Network function virtualization (NFV) in Elastic Optical Inter-DataCenter Networks (EO-DCNs) enables a flexible, adaptive, effective, and economic network services deployment and upgrade. However, it is facing critical threats from large-scale network failures due to natural disasters. This is driving the need for efficient network protection schemes of service function chain (SFC) provisioning. In this paper, we investigate the disaster-resilient SFC provisioning problem leveraging power-efficient path protection with distance-adaptive modulation format (MF) assignment, concerning virtual network function (VNF) placement, SFC mapping, path protection, constrained recovery delay, and spectrum allocation simultaneously. An integer linear program (ILP) model is formulated to jointly minimize power consumption and spectrum usage, subject to disaster resilience. A heuristic algorithm is further developed for the sake of scalability. Numerical simulation results demonstrate that the proposed disaster protection schemes enable saving up to 32.05% power consumption.

Network function virtualization (NFV) in Elastic Optical Inter-DataCenter Networks (EO-DCNs) enables a flex-ible, adaptive, effective, and economic network services de-ployment and upgrade. However, it is facing critical threats from large-scale network failures, due to natural disasters. This is driving the need for efficient network protection schemes of service function chain (SFC) provisioning. In this paper, we investigate the disaster-resilient SFC provisioning problem leveraging power-efficient path protection with distance-adaptive modulation format (MF) assignment, concerning virtual network function (VNF) placement, SFC mapping, path protection, and spectrum allocation simultaneously. An integer linear program (ILP) model is formulated to jointly minimize power consumption and spectrum usage, subject to disaster resilience. A heuristic algorithm is also developed for the sake of scalability. Numerical Simulation results demonstrate that the proposed disaster pro-tection schemes enable saving up to 32.05 % power consumption.

As the increasing traffic patterns show asymmetric feature, directed pre-configured-cycle (p-cycle) has indicated the ability of better protection in elastic optical networks (EONs). In this paper, we investigate three different integer linear program (ILP) models of directed p-cycle without candidate cycle enumeration leveraging flow conservation. Three directed p-cycle designs are based on the same directed p-cycle strategy but differ from each other in how the flows can construct the directed p-cycles, namely individual link flow (ILF) directed p-cycle, aggregated link flows (ALF) directed p-cycle, and loop-eliminating flow (LEF) directed p-cycle, respectively. These ILPs aim to jointly minimize power consumption and spectrum usage of all directed p-cycles configured in EONs. The problem formulation involves directed p-cycle generation, modulation format (MF) selection, power consumption optimization, and spectrum allocation. Furthermore, the proposed directed p-cycle strategy is designed with a compact and novel MF adaptation relying on accurate protection path lengths. Simulations are conducted to compare the proposed ILPs with the conventional method which uses a rough upper bound on MF adaptation. Numerical results demonstrate that all of the three proposed ILPs have better performances on the joint objective, in which the improvement is up to 24.31%. Although the proposed ILPs are with the same performance on the objective due to the same directed p-cycle strategy, the LEF directed p-cycle shows the best efficiency.

Natural disasters have challenged the survivability of Elastic Optical Inter-DataCenter Networks (EO-DCNs), and it is urgent to establish efficient disaster protection schemes. In this paper, we investigate the disaster-resilient service provisioning problem leveraging cooperative storage system (CSS). Instead of mirrored content backup on a single DC, our proposed CSS partitions a required content into no less than three fragments if possible, each of which is then stored on a DC located in different disaster zones. Accordingly, multi-path routing with the adaptive number of working paths to distinct DCs is employed to serve each request, while a protection path is computed to protect against a disaster failure. Our main objective is to jointly minimize the spectrum usage and maximal occupied frequency slot index (MOFI) subject to disaster resilience. Besides, we also expect to cut the content storage space. To this end, we propose for the first time a CSS-based dedicated end-to-content path protection (CDP), which allows service provisioning through multiple paths with the adaptive number of paths rather than a single path. This consequently reduces at least half of the reserved spectrum on the protection path. To find the optimal CDP strategy, we formulate the studied problem as an integer linear program (ILP) and then propose a fast heuristic algorithm. Observing the trade-off between the spectrum usage and content storage space, we further design a maximum-CDP (M-CDP), which generates the maximum number of working paths to reduce the content storage space. Simulations are conducted to compare the proposed schemes with the traditional protection strategy using mirrored storage and single-path routing. Numerical results demonstrate that the proposed CSS-based protection schemes enable to cut up to 21.6% of the spectrum usage and 15% of the content storage space.

Natural disasters have challenged the survivability of Elastic Optical Inter-DataCenter Networks (EO-DCNs), and it is urgent to establish efficient disaster protection schemes. In this paper, we investigate the disaster-resilient service provisioning problem leveraging cooperative storage system (CSS) and multipath routing. The studied problem involves data center (DC) assignment, content partition and placement, working/protection paths computation, as well as spectrum allocation. Our main objective is to jointly minimize the spectrum usage and maximal frequency slot index. Besides, we also expect to cut the content storage space. To this end, we first formulate the studied CSS-based protection problem as an integer linear program (ILP), and then propose a fast heuristic algorithm to improve the network scalability in large instances. Numerical simulations are conducted to compare the proposed schemes with the traditional protection strategy using entire content replication and single path routing. Simulation results demonstrate that the CSS-based protection scheme enables to cut up to 17.8% of the spectrum usage and half of the content storage space.

This article describes a method to provide adapted visit tours in art museums according to the preferences expressed by the visitor and exhibits prestige. It is based on a dual approach with, on the one hand an automatic textual analysis of the official information available online (labels of exhibits) that allows to rank the exhibit attractiveness for a standard museum visitor. On the other hand, individual preferences are also taken into account to adapt the visit according to the personal cultural awareness of the visitor. We use operations research to solve a routing optimization problem, aiming at finding a visit tour with time constraints and maximization of the visitor satisfaction. Depending on the instance size and the problem scale, an integer linear programming (ILP) model and a greedy algorithm are proposed to recommend personalized visit tours and applied on two museums: ‘Musée de l’Orangerie’ in Paris and ‘National Gallery’ in London. The obtained results show that it is possible to recommend a good tour to visitors of an art museum by taking into account the common prestige of the exhibits and the individual interests, joining automatic text summarization and routing optimization in a limited geographical space.

This paper proposes a new method to provide personalized tour recommendation for museum visits.It combines an optimization of preference criteria of visitors with an automatic extraction of artwork importance from museum information based on Natural Language Processing using textual energy.This project includes researchers from computer and social sciences.Some results are obtained with numerical experiments.They show that our model clearly improves the satisfaction of the visitor who follows the proposed tour.This work foreshadows some interesting outcomes and applications about on-demand personalized visit of museums in a very near future.

This article presents a new incremental learning algorithm for classification tasks, called NetLines, which is well adapted for both binary and real-valued input patterns. It generates small, compact feedforward neural networks with one hidden layer of binary units and binary output units. A convergence theorem ensures that solutions with a finite number of hidden units exist for both binary and real-valued input patterns. An implementation for problems with more than two classes, valid for any binary classifier, is proposed. The generalization error and the size of the resulting networks are compared to the best published results on well-known classification benchmarks. Early stopping is shown to decrease overfitting, without improving the generalization performance.