the best of our knowledge, we are the first to focus on censorship detection on 3G/4G (hereafter mobile) network operators, investigating the extent of differences in applying censorship inside a single country. To do so we performed an experimental campaign in Italy using the five major mobile operators. We introduce the censorship detection platform and tests we adopted, and aggregate the results according to the outcome of the tests in classes, related with censoring techniques and circumvention capabilities. Overall 15 different aggregated behaviors have been found in the experimental campaign. The analysis of measurement results reveals wide dis-homogeneity of treatment for a given censored resource across different mobile operators, with 99.5% of resources showing at least two different behaviors when probed. The discussion of reported results informs about the unexpected variability on transparency and precision of censorship, and also on effective detection and circumvention strategies, as measured from mobile networks in a single country. © 2017 IEEE.


Botta A.,University of Naples Federico II | De Donato W.,NM2 SRL | Persico V.,NM2 SRL | Pescape A.,NM2 SRL
Future Generation Computer Systems | Year: 2016

Cloud computing and Internet of Things (IoT) are two very different technologies that are both already part of our life. Their adoption and use are expected to be more and more pervasive, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and as an enabler of a large number of application scenarios. In this paper, we focus our attention on the integration of Cloud and IoT, which is what we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately and, more precisely, their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the new CloudIoT paradigm, which involves completely new applications, challenges, and research issues. To bridge this gap, in this paper we provide a literature survey on the integration of Cloud and IoT. Starting by analyzing the basics of both IoT and Cloud Computing, we discuss their complementarity, detailing what is currently driving to their integration. Thanks to the adoption of the CloudIoT paradigm a number of applications are gaining momentum: we provide an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges. These challenges are then analyzed in details to show where the main body of research is currently heading. We also discuss what is already available in terms of platforms-both proprietary and open source-and projects implementing the CloudIoT paradigm. Finally, we identify open issues and future directions in this field, which we expect to play a leading role in the landscape of the Future Internet. © 2015 Elsevier B.V.


Mostafaei H.,Third University of Rome | Montieri A.,NM2 srl | Persico V.,University of Naples Federico II | Pescape A.,NM2 srl | Pescape A.,University of Naples Federico II
Proceedings - IEEE Symposium on Computers and Communications | Year: 2016

Wireless sensor networks (WSNs) are currently adopted in a vast variety of domains. Due to practical energy constraints, in this field minimizing sensor energy consumption is a critical challenge. Sleep scheduling approaches give the opportunity of turning off a subset of the nodes of a network - without suspending the monitoring activities performed by the WSN - in order to save energy and increase the lifetime of the sensing system. Our study focuses on partial coverage, targeting scenarios in which the continuous monitoring of a limited portion of the area of interest is enough. In this paper, we present PCLA, an efficient algorithm based on Learning Automata that aims at minimizing the number of sensors to activate, such that a given portion of the area of interest is covered and connectivity among sensors is preserved. Simulation results show how PCLA can select sensors in an efficient way to satisfy the imposed constraints, thus guaranteeing better performance in terms of both working-node ratio and WSN lifetime. Also, we show how PCLA outperforms state-of-the-art partial-coverage algorithms. © 2016 IEEE.


Botta A.,University of Naples Federico II | Botta A.,NM2 Srl | Avallone A.,University of Naples Federico II | Garofalo M.,University of Naples Federico II | Ventre G.,University of Naples Federico II
ICISSP 2016 - Proceedings of the 2nd International Conference on Information Systems Security and Privacy | Year: 2016

Network neutrality is a hot topic since a few years and involves different aspects of interest (e.g. economic, regulatory and privacy) for a wide range of stakeholders, including policy makers, researchers, economists, and service providers. When referring to video streaming, a killer web service of the Internet, much has been discussed regarding if and how video providers violate or may violate neutrality principles, in order to give users a "better" service compared to other services or to other providers. In this paper we provide a contribution to this discussion analyzing the performance of three main video hosting providers (i.e. YouTube, Vimeo, and Dailymotion) from an user viewpoint. We measure the throughput and RTT experienced by users watching real videos of different popularity, at different day hours and at several locations from around the world. We uncover the performance differences of these providers as a function of the different variables under control and move a step forward to understand what causes such differences. Our results allow to understand what are the real performance users currently get from these providers and if the performance differences observed can be due or to considered as a violation of network neutrality principles, providing a ground for people interested in legal and regulatory issues of web applications and services. Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.


Berger A.,Telecommunications Research Center Vienna | D'Alconzo A.,AIT Austrian Institute of Technology | Gansterer W.N.,University of Vienna | Pescape A.,University of Naples Federico II | Pescape A.,NM2 Srl
Computer Networks | Year: 2016

We consider the analysis of network traffic data for identifying highly agile DNS patterns which are widely considered indicative for cybercrime. In contrast to related approaches, our methodology is capable of explicitly distinguishing between the individual, inherent agility of benign Internet services and criminal sites. Although some benign services use a large number of addresses, they are confined to a subset of IP addresses, due to operational requirements and contractual agreements with certain Content Distribution Networks. We discuss DNSMap, a system which analyzes observed DNS traffic, and continuously learns which FQDNs are hosted on which IP addresses. Any significant changes over time are mapped to bipartite graphs, which are then further pruned for cybercrime activity. Graph analysis enables the detection of transitive relations between FQDNs and IPs, and reveals clusters of malicious FQDNs and IP addresses hosting them. We developed a prototype system which is designed for realtime analysis, requires no costly classifier retraining, and no excessive whitelisting. We evaluate our system using large data sets from an ISP with several 100,000 customers, and demonstrate that even moderately agile criminal sites can be detected reliably and almost immediately. © 2016 Elsevier B.V. All rights reserved.


Botta A.,University of Naples Federico II | de Donato W.,University of Naples Federico II | Persico V.,NM2 SRL | Pescape A.,NM2 SRL
Future Generation Computer Systems | Year: 2015

Cloud computing and Internet of Things (IoT) are two very different technologies that are both already part of our life. Their adoption and use are expected to be more and more pervasive, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and as an enabler of a large number of application scenarios.In this paper, we focus our attention on the integration of Cloud and IoT, which is what we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately and, more precisely, their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the new CloudIoT paradigm, which involves completely new applications, challenges, and research issues. To bridge this gap, in this paper we provide a literature survey on the integration of Cloud and IoT. Starting by analyzing the basics of both IoT and Cloud Computing, we discuss their complementarity, detailing what is currently driving to their integration. Thanks to the adoption of the CloudIoT paradigm a number of applications are gaining momentum: we provide an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges. These challenges are then analyzed in details to show where the main body of research is currently heading. We also discuss what is already available in terms of platforms-both proprietary and open source-and projects implementing the CloudIoT paradigm. Finally, we identify open issues and future directions in this field, which we expect to play a leading role in the landscape of the Future Internet. © 2015 Elsevier B.V.

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