| Titre : | Application of Artificial Intelligence to Software Defined Network |
| Auteurs : | HADJER MAHDI, Auteur ; Sadek Labib Terrissa, Directeur de thèse |
| Type de document : | Monographie imprimée |
| Editeur : | Biskra [Algérie] : Faculté des Sciences Exactes et des Sciences de la Nature et de la Vie, Université Mohamed Khider, 2019 |
| Format : | 1 vol. (84 p.) / ill. / 29 cm |
| Langues: | Anglais |
| Mots-clés: | SDN,Controller,Floodlight,OpenFlow,Openvswitch,Artificial Neural Networks. |
| Résumé : |
SDN - Software Defined Networking - is certainly the hot topic that has shaken the network world in recent years, the SDN technology has triggered a radical long-term change in network design, and the market has quickly appropriated the SDN as a set of solutions / architectures to remove the existing boundaries between the worlds of applications and the network. While the deployment of applications is always easier and dynamic, thanks to virtualization and the Cloud. In this work, we evaluated SDN controller performance, namely, Ryu. We have also developed an application for managing ANNs in an SDN environment. |
| Sommaire : |
GENERAL INTRODUCTION CHAPTER I: Towards a programmable network: SDN 10 I.1 Introduction 11 1.2 History 11 1.3 Software Defined Networking (SDN) 11 1.4 The objective 12 1.5 SDN Architecture 13 1.6 Northern API 15 1.7 Southern API 15 OpenFlow 16 1.9 Benefits of SDN 25 1.10 SDN controller types 26 1.1 I Conclusion 26 CHAPTER II: Dynamic LOAD BALANCING using Software Defined Networks 27 II. I Introduction 11.2 load Balancing 28 H.3 Algorithm of load Balancing 31 H.4 SDN Load Balancing Defmition 31 11.5 What are the benefits of SDN load balancing? 32 H.6 Strategies 32 11.7 Load Balance System Design 33 H.8 Load balancing method using an artificial neural network 34 11.9 Conclusion 38 CHAPTER Load Balance Approach Based on Artificial Neural Network 39 III. Introduction 40 111.2 Artificial Neural Network 40 Backpropagation 41 111.4 Types of Artificial Network 42 111.5 Load Balance Approach Based on Artificial Neural Network 42 M.6 Experi mental 44 HI.7 Conclusion 49 CHAPTER IV: Implementation 50 IV.1 Introduction 51 IV.2 Tools 51 IV.3 Artificial Neural Network 61 IV.4 Set up a topology using Mininet 64 1V.5 Conclusion 78 GENERAL CONCLUSION 79 References 80 |
| Type de document : | Mémoire master |
Disponibilité (1)
| Cote | Support | Localisation | Statut |
|---|---|---|---|
| MINF/443 | Mémoire master | bibliothèque sciences exactes | Consultable |
