@inproceedings {Ruf2014b, title = {Bandwidth Measurements and Capacity Exploitation in Gigabit Passive Optical Networks}, booktitle = {Fotonica 2014}, year = {2014}, month = {05/2014}, abstract = {

We report an experimental investigation on the
measurement of the available bandwidth for users in Gigabit
Passive Optical Networks (GPON) and the limitations caused by
the Internet protocols. We point out that the huge capacity
offered by the GPON highlights the enormous differences that
can be showed among the available and actually exploitable
bandwidth in the case of TCP. In this ultrabroadband
environment we also investigated on use of the UDP and of the
multisession TCP. A correlation in terms of QoE is also reported.

}, keywords = {access capacity, GPON, QoE, QoS, Throughput}, author = {Arianna Rufini and Edion Tego and Francesco Matera and Marco Mellia} } @article {Mic2013, title = {Basic Network Data Analysis}, number = {D3.1}, year = {2013}, month = {05/2013}, institution = {mPlane Consortium}, type = {Public Deliverable}, address = {Torino}, abstract = {

This document describes the requirements, input, output for the algorithms needed to perform analytic tasks on a large amount of data, in the context of WP3. Starting from the use cases defined in WP1, we identify the algorithms needed to address the various scenario requirements. Operating on a large amount of data, these algorithms strive for parallel and scalable approaches; the designing and implementation of the algorithm itself can be a challenging research task since today very little is known concerning how to develop efficient and scalable algorithms that runs on parallel processing frameworks.
The algorithm in the storage layer are characterized by the fact that they operate on a large amount of data, and produce a concise representation of it, extracting features and aggregating it, so that the produced output is easier to handle and understand. Depending on the amount of data produced, on the scenario characteristics and on the time constraints, algorithms can require a real time (or near real time) or a batch processing.
For each algorithm and use case, the input data and the initial state, the computation to run and the output produced are described.

}, keywords = {algorithms, big data, storage}, issn = {D3.1}, author = {Pietro Michiardi and Antonio Barbuzzi and Alessandro Finamore and Stefano Traverso and Daniele Apiletti and Elena Baralis and Tania Cerquitelli and Silvia Chiusano and Luigi Grimaudo and A. Rufini and Francesco Matera and A. Valentii and Maurizio Dusi and Mohamed Ahmed and Tivadar Szemethy and L. N{\'e}meth and R. Szalay and Ilias Leontiadis and Yan Grunenberger and P. Casas and Alessandro D{\textquoteright}Alconzo and A B{\"a}r and D Rossi and YiXi Gong} }