Energy management in communication networks: a journey through modelling and optimization glasses

The widespread proliferation of Internet and wireless applications has produced a significant increase of ICT energy footprint. As a response, in the last five years, significant efforts have been undertaken to include energy-awareness into network management. Several green networking frameworks have been proposed by carefully managing the network routing and the power state of network devices. Even though approaches proposed differ based on network technologies and sleep modes of nodes and interfaces, they all aim at tailoring the active network resources to the varying traffic needs in order to minimize energy consumption. From a modeling point of view, this has several commonalities with classical network design and routing problems, even if with different objectives and in a dynamic context. With most researchers focused on addressing the complex and crucial technological aspects of green networking schemes, there has been so far little attention on understanding the modeling similarities and differences of proposed solutions. This paper fills the gap surveying the literature with optimization modeling glasses, following a tutorial approach that guides through the different components of the models with a unified symbolism. A detailed classification of the previous work based on the modeling issues included is also proposed

Understanding Internet topology: principles, models, and validation

Building on a recent effort that combines a first-principles approach to modeling router-level connectivity with a more pragmatic use of statistics and graph theory, we show in this paper that for the Internet, an improved understanding of its physical infrastructure is possible by viewing the physical connectivity as an annotated graph that delivers raw connectivity and bandwidth to the upper layers in the TCP/IP protocol stack, subject to practical constraints (e.g., router technology) and economic considerations (e.g., link costs). More importantly, by relying on data from Abilene, a Tier-1 ISP, and the Rocketfuel project, we provide empirical evidence in support of the proposed approach and its consistency with networking reality. To illustrate its utility, we: 1) show that our approach provides insight into the origin of high variability in measured or inferred router-level maps; 2) demonstrate that it easily accommodates the incorporation of additional objectives of network design (e.g., robustness to router failure); and 3) discuss how it complements ongoing community efforts to reverse-engineer the Internet

Performance of voice and video conferencing over ATM and gigabit ethernet backbone networks

Gigabit Ethernet and ATM network technologies have been modeled as campus network backbones for the simulation-based comparison of their performance. Real-time voice and video conferencing traffic is used to compare the performance of both backbone technologies in terms of response times and packet end-to-end delays. Simulation results show that Gigabit Ethernet has been able to perform the same and in some cases better than ATM as a backbone network for video and voice conferencing providing network designers with a cheaper solution to meet the growing needs of bandwidth-hungry applications in a campus environment

Traffic matrix estimation on a large IP backbone: a comparison on real data

This paper considers the problem of estimating the point-to-point traffic matrix in an operational IP backbone. Contrary to previous studies, that have used a partial traffic matrix or demands estimated from aggregated Netflow traces, we use a unique data set of complete traffic matrices from a global IP network measured over five-minute intervals. This allows us to do an accurate data analysis on the time-scale of typical link-load measurements and enables us to make a balanced evaluation of different traffic matrix estimation techniques. We describe the data collection infrastructure, present spatial and temporal demand distributions, investigate the stability of fan-out factors, and analyze the mean-variance relationships between demands. We perform a critical evaluation of existing and novel methods for traffic matrix estimation, including recursive fanout estimation, worst-case bounds, regularized estimation techniques, and methods that rely on mean-variance relationships. We discuss the weaknesses and strengths of the various methods, and highlight differences in the results for the European and American subnetworks

Global Modeling and Prediction of Computer Network Traffic

We develop a probabilistic framework for global modeling of the traffic over a computer network. This model integrates existing single-link (-flow) traffic models with the routing over the network to capture the global traffic behavior. It arises from a limit approximation of the traffic fluctuations as the time--scale and the number of users sharing the network grow. The resulting probability model is comprised of a Gaussian and/or a stable, infinite variance components. They can be succinctly described and handled by certain 'space-time' random fields. The model is validated against simulated and real data. It is then applied to predict traffic fluctuations over unobserved links from a limited set of observed links. Further, applications to anomaly detection and network management are briefly discussed

Smart Dimensioning of IP Network Links

Link dimensioning is generally considered as an effective and (operationally) simple mechanism to meet (given) performance requirements. In practice, the required link capacity C is often estimated by rules of thumb, such as C = d·M, where M is the (envisaged) average traffic rate, and d some (empirically determined) constant larger than 1. This paper studies the viability of this class of ‘simplistic’ dimensioning rules. Throughout, the performance criterion imposed is that the fraction of intervals of length T in which the input exceeds the vailable output capacity (i.e., CT) should not exceed $\varepsilon$, for given T and $\varepsilon$.\ud We first present a dimensioning formula that expresses the required link capacity as a function of M and a variance term V(T), which captures the burstiness on timescale T. We explain how M and V(T) can be estimated with low measurement effort. The dimensioning formula is then used to validate dimensioning rules of the type C = d·M. Our main findings are: (i) the factor d is strongly affected by the nature of the traffic, the level of aggregation, and the network infrastructure; if these conditions are more or less constant, one could empirically determine d; (ii) we can explicitly characterize how d is affected by the ‘performance parameters’, i.e., T and $\varepsilon$