Federico Larroca

Energy-aware routing: A reality check

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 full-relief 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.

Routing Games for Traffic Engineering

Current data network scenario makes traffic engineering (TE) a very challenging task. The ever growing access rates and new applications running on end-hosts result in more variable and unpredictable traffic patterns. By providing origin-destination pairs with several possible paths, load-balancing has proved itself an excellent tool to face this uncertainty. In particular, mechanisms where routers greedily minimize a path cost function (thus requiring minimum coordination) have been studied from a game-theoretic perspective in what is known as a routing game (RG). The contribution of this paper is twofold. We first propose a new RG specifically designed for elastic traffic, where we maximize the total utility through load-balancing only. Secondly, we consider several important RGs from a TE perspective and, using several real topologies and traffic demands, present a thorough comparison of their performance. This paper brings insight into several RGs, which will help one in choosing an adequate dynamic load-balancing mechanism. The comparison shows that the performance gain of the proposed game can be important.

An Overview of WLAN Performance, Some Important Case-Scenarios and Their Associated Models

The study of the 802.11 standard has been very intense for more than a decade now. Several works have striven at understanding its performance, even in the simplest topology of a wireless local area network (WLAN) with a single access point. The present survey is an effort to classify and present the enormous literature on the subject into several important case-scenarios, and summarizes the current understanding of WLAN performance. The resulting performance and associated models are discussed (and sometimes extended) and simulation results are used to illustrate them. We also highlight interesting open research problems that we believe the community should address.

Network bandwidth allocation with end-to-end QoS constraints and revenue sharing in multi-domain federations

Internet is evolving, traffic continues to grow, new revenue sources are sought by Network and Service Providers. Value added services with real time characteristics are likely to be common currency in the near future. Quality of Service (QoS) could allow Application/Service Providers (APs) to offer better services to the end users. At the same time, all actors claim for a fair distribution of revenues. Inspired by this scenario, we propose a complete framework for selling interdomain quality assured services, and subsequently distributing revenues, in an Autonomous System (AS) association context. We state the problem as a network utility maximization problem with QoS constraints and show that a distributed solution can be carried out. In order to fairly share the resulting revenue we study concepts from coalitional game theory and propose a solution based on the Shapley value and statistics on the revenues. Simulations of the whole proposal are shown.

Optimal multipath forwarding in planned Wireless Mesh Networks

Wireless Mesh Networks (WMNs) have emerged in the last years as a cost-efficient alternative to traditional wired access networks. In the context of WMNs resources are intrinsically scarce, which has led to the proposal of dynamic routing in order to fully exploit the network capacity. We argue instead in favour of separating routing from forwarding (i.e. a la MPLS). Our proposal is a dynamic load-balancing scheme that forwards incoming packets along several pre-established paths in order to minimize a certain congestion function. We consider a particular but very typical scenario: a planned WMN where all links do not interfere with each other. We use a simple and versatile congestion function: the sum of the average queue length over all network nodes interfaces. We present a method to learn this function from measurements and several simulations to illustrate the framework, comparing our proposal with the IEEE 802.11s standard.

Minimum-Delay Load-Balancing through Non-parametric Regression

Network convergence and new applications running on end-hosts result in increasingly variable and unpredictable traffic patterns. By providing origin-destination pairs with several possible paths, load-balancing 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.

Design and Analysis of Flow Aware Load Balancing Mechanisms for Multi-Service Networks

Ethernet technology is being deployed in metro and wide area networks. However, despite recent evolutions, Ethernet cannot be considered a carrier class technology due mainly to the lack of facilities for efficient traffic engineering (TE). In this article, we propose a Flow-aware TE approach for carrier class Ethernet networks providing services like those defined by the Metro Ethernet Forum. The flow-aware networking approach we consider is based on the Cross-protect router mechanisms, allowing satisfactory quality for streaming and elastic flows without explicitly identifying traffic classes by the combined use of fair queuing and admission control. Our proposal applies specifically to architectures where a tunnel is established between ingress and egress nodes (like Ethernet over MPLS). In this specific context, the Cross-protect mechanisms are applied on a per tunnel basis. We analyze the proposed approach in terms of flow blocking probabilities for which explicit formula are derived. We also extend the study framework to the case where several paths are established between a pair of ingress/egress nodes, and propose a simple load balancing scheme. We analyze its performance and derive approximate formula for the flow blocking probability in this case. The analysis is validated by extensive simulations.

Minimum queue length load-balancing in planned Wireless Mesh Networks

Wireless Mesh Networks (WMNS) have emerged in the last years as a cost-efficient alternative to traditional wired access networks. In order to fully exploit the intrinsically scarce resources WMNS possess, the use of dynamic routing has been proposed. We argue instead in favour of separating routing from forwarding (i.e. à la MPLS) and implementing a dynamic load-balancing scheme that forwards incoming packets along several pre-established paths in order to minimize a certain congestion function. In this paper, we consider a particular but very important scenario: a planned WMN where all bidirectional point-to-point links do not interfere with each other. Due to its versatility and simplicity, we use the sum over all links of the mean queue length as congestion function. A method to learn this function from measurements is presented, whereas simulations illustrate the framework.

Minimum delay load-balancing via nonparametric regression and no-regret algorithms

In the current network scenario, where traffic is increasingly dynamic and resource demanding, Dynamic Load-Balancing (DLB) has been shown to be an excellent Traffic Engineering tool. In particular, we are interested in the problem of minimum delay load-balancing. That is to say, we assume that the queueing delay of a link is given by a function of its load. The objective is then to adjust the traffic distribution over paths so that, for the current traffic demand, the addition of these functions times the load is minimized. The contribution of our article is twofold. Firstly, we analyze the possibility of using so-called no-regret algorithms to perform the load balancing. As opposed to other distributed optimization algorithms (such as the classical gradient descent) the algorithm we discuss requires no fine-tuning of any speed-controlling parameter. Secondly, we present a framework that does not assume any particular model for the queueing delay function, and instead learns it from measurements. This way, the resulting mean delay of optimizing with this learnt function is an excellent approximation of the real minimum delay traffic distribution. The whole framework is illustrated by several packet and flow level simulations.

Taming traffic dynamics: Analysis and improvements

Internet traffic is highly dynamic and difficult to predict in current network scenarios, which enormously complicates network management and resources optimization. To address this uncertainty in a robust and efficient way, two almost antagonist Traffic Engineering (TE) techniques have been proposed in the last years: Robust Routing and Dynamic Load Balancing. Robust Routing (RR) copes with traffic uncertainty in an off-line preemptive fashion, computing a single static routing configuration that is optimized for traffic variations within some predefined uncertainty set. On the other hand, Dynamic Load Balancing (DLB) balances traffic among multiple paths in an on-line reactive fashion, adapting to traffic variations in order to optimize a certain congestion function. In this article we present the first comparative study between these two alternative methods. We are particularly interested in the performance loss of RR with respect to DLB, and on the response of DLB when faced with abrupt changes. This study brings insight into several RR and DLB algorithms, evaluating their virtues and shortcomings, which allows us to introduce new mechanisms that improve previous proposals.