Integration of 3G connectivity in planetlab Europe :A step of an evolutionary path towards heterogeneous large scale network testbeds

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On the Integration of Blockchain and SDN: Overview, Applications, and Future Perspectives

Blockchain (BC) and software-defined networking (SDN) are leading technologies which have recently found applications in several network-related scenarios and have consequently experienced a growing interest in the research community. Indeed, current networks connect a massive number of objects over the Internet and in this complex scenario, to ensure security, privacy, confidentiality, and programmability, the utilization of BC and SDN have been successfully proposed. In this work, we provide a comprehensive survey regarding these two recent research trends and review the related state-of-the-art literature. We first describe the main features of each technology and discuss their most common and used variants. Furthermore, we envision the integration of such technologies to jointly take advantage of these latter efficiently. Indeed, we consider their group-wise utilization—named BC–SDN—based on the need for stronger security and privacy. Additionally, we cover the application fields of these technologies both individually and combined. Finally, we discuss the open issues of reviewed research and describe potential directions for future avenues regarding the integration of BC and SDN. To summarize, the contribution of the present survey spans from an overview of the literature background on BC and SDN to the discussion of the benefits and limitations of BC–SDN integration in different fields, which also raises open challenges and possible future avenues examined herein. To the best of our knowledge, compared to existing surveys, this is the first work that analyzes the aforementioned aspects in light of a broad BC–SDN integration, with a specific focus on security and privacy issues in actual utilization scenarios

Astrophysical data mining with GPU. A case study: Genetic classification of globular clusters

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource, http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200× in the training phase with respect to the CPU based version

Do You Know What You Are Generating?

Software-based traffic generators are commonly used in ex-perimental research on computer networks. However, there are no much studies focusing on how such instruments are accurate. Here we start a discussion reviewing the prob-lem of using software-based traffic generators over common hardware/software, highlighting interesting issues that pose some threats to common beliefs. We started comparing the operator-requested traffic profile against the real behavior of commonly used software-based traffic generators. We aim at performing tests under different conditions and looking both at packet/bit rate and inter-packet time distribution. Prelimi-nary results show notable differences in some cases, opening the way to interesting discussions and further investigations

TCP port 80 traffic traces

TCP port 80 traffic traces This is traffic related to TCP port 80 generated by clients inside the network of University of Napoli, Federico II - UNINA - reaching the outside world (i.e. src host from UNINA and dst port tcp 80 OR dst host from UNINA and src port tcp 80). The link observed is a link at 200Mbps connecting the University of Napoli "Federico II" network to the rest of the Internet. These traces are in tcpdump format. Packet lenghts are variable because, for each packet, we stored full TCP headers including optional headers (e.g. MSS). Because of privacy concerns, IP addresses have been replaced preserving network membership. These traces constitute a subset of many traces we started collecting since year 2004. When refering to our Traffic Traces, please cite the following references: A. Dainotti, A. Pescapè, and G. Ventre, "A Packet-level Characterization of Network Traffic", 11th IEEE International Workshop on Computer-Aided Modeling, Analysis and Design of Communication Links and Networks (CAMAD 2006), ISBN 0-7803-9536-0, pp. 38-45 - Trento (Italy), June 2006