@inproceedings {7249283, title = {Lagrangian relaxation for the time-dependent combined network design and routing problem}, booktitle = {Communications (ICC), 2015 IEEE International Conference on}, year = {2015}, month = {June}, publisher = {IEEE XPlore}, organization = {IEEE XPlore}, address = {London, United-Kingdom}, keywords = {allocation planning process, flow conservation constraints, integer programming, integer programming methods, Lagrangian relaxation, linear programming, Maintenance engineering, multi-commodity capacitated fixed charge network design, network routing problem, Optimization, Quality of Service, Reliability, resource allocation, resource installation, Resource management, Routing, routing decision process, telecommunication network routing, telecommunication traffic, time-dependent combined network design, traffic demands}, doi = {10.1109/ICC.2015.7249283}, author = {Dimitri Papadimitriou and Bernard Fortz and Enrico Gorgone} } @inproceedings {DR:INFOCOM-15b, title = {A Lightweight Anycast Enumeration and Geolocation}, booktitle = {IEEE INFOCOM, Demo Session}, year = {2015}, address = {Hong Kong, China}, abstract = {

Several Internet services such as CDNs, DNS name servers, and sinkholes use IP-layer anycast to reduce user response times and increase robustness with respect to network failures and denial of service attacks. However, current geolocation tools fail with anycast IP addresses. In our recent work [1], we remedy to this by developing an anycast detection, enumeration, and geolocation technique based on a set of delay measurements from a handful of geographically distributed vantage points. The technique (i) detects if an IP is anycast, (ii) enumerates replicas by finding the maximum set of non-overlapping disks (i.e., areas centered around vantage points), and (iii) geolocates the replicas by solving a classification problem and assigning the server location to the most likely city. We propose to demo this technique. In particular, we visually show how to detect an anycast IP, enumerate its replicas, and geolocate them on a map. The demo allows to browse previously geolocated services, as well as to explore new targets on demand.

}, url = {http://www.enst.fr/~drossi/paper/rossi15infocom-b.pdf}, author = {Cicalese, Danilo and Joumblatt, Diana and Rossi, Dario and Buob, Marc-Olivier and Auge, Jordan and Friedman, Timur} } @inproceedings {Yon2015, title = {A Lightweight Network Proximity Service Based On Neighborhood Models}, booktitle = {22nd IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT)}, year = {2015}, month = {11/2015}, publisher = {IEEE}, organization = {IEEE}, address = {Luxembourg}, abstract = {

This paper proposes a network proximity service\ based on the neighborhood models used in recommender systems.\ Unlike previous approaches, our service infers network proximity\ without trying to recover the latency between network nodes. By\ asking each node to probe a number of landmark nodes which\ can be servers at Google, Yahoo and Facebook, etc., a simple\ proximity measure is computed and allows the direct ranking\ and rating of network nodes by their proximity to a target node.\ The service is thus lightweight and can be easily deployed in\ e.g. P2P and CDN applications. Simulations on existing datasets\ and experiments with a deployment over PlanetLab showed\ that our service achieves an accurate proximity inference that\ is comparable to state-of-the-art latency prediction approaches,\ while being much simpler.

}, author = {Liao, Yongjun and Du, Wei and Leduc, Guy} } @inproceedings {Bae2014a, title = {Large-Scale Network Traffic Monitoring with DBStream, a System for Rolling Big Data Analysis}, booktitle = {International Conference on Big Data, IEEE BigData}, year = {2014}, month = {11/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Washington D.C., USA}, abstract = {

The complexity of the Internet has rapidly increased, making it more important and challenging to design scalable network monitoring tools. Network monitoring typically requires rolling data analysis, i.e., continuously and incrementally updating (rolling-over) various reports and statistics over high-volume data streams. In this paper, we describe DBStream, which is an SQL-based system that explicitly supports incremental queries for rolling data analysis. We also present a performance comparison of DBStream with a parallel data processing engine (Spark), showing that, in some scenarios, a single DBStream node can outperform a cluster of ten Spark nodes on rolling network monitoring workloads. Although our performance evaluation is based on network monitoring data, our results can be generalized to other big data problems with high volume and velocity.

}, keywords = {Big Data Analysis, Data Stream Processing, network data analysis, System Performance}, author = {Arian B{\"a}r and Alessandro Finamore and Pedro Casas and Lukasz Golab and Marco Mellia} }