Ramón Fabregat

Multi-objective optimization scheme for multicast flows: a survey, a model and a MOEA solution

This paper presents a new traffic engineering load balancing taxonomy, classifying several publications and including their objective functions, constraints and proposed heuristics. Using this classification, a novel Generalized Multiobjective Multitree model (GMM-model) is proposed. This model considers for the first time multitree-multicast load balancing with splitting in a multiobjective context, whose mathematical solution is a whole Pareto optimal set that can include several results than it has been possible to find in the publications surveyed. To solve the GMM-model, a multi-objective evolutionary algorithm (MOEA) inspired by the Strength Pareto Evolutionary Algorithm (SPEA) is proposed. Experimental results considering up to 11 different objectives are presented for the well-known NSF network, with two simultaneous data flows.

On optimal computation of MPLS label binding for multipoint-to-point connections

Most network operators have considered reducing Label Switched Routers (LSR) label spaces (i.e. the number of labels that can be used) as a means of simplifying management of underlaying Virtual Private Networks (VPNs) and, hence, reducing operational expenditure (OPEX). This letter discusses the problem of reducing the label spaces in Multiprotocol Label Switched (MPLS) networks using label merging - better known as MultiPoint-to-Point (MP2P) connections. Because of its origins in IP, MP2P connections have been considered to have tree- shapes with Label Switched Paths (LSP) as branches. Due to this fact, previous works by many authors affirm that the problem of minimizing the label space using MP2P in MPLS - the Merging Problem - cannot be solved optimally with a polynomial algorithm (NP-complete), since it involves a hard- decision problem. However, in this letter, the Merging Problem is analyzed, from the perspective of MPLS, and it is deduced that tree-shapes in MP2P connections are irrelevant. By overriding this tree-shape consideration, it is possible to perform label merging in polynomial time. Based on how MPLS signaling works, this letter proposes an algorithm to compute the minimum number of labels using label merging: the Full Label Merging algorithm. As conclusion, we reclassify the Merging Problem as Polynomial-solvable, instead of NP-complete. In addition, simulation experiments confirm that without the tree-branch selection problem, more labels can be reduced.

Full label space reduction in MPLS networks: asymmetric merged tunneling

Traffic Engineering objective is to optimize network resource utilization. Although several works have been published about minimizing network resource utilization in MPLS networks, few of them have been focused in LSR label space reduction. This letter studies Asymmetric Merged Tunneling (AMT) as a new method for reducing the label space in MPLS network. The proposed method may be regarded as a combination of label merging (proposed in the MPLS architecture) and asymmetric tunneling (proposed recently in our previous works). Finally, simulation results are performed by comparing AMT with both ancestors. They show a great improvement in the label space reduction factor.

Label space reduction in MPLS networks: how much can a single stacked label do?

Most network operators have considered reducing LSR label spaces (number of labels used) as a way of simplifying management of underlaying Virtual Private Networks (VPNs) and therefore reducing operational expenditure (OPEX). The IETF outlined the label merging feature in MPLS-allowing the configuration of MultiPoint-to-Point connections (MP2P)-as a means of reducing label space in LSRs. We found two main drawbacks in this label space reduction scheme: a it should be separately applied to a set of LSPs with the same egress LSR--which decreases the options for better reductions, and b LSRs close to the edge of the network experience a greater label space reduction than those close to the core. The later implies that MP2P connections reduce the number of labels asymmetrically. In this article we propose a solution to these drawbacks achieved by stacking an additional label onto the packet header. We call this type of reduction Asymmetric Merged Tunnels (AMT). A fast framework for computing the optimal reduction using AMTs is proposed. Our simulations show that the label space can be reduced by up to 20% more than when label merging is used.

Multi-objective scheme over multi-tree routing in multicast MPLS networks

This paper proposes a traffic engineering scheme using different distribution trees to several multicast flows. We formulate this problem as one with Non Linear programming with discontinuous derivatives (DNLP). The aim is to combine into a single aggregated metric the following weighting objectives: the maximum link utilization, the hop count, the total bandwidth consumption and the total end-to-end delay. Our proposal solves the traffic split ratio for multiple trees. The proposed approach is applied in MPLS networks by allowing the establishment of explicit routes in multicast events. Furthermore, the results obtained with GAMS tools show that several objectives are decreased; the maximum link utilization is minimized. The main contribution of this paper is the optimization model and the formulation of the multi-objective function.

A Multi-Objective Optimization Scheme for Multicast Routing: A Multitree Approach

In this paper, we propose a multi-objective traffic engineering scheme using different distribution trees to multicast several flows. The aim is to combine into a single aggregated metric, the following weighting objectives: the maximum link utilization, the hop count, the total bandwidth consumption, and the total end-to-end delay. Moreover, our proposal solves the traffic split ratio for multiple trees. We formulate this multi-objective function as one with Non Linear programming with discontinuous derivatives (DNLP). Results obtained using SNOPT solver show that several weighting objectives are decreased and the maximum link utilization is minimized. The problem is NP-hard, therefore, a novel SPT algorithm is proposed for optimizing the different objectives. The behavior we get using this algorithm is similar to what we get with SNOPT solver. The proposed approach can be applied in MPLS networks by allowing the establishment of explicit routes in multicast events. The main contributions of this paper are the optimization model and the formulation of the multi-objective function; and that the algorithm proposed shows polynomial complexity.

A web-based e-learning tool for UML class diagrams

The paper presents a web-based tool designed to give support to teaching and learning of UML class diagrams. The proposed UML environment is capable to correct automatically UML class diagrams exercises providing feedback to the student immediately. The tool is part of a more general framework, denoted ACME, which provides the main functionalities of an e-learning platform. The tool has been used in a first experimental group of an introductory database course.

Asymmetric tunnels in P2MP LSPs as a label space reduction method

The objective of traffic engineering is to optimize network resource utilization. Although several works have been published about minimizing network resource utilization, few works have focused on LSR (label switched router) label space. This paper proposes an algorithm that takes advantage of the MPLS label stack features in order to reduce the number of labels used in LSPs. Some tunnelling methods and their MPLS implementation drawbacks are also discussed. The described algorithm sets up NHLFE (next hop label forwarding entry) tables in each LSR, creating asymmetric tunnels when possible. Experimental results show that the described algorithm achieves a great reduction factor in the label space. The presented works apply for both types of connections: P2MP (point-to-multipoint) and P2P (point-to-point).

Multidimensional Adaptations for Open Learning Management Systems

Our work is focused on alleviating the workload for designers of adaptive courses on the complexity task of authoring adaptive learning designs adjusted to specific user characteristics and the user context. We propose an adaptation platform that consists in a set of intelligent agents where each agent carries out an independent adaptation task. The agents apply machine learning techniques to support the user modelling for the adaptation process.

All-Optical Label Stacking: Easing the Trade-offs Between Routing and Architecture Cost in All-Optical Packet Switching

All-optical label swapping (AOLS) forms a key technology towards the implementation of all-optical packet switching nodes (AOPS) for the future optical Internet. The capital expenditures of the deployment of AOLS increases with the size of the label spaces (i.e. the number of used labels), since a special optical device is needed for each recognized label on every node. Label space sizes are affected by the way in which demands are routed. For instance, while shortest-path routing leads to the usage of fewer labels but high link utilization, minimum interference routing leads to the opposite. This paper studies all-optical label stacking (AOLStack), which is an extension of the AOLS architecture. AOLStack aims at reducing label spaces while easing the compromise with link utilization. In this paper, an integer lineal program is proposed with the objective of analyzing the softening of the aforementioned trade-off due to AOLStack. Furthermore, a heuristic aiming at finding good solutions in polynomial-time is proposed as well. Simulation results show that AOLStack either a) reduces the label spaces with a low increase in the link utilization or, similarly, b) uses better the residual bandwidth to decrease the number of labels even more.