Abstract-In this work, we analyze the design of green routing algorithms and evaluate the achievable energy savings that such mechanisms could allow in several realistic network scenarios. We formulate the problem as a minimum energy routing optimization, which we numerically solve considering a core-network scenario, which can be seen as a worst-case for energy saving performance (as nodes cannot be switched off). To gather fullrelief results, we analyze the energy savings in various conditions (i.e., network topology and traffic matrix) and under different technology assumptions (i.e., the energy profile of the network devices).These results give us insight into the potential benefits of different "green" technologies and their interactions. In particular, we show that depending on the topology and traffic matrices, the optimal energy savings can be modest, partly limiting the interest for green routing approaches for some scenarios. At the same time, we also show that the common belief that there is a trade off between green network optimization and performance does not necessarily hold: in the considered environment, green routing has no effect on the main network performances such as maximum link utilization.

Abstract. Network convergence and new applications running on endhosts result in increasingly variable and unpredictable traffic patterns. By providing origin-destination pairs with several possible paths, loadbalancing has proved itself an excellent tool to face this uncertainty. Formally, load-balancing is defined in terms of a convex link cost function of its load, where the objective is to minimize the total cost. Typically, the link queueing delay is used as this cost since it measures its congestion. Over-simplistic models are used to calculate it, which have been observed to result in suboptimal resource usage and total delay. In this paper we investigate the possibility of learning the delay function from measurements, thus converging to the actual minimum. A novel regression method is used to make the estimation, restricting the assumptions to the minimum (e.g. delay should increase with load). The framework is relatively simple to implement, and we discuss some possible variants.

We propose a new methodology to estimate the spatial reuse of CSMA-like scheduling. Instead of focusing on spatial configurations of users, we model the interferences between users as a random graph. Using configuration models for random graphs, we show how the properties of the medium access mechanism are captured by some deterministic differential equations, when the size of the graph gets large. Performance indicators such as the probability of connection of a given node can then be efficiently computed from these equations. We also perform simulations to illustrate the results on different types of random graphs. Even on spatial structures, these estimates get very accurate as soon as the variance of the interference is not negligible.

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