NSF Award
0728980
The main focus of the research is to develop good algorithms for designing networks. With the popularity and ubiquity of the Internet, it has become important to develop simple and scalable algorithms to design good networks which offer the most flexibility and functionality. The<br/>network designer has to build networks given only partial information and loose estimates of the traffic that will eventually be carried, to build networks knowing that faults will almost surely occur and to provision for handling these faults gracefully, and to do this in the most economic and efficient fashion. Along with this, the network designer today must take into account the heterogeneity of networks (which will include wireless and optical parts), and the fact that each<br/>network has to interact with potentially many other networks. In addition, these interacting networks may be controlled by different entities having different pricing schemes and different incentive structures.<br/><br/>The investigators from Carnegie Mellon University and Bell Laboratories draw on their mix of backgrounds to mathematically model the problems faced in network design contexts, and to develop algorithmic tools and good algorithms with provable guarantees for these problems. To achieve these goals, the research adapts and augments a rich set of algorithmic techniques from linear and convex programming, stochastic optimization, metric embeddings, and randomization, as well as complexity-theoretic techniques that have developed in theoretical computer science over the past few years. The research reflects a collaboration between academia and research laboratories to transfer ideas, problems and algorithms between theory and practice: in particular, the research encourages students to learn problem modeling and solving, and to move between the<br/>two environments gaining a balanced view of issues in network design. Research progress is propagated into the curriculum via specialized courses presenting the theoretical advances in the context of their applications, as well as basic courses teaching the fundamental ideas and techniques behind these research advances.
