Prévision du trafic Internet : modèles et applications

Avec l'essor de la métrologie de l'Internet, la prévision du trafic s'est imposée comme une de ses branches les plus importantes. C'est un outil puissant qui permet d'aider à la conception, la mise en place et la gestion des réseaux ainsi qu'à l'ingénierie du trafic et le contrôle des paramètres de qualité de service. L'objectif de cette thèse est d'étudier les techniques de prévision et d'évaluer la performance des modèles de prévision et de les appliquer pour la gestion des files d'attente et le contrôle du taux de perte dans les réseaux à commutation de rafales. Ainsi, on analyse les différents paramètres qui permettent d'améliorer la performance de la prévision en termes d'erreur. Les paramètres étudiés sont : la quantité de données nécessaires pour définir les paramètres du modèle, leur granularité, le nombre d'entrées du modèle ainsi que les caractéristiques du trafic telles que sa variance et la distribution de la taille des paquets. Nous proposons aussi une technique d'échantillonnage baptisée échantillonnage basé sur le maximum (Max-Based Sampling - MBS). Nous prouvons son efficacité pour améliorer la performance de la prévision et préserver l'auto-similarité et la dépendance à long terme du trafic. \ud Le travail porte aussi sur l'exploitation de la prévision du trafic pour la gestion du trafic et le contrôle du taux de perte dans les réseaux à commutation de rafales. Ainsi, nous proposons un nouveau mécanisme de gestion de files d'attente, baptisé α_SNFAQM, qui est basé sur la prévision du trafic. Ce mécanisme permet de stabiliser la taille de la file d'attente et par suite, contrôler les délais d'attente des paquets. Nous proposons aussi une nouvelle technique qui permet de garantir la qualité de service dans les réseaux à commutation de rafales en termes de taux de perte. Elle combine entre la modélisation, la prévision du trafic et les systèmes asservis avec feedback. Elle permet de contrôler efficacement le taux de perte des rafales pour chaque classe de service. Le modèle est ensuite amélioré afin d'éviter les feedbacks du réseau en utilisant la prévision du taux de perte au niveau TCP. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Modélisation et prévision du trafic, techniques d'échantillonnage, gestion des files d'attente, réseaux à commutation de rafales, contrôle du taux de perte, qualité de service, l'automatique

Collaborative Multi-domain Routing in SDN Environments

Today’s Internet is a collection of multi-domain networks where each domain is usually administrated and managed by a single network operator. Unfortunately, network operators share minimal information with each other and do not collaborate much to improve their routing decisions and the overall performance of the resulting large-scale mutli-domain network. Motivated by the need to solve this problem, in this paper, we look at this particular challenge and propose a novel collaborative multi-domain routing framework that is able to efficiently route the incoming flows through the different domains while ensuring their performance requirements in terms of delay and bandwidth and maximizing the overall network utilization. We hence propose an integer linear program to solve this problem and develop a greedy algorithm to cope with large-scale instances of the problem. Simulation results show that the proposed collaboration mechanism is able to significantly optimize network utilization and maximize the number of routed flows with guaranteed performance

On Ensuring Full Yet Cost-Efficient Survivability of Service Function Chains in NFV Environments

The emergence of Network Function Virtualization enables the deployment of network services in the form of service function chains. In this context, one of the key challenges is to ensure the survivability of these chains in face of single or multiple simultaneous physical node failures. In this paper, we address this challenge and propose solutions to guarantee the survivability of service chains by ensuring that there are enough backups ready to take over when failures occur. Specifically, we put forward a Survivability Management Framework that predicts traffic demand in service function chains and provision enough backups for network functions with minimal costs. To this end, we leverage the AutoRegressive Integrated Moving Average (ARIMA) model to predict future demand. We mathematically model the service chain survivability problem as an integer linear program that determines the minimal number of shared backups and their optimal location in the infrastructure such that backup operational costs are minimized. We also devise two greedy algorithms to deal with the problem in large-scale scenarios. We show, through several simulations, the performance and efficiency of the proposed solutions in different scenarios. We also show that demand prediction could help to avoid unnecessary provisioning of backups, and thereby reduce their operational costs

Towards optimal synchronization in NFV-based environments

Network Function Virtualization (NFV) is known for its ability to reduce deployment costs and improve the flexibility and scalability of network functions. Due to processing capacity limitations, the infrastructure provider may need to instantiate multiple instances of the same network function. However, most of network functions are stateful, meaning that the instances of the same function need to keep a common state and hence the need for synchronization among them. In this paper, we address this problem with the goal of identifying the optimal synchronization pattern between the instances in order to minimize the synchronization costs and delay. We propose a novel network function named Synchronization Function able to carry out data collection and further minimize these costs. We first mathematically model this problem as an integer linear program that finds the optimal synchronization pattern and the optimal placement and number of synchronization functions that minimize synchronization costs and ensure a bounded synchronization delay. We also put forward three greedy algorithms to cope with large-scale scenarios of the problem, and we explore the possibility to migrate network function instances to further reduce costs. Extensive simulations show that the proposed algorithms efficiently find near-optimal solutions with minimal computation time and provide better results compared to existing solutions

On minimizing flow monitoring costs in large-scale software-defined network networks

Recent years have witnessed the rise of novel network applications such as telesurgery, telepresence, and holoportation. As such applications have stringent performance requirements, timely and accurate traffic monitoring becomes of paramount importance to be able to react in a timely and efficient manner, and swiftly adjust the network configuration to achieve the sought-after requirements. However, existing monitoring schemes are either incurring high cost (e.g., high bandwidth consumption) due to the large number of monitoring messages or inefficient when they incur high reporting delay (i.e., the time needed for a monitoring message to reach the controller) making the collected statistics obsolete. In this paper, we address this problem and propose monitoring mechanisms for software defined networks that minimize the monitoring cost while satisfying an upper bound on the reporting delay of the statistics. Our solutions allow to carefully select the switch that should report the statistics about each flow crossing the network taking into consideration the available bandwidth and the capacity of the switch (i.e., the maximum number of flows that it can monitor). In particular, we formulate the switch-to-flow selection problem as an integer linear program and propose two heuristic algorithms to cope with large-scale instances of the problem. We consider the scenario where a single controller is collecting statistics and another where statistics are collected by multiple controllers. Simulation results show that the proposed algorithms provide near-optimal solutions with minimal computation time and outperform existing monitoring strategies in terms of monitoring cost and reporting delay

On Optimizing Backup Sharing Through Efficient VNF Migration

With the emergence of software defined networking and network function virtualization technologies, network services are expected to be offered as service function chains made out from virtual network functions that are connected to steer and process the incoming traffic. In this context, achieving the survivability of these chains against failures is a key challenge to ensure high availability and continuity of the services. A promising solution proposed in the literature is to provision backups for the virtual network functions that could be shared among multiple service chains. These backups are used in case of a failure to take over the failed functions and ensure service continuity. In this paper, we propose two solutions to efficiently place and provision the shared backups in order to ensure the survivability of the service chains against single node failures. The originality of these solutions is that they leverage the migration of virtual network functions to minimize the resources consumed by the backups. Simulation results show that, compared to existing solutions, the proposed schemes leveraging migration are able to reduce by up to 20% the amount of resources allocated for the shared backups while ensuring the survivability of the service chains

Price and Performance of Cloud-hosted Virtual Network Functions:Analysis and Future Challenges

The concept of Network Function Virtualization (NFV) has been introduced as a new paradigm in the recent few years. NFV offers a number of benefits including significantly increased maintainability and reduced deployment overhead. Several works have been done to optimize deployment (also called embedding) of virtual network functions (VNFs). However, no work to date has looked into optimizing the selection of cloud instances for a given VNF and its specific requirements. In this paper, we evaluate the performance of VNFs when embedded on different Amazon EC2 cloud instances. Specifically, we evaluate three VNFs (firewall, IDS, and NAT) in terms of arrival packet rate, resources utilization, and packet loss. Our results indicate that performance varies across instance types, departing from the intuition of "you get what you pay for'' with cloud instances. We also find out that CPU is the critical resource for the tested VNFs, although their peak packet processing capacities differ considerably from each other. Finally, based on the obtained results, we identify key research challenges related to VNF instance selection and service chain provisioning

On improving Service Chains Survivability Through Efficient Backup Provisioning

With the growing adoption of Software Defined Networking (SDN) and Network Function Virtualization (NFV), large-scale NFV infrastructure deployments are gaining momentum. Such infrastructures are home to thousands of network Service Function Chains (SFCs), each composed of a chain of virtual network functions (VNFs) that are processing incoming traffic flows. Unfortunately, in such environments, the failure of a single node may break down several VNFs and thereby breaking many service chains at the same time. In this paper, we address this particular problem and investigate possible solutions to ensure the survivability of the affected service chains by provisioning backup VNFs that can take over in case of failure. Specifically, we propose a survivability management framework to efficiently manage SFCs and the backup VNFs. We formulate the SFC survivability problem as an integer linear program that determines the minimum number of required backups to protect all the SFCs in the system and identifies their optimal placement in the infrastructure. We also propose two heuristic algorithms to cope with the large-scale instances of the problem. Through extensive simulations of different deployment scenarios, we show that these algorithms provide near-optimal solutions with minimal computation time