Leandros Tassiulas
- Title: Optimizing the network edge for flexible service provisioning
- Abstract: The virtualization of network resources provides unique flexibility in service provisioning in most levels of the network stack. Softwarization of the network control and operation (SDN) is a key enabler of that development. Starting from the network core, SDN is a dominant trend in the evolution of network architectures with increased emphasis recently on the network edge. I will present some recent results in this area starting with a study on migration from legacy networking to SDN enabled network modules. The trade-off between the benefits of SDN upgrades and the cost of deployment is addressed and captured by an appropriate sub-modular function that allows to optimize the penetration pace of the technology. Validation on some real-world network topologies and traffic matrices will be presented as well. Then we move our attention to the network periphery. A wireless multi-hop extension at the network edge is considered and the problem of enabling SDN is addressed via replication of SDN controllers. The delay constraints of the controlled data-path elements is appropriately modeled and the problem of locating the controllers is addressed via optimization and a proof-of concept implementation. An alternate approach is considered then for the wireless network where we assume coexistence of SDN enabled components with network islands operating under distributed adhoc routing protocols. The trade-off of the coexistence is studied and the impact of SDN penetration is evaluated. Some paradigms of collaborative network services are presented finally as they are enabled by the above architectural evolution.
Bio: Leandros Tassiulas is the John C. Malone Professor of Electrical Engineering and member of the Institute for Network Science, Yale University. He received the IEEE Koji Kobayashi Computer And Communications Award (2016), the Inaugural INFOCOM 2007 Achievement Award, the INFOCOM 1994 Best Paper Award, the National Science Foundation (NSF) Research Initiation Award (1992), the NSF CAREER Award (1995), the Office of Naval Research Young Investigator Award (1997), and the Bodossaki Foundation Award(1999).
Patrick Thiran
- Title: Locating the source of diffusion in large-scale and random networks.
- Abstract: We survey some results on the localization of the source of diffusion in a network. There have been significant efforts in studying the dynamics of epidemic propagations on networks, and more particularly on the forward problem of epidemics: understanding the diffusion process and its dependence on the infecting and curing rates. We address here the inverse problem of inferring the original source of diffusion, given the infection data gathered at some of the nodes in the network. Indeed, because of the large size of many real networks, the state of all nodes in a network cannot in general be observed. We show that it is fundamentally possible to estimate the location of the source from measurements collected by sparsely placed observers. We present a strategy that is optimal for arbitrary trees, achieving maximum probability of correct localization, and describe efficient implementations for arbitrary graphs. We then move to the sensor placement problem, and distinguish two regimes depending on the variance of the propagation delays. In the low noise regime, when propagation delays are (close to) deterministic values, the smallest number of sensors needed to exactly localize the source is the double metric dimension (DMD) of the network. We compute bounds for this quantity in different classes of (random) graphs, and we comment on its implications for the detectability of a source in actual networks. In the high noise regime, when the propagation delays have a large variance, the DMD is no longer a good proxy for computing an optimal sensor set. A possible solution is then to replace off-line sensor placement by an on-line placement. We discuss a general framework for source localization that encompasses both off-line and on-line sensor placement, and find that replacing static by dynamic sensors can strongly outperform a purely static strategy with the same sensor budget. The results presented in this talk come from joint works with Brunella Spinelli, L. Elisa Celis, Pedro Pinto, Martin Vetterli, Mladen Dimovski, Gergely Odor, Farzad Pourkamali.
Bio: Patrick Thiran is a full professor in the School of Computer and Communication Sciences at EPFL. He holds an electrical engineering degree from the Université Catholique de Louvain, an M.S. degree in electrical engineering from the University of California at Berkeley, and the PhD degree from EPFL. He was with Sprint Advanced Technology Labs, Burlingame, CA, in 2000-01 and with Nokia NRC in 2008. His research interests include networks, performance analysis and stochastic models. He is currently active in the analysis and design of wireless and PLC networks, in network inference, in active sampling for optimisation and in dynamic processes on graphs. He served as an Associate Editor for the IEEE Transactions on Circuits and Systems in 1997-99, for the IEEE/ACM Transactions on Networking in 2006-10 and for the ACM Transactions on Modeling and Performance Evaluation of Computing Systems in 2015-18. He is currently serving on the editorial board of the IEEE Journal on Selected Areas in Communication. He received the 1996 EPFL Doctoral Prize and the 2008 Crédit Suisse Teaching Award.
