@article {D43,
	title = { Cross-check of Analysis Modules and Reasoner Interactions},
	number = {D4.3},
	year = {2014},
	month = {10/2014},
	type = {Deliverable},
	keywords = {reasoner, WP4},
	author = {Umberto Manferdini and Stefano Traverso and Marco Mellia and Edion Tego and Francesco Matera and Zied Ben Houidi and Marco Milanesio and Pietro Michiardi and Dario Rossi and D. Cicalese and D. Joumblatt and Jordan Aug{\'e} and Maurizio Dusi and Sofia Nikitaki and Mohamed Ahmed and Ilias Leontiadis and L. Baltrunas and M. Varvello and Pedro Casas and Alessandro D{\textquoteright}Alconzo and Benoit Donnet and W. Du and Guy Leduc and Y. Liao and Alessandro Capello and Fabrizio Invernizzi}
}
@article {Pap2013,
	title = {Design of Analysis Modules},
	number = {D4.1},
	year = {2013},
	month = {11/2013},
	institution = {mPlane Consortium},
	type = {Public Deliverable},
	address = {Torino},
	keywords = {algorithms, analysis},
	isbn = {D4.1},
	issn = {D4.1},
	author = {Dimitri Papadimitriou and Zied Ben-Houidi and Samir Ghamri-Doudane and D Rossi and M. Milanesio and P. Casas and Alessandro D{\textquoteright}Alconzo and Edion Tego and Francesco Matera and Maurizio Dusi and Tivadar Szemethy and L. M{\'a}th{\'e} and Alessandro Finamore and Stefano Traverso and Ilias Leontiadis and Yan Grunenberger and L. Baltrunas and Benoit Donnet and Guy Leduc and Y. Liao}
}
@article {Lia2013,
	title = {DMFSGD: A decentralized matrix factorization algorithm for network distance prediction},
	journal = {IEEE/ACM Transactions on Networking},
	volume = {21},
	year = {2013},
	month = {10/2013},
	chapter = {1511},
	abstract = {<p>The knowledge of end-to-end network distances is\&nbsp;essential to many Internet applications. As active probing of all\&nbsp;pairwise distances is infeasible in large-scale networks, a natural\&nbsp;idea is to measure a few pairs and to predict the other ones\&nbsp;without actually measuring them. This paper formulates the\&nbsp;prediction problem as matrix completion where the unknown\&nbsp;entries in a pairwise distance matrix constructed from a network\&nbsp;are to be predicted. By assuming that the distance matrix has\&nbsp;a low-rank characteristics, the problem is solvable by lowrank\&nbsp;approximation based on matrix factorization. The new\&nbsp;formulation circumvents the well-known drawbacks of existing\&nbsp;approaches based on Euclidean embedding.</p><p>A new algorithm, so-called Decentralized Matrix Factorization\&nbsp;by Stochastic Gradient Descent (DMFSGD), is proposed. By\&nbsp;letting network nodes exchange messages with each other, the\&nbsp;algorithm is fully decentralized and only requires each node\&nbsp;to collect and to process local measurements, with neither\&nbsp;explicit matrix constructions nor special nodes such as landmarks\&nbsp;and central servers. In addition, we compared comprehensively\&nbsp;matrix factorization and Euclidean embedding to demonstrate\&nbsp;the suitability of the former on network distance prediction. We\&nbsp;further studied the incorporation of a robust loss function and\&nbsp;of non-negativity constraints. Extensive experiments on various\&nbsp;publicly-available datasets of network delays show not only the\&nbsp;scalability and the accuracy of our approach, but also its usability\&nbsp;in real Internet applications.</p>},
	keywords = {matrix completion, matrix factorization, network distance prediction, stochastic gradient descent},
	author = {Yongjun Liao and Wei Du and Pierre Geurts and Guy Leduc}
}
